https://is.theorizeit.org/w/api.php?action=feedcontributions&user=Pblowry&feedformat=atomIS Theory - User contributions [en]2024-03-28T13:11:16ZUser contributionsMediaWiki 1.35.3https://is.theorizeit.org/w/index.php?title=Selective_organizational_information_privacy_and_security_violations_model_(SOIPSVM)&diff=937Selective organizational information privacy and security violations model (SOIPSVM)2016-01-26T07:54:20Z<p>Pblowry: /* Seminal articles */</p>
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<div>== Acronym ==<br />
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SOIPSVM<br />
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== Alternate name(s) ==<br />
<br />
n/a<br />
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== Main dependent construct(s)/factor(s) ==<br />
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Likelihood of a privacy or security rule violation [by the organization]<br />
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== Main independent construct(s)/factor(s) ==<br />
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Contextual conditions of formal and informal communication structures, and coupling between violation and outcomes. Rule characteristics of enforceability through certainty, severity, and celerity of sanctions; goal clarity of rules, and rule connectedness, perceived risk of violating a privacy or security rule, economic strain, and non-economic strain.<br />
== Motivation for SOIPSVM / Why IS researchers can benefit from using it ==<br />
Motivation for creation of the theory: Privacy and security concerns are pervasive because of the ease of access to information. Recurrent negative cases in the popular press attest to the failure of current privacy regulations to keep consumer and protected health information sufficiently secure in today’s climate of increased IT use. One reason for such failure is that organizations violate these regulations for multiple reasons. To address this issue, Wall, Lowry, and Barlow (2016) proposed a theoretical model to explain the likelihood that organizations will select an externally governed privacy or security rule for violation in response to organizational strain or slack resources. This proposed theoretical model, is the selective organizational information privacy and security violations model (SOIPSVM).<br />
== Concise description of theory ==<br />
The selective organizational information privacy and security violations model (SOIPSVM), explains how organizational structures and processes, along with characteristics of regulatory rules, alter perceptions of risk when an organization’s performance does not match its aspiration levels and, thereby, affects the likelihood of rule violations. Importantly, SOIPSVM is contextualized for the domain of organizational privacy and security violations. SOIPSVM builds on and extends the selective organizational rule violations model (SORVM), which posits that organizational rule violations are selective. <br />
SOIPSVM provides at least four contributions to the privacy and security literature that can further guide empirical research and practice. First, SOIPSVM introduces the concept of selectivity in rule violations to privacy and security research. This concept can improve privacy and security research by showing that organizational violations of privacy and security rules are dynamic and selective yet influenced by external forces. <br />
Second, SOIPSVM extends the boundaries of SORVM, which is limited to explaining the behavior of organizations under strain, such as economic hardship. SOIPSVM contributes to the theory of selective deviance by proposing that selectivity extends to organizations with slack resources. <br />
Third, SOIPSVM addresses ideas of non-economic risk and strain in addition to economic risk and strain. Thus, SOIPSVM explains organizational rule-violating behavior as an attempt to protect core organizational values from external entities that pressure organizations to change their values to comply with rules. <br />
Fourth, SOIPSVM broadens the theoretical scope of two important constructs (namely, structural secrecy and procedural emphasis) to improve the model’s explanatory power. <br />
Fifth, SOIPSVM identifies important elements of rule enforcement by drawing from the tenets of general deterrence theory. <br />
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== Diagram/schematic of theory ==<br />
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[[File:SOIPSVM_figure1.jpg]]<br />
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Figure 1. Overview of the selective organizational information privacy and security violations model (SOIPSVM), from Wall, Lowry, and Barlow (2016).<br />
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== Originating author(s) ==<br />
<br />
Jeffrey D. Wall, Paul Benjamin Lowry, and Jordan B. Barlow<br />
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== Seminal articles ==<br />
Jeffrey D. Wall, Paul Benjamin Lowry, and Jordan Barlow (2016). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2611567 Organizational violations of externally governed privacy and security rules: Explaining and predicting selective violations under conditions of strain and excess],” Journal of the Association for Information Systems (JAIS), vol. 17(1), pp. 39-76 (http://aisel.aisnet.org/jais/vol17/iss1/).<br />
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== Originating area ==<br />
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Information systems (native theory)<br />
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== Level of analysis ==<br />
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Organization<br />
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== IS and non-IS articles that use the theory ==<br />
TBD<br />
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== Links from this theory to other theories ==<br />
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GDT or [[General_deterrence_theory]]<br />
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== External links ==<br />
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n/a<br />
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== Original Contributor(s) ==<br />
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[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Delone_and_McLean_IS_success_model&diff=935Delone and McLean IS success model2016-01-09T11:35:37Z<p>Pblowry: /* IS articles that use the theory */</p>
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== '''Delone and McLean IS success model''' ==<br />
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== Acronym ==<br />
n/a<br />
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== Alternate name(s)==<br />
DeLone & McLean Information Systems Success Model, DeLone & McLean IS Success Model, D&M IS Success Model<br />
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== Main dependent construct(s)/factor(s)==<br />
Net Benefits, (Intention to) Use, User Satisfaction<br />
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== Main independent construct(s)/factor(s) ==<br />
System Quality, Information Quality, Service Quality<br />
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== Concise description of theory ==<br />
In order to provide a general and comprehensive definition of IS success that covers different perspectives of evaluating information systems, DeLone and McLean reviewed the existing definitions of IS success and their corresponding measures, and classified them into six major categories. Thus, they created a multidimensional measuring model with interdependencies between the different success categories (DeLone & McLean 1992). <br><br />
<br><br />
Motivated by DeLone and McLean’s call for further development and validation of their model, many researchers have attempted to extend or respecify the original model. Ten years after the publication of their first model and based on the evaluation of the many contributions to it, DeLone and McLean proposed an updated IS success model (DeLone & McLean 2002, 2003).<br><br />
<br><br />
The updated model consists of six interrelated dimensions of IS success: information, system and service quality, (intention to) use, user satisfaction, and net benefits. The arrows demonstrate proposed associations between the success dimensions. The model can be interpreted as follows: A system can be evaluated in terms of information, system, and service quality; these characteristics affect the subsequent use or intention to use and user satisfaction. As a result of using the system, certain benefits will be achieved. The net benefits will (positively or negatively) influence user satisfaction and the further use of the information system.<br />
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== Diagram/schematic of theory ==<br />
[[Image:D&M1992.jpg]]<br><br />
Information Systems Success Model (DeLone & McLean 1992)<br><br />
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[[Image:D&M2002.jpg]]<br><br />
Updated Information Systems Success Model (DeLone & McLean 2002, 2003)<br><br />
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== Originating author(s) ==<br />
DeLone & McLean (1992); DeLone & McLean (2002); DeLone & McLean (2003)<br />
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== Seminal articles ==<br />
DeLone, W.H., and McLean, E.R. 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research (3:1), pp 60-95.<br />
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DeLone, W.H., and McLean, E.R. 2002. "Information Systems Success Revisited," in: Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS 02). Big Island, Hawaii: pp. 238-249.<br />
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DeLone, W.H., and McLean, E.R. 2003. "The DeLone and McLean Model of Information Systems Success: A Ten-Year Update," Journal of Management Information Systems (19:4), Spring, pp 9-30.<br />
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== Originating area ==<br />
Information Systems<br />
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== Level of analysis ==<br />
Individual, Organization<br />
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== IS articles that use the theory ==<br />
Almutairi, H., and Subramanian, G.H. 2005. "An Empirical Application of the DeLone and McLean Model in the Kuwaiti Private Sector," Journal of Computer Information Systems (45:3), Spring, pp 113-122.<br />
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Bharati, P. and Chaudhury, A. (2006), “Product Customization on the Web: An Empirical Study of Factors Impacting Choiceboard User Satisfaction,” Information Resources Management Journal, Vol. 19, No. 2, pp. 69 - 81. <br />
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Bharati, P. and Berg, D. (2005), “Service Quality from the Other Side: Information Systems Management at Duquesne Light”, International Journal of Information Management, Vol. 25, No. 4, pp. 367-380.<br />
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Bharati, P. and Chaudhury, A. (2004), “An Empirical Investigation of Decision-Making Satisfaction in Web-Based Decision Support Systems”, Decision Support Systems, Vol. 37, No. 2, pp. 187-197. <br />
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Bharati, P. and Berg, D. (2003), “Managing Information Technology for Service Quality: A Study from the Other Side”, IT and People, Vol. 16, No. 2, pp. 183-202. <br />
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Bharati, P. (2002-2003), “People and Information Matter: Task Support Satisfaction from the Other Side”, Journal of Computer Information Systems, Winter.<br />
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Chae, H.-C.M. 2007. "Is Success Model and Perceived It Value," in: Proceedings of the 13th Americas Conference on Information Systems (AMCIS 07). Keystone, CO, USA.<br />
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DeLone, W.H., and McLean, E.R. 2004. "Measuring E-Commerce Success: Applying the DeLone & McLean Information Systems Success Model," International Journal of Electronic Commerce (9:1), Fall, pp 31-47.<br />
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Halawi,L.A, McCarthy,R. V and Aronson, J. E. 2007-2008. "An Empirical Investigation of Knowledge Management Systems Success”. Journal of Computer Information Systems (JCIS) Winter, 121-135.<br />
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Hu, P.J.-H. 2003. "Evaluating Telemedicine Systems Success: A Revised Model," in: Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS 03). Big Island, Hawaii.<br />
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Hwang, M., and McLean, E.R. 1996. "The Use of Meta-Analysis in Validating the DeLone and McLean Information Systems Success Model," in: Proceedings of the 29th Hawaii International Conference on System Sciences (HICSS 96). Big Island, Hawaii.<br />
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Iivari, J. 2005. "An Empirical Test of the DeLone-McLean Model of Information System Success," The DATA BASE for Advances in Information Systems (26:2), pp 8-27.<br />
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Jennex, M., Olfman, L., Panthawi, P., and Park, Y.-T. 1998. "An Organizational Memory Information Systems Success Model: An Extension of DeLone and McLean's I/S Success Model " in: Proceedings of the 31st Hawaii International Conference on System Sciences (HICSS 98). Big Island, Hawaii.<br />
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Jennex, M., and Olfman, L. 2003. "A Knowledge Management Success Model: An Extension of DeLone and McLean’s Is Success Model," in: Proceedings of the 9th Americas Conference on Information Systems (AMCIS 03). Tampa, Florida.<br />
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Kulkarni, U.R., Ravindran, S., and Freeze, R. 2006. "A Knowledge Management Success Model: Theoretical Development and Empirical Validation," Journal of Management Information Systems (23:3), 12, pp 309-347.<br />
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Mao, E., and Ambrose, P. 2004. "A Theoretical and Empirical Validation of Is Success Models in a Temporal and Quasi Volitional Technology Usage Context," in: Proceedings of the 10th Americas Conference on Information Systems (AMCIS 04). New York City, New York.<br />
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McGill, T., Hobbs, V., and Klobas, J. 2003. "User-Developed Applications and Information Systems Success: A Test of DeLone and McLean's Model," Information Resources Management Journal (16:1), p 24.<br />
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Molla, A., and Licker, P.S. 2001. "E-Commerce Systems Success: An Attempt to Extend and Respecify the DeLone and MacLean Model of Is Success," Journal of Electronic Commerce Research (2:4), pp 131-141.<br />
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Pare, G., Aubry, D., Lepanto, L., and Sicotte, C. 2005. "Evaluating Pacs Success: A Multidimensional Model," in: Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS 05). Big Island, Hawaii.<br />
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Qian, Z., and Bock, G.-W. 2005. "An Empirical Study on Measuring the Success of Knowledge Repository Systems," in: Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS 05). Big Island, Hawaii.<br />
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Rai, A., Lang, S.S., and Welker, R.B. 2002. "Assessing the Validity of Is Success Models: An Empirical Test and Theoretical Analysis," Information Systems Research (13:1), pp 50-69.<br />
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Roldán, J.L., and Leal, A. 2003. "A Validation Test of an Adaptation of the DeLone and McLean’s Model in the Spanish EIS Field," in: Critical Reflections on Information Systems: A Systemic Approach, J.J. Cano (ed.). Hershey, PA, USA: Idea Group Publishing, pp. 66-84.<br />
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Rouibah, Kamel, Paul Benjamin Lowry, and Laila Al-Mutairi (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2525129 Dimensions of Business-to-Consumer (B2C) Systems Success in Kuwait: Testing a Modified DeLone and McLean IS Success Model in an E-Commerce Context],” Journal of Global Information Management, vol. 23(3), pp. 41–70 (doi: http://dx.doi.org/10.4018/JGIM.2015.07.0103).<br />
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Rosemann, M., and Vessey, I. 2005. "Linking Theory and Practice: Performing a Reality Check on a Model of Is Success," in: Proceedings of the 13th European Conference on Information Systems (ECIS 05). Regensburg, Germany.<br />
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Seddon, P.B. 1997. "A Respecification and Extension of the DeLone and McLean Model of Is Success," Information Systems Research (8:3), pp 240-253.<br />
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Seddon, P.B., and Kiew, M.-Y. 1994. "A Partial Test and Development of the DeLone and McLean Model of Is Success," in: Proceedings of the 15th International Conference on Information Systems (ICIS 94). Vancouver, Canada: pp. 99-110.<br />
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Seddon, P.B., Staples, S., Patnayakuni, R., and Bowtell, M. 1999. "Dimensions of Information Systems Success," Communication of the AIS (2), pp 1-60.<br />
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Sedera, D. 2006. "An Empirical Investigation of the Salient Characteristics of Is-Success Models," in: Proceedings of the 12th Americas Conference on Information Systems (AMCIS 06). Acapulco, Mexico.<br />
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Seen, M., Rouse, A.C., and Beaumont, N. 2007. "Explaining and Predicting Information Systems Acceptance and Success: An Integrative Model," in: Proceedings of the 15th European Conference on Information Systems (ECIS 07). St Gallen, Switzerland.<br />
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Skok, W., Kophamel, A., and Richardson, I. 2001. "Diagnosing Information Systems Success: Importance-Performance Maps in the Health Club Industry," Information & Management (38:7), pp 409-419.<br />
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Thomas, P. 2006. "Information Systems Success and Technology Acceptance within Government Organization," in: Proceedings of the 12th Americas Conference on Information Systems (AMCIS 06). Acapulco, Mexico.<br />
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[http://ssrn.com/abstract=1612176 Trkman,M., Trkman,P.2009."A Wiki as Intranet – a Critical Analysis Using the DeLone & McLean Model," Online Information Review, 33(6), pp 1087-1102.]<br />
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Urbach, N., Smolnik, S., and Riempp, G. 2008. "A Methodological Examination of Empirical Research on Information Systems Success: 2003 to 2007," in: Proceedings of the 14th Americas Conference on Information Systems (AMCIS 2008). Toronto, Ontario, Canada.<br />
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Wu, J.-H., and Wang, Y.-M. 2006. "Measuring Kms Success: A Respecification of the DeLone and McLean's Model," Information & Management (43:6), pp 728-739.<br />
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== Links from this theory to other theories ==<br />
[[Multi-motive information systems continuance model (MISC)]]<br><br />
[[Technology acceptance model]]<br><br />
[[Unified theory of acceptance and use of technology]]<br />
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== External links ==<br />
[http://business.clemson.edu/ise/index.html] Information Systems Effectiveness Home Page<br />
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== Original Contributor(s) ==<br />
Nils Urbach & Benjamin Müller<br />
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Please feel free to make modifications to this site. In order to do so, you must register.<br />
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[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Theory_of_reasoned_action&diff=934Theory of reasoned action2016-01-09T11:30:46Z<p>Pblowry: /* IS articles that use the theory */</p>
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== '''Theory of reasoned action''' ==<br />
----<br />
== Acronym ==<br />
TRA<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention, Behavior<br />
== Main independent construct(s)/factor(s) ==<br />
Attitude toward behavior, Subjective norm,<br />
== Concise description of theory ==<br />
TRA posits that individual behavior is driven by behavioral intentions where behavioural intentions are a function of an individual's attitude toward the behaviour and subjective norms surrounding the performance of the behavior. Attitude toward the behavior is defined as the individual's positive or negative feelings about performing a behaviour. It is determined through an assessment of one's beliefs regarding the consequences arising from a behavior and an evaluation of the desirability of these consequences. Formally, overall attitude can be assessed as the sum of the individual consequence x desirability assessments for all expected consequences of the behavior. Subjective norm is defined as an individual's perception of whether people important to the individual think the behavior should be performed. The contribution of the opinion of any given referent is weighted by the motivation that an individual has to comply with the wishes of that referent. Hence, overall subjective norm can be expressed as the sum of the individual perception x motivation assessments for all relevant referents. Algebraically TRA can be represented as B ≈ BI = w1AB + w2SN where B is behavior, BI is behavioral intention, AB is attitude toward behavior, SN is subjective norm, and w1 and w2 are weights representing the importance of each term.<br />
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The model has some limitations including a significant risk of confounding between attitudes and norms since attitudes can often be reframed as norms and vice versa. A second limitation is the assumption that when someone forms an intention to act, they will be free to act without limitation. In practice, constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act. The theory of planned behavior (TPB) attempts to resolve this limitation. <br />
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Sources:<br />
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http://en.wikipedia.org/wiki/Technology_acceptance_model <br><br />
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth: Harcourt Brace Jovanovich College Publishers.<br />
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== Diagram/schematic of theory ==<br />
[[Image:Tra.JPG]]<br />
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Source: Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior : An introduction to theory and research. Reading, Mass. ; Don Mills, Ontario: Addison-Wesley Pub. Co. <br />
== Originating author(s) ==<br />
Fishbein (1967); Ajzen and Fishbein (1973); Fishbein and Ajzen (1975)<br />
== Seminal articles ==<br />
Ajzen, I., & Fishbein, M. (1973). Attitudinal and normative variables as predictors of specific behavior. Journal of Personality and Social Psychology, 27(1), 41-57. <br />
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Fishbein, M. (1967). Attitude and the prediction of behavior. In M. Fishbein (Ed.), Readings in attitude theory and measurement (pp. 477-492). New York: Wiley. <br />
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Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior : An introduction to theory and research. Reading, Mass. ; Don Mills, Ontario: Addison-Wesley Pub. Co. <br />
== Originating area ==<br />
Social psychology<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Bagchi, S., Kanungo, S., & Dasgupta, S. (2003). Modeling use of enterprise resource planning systems: A path analytic study. European Journal of Information Systems, 12(2), 142-158. <br />
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Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service: The internet as an illustration. International Journal of Service Industry Management, 12(5), 423-450. <br />
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Celuch, K., Taylor, S. A., & Goodwin, S. (2004). Understanding insurance salesperson internet information management intentions: A test of competing models. Journal of Insurance Issues, 27(1), 22-40. <br />
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Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). [http://inn.colorado.edu/Details/Paper/199 User acceptance of computer technology: A comparison of two theoretical models]. Management Science, 35(8), 982-1003. <br />
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Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
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Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior. International Journal of Information Management, 24(6), 539-550. <br />
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Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440-465. <br />
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Hebert, M., & Benbasat, I. (1994). Adopting information technology in hospitals: The relationship between attitudes/expectations and behavior. Hospital & Health Services Administration, 39(3), 369-383. <br />
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Jae-Nam, L., & Young-Gul, K. (2005). Understanding outsourcing partnership: A comparison of three theoretical perspectives. IEEE Transactions on Engineering Management, 52(1), 43-58. <br />
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Jeffrey, A. C., & Fawzy, S. (1999). A graphical method for assessing knowledge-based systems investments. Logistics Information Management, 12(1/2), 63-77. <br />
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Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). [http://inn.colorado.edu/Details/Paper/83 Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs]. MIS Quarterly, 23(2), 183-213. <br />
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Leonard, L. N. K., Cronan, T. P., & Kreie, J. (2004). What influences IT ethical behavior intentions-planned behavior, reasoned action, perceived importance, or individual characteristics? Information & Management, 42(1), 143-158. <br />
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Liker, J. K., & Sindi, A. A. (1997). User acceptance of expert systems: A test of the theory of reasoned action. Journal of Engineering and Technology Management, 14(2), 147-173. <br />
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Lowry, Paul Benjamin, Jinwei Cao, and Andrea Everard (2011). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1668113 Privacy concerns versus desire for interpersonal awareness in driving the use of self-disclosure technologies: The case of instant messaging in two cultures],” Journal of Management Information Systems (JMIS), vol. 27(4), pp. 163–200 (doi: http://dx.doi.org/10.2753/MIS0742-1222270406). <br />
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Lowry, Paul Benjamin, Gaskin, James, Twyman, Nathan W., Hammer, Bryan, and Roberts, Tom L. (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
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Lowry, Paul Benjamin, Jenkins, Jeffrey L., Twyman, Nathan W., Hammer, Bryan, Gaskin, James, and Hassell, Martin (2008). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287401 Proposing the hedonic affect model (HAM) to explain how stimuli and performance expectations predict affect in individual and group hedonic systems use],” Proceedings of the Journal of the Association for Information Systems Theory Development Workshop at the International Conference on Systems Sciences, Paris, France, December 13. All Sprouts Content, vol. 8(24), paper 230, pp. 1–51.<br />
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Lowry, Paul Benjamin, Twyman, Nathan W., Gaskin, James, Hammer, Bryan, Bailey, Aaron, and Roberts, Tom L. (2007). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1482844 Proposing the interactivity-stimulus-attention model (ISAM) to explain and predict enjoyment, immersion, and adoption of purely hedonic systems],” Proceedings of the Special Interest Group on Human-Computer Interaction 2007 Pre-ICIS Workshop at the International Conference on System Sciences, Montréal, Canada, December 8, paper 11, pp. 72–76 (best-paper nomination) http://aisel.aisnet.org/sighci2007/11/.<br />
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Mykytyn, P. P. J., & Harrison, D. A. (1993). The application of the theory of reasoned action to senior management and strategic information systems. Information Resources Management Journal, 6(2), 15-26. <br />
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Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15(3), 325-343. <br />
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Shih, Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study internet banking in taiwan. Internet Research, 14(3), 213-223. <br />
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Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). [http://inn.colorado.edu/Details/Paper/75 User acceptance of information technology: Toward a unified view]. MIS Quarterly, 27(3), 425-478. <br />
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Yoh, E., Damhorst, M. L., Sapp, S., & Laczniak, R. (2003). Consumer adoption of the internet: The case of apparel shopping. Psychology & Marketing, 20(12), 1095-1118.<br />
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== Links from this theory to other theories ==<br />
[[Hedonic-motivation system adoption model (HMSAM)]], [[Theory of planned behavior]], [[Technology acceptance model]], [[Unified theory of acceptance and use of technology]]<br />
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== External links ==<br />
http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Health%20Communication/theory_planned_behavior.doc/, The University of Twente in the Netherlands presents an overview of the theory of planned behavior and the theory of reasoned action including references.<br />
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== Original Contributor(s) ==<br />
Brent Furneaux<br />
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Please feel free to make modifications to this site. In order to do so, you must register.<br />
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[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Social_exchange_theory&diff=933Social exchange theory2016-01-09T11:28:27Z<p>Pblowry: /* IS articles that use the theory */</p>
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== '''Social exchange theory''' ==<br />
----<br />
== Acronym ==<br />
N/A<br />
== Alternate name(s)==<br />
Exchange theory<br />
== Main dependent construct(s)/factor(s)==<br />
Value and utility: profit, rewards, approval, status, reputation, flexibility, and trust.<br />
== Main independent construct(s)/factor(s) ==<br />
Exchange relation, dependency, and power<br />
== Concise description of theory ==<br />
Social exchange theory grew out of the intersection of economics, psychology and sociology. According to Hormans (1958), the initiator of the theory, it was developed to understand the social behavior of humans in economic undertakings. The fundamental difference between economic exchange and social exchange theory is in the way actors are viewed. Exchange theory “views actors (person or firm) as dealing not with another actor but with a market” (Emerson,1987, P.11), responding to various market characteristics; while social exchange theory views the exchange relationship between specific actors as “actions contingent on rewarding reactions from others.” (Blau, 1964, P.91)<br />
<br />
Today, social exchange theory exists in many forms, but all of them are driven by the same central concept of actors exchanging resources via a social exchange relationship. Where social exchange (e.g., Ax; By ) is the voluntary transfer of resources (x,y,…) between multiple actors (A,B,…) (Cook,1977). The theory has evolved from a dyadic model to a network model (Cook, 1977) with market properties (Emerson, 1987). The crux of the theory is still best captured in Homans’s own words (1958, P.606)<br />
<br />
“Social behavior is an exchange of goods, material goods but also non-material ones, such as the symbols of approval or prestige. Persons that give much to others try to get much from them, and persons that get much from others are under pressure to give much to them. This process of influence tends to work out at equilibrium to a balance in the exchanges. For a person in an exchange, what he gives may be a cost to him, just as what he gets may be a reward, and his behavior changes less as the difference of the two, profit, tends to a maximum.” <br />
<br />
This interaction between two actors (people, firms etc.) results in various contingencies, where the actors modify their resources to each others expectations. Power is the mechanics that can explain the relation of the actors (Emerson, 1962 and Blau, 1964). According to Emerson (1962), power is the property of a relation and not of an actor, because it “resides implicitly in the other’s dependency.” (P.32). <br />
<br />
Where “dependence of A upon Bj (DABJ) is a joint function, (1) varying directly with the value to A of the resources received from B and (2) varying inversely with comparison level for alternative exchange relations.” (Emerson and Cook, 1972b: 64). Power results from resource dependency (Emerson, 1962) in a dyadic relation but in a network exchange model, it is also derived from the structure (Cook,1977) - structural power. Here, power of A over B (PAB) in any relation Ax; By is the ability of A to decrease the exchange ratio, x/y (Emerson and Cook,1974, P. 25).<br />
<br />
To conclude, social exchange theory is best understood as a framework for explicating movement of resources, in imperfect market conditions, between dyads or a network via a social process (Emerson, 1987). <br />
== Diagram/schematic of theory ==<br />
[[Image:sxgt.jpg]]<br />
<br />
(Figure source: Cook, 1977)<br><br />
In the above diagram, the letters represents actors and the arrows depict the movement of resources. The arrow head points to the sourcing actors with the ends at the source actors. Here, B1 and B2 represent alternative exchange relations.<br />
== Originating author(s) ==<br />
George Homans (1958)<br />
== Seminal articles ==<br />
Homans,G.C.1958. Social Behavior as Exchange. American Journal of Sociology, 63 (6): 597-606.<br />
<br />
Emerson, R. 1962.Power-Dependence Relations. American Sociological Review, 27(1): 31-41.<br />
<br />
Blau,P.1964. Exchange and Power in Social Life. New York: Wiley.<br />
<br />
Levine,S. and White,P. 1961.Exchange as a Conceptual Framework for the Study of Interorganizational Relationships. Administrative Science Quarterly 5(4): 583-601.<br />
<br />
Cook.K.S. 1977.Exchange and Power in Networks of Interorganizational Relations. The Sociological Quarterly 18 (Winter 1977): 62-82.<br />
== Originating area ==<br />
Economics, Psychology and Sociology<br />
== Level of analysis ==<br />
Individual, group, and organizational<br />
== IS articles that use the theory ==<br />
Gefen,D. and M. Keil.1998. The impact of developer responsiveness on perceptions of usefulness and ease of use: an extension of the Technology Acceptance Model. ACM SIGMIS Database 29(2): 35-49.<br />
<br />
Gefen, D. and C. M., Ridings. 2002. [http://inn.colorado.edu/Details/Paper/7502 Implementation Team Responsiveness and User Evaluation of Customer Relationship Management: A Quasi-Experimental Design Study of Social Exchange Theory]. Journal of Management Information Systems 19(1): 47-69.<br />
<br />
Hall, H. 2003. Borrowed theory - Applying exchange theories in information science research. Library and Information Science Research 25(3): 287-306.<br />
<br />
Kankanhalli, A., B.C. Y. Tan, and K.K., Wei. 2005. [http://inn.colorado.edu/Details/Paper/111 Contributing Knowledge to Electronic Knowledge Repositories: An Empirical Investigation], MIS Quarterly 29(1): 113-143.<br />
<br />
Kern, T & Willcocks, LP. 2000. Exploring information technology outsourcing relationships: Theory and practice, Journal of Strategic Information Systems, 9(2000): 321-350.<br />
<br />
Lee, JN and YG, Kim.1999. [http://inn.colorado.edu/Details/Paper/8077 Effect of Partnership Quality on IS Outsourcing Success: Conceptual Framework and Validation], Journal of Management Information Systems, 15(4): 29-61.<br />
<br />
Paul Benjamin Lowry, Jinwei Cao, and Andrea Everard (2011). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1668113 Privacy concerns versus desire for interpersonal awareness in driving the use of self-disclosure technologies: The case of instant messaging in two cultures],” Journal of Management Information Systems (JMIS), vol. 27(4), pp. 163–200 (doi: http://dx.doi.org/10.2753/MIS0742-1222270406). <br />
<br />
Clay Posey, Paul Benjamin Lowry, Tom L. Roberts, and Selwyn Ellis (2010). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1501447 Proposing the online community self-disclosure model: The case of working professionals in France and the UK who use online communities],” European Journal of Information Systems (EJIS), vol. 19(2), pp. 181–195 (doi: http://dx.doi.org/10.1057/ejis.2010.15). <br />
<br />
Son, JY., S. Narasimhan and F.J. Riggins. 2005. “[http://inn.colorado.edu/Details/Paper/6834 Effects of Relational Factors and Channel Climate on EDI Usage in the Customer-Supplier Relationship. Journal of Management Information Systems. 22(1): 321-353.<br />
<br />
== Links from this theory to other theories ==<br />
[[Resource dependency theory]], [[Expectation confirmation theory]], Economic rational choice theory, Power-Politics theory<br />
<br />
== External links ==<br />
http://theoryandscience.icaap.org/content/vol004.002/01_zafirovski.html, A paper exploring, discussing and extending social exchange theory from a sociological perspective by Milan Zafirovski.<br />
== Original Contributor(s) ==<br />
Jijesh Devan<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Social_exchange_theory&diff=932Social exchange theory2016-01-09T11:07:15Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Social exchange theory''' ==<br />
----<br />
== Acronym ==<br />
N/A<br />
== Alternate name(s)==<br />
Exchange theory<br />
== Main dependent construct(s)/factor(s)==<br />
Value and utility: profit, rewards, approval, status, reputation, flexibility, and trust.<br />
== Main independent construct(s)/factor(s) ==<br />
Exchange relation, dependency, and power<br />
== Concise description of theory ==<br />
Social exchange theory grew out of the intersection of economics, psychology and sociology. According to Hormans (1958), the initiator of the theory, it was developed to understand the social behavior of humans in economic undertakings. The fundamental difference between economic exchange and social exchange theory is in the way actors are viewed. Exchange theory “views actors (person or firm) as dealing not with another actor but with a market” (Emerson,1987, P.11), responding to various market characteristics; while social exchange theory views the exchange relationship between specific actors as “actions contingent on rewarding reactions from others.” (Blau, 1964, P.91)<br />
<br />
Today, social exchange theory exists in many forms, but all of them are driven by the same central concept of actors exchanging resources via a social exchange relationship. Where social exchange (e.g., Ax; By ) is the voluntary transfer of resources (x,y,…) between multiple actors (A,B,…) (Cook,1977). The theory has evolved from a dyadic model to a network model (Cook, 1977) with market properties (Emerson, 1987). The crux of the theory is still best captured in Homans’s own words (1958, P.606)<br />
<br />
“Social behavior is an exchange of goods, material goods but also non-material ones, such as the symbols of approval or prestige. Persons that give much to others try to get much from them, and persons that get much from others are under pressure to give much to them. This process of influence tends to work out at equilibrium to a balance in the exchanges. For a person in an exchange, what he gives may be a cost to him, just as what he gets may be a reward, and his behavior changes less as the difference of the two, profit, tends to a maximum.” <br />
<br />
This interaction between two actors (people, firms etc.) results in various contingencies, where the actors modify their resources to each others expectations. Power is the mechanics that can explain the relation of the actors (Emerson, 1962 and Blau, 1964). According to Emerson (1962), power is the property of a relation and not of an actor, because it “resides implicitly in the other’s dependency.” (P.32). <br />
<br />
Where “dependence of A upon Bj (DABJ) is a joint function, (1) varying directly with the value to A of the resources received from B and (2) varying inversely with comparison level for alternative exchange relations.” (Emerson and Cook, 1972b: 64). Power results from resource dependency (Emerson, 1962) in a dyadic relation but in a network exchange model, it is also derived from the structure (Cook,1977) - structural power. Here, power of A over B (PAB) in any relation Ax; By is the ability of A to decrease the exchange ratio, x/y (Emerson and Cook,1974, P. 25).<br />
<br />
To conclude, social exchange theory is best understood as a framework for explicating movement of resources, in imperfect market conditions, between dyads or a network via a social process (Emerson, 1987). <br />
== Diagram/schematic of theory ==<br />
[[Image:sxgt.jpg]]<br />
<br />
(Figure source: Cook, 1977)<br><br />
In the above diagram, the letters represents actors and the arrows depict the movement of resources. The arrow head points to the sourcing actors with the ends at the source actors. Here, B1 and B2 represent alternative exchange relations.<br />
== Originating author(s) ==<br />
George Homans (1958)<br />
== Seminal articles ==<br />
Homans,G.C.1958. Social Behavior as Exchange. American Journal of Sociology, 63 (6): 597-606.<br />
<br />
Emerson, R. 1962.Power-Dependence Relations. American Sociological Review, 27(1): 31-41.<br />
<br />
Blau,P.1964. Exchange and Power in Social Life. New York: Wiley.<br />
<br />
Levine,S. and White,P. 1961.Exchange as a Conceptual Framework for the Study of Interorganizational Relationships. Administrative Science Quarterly 5(4): 583-601.<br />
<br />
Cook.K.S. 1977.Exchange and Power in Networks of Interorganizational Relations. The Sociological Quarterly 18 (Winter 1977): 62-82.<br />
== Originating area ==<br />
Economics, Psychology and Sociology<br />
== Level of analysis ==<br />
Individual, group, and organizational<br />
== IS articles that use the theory ==<br />
Gefen,D. and M. Keil.1998. The impact of developer responsiveness on perceptions of usefulness and ease of use: an extension of the Technology Acceptance Model. ACM SIGMIS Database 29(2): 35-49.<br />
<br />
Gefen, D. and C. M., Ridings. 2002. [http://inn.colorado.edu/Details/Paper/7502 Implementation Team Responsiveness and User Evaluation of Customer Relationship Management: A Quasi-Experimental Design Study of Social Exchange Theory]. Journal of Management Information Systems 19(1): 47-69.<br />
<br />
Hall, H. 2003. Borrowed theory - Applying exchange theories in information science research. Library and Information Science Research 25(3): 287-306.<br />
<br />
Kankanhalli, A., B.C. Y. Tan, and K.K., Wei. 2005. [http://inn.colorado.edu/Details/Paper/111 Contributing Knowledge to Electronic Knowledge Repositories: An Empirical Investigation], MIS Quarterly 29(1): 113-143.<br />
<br />
Kern, T & Willcocks, LP. 2000. Exploring information technology outsourcing relationships: Theory and practice, Journal of Strategic Information Systems, 9(2000): 321-350.<br />
<br />
Lee, JN and YG, Kim.1999. [http://inn.colorado.edu/Details/Paper/8077 Effect of Partnership Quality on IS Outsourcing Success: Conceptual Framework and Validation], Journal of Management Information Systems, 15(4): 29-61.<br />
<br />
Clay Posey, Paul Benjamin Lowry, Tom L. Roberts, and Selwyn Ellis (2010). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1501447 Proposing the online community self-disclosure model: The case of working professionals in France and the UK who use online communities],” European Journal of Information Systems (EJIS), vol. 19(2), pp. 181–195 (doi: http://dx.doi.org/10.1057/ejis.2010.15). <br />
<br />
Son, JY., S. Narasimhan and F.J. Riggins. 2005. “[http://inn.colorado.edu/Details/Paper/6834 Effects of Relational Factors and Channel Climate on EDI Usage in the Customer-Supplier Relationship. Journal of Management Information Systems. 22(1): 321-353.<br />
<br />
== Links from this theory to other theories ==<br />
[[Resource dependency theory]], [[Expectation confirmation theory]], Economic rational choice theory, Power-Politics theory<br />
<br />
== External links ==<br />
http://theoryandscience.icaap.org/content/vol004.002/01_zafirovski.html, A paper exploring, discussing and extending social exchange theory from a sociological perspective by Milan Zafirovski.<br />
== Original Contributor(s) ==<br />
Jijesh Devan<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Elaboration_likelihood_model&diff=931Elaboration likelihood model2016-01-09T10:43:35Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>== '''Elaboration Likelihood Model''' ==<br />
<br />
== Acronym ==<br />
ELM<br />
<br />
== Alternate name(s)==<br />
None<br />
<br />
== Main dependent construct(s)/factor(s)==<br />
Changed Attitude<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
Argument Quality<br />
Peripheral Cues<br />
<br />
== Concise description of theory ==<br />
The elaboration likelihood model (ELM) is a psychological theory that addresses the process of persuasion. Specifically, it is a “dual-process” theory – that is, a theory that explains that there are two routes through which persuasion takes place, the central route and the peripheral route. <br />
<br />
In the ELM, information is the primary driver of attitude change. When information is carefully considered, cognitive effort is expended, and then an informed judgment is made, an individual is using the central route of information processing. When using the central route, individuals cognitively elaborate on the content of an informational message, evaluate its content, and consider other issues relevant to the information. Elaboration in the ELM thus refers to “the extent to which a person scrutinizes the issue-relevant arguments contained in the persuasive communication.” (Petty and Cacioppo, 1986, p. 7). When elaboration levels are high, the individual is using the central route.<br />
<br />
When elaboration levels are low, the individual is using the peripheral route. This route requires less cognitive effort than the aforementioned central route. Heuristics, cues, and affinity with the source of the information form the basis for an attitude change when using the peripheral route. Simple decision rules are used here rather than active, effortful analysis of information.<br />
<br />
The ELM explains that changes in attitudes are a function of (1) the quality of the information or argument, (2) peripheral cues, including heuristics and other stimuli that influence persuasion, and (3) elaboration likelihood. <br />
<br />
== Diagram/schematic of theory ==<br />
[[File:ELM_GeneralModel.jpg|400px|thumb|left|ELM model]]<br />
<br style="clear:both" /><br />
<br />
== Originating author(s) ==<br />
Petty, R.E., and Cacioppo, J.T. 1986. Communication and Persuasion: Central and Peripheral Routes to Attitude Change. New York: Springer-Verlag.<br />
<br />
Petty, R.E., Cacioppo, J.T., and Goldman, R. 1981. "Personal Involvement as a Determinant of Argument-Based Persuasion," Journal of Personality and Social Psychology (41:5), p 847.<br />
<br />
== Seminal articles ==<br />
Eagly, A.H., and Chaiken, S. 1993. The Psychology of Attitudes. Harcourt Brace Jovanovich College Publishers.<br />
<br />
Chaiken, S., and Trope, Y. 1999. Dual-Process Theories in Social Psychology. Guilford Press.<br />
<br />
== Originating area ==<br />
Psychology<br />
<br />
== Level of analysis ==<br />
Individual<br />
<br />
== Links to WWW sites describing theory ==<br />
Elaboration Likelihood Model - Wikipedia Entry - [http://en.wikipedia.org/wiki/Elaboration_likelihood_model] <br />
<br />
Elaboration Likelihood Model – Psychwiki.com - [http://www.psychwiki.com/wiki/Elaboration_Likelihood_Model]<br />
<br />
Elaboration Likelihood Model – University of Twente - [http://www.utwente.nl/cw/theorieenoverzicht/Theory%20clusters/Health%20Communication/Elaboration_Likelihood_Model/]<br />
<br />
Elaboration Likelihood Model – video from “The Psych Files” - [http://www.youtube.com/watch?v=VlqUPJ_LCrs]<br />
<br />
== Links from this theory to other theories ==<br />
[[Technology acceptance model]]<br />
<br />
[[Theory of planned behavior]]<br />
<br />
[[Unified theory of acceptance and use of technology]]<br />
<br />
== IS articles that use the theory ==<br />
Angst, C. M., and R. Agarwal (2009) "[http://inn.colorado.edu/Details/Paper/6317 Adoption of Electronic Health Records in the Presence of Privacy Concerns: The Elaboration Likelihood Model and Individual Persuasion]", MIS Quarterly, (33) 2, pp. 339-370.<br />
<br />
Bhattacherjee, A., and C. Sanford (2006) "[http://inn.colorado.edu/Details/Paper/6722 Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model]", MIS Quarterly, pp. 805-825.<br />
<br />
Cheung, C. M.-Y., C.-L. Sia, and K. K. Kuan (2012) "Is This Review Believable? A Study of Factors Affecting the Credibility of Online Consumer Reviews from an ELM Perspective", Journal of the Association for Information Systems, (13) 8.<br />
<br />
Dinev, T. (2014) "Why Would We Care about Privacy?", European Journal of Information Systems, (23) 2, pp. 97-102.<br />
<br />
Fui-Hoon Nah, F., and I. Benbasat (2004) "Knowledge-based Support in a Group Decision Making Context: An Expert-Novice Comparison", Journal of the Association for Information Systems, (5) 3.<br />
<br />
Jahng, J., H. Jain, and K. Ramamurthy (2007) "Effects of Interaction Richness on Consumer Attitudes and Behavioral Intentions in E-commerce: Some Experimental Results", European Journal of Information Systems, (16) 3, pp. 254-269.<br />
<br />
Kim, D., and I. Benbasat (2006) "[http://inn.colorado.edu/Details/Paper/6915 The Effects of Trust-Assuring Arguments on Consumer Trust in Internet Stores: Application of Toulmin's Model of Argumentation]", Information Systems Research, (17) 3, pp. 286-300.<br />
<br />
Kim, D., and I. Benbasat (2009) "Trust-Assuring Arguments in B2C E-commerce: Impact of Content, Source, and Price on Trust", Journal of Management Information Systems, (26) 3, pp. 175-206.<br />
<br />
Paul Benjamin Lowry, Gregory D. Moody, Anthony Vance, Matthew L. Jensen, Jeffrey L. Jenkins, and Taylor Wells (2012). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1948055 Using an elaboration likelihood approach to better understand the persuasiveness of website privacy assurance cues for online consumers],” Journal of the Association for Information Science and Technology (JASIST), vol. 63(4), pp. 755-766 (doi: http://dx.doi.org/10.1002/asi.21705).<br />
<br />
Meservy, T. O., M. L. Jensen, and K. J. Fadel (2013) "Evaluation of Competing Candidate Solutions in Electronic Networks of Practice", Information Systems Research, (25) 1, pp. 15-34.<br />
<br />
Sussman, S. W., and W. S. Siegal (2003) "[http://inn.colorado.edu/Details/Paper/6804 Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption]", Information Systems Research, (14) 1, pp. 47-65.<br />
<br />
Tam, K. Y., and S. Y. Ho (2005) "[http://inn.colorado.edu/Details/Paper/6821 Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective]", Information Systems Research, (16) 3, pp. 271-291.<br />
<br />
Zhang, W., and S. A. Watts (2008) "Capitalizing on Content: Information Adoption in Two Online Communities", Journal of the Association for Information Systems, (9) 2.<br />
<br />
== Contributor(s) ==<br />
Jeff Baker<br />
<br />
== Date last updated ==<br />
May 26, 2014<br />
<br />
Please feel free to make modifications to this site. In order to do so, you must register.</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=General_deterrence_theory&diff=930General deterrence theory2016-01-09T10:35:50Z<p>Pblowry: </p>
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== '''General deterrence theory''' ==<br />
----<br />
== Acronym ==<br />
GDT<br />
== Alternate name(s)==<br />
Deterrence theory (DT)<br />
== Main dependent construct(s)/factor(s)==<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
== Concise description of theory ==<br />
Schuessler (2009)<ref>Schuessler, J. 2009. General deterrence theory: Assessing information systems security effectiveness in large versus small businesses. Ph.D. dissertation, University of North Texas, United States -- Texas. (Publication No. AAT 3377466).</ref> wrote that General Deterrence Theory (GDT) "posits that individuals can be dissuaded from committing antisocial acts through the use of countermeasures, which include strong disincentives and sanctions relative to the act" (p. 11). He references Straub and Welke (1998) <ref>Straub, D. W., & Welke, R. J. (1998). Coping with systems risk: Security planning models for management decision making. Management Information Systems Quarterly, 22(4), 441.</ref> for the foundational work on this subject. Schuessler also noted "Using GDT as a guideline, countermeasures could be put in place to eliminate such a threat or at least mitigate some of the risk should the event occur. Countermeasures such as education and training, backups, reprimands and so on can all serve as tools to eliminate or mitigate such risk. The current research expands this conceptual view of GDT to include other sources of threats such as non-humans threats. In this way, other threats such as natural disasters and technical failures can also be examined. It is believed that this extension is valid because often times, preemptive planning can help to mitigate these threats as well. For example, backups can replace lost data after hardware failure or a natural disaster (p. 12)."<br />
<br />
<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
== Originating author(s) ==<br />
Straub, D. W., & Welke, R. J.<br />
== Seminal articles ==<br />
Straub, D. W., & Welke, R. J. (1998). Coping with systems risk: Security planning models for management decision making. Management Information Systems Quarterly, 22(4), 441.<br />
== Originating area ==<br />
Information Systems<br />
== Level of analysis ==<br />
<br />
== IS articles that use the theory ==<br />
<br />
Straub, D. W., & Welke, R. J. (1998). Coping with systems risk: Security planning models for management decision making. Management Information Systems Quarterly, 22(4), 441.<br />
<br />
Jeffrey D. Wall, Paul Benjamin Lowry, and Jordan Barlow (2016). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2611567 Organizational violations of externally governed privacy and security rules: Explaining and predicting selective violations under conditions of strain and excess],” Journal of the Association for Information Systems (JAIS), vol. 17(1) [in press] (http://aisel.aisnet.org/jais/vol17/iss1/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
[[Selective_organizational_information_privacy_and_security_violations_model_(SOIPSVM)]]<br />
<br />
== External links ==<br />
<br />
== Original Contributor(s) ==<br />
<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=General_deterrence_theory&diff=929General deterrence theory2016-01-09T10:32:53Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''General deterrence theory''' ==<br />
----<br />
== Acronym ==<br />
GDT<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
== Concise description of theory ==<br />
Schuessler (2009)<ref>Schuessler, J. 2009. General deterrence theory: Assessing information systems security effectiveness in large versus small businesses. Ph.D. dissertation, University of North Texas, United States -- Texas. (Publication No. AAT 3377466).</ref> wrote that General Deterrence Theory (GDT) "posits that individuals can be dissuaded from committing antisocial acts through the use of countermeasures, which include strong disincentives and sanctions relative to the act" (p. 11). He references Straub and Welke (1998) <ref>Straub, D. W., & Welke, R. J. (1998). Coping with systems risk: Security planning models for management decision making. Management Information Systems Quarterly, 22(4), 441.</ref> for the foundational work on this subject. Schuessler also noted "Using GDT as a guideline, countermeasures could be put in place to eliminate such a threat or at least mitigate some of the risk should the event occur. Countermeasures such as education and training, backups, reprimands and so on can all serve as tools to eliminate or mitigate such risk. The current research expands this conceptual view of GDT to include other sources of threats such as non-humans threats. In this way, other threats such as natural disasters and technical failures can also be examined. It is believed that this extension is valid because often times, preemptive planning can help to mitigate these threats as well. For example, backups can replace lost data after hardware failure or a natural disaster (p. 12)."<br />
<br />
<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
== Originating author(s) ==<br />
Straub, D. W., & Welke, R. J.<br />
== Seminal articles ==<br />
Straub, D. W., & Welke, R. J. (1998). Coping with systems risk: Security planning models for management decision making. Management Information Systems Quarterly, 22(4), 441.<br />
== Originating area ==<br />
Information Systems<br />
== Level of analysis ==<br />
<br />
== IS articles that use the theory ==<br />
<br />
== Links from this theory to other theories ==<br />
<br />
[[Selective_organizational_information_privacy_and_security_violations_model_(SOIPSVM)]]<br />
<br />
== External links ==<br />
<br />
== Original Contributor(s) ==<br />
<br />
<br><br />
<br><br />
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[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Selective_organizational_information_privacy_and_security_violations_model_(SOIPSVM)&diff=928Selective organizational information privacy and security violations model (SOIPSVM)2016-01-09T10:22:19Z<p>Pblowry: /* Diagram/schematic of theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
SOIPSVM<br />
<br />
== Alternate name(s) ==<br />
<br />
n/a<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Likelihood of a privacy or security rule violation [by the organization]<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Contextual conditions of formal and informal communication structures, and coupling between violation and outcomes. Rule characteristics of enforceability through certainty, severity, and celerity of sanctions; goal clarity of rules, and rule connectedness, perceived risk of violating a privacy or security rule, economic strain, and non-economic strain.<br />
== Motivation for SOIPSVM / Why IS researchers can benefit from using it ==<br />
Motivation for creation of the theory: Privacy and security concerns are pervasive because of the ease of access to information. Recurrent negative cases in the popular press attest to the failure of current privacy regulations to keep consumer and protected health information sufficiently secure in today’s climate of increased IT use. One reason for such failure is that organizations violate these regulations for multiple reasons. To address this issue, Wall, Lowry, and Barlow (2016) proposed a theoretical model to explain the likelihood that organizations will select an externally governed privacy or security rule for violation in response to organizational strain or slack resources. This proposed theoretical model, is the selective organizational information privacy and security violations model (SOIPSVM).<br />
== Concise description of theory ==<br />
The selective organizational information privacy and security violations model (SOIPSVM), explains how organizational structures and processes, along with characteristics of regulatory rules, alter perceptions of risk when an organization’s performance does not match its aspiration levels and, thereby, affects the likelihood of rule violations. Importantly, SOIPSVM is contextualized for the domain of organizational privacy and security violations. SOIPSVM builds on and extends the selective organizational rule violations model (SORVM), which posits that organizational rule violations are selective. <br />
SOIPSVM provides at least four contributions to the privacy and security literature that can further guide empirical research and practice. First, SOIPSVM introduces the concept of selectivity in rule violations to privacy and security research. This concept can improve privacy and security research by showing that organizational violations of privacy and security rules are dynamic and selective yet influenced by external forces. <br />
Second, SOIPSVM extends the boundaries of SORVM, which is limited to explaining the behavior of organizations under strain, such as economic hardship. SOIPSVM contributes to the theory of selective deviance by proposing that selectivity extends to organizations with slack resources. <br />
Third, SOIPSVM addresses ideas of non-economic risk and strain in addition to economic risk and strain. Thus, SOIPSVM explains organizational rule-violating behavior as an attempt to protect core organizational values from external entities that pressure organizations to change their values to comply with rules. <br />
Fourth, SOIPSVM broadens the theoretical scope of two important constructs (namely, structural secrecy and procedural emphasis) to improve the model’s explanatory power. <br />
Fifth, SOIPSVM identifies important elements of rule enforcement by drawing from the tenets of general deterrence theory. <br />
<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:SOIPSVM_figure1.jpg]]<br />
<br />
Figure 1. Overview of the selective organizational information privacy and security violations model (SOIPSVM), from Wall, Lowry, and Barlow (2016).<br />
<br />
== Originating author(s) ==<br />
<br />
Jeffrey D. Wall, Paul Benjamin Lowry, and Jordan B. Barlow<br />
<br />
== Seminal articles ==<br />
Jeffrey D. Wall, Paul Benjamin Lowry, and Jordan Barlow (2016). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2611567 Organizational violations of externally governed privacy and security rules: Explaining and predicting selective violations under conditions of strain and excess],” Journal of the Association for Information Systems (JAIS), vol. 17(1) [in press] (http://aisel.aisnet.org/jais/vol17/iss1/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Organization<br />
<br />
== IS and non-IS articles that use the theory ==<br />
TBD<br />
<br />
== Links from this theory to other theories ==<br />
<br />
GDT or [[General_deterrence_theory]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=File:SOIPSVM_figure1.JPG&diff=927File:SOIPSVM figure1.JPG2016-01-09T10:19:50Z<p>Pblowry: Overview of the selective organizational information privacy and security violations model (SOIPSVM)</p>
<hr />
<div>Overview of the selective organizational information privacy and security violations model (SOIPSVM)</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=File:SOIPSVM_figure1.jpg&diff=926File:SOIPSVM figure1.jpg2016-01-09T10:10:53Z<p>Pblowry: Overview of the selective organizational information privacy and security violations model (SOIPSVM)</p>
<hr />
<div>Overview of the selective organizational information privacy and security violations model (SOIPSVM)</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Selective_organizational_information_privacy_and_security_violations_model_(SOIPSVM)&diff=925Selective organizational information privacy and security violations model (SOIPSVM)2016-01-09T10:09:46Z<p>Pblowry: Created page with "== Acronym == SOIPSVM == Alternate name(s) == n/a == Main dependent construct(s)/factor(s) == Likelihood of a privacy or security rule violation [by the organization] ==..."</p>
<hr />
<div>== Acronym ==<br />
<br />
SOIPSVM<br />
<br />
== Alternate name(s) ==<br />
<br />
n/a<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Likelihood of a privacy or security rule violation [by the organization]<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Contextual conditions of formal and informal communication structures, and coupling between violation and outcomes. Rule characteristics of enforceability through certainty, severity, and celerity of sanctions; goal clarity of rules, and rule connectedness, perceived risk of violating a privacy or security rule, economic strain, and non-economic strain.<br />
== Motivation for SOIPSVM / Why IS researchers can benefit from using it ==<br />
Motivation for creation of the theory: Privacy and security concerns are pervasive because of the ease of access to information. Recurrent negative cases in the popular press attest to the failure of current privacy regulations to keep consumer and protected health information sufficiently secure in today’s climate of increased IT use. One reason for such failure is that organizations violate these regulations for multiple reasons. To address this issue, Wall, Lowry, and Barlow (2016) proposed a theoretical model to explain the likelihood that organizations will select an externally governed privacy or security rule for violation in response to organizational strain or slack resources. This proposed theoretical model, is the selective organizational information privacy and security violations model (SOIPSVM).<br />
== Concise description of theory ==<br />
The selective organizational information privacy and security violations model (SOIPSVM), explains how organizational structures and processes, along with characteristics of regulatory rules, alter perceptions of risk when an organization’s performance does not match its aspiration levels and, thereby, affects the likelihood of rule violations. Importantly, SOIPSVM is contextualized for the domain of organizational privacy and security violations. SOIPSVM builds on and extends the selective organizational rule violations model (SORVM), which posits that organizational rule violations are selective. <br />
SOIPSVM provides at least four contributions to the privacy and security literature that can further guide empirical research and practice. First, SOIPSVM introduces the concept of selectivity in rule violations to privacy and security research. This concept can improve privacy and security research by showing that organizational violations of privacy and security rules are dynamic and selective yet influenced by external forces. <br />
Second, SOIPSVM extends the boundaries of SORVM, which is limited to explaining the behavior of organizations under strain, such as economic hardship. SOIPSVM contributes to the theory of selective deviance by proposing that selectivity extends to organizations with slack resources. <br />
Third, SOIPSVM addresses ideas of non-economic risk and strain in addition to economic risk and strain. Thus, SOIPSVM explains organizational rule-violating behavior as an attempt to protect core organizational values from external entities that pressure organizations to change their values to comply with rules. <br />
Fourth, SOIPSVM broadens the theoretical scope of two important constructs (namely, structural secrecy and procedural emphasis) to improve the model’s explanatory power. <br />
Fifth, SOIPSVM identifies important elements of rule enforcement by drawing from the tenets of general deterrence theory. <br />
<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:SOIPSVM_figure1.JPG]]<br />
<br />
Figure 1. Overview of the selective organizational information privacy and security violations model (SOIPSVM), from Wall, Lowry, and Barlow (2016).<br />
<br />
== Originating author(s) ==<br />
<br />
Jeffrey D. Wall, Paul Benjamin Lowry, and Jordan B. Barlow<br />
<br />
== Seminal articles ==<br />
Jeffrey D. Wall, Paul Benjamin Lowry, and Jordan Barlow (2016). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2611567 Organizational violations of externally governed privacy and security rules: Explaining and predicting selective violations under conditions of strain and excess],” Journal of the Association for Information Systems (JAIS), vol. 17(1) [in press] (http://aisel.aisnet.org/jais/vol17/iss1/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Organization<br />
<br />
== IS and non-IS articles that use the theory ==<br />
TBD<br />
<br />
== Links from this theory to other theories ==<br />
<br />
GDT or [[General_deterrence_theory]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Main_Page&diff=924Main Page2016-01-09T10:08:09Z<p>Pblowry: /* Theories */</p>
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<br />
== Theories ==<br />
<br />
*[[Absorptive capacity theory]]<br />
*[[Actor network theory]]<br />
*[[Accountability theory|Accountability theory (NEW entry!)]]<br />
*[[Adaptive structuration theory]]<br />
*[[Administrative behavior, theory of]]<br />
*[[Agency theory]] <br />
*[[Argumentation theory]]<br />
*[[Behavioral decision theory]]<br />
*[[Boundary object theory]]<br />
*[[Chaos theory]]<br />
*[[Cognitive dissonance theory]]<br />
*[[Cognitive fit theory]]<br />
*[[Cognitive load theory]]<br />
*[[Competitive strategy (Porter)]]<br />
*[[Complexity theory]]<br />
*[[Contingency theory]]<br />
*[[Critical realism theory]]<br />
*[[Critical social theory]] <br />
*[[Critical success factors, theory of]]<br />
*[[Customer Focus Theory]]<br />
*[[Deferred action, theory of]] <br />
*[[Delone and McLean IS success model]]<br />
*[[Diffusion of innovations theory]]<br />
*[[Dynamic capabilities]]<br />
*[[Elaboration likelihood model]]<br />
*[[Embodied social presence theory]]<br />
*[[Equity theory]] <br />
*[[Evolutionary theory]]<br />
*[[Expectation confirmation theory]] <br />
*[[Feminism theory]]<br />
*[[Fit-Viability theory]]<br />
*[[Flow theory]]<br />
*[[Game theory]]<br />
*[[Garbage can theory]] <br />
*[[General systems theory]]<br />
*[[General deterrence theory]]<br />
*[[Hedonic-motivation system adoption model (HMSAM)|Hedonic-motivation system adoption model (HMSAM) (NEW Entry!)]]<br />
*[[Hermeneutics]]<br />
*[[Illusion of control]]<br />
*[[Impression management, theory of]]<br />
*[[Information processing theory]]<br />
*[[Institutional theory]]<br />
*[[International information systems theory]]<br />
*[[Kellers Motivational Model |Keller's Motivational Model]]<br />
*[[Knowledge-based theory of the firm]]<br />
*[[Language action perspective]] <br />
*[http://istheory.byu.edu/wiki/Lemon_Market_Theory Information asymmetry theory (lemon market)]<br />
*[[Management fashion theory]]<br />
*[[Media richness theory]]<br />
*[[Media synchronicity theory]]<br />
*[[Modal aspects, theory of]]<br />
*[[Multi-attribute utility theory]] <br />
*[[Multi-motive information systems continuance model (MISC)]]<br />
*[[Organizational culture theory]] <br />
*[[Organizational information processing theory]]<br />
*[[Organizational knowledge creation]]<br />
*[[Organizational learning theory]]<br />
*[[Portfolio theory]] <br />
*[[Process virtualization theory]]<br />
*[[Prospect theory]] <br />
*[[Protection motivation theory (NEW entry!)]]<br />
*[[Punctuated equilibrium theory]]<br />
*[[Real options theory]]<br />
*[[Resource-based view of the firm]]<br />
*[[Resource dependency theory]]<br />
*[[Selective organizational information privacy and security violations model (SOIPSVM)]]<br />
*[[Self-efficacy theory]]<br />
*[[SERVQUAL]]<br />
*[http://is.theorizeit.org/wiki/Signaling Signaling theory]<br />
*[[Social capital theory]]<br />
*[[Social cognitive theory]]<br />
*[[Social exchange theory]]<br />
*[[Social learning theory]]<br />
*[[Social network theory]]<br />
*[[Social shaping of technology]]<br />
*[[Socio-technical theory]]<br />
*[[Soft systems theory]]<br />
*[[Stakeholder theory]] <br />
*[[Structuration theory]]<br />
*[[Task closure theory]] <br />
*[[Task-technology fit]]<br />
*[[Technological frames of reference]]<br />
*[[Technology acceptance model]] <br />
*[[Technology dominance, theory of]] <br />
*[[Technology-organization-environment framework]]<br />
*[[Theory of collective action]]<br />
*[[Theory of planned behavior]]<br />
*[[Theory of reasoned action]]<br />
*[[Transaction cost economics]] <br />
*[[Transactive memory theory]] <br />
*[[Unified theory of acceptance and use of technology]]<br />
*[[Usage control model]]<br />
*[[Work systems theory]]<br />
*[[Yield shift theory of satisfaction]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=922Multi-motive information systems continuance model (MISC)2015-11-22T10:09:46Z<p>Pblowry: /* IS and non-IS articles that use the theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:MISC_figure1.JPG]]<br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS and non-IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/).<br />
<br />
Jin-Liang Wang, Hai-Zhen Wang, James Gaskin, Li-Hui Wang, “[http://www.sciencedirect.com/science/article/pii/S0747563215300169 The role of stress and motivation in problematic smartphone use among college students],” Computers in Human Behavior, Volume 53, December 2015, Pages 181-188, ISSN 0747-5632, http://dx.doi.org/10.1016/j.chb.2015.07.005.<br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=921Multi-motive information systems continuance model (MISC)2015-11-22T10:08:49Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:MISC_figure1.JPG]]<br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS and non-IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/).<br />
<br />
Jin-Liang Wang, Hai-Zhen Wang, James Gaskin, Li-Hui Wang, [http://www.sciencedirect.com/science/article/pii/S0747563215300169 The role of stress and motivation in problematic smartphone use among college students’, Computers in Human Behavior, Volume 53, December 2015, Pages 181-188, ISSN 0747-5632, http://dx.doi.org/10.1016/j.chb.2015.07.005.<br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Accountability_theory&diff=920Accountability theory2015-11-21T14:12:32Z<p>Pblowry: /* Diagram/schematic of theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
Acronyms are not commonly used for accountability theory.<br />
<br />
== Alternate name(s) ==<br />
<br />
Felt Accountability Theory, Accountability Model<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
Accountability is “is a process in which a person has a potential obligation to explain his/her actions to another party who has the right to pass judgment on those actions and to administer potential positive or negative consequences in response to them” (Vance, Lowry and Eggett 2015, p. 347).<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Identifiability, expectation of evaluation, awareness of monitoring, social presence<br />
<br />
== Originating area ==<br />
<br />
Organization science; Management<br />
<br />
== Level of analysis ==<br />
<br />
Individual; organizational<br />
== Concise description of theory ==<br />
As explained by Vance, Lowry and Eggett (2015), accountability theory explains how the perceived need to justify one’s behaviors to another party causes one to consider and feel accountable for the process by which decisions and judgments have been reached. In turn, this perceived need to account for a decision-making process and outcome increases the likelihood that one will think deeply and systematically about one’s procedural behaviors. This theory was originally developed by Tetlock, Lerner, and colleagues and has been effectively applied in organizational research.<br />
<br />
Importantly, as explained carefully by Vance, Lowry, and Eggett (2013), a useful way to understand accountability is to distinguish between its two most prevalent uses: (1) as a virtue and (2) as a mechanism. As a virtue, accountability is seen as a quality in which a person displays a willingness to accept responsibility, a desirable trait in public officials, government agencies, or firms; hence, in this use, accountability is a positive feature of an entity. As a mechanism, accountability is seen as a process in which a person has a potential obligation to explain his or her actions to another party who has the right to pass judgment on the actions as well as to subject the person to potential consequences for his or her actions. Accountability theory focuses on the process of accountability.<br />
<br />
Accountability theory proposes several mechanisms that increase accountability perceptions. For example, “even the simplest accountability manipulation necessarily implicates several empirically distinguishable submanipulations” (Lerner and Tetlock 1999, p. 255), including the presence of another person, identifiability, and expectation of evaluation. Recent research has shown that IT design artifacts of systems can manipulate the four core components of accountability theory and thus improve employees’ felt accountability toward organizational system security without disruptive interventions or traininging (Vance et al. 2013; 2015): (1) identifiability, (2) expectation of evaluation, (3) awareness of monitoring, and (4) social presence.<br />
<br />
''Identifiability'' is a person’s “knowledge that his outputs could be linked to him” and thus reveal his/her true identity (Williams, Harkins and Latane 1981, p. 309)<br />
<br />
''Expectation of evaluation'' is the belief that one’s “performance will be assessed by another [party] according to some normative ground rules and with some implied consequences” (Lerner and Tetlock 1999, p. 255).<br />
<br />
''Awareness of monitoring'' is a user’s state of active cognition that his/her system-related work is monitored (Vance, Lowry, and Eggett 2015).<br />
<br />
''Social presence'' is the awareness of other users in the system (Vance, Lowry, and Eggett 2015).<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:Accountability_figure1.jpg]]<br />
<br />
'''Figure 1. Overview of Accountability Theory Adapted to Preventing Access Policy Violations by Vance, Lowry, and Eggett (2015, p. 348).'''<br />
<br />
== Originating author(s) ==<br />
P. E. Tetlock developed the initial concepts and mechanisms on accountability through several key papers. <br />
<br />
J.S. Lerner later worked with P.E. Tetlock in (Lerner and Tetlock 1999) to develop what is largely referred to as accountability theory.<br />
<br />
Anthony Vance and Paul Benjamin Lowry later re-contexualized accountability for use with deterring security access policy violations with organizational employees by designing system features that promote accountability in end-users.<br />
== Seminal articles ==<br />
Lerner, J. S., and Tetlock, P. E. 1999. “Accounting for the Effects of Accountability,” Psychological Bulletin (125:2), pp. 255-275<br />
<br />
Tetlock, P. E. 1983a. “Accountability and Complexity of Thought,” Journal of Personality and Social Psychology (45:1), pp. 74-83.<br />
<br />
Tetlock, P. E. 1983b. “Accountability and the Perseverance of First Impressions,” Social Psychology Quarterly (46:4), pp. 285-292.<br />
<br />
== Other key references outside of IS ==<br />
<br />
Tetlock, P. E. 1985. “Accountability: A Social Check on the Fundamental Attribution Error,” Social Psychology Quarterly (48:3), pp. 227-236.<br />
<br />
Tetlock, P. E. 1992. “The Impact of Accountability on Judgment and Choice: Toward a Social Contingency Model,” Advances in Experimental Social Psychology (25:1992), pp. 331-376.<br />
<br />
Tetlock, P. E. 1999. “Accountability Theory: Mixing Properties of Human Agents with Properties of Social Systems,” in Shared Cognition in Organizations: The Management of Knowledge, <br />
<br />
L. Thompson, J. M. Levine, and D. M. Messick (eds.), Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 117-138.<br />
<br />
Tetlock, P. E., and Boettger, R. 1989. “Accountability: A Social Magnifier of the Dilution Effect,” Journal of Personality and Social Psychology (57:3), pp. 388-398.<br />
<br />
Tetlock, P. E., and Boettger, R. 1994. “Accountability Amplifies the Status-Quo Effect When Change Creates Victims,” Journal of Behavioral Decision Making (7:1), pp. 1-23.<br />
<br />
Tetlock, P. E., and Kim, J. I. 1987. “Accountability and Judgment Processes in a Personality Prediction Task,” Journal of Personality and Social Psychology (52:4), pp. 700-709.<br />
<br />
Tetlock, P. E., Skitka, L., and Boettger, R. 1989. “Social and Cognitive Strategies for Coping with Accountability: Conformity, Complexity, and Bolstering,” Journal of Personality andSocial Psychology (57:4), pp. 632-640.<br />
<br />
== Leading IS Articles that Contextualized Accountability Theory to IS context ==<br />
Anthony Vance, Paul Benjamin Lowry, and Dennis Eggett (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2549000 A new approach to the problem of access policy violations: Increasing perceptions of accountability through the user interface],” MIS Quarterly (MISQ), vol. 39(2), pp. 345–366.<br />
<br />
Anthony Vance, Paul Benjamin Lowry, and Dennis Eggett (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2173671 Using accountability to reduce access policy violations in information systems],” Journal of Management Information Systems (JMIS), vol. 29(4), pp. 263–289 (doi: http://dx.doi.org/10.2753/MIS0742-1222290410).<br />
<br />
Vance, Lowry, and Eggett (2015; 2013) uniquely contextualized accountability theory to the context of organizational security—namely for dealing with access policy violations. Access-policy violations are a growing problem with substantial costs for organizations. Although training programs and sanctions have been suggested as a means of reducing these violations, evidence shows these persist. It is thus imperative to identify additional ways to reduce access-policy violations, especially for systems providing broad access to data. They used accountability theory to develop four user-interface (UI) design artifacts that raise users’ accountability perceptions within systems and in turn decrease access-policy violations. To test their new accountability model, they uniquely applied the scenario-based factorial survey method to various graphical manipulations of a records system containing sensitive information at a large organization with over 300 end-users who use the system daily. They showed that the UI design artifacts corresponding to four submanipulations of accountability can raise accountability and reduce access policy violation intentions. Importantly, this approach increases accountability without harsh policies (e.g., threats through sanctions) or disruption intervention (e.g., training).<br />
== IS articles that use the theory ==<br />
David Eargle, Anthony Vance, and Paul Benjamin Lowry (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2529609 How moral Intensity and impulsivity moderate the influence of accountability on access policy violations in information systems],” Seventh Workshop on Information Security and Privacy 2013 (WISP 2013) at the 2013 International Conference on Information Systems (ICIS 2013), Milan, Italy, December 14 (doi: http://dx.doi.org/10.13140/2.1.3754.4644). <br />
<br />
Anthony Vance, Paul Benjamin Lowry, and Dennis Eggett (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2549000 A new approach to the problem of access policy violations: Increasing perceptions of accountability through the user interface],” MIS Quarterly (MISQ), vol. 39(2), pp. 345–366. <br />
<br />
Anthony Vance, Paul Benjamin Lowry, and Dennis Eggett (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2173671 Using accountability to reduce access policy violations in information systems],” Journal of Management Information Systems (JMIS), vol. 29(4), pp. 263–289 (doi: http://dx.doi.org/10.2753/MIS0742-1222290410). <br />
<br />
Anthony Vance, Braden Molyneux, Paul Benjamin Lowry, and Dennis Eggett (2011). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2273570 A new approach to the problem of unauthorized access: Raising perceptions of accountability through user interface design features],” Proceedings of the Dewald Roode Workshop in Information Systems Security 2011, IFIP WG 8.11 / 11.13, Blacksburg, VA, September 22–23, pp. 1–38. <br />
<br />
Anthony Vance, Gove Allen, Braden Molyneux, and Paul Benjamin Lowry (2010). “Making systems users accountable: Using accountability to deter access policy violations,” MIS Quarterly Pre-ICIS Workshop for Authors at the International Conference on System Sciences, St. Louis, MO, December 12. <br />
<br />
Anthony Vance, Gove Allen, Braden Molyneux, and Paul Benjamin Lowry (2010). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1668117 Making systems users accountable: Using accountability to deter access policy violations],” Proceedings of the Dewald Roode Workshop on IS Security Research 2010, IFIP WG 8.11 / 11.13, Waltham, MA, October 8–9, pp. 369–391. <br />
== Links from this theory to other theories ==<br />
<br />
n/a<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Hedonic-motivation_system_adoption_model_(HMSAM)&diff=919Hedonic-motivation system adoption model (HMSAM)2015-11-21T14:12:01Z<p>Pblowry: /* Diagram/schematic of theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
HMSAM<br />
<br />
== Alternate name(s) ==<br />
<br />
n/a<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Behavioral intention to use, Immersion<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Perceived ease of use, perceived usefulness, curiosity, joy, control<br />
<br />
== Concise description of theory ==<br />
<br />
The hedonic-motivation system adoption model (HMSAM) is a native information systems theory to improve the understanding of hedonic-motivation systems (HMS) adoption. HMS are systems used primarily to fulfill users’ intrinsic motivations, such for online gaming, virtual worlds, online shopping, learning/education, online dating, digital music repositories, social networking, only pornography, gamified systems, and for general gamification. Instead of a minor, general technology acceptance model (TAM) extension, HMSAM is an HMS-specific system acceptance model based on an alternative theoretical perspective, which is in turn grounded in flow-based cognitive absorption (CA). The HMSAM further builds on van der Heijden’s (2004) model of hedonic system adoption by including CA as a key mediator of perceived ease of use (PEOU) and of behavioral intentions to use (BIU) hedonic-motivation systems. Typically, models simplistically represent “intrinsic motivations” by mere perceived enjoyed. Instead, HMSAM uses the more complex, rich construct of CA, which includes joy, control, curiosity, focused immersion, and temporal dissociation. CA is construct that is grounded in the seminal flow literature, yet ironically CA has traditionally been used as a static construct, as if all five of its subconstructs occur at the same time—in direct contradiction to the flow literature. Thus, part of HMSAM’s contribution is to return CA closer to its flow roots by re-ordering these CA subconstructs into more natural process-variance order as predicted by flow. Empirical data collection along with mediation tests further support this modeling approach. Figure 1 overviews HMSAM.<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:HMSAM_overview.jpg]]<br />
<br />
Figure 1. Overview of HMSAM, from Lowry et al. (2013)<br />
<br />
== Originating author(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry], James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts.<br />
<br />
== Seminal articles ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
== Originating area ==<br />
<br />
Information Systems (native IS theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1<br />
<br />
Yichuan Wang, Pramod Rajan, Chetan S. Sankar, P. K. Raju (2014). “Relationships between Goal Clarity, Concentration and Learning Effectiveness When Playing Serious Games,” Proceeding of Twentieth Americas Conference on Information Systems, Savannah, Georgia.<br />
<br />
Tobias Kowatsch and Flavius Kehr (2014). “Towards a Design Theory for IS Services Enabling Incentive-based Health Promotion in Organizations,” Wirtschaftsinformatik (MKWI 2014)<br />
<br />
Mark Keith, Greg Anderson, Douglas Dean, and James Eric Gaskin (2014). “The Effects of Team Flow on Performance: A Video Game Experiment,” SIGHCI 2014 Proceedings.<br />
<br />
Mosiane, Segomotso; Brown, Irwin (2014). “Exploring antecedents of game-based learning effectiveness,” Proceedings of the 25th Australasian Conference on Information Systems, 8th - 10th December, Auckland, New Zealand<br />
<br />
Thomas Wiegand and Stefan Stieglitz (2015) “Serious Fun - Effects of Gamification on Knowledge Exchange in Enterprises” Lecture Notes in Informatics Proceedings<br />
<br />
Lisa-Maria Putz and Horst Treiblmaier (2015). “Creating a Theory-Based Research Agenda for Gamification,” Proceeding of Twenty-First Americas Conference on Information Systems, Puerto Rico.<br />
<br />
== Links from this theory to other theories ==<br />
<br />
[[Multi-motive information_systems continuance model (MISC)]]<br><br />
<br />
[[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=918Multi-motive information systems continuance model (MISC)2015-11-21T14:09:32Z<p>Pblowry: /* Diagram/schematic of theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:MISC_figure1.JPG]]<br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=File:MISC_figure1.JPG&diff=917File:MISC figure1.JPG2015-11-21T14:08:51Z<p>Pblowry: Overview of the multimotive information systems continuance model (MISC) model, by Lowry et al. (2015)</p>
<hr />
<div>Overview of the multimotive information systems continuance model (MISC) model, by Lowry et al. (2015)</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=916Multi-motive information systems continuance model (MISC)2015-11-21T10:59:36Z<p>Pblowry: /* Diagram/schematic of theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:MISC_figure_1.jpg]]<br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=915Multi-motive information systems continuance model (MISC)2015-11-21T10:57:41Z<p>Pblowry: /* Diagram/schematic of theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:MISC_figure_1.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=914Multi-motive information systems continuance model (MISC)2015-11-21T10:56:10Z<p>Pblowry: /* Diagram/schematic of theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
[[File:MISC_figure_1.jpg]]<br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=913Multi-motive information systems continuance model (MISC)2015-11-21T10:54:57Z<p>Pblowry: /* Diagram/schematic of theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
[[File:MISC_figure_1.jpg]]<br />
</gallery><br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=File:MISC_figure_1.JPG&diff=912File:MISC figure 1.JPG2015-11-21T10:52:32Z<p>Pblowry: Depiction of the multimotive information systems continuance model (MISC) from Lowry et al. (2015)</p>
<hr />
<div>Depiction of the multimotive information systems continuance model (MISC) from Lowry et al. (2015)</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Hedonic-motivation_system_adoption_model_(HMSAM)&diff=911Hedonic-motivation system adoption model (HMSAM)2015-11-21T10:50:56Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>== Acronym ==<br />
<br />
HMSAM<br />
<br />
== Alternate name(s) ==<br />
<br />
n/a<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Behavioral intention to use, Immersion<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Perceived ease of use, perceived usefulness, curiosity, joy, control<br />
<br />
== Concise description of theory ==<br />
<br />
The hedonic-motivation system adoption model (HMSAM) is a native information systems theory to improve the understanding of hedonic-motivation systems (HMS) adoption. HMS are systems used primarily to fulfill users’ intrinsic motivations, such for online gaming, virtual worlds, online shopping, learning/education, online dating, digital music repositories, social networking, only pornography, gamified systems, and for general gamification. Instead of a minor, general technology acceptance model (TAM) extension, HMSAM is an HMS-specific system acceptance model based on an alternative theoretical perspective, which is in turn grounded in flow-based cognitive absorption (CA). The HMSAM further builds on van der Heijden’s (2004) model of hedonic system adoption by including CA as a key mediator of perceived ease of use (PEOU) and of behavioral intentions to use (BIU) hedonic-motivation systems. Typically, models simplistically represent “intrinsic motivations” by mere perceived enjoyed. Instead, HMSAM uses the more complex, rich construct of CA, which includes joy, control, curiosity, focused immersion, and temporal dissociation. CA is construct that is grounded in the seminal flow literature, yet ironically CA has traditionally been used as a static construct, as if all five of its subconstructs occur at the same time—in direct contradiction to the flow literature. Thus, part of HMSAM’s contribution is to return CA closer to its flow roots by re-ordering these CA subconstructs into more natural process-variance order as predicted by flow. Empirical data collection along with mediation tests further support this modeling approach. Figure 1 overviews HMSAM.<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:HMSAM_overview.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of HMSAM, from Lowry et al. (2013)<br />
<br />
== Originating author(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry], James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts.<br />
<br />
== Seminal articles ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
== Originating area ==<br />
<br />
Information Systems (native IS theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1<br />
<br />
Yichuan Wang, Pramod Rajan, Chetan S. Sankar, P. K. Raju (2014). “Relationships between Goal Clarity, Concentration and Learning Effectiveness When Playing Serious Games,” Proceeding of Twentieth Americas Conference on Information Systems, Savannah, Georgia.<br />
<br />
Tobias Kowatsch and Flavius Kehr (2014). “Towards a Design Theory for IS Services Enabling Incentive-based Health Promotion in Organizations,” Wirtschaftsinformatik (MKWI 2014)<br />
<br />
Mark Keith, Greg Anderson, Douglas Dean, and James Eric Gaskin (2014). “The Effects of Team Flow on Performance: A Video Game Experiment,” SIGHCI 2014 Proceedings.<br />
<br />
Mosiane, Segomotso; Brown, Irwin (2014). “Exploring antecedents of game-based learning effectiveness,” Proceedings of the 25th Australasian Conference on Information Systems, 8th - 10th December, Auckland, New Zealand<br />
<br />
Thomas Wiegand and Stefan Stieglitz (2015) “Serious Fun - Effects of Gamification on Knowledge Exchange in Enterprises” Lecture Notes in Informatics Proceedings<br />
<br />
Lisa-Maria Putz and Horst Treiblmaier (2015). “Creating a Theory-Based Research Agenda for Gamification,” Proceeding of Twenty-First Americas Conference on Information Systems, Puerto Rico.<br />
<br />
== Links from this theory to other theories ==<br />
<br />
[[Multi-motive information_systems continuance model (MISC)]]<br><br />
<br />
[[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Expectation_confirmation_theory&diff=910Expectation confirmation theory2015-11-21T10:49:22Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Expectation confirmation theory''' ==<br />
----<br />
== Acronym ==<br />
ECT<br />
== Alternate name(s)==<br />
Expectation disconfirmation theory (EDT)<br />
== Main dependent construct(s)/factor(s)==<br />
Satisfaction<br />
== Main independent construct(s)/factor(s) ==<br />
Expectations, Perceived performance, Disconfirmation<br />
== Concise description of theory ==<br />
Expectations-confirmation theory posits that expectations, coupled with perceived performance, lead to post-purchase satisfaction. This effect is mediated through positive or negative disconfirmation between expectations and performance. If a product outperforms expectations (positive disconfirmation) post-purchase satisfaction will result. If a product falls short of expectations (negative disconfirmation) the consumer is likely to be dissatisfied (Oliver, 1980; Spreng et al. 1996). <br />
<br />
The four main constructs in the model are: expectations, performance, disconfirmation, and satisfaction. Expectations reflect anticipated behavior (Churchill and Suprenant, 1982). They are predictive, indicating expected product attributes at some point in the future (Spreng et al. 1996). Expectations serve as the comparison standard in ECT – what consumers use to evaluate performance and form a disconfirmation judgment (Halstead, 1999). Disconfirmation is hypothesized to affect satisfaction, with positive disconfirmation leading to satisfaction and negative disconfirmation leading to dissatisfaction. <br />
<br />
A major debate within the marketing literature concerns the nature of the effect of disconfirmation on satisfaction. The root of the problem lies in the definition of predictive expectations as the comparison standard for perceived performance. In such case, the confirmation of negative expectations is not likely to lead to satisfaction (Santos and Boote 2003). To overcome this problem, researchers have proposed other comparison standards such as desires, ideals, equity, or past product and brand experience (see reviews by Halstead, 1999; Yi 1990 and analysis by Tse and Wilton, 1988. Also see Spreng et al. 1996; Woodruff et al., 1983). <br />
== Diagram/schematic of theory ==<br />
[[Image:Ect.JPG]]<br />
== Originating author(s) ==<br />
Oliver (1977, 1980)<br />
== Seminal articles ==<br />
Oliver R. L, 1977, "Effect of Expectation and Disconfirmation on Postexposure Product Evaluations - an Alternative Interpretation," Journal of Applied Psychology, 62(4), p. 480. <br />
<br />
Oliver R. L, 1980, "A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions," JMR, Journal of Marketing Research, 17(4), p. 460. <br />
<br />
Spreng R. A, S.B. MacKenzie and R.W. Olshavsky, 1996, "A reexamination of the determinants of consumer satisfaction," Journal of Marketing, 60(3), p. 15.<br />
== Originating area ==<br />
Marketing, Consumer behavior<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Au, N., Ngai, E., Cheng, E. "A Critical Review of End-User Information System Satisfaction Research and a New Research Framework," Omega (30), 2002, pp. 451-478. <br />
<br />
Bhattacherjee, A. (2001a). [http://inn.colorado.edu/Details/Paper/137 Understanding information systems continuance: An expectation-confirmation model]. MIS Quarterly, 25(3), 351.<br />
<br />
Bhattacherjee, A. (2001b). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32, 201.<br />
<br />
Erevelles, S., Srinivasan, S., & Rangel, S. (2003). Consumer satisfaction for internet service providers: An analysis of underlying processes. Information Technology and Management, 4(1), 69.<br />
<br />
Hsu, M.H., Chiu, C.M., & Ju, T.L. (2004). Determinants of continued use of the WWW: An integration of two theoretical models. Industrial Management & Data Systems, 104 (9), 766.<br />
<br />
Khalifa M. and V. Liu, 2004, "The State of Research on Information System Satisfaction," JITTA : Journal of Information Technology Theory and Application, 5(4), p. 37.<br />
<br />
Lin, C.S., Wu, S., & Tsai, R.J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management 42, 683.<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1<br />
<br />
<nowiki> </nowiki>McKinney, V., Yoon, K., & Zahedi, F. (2002). [http://inn.colorado.edu/Details/Paper/6802 The measurement of web-customer satisfaction: An expectation and disconfirmation approach.] Information Systems Research, 13(3), 296.<br />
<br />
Nevo, D. and Wade, M., “How to Avoid Disappointment by Design”, The Communications of the ACM, Vol. 50, No. 4, pp. 43-48, 2007.<br />
<br />
Piccoli, G., M.K. Brohman, R. Watson, and A. Parasuraman, “Net-Based Customer Service Systems: Evolution and Revolution in Website Functionalities”, Decision Sciences. 35(3), Summer 2004, pp. 423-455.<br />
<br />
Staples, D.S., Wong, I., & Seddon, P.B. (2002). Having expectations of information systems benefits that match received benefits: Does it really matter?. Information & Management, 40, 115.<br />
<br />
Susarla, A., Barua, A., & Whinston, A. B. (2003).[http://inn.colorado.edu/Details/Paper/127 Understanding the service component of application service provision: An empirical analysis of satisfaction with ASP services.] MIS Quarterly, 27(1), 91.<br />
<br />
Thong, J.Y.L., Hong S.-J., & Tam, K.Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. ''International Journal of Human-Computer Studies, 64'', 799-810.<br />
<br />
== Links from this theory to other theories ==<br />
[[Multi-motive information_systems continuance model (MISC)]]<br><br />
[[SERVQUAL]], [[Cognitive dissonance theory]], [[Social exchange theory]], [[Equity theory]], adaptation theory<br />
<br />
== External links ==<br />
N/A<br />
<br />
== Original Contributor(s) ==<br />
Dorit Nevo<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Expectation_confirmation_theory&diff=909Expectation confirmation theory2015-11-21T10:48:44Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Expectation confirmation theory''' ==<br />
----<br />
== Acronym ==<br />
ECT<br />
== Alternate name(s)==<br />
Expectation disconfirmation theory (EDT)<br />
== Main dependent construct(s)/factor(s)==<br />
Satisfaction<br />
== Main independent construct(s)/factor(s) ==<br />
Expectations, Perceived performance, Disconfirmation<br />
== Concise description of theory ==<br />
Expectations-confirmation theory posits that expectations, coupled with perceived performance, lead to post-purchase satisfaction. This effect is mediated through positive or negative disconfirmation between expectations and performance. If a product outperforms expectations (positive disconfirmation) post-purchase satisfaction will result. If a product falls short of expectations (negative disconfirmation) the consumer is likely to be dissatisfied (Oliver, 1980; Spreng et al. 1996). <br />
<br />
The four main constructs in the model are: expectations, performance, disconfirmation, and satisfaction. Expectations reflect anticipated behavior (Churchill and Suprenant, 1982). They are predictive, indicating expected product attributes at some point in the future (Spreng et al. 1996). Expectations serve as the comparison standard in ECT – what consumers use to evaluate performance and form a disconfirmation judgment (Halstead, 1999). Disconfirmation is hypothesized to affect satisfaction, with positive disconfirmation leading to satisfaction and negative disconfirmation leading to dissatisfaction. <br />
<br />
A major debate within the marketing literature concerns the nature of the effect of disconfirmation on satisfaction. The root of the problem lies in the definition of predictive expectations as the comparison standard for perceived performance. In such case, the confirmation of negative expectations is not likely to lead to satisfaction (Santos and Boote 2003). To overcome this problem, researchers have proposed other comparison standards such as desires, ideals, equity, or past product and brand experience (see reviews by Halstead, 1999; Yi 1990 and analysis by Tse and Wilton, 1988. Also see Spreng et al. 1996; Woodruff et al., 1983). <br />
== Diagram/schematic of theory ==<br />
[[Image:Ect.JPG]]<br />
== Originating author(s) ==<br />
Oliver (1977, 1980)<br />
== Seminal articles ==<br />
Oliver R. L, 1977, "Effect of Expectation and Disconfirmation on Postexposure Product Evaluations - an Alternative Interpretation," Journal of Applied Psychology, 62(4), p. 480. <br />
<br />
Oliver R. L, 1980, "A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions," JMR, Journal of Marketing Research, 17(4), p. 460. <br />
<br />
Spreng R. A, S.B. MacKenzie and R.W. Olshavsky, 1996, "A reexamination of the determinants of consumer satisfaction," Journal of Marketing, 60(3), p. 15.<br />
== Originating area ==<br />
Marketing, Consumer behavior<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Au, N., Ngai, E., Cheng, E. "A Critical Review of End-User Information System Satisfaction Research and a New Research Framework," Omega (30), 2002, pp. 451-478. <br />
<br />
Bhattacherjee, A. (2001a). [http://inn.colorado.edu/Details/Paper/137 Understanding information systems continuance: An expectation-confirmation model]. MIS Quarterly, 25(3), 351.<br />
<br />
Bhattacherjee, A. (2001b). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32, 201.<br />
<br />
Erevelles, S., Srinivasan, S., & Rangel, S. (2003). Consumer satisfaction for internet service providers: An analysis of underlying processes. Information Technology and Management, 4(1), 69.<br />
<br />
Hsu, M.H., Chiu, C.M., & Ju, T.L. (2004). Determinants of continued use of the WWW: An integration of two theoretical models. Industrial Management & Data Systems, 104 (9), 766.<br />
<br />
Khalifa M. and V. Liu, 2004, "The State of Research on Information System Satisfaction," JITTA : Journal of Information Technology Theory and Application, 5(4), p. 37.<br />
<br />
Lin, C.S., Wu, S., & Tsai, R.J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management 42, 683.<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
<br />
McKinney, V., Yoon, K., & Zahedi, F. (2002). [http://inn.colorado.edu/Details/Paper/6802 The measurement of web-customer satisfaction: An expectation and disconfirmation approach.] Information Systems Research, 13(3), 296.<br />
<br />
Nevo, D. and Wade, M., “How to Avoid Disappointment by Design”, The Communications of the ACM, Vol. 50, No. 4, pp. 43-48, 2007.<br />
<br />
Piccoli, G., M.K. Brohman, R. Watson, and A. Parasuraman, “Net-Based Customer Service Systems: Evolution and Revolution in Website Functionalities”, Decision Sciences. 35(3), Summer 2004, pp. 423-455.<br />
<br />
Staples, D.S., Wong, I., & Seddon, P.B. (2002). Having expectations of information systems benefits that match received benefits: Does it really matter?. Information & Management, 40, 115.<br />
<br />
Susarla, A., Barua, A., & Whinston, A. B. (2003).[http://inn.colorado.edu/Details/Paper/127 Understanding the service component of application service provision: An empirical analysis of satisfaction with ASP services.] MIS Quarterly, 27(1), 91.<br />
<br />
Thong, J.Y.L., Hong S.-J., & Tam, K.Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. ''International Journal of Human-Computer Studies, 64'', 799-810.<br />
<br />
== Links from this theory to other theories ==<br />
[[Multi-motive information_systems continuance model (MISC)]]<br><br />
[[SERVQUAL]], [[Cognitive dissonance theory]], [[Social exchange theory]], [[Equity theory]], adaptation theory<br />
<br />
== External links ==<br />
N/A<br />
<br />
== Original Contributor(s) ==<br />
Dorit Nevo<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Hedonic-motivation_system_adoption_model_(HMSAM)&diff=908Hedonic-motivation system adoption model (HMSAM)2015-11-21T09:32:01Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>== Acronym ==<br />
<br />
HMSAM<br />
<br />
== Alternate name(s) ==<br />
<br />
n/a<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Behavioral intention to use, Immersion<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Perceived ease of use, perceived usefulness, curiosity, joy, control<br />
<br />
== Concise description of theory ==<br />
<br />
The hedonic-motivation system adoption model (HMSAM) is a native information systems theory to improve the understanding of hedonic-motivation systems (HMS) adoption. HMS are systems used primarily to fulfill users’ intrinsic motivations, such for online gaming, virtual worlds, online shopping, learning/education, online dating, digital music repositories, social networking, only pornography, gamified systems, and for general gamification. Instead of a minor, general technology acceptance model (TAM) extension, HMSAM is an HMS-specific system acceptance model based on an alternative theoretical perspective, which is in turn grounded in flow-based cognitive absorption (CA). The HMSAM further builds on van der Heijden’s (2004) model of hedonic system adoption by including CA as a key mediator of perceived ease of use (PEOU) and of behavioral intentions to use (BIU) hedonic-motivation systems. Typically, models simplistically represent “intrinsic motivations” by mere perceived enjoyed. Instead, HMSAM uses the more complex, rich construct of CA, which includes joy, control, curiosity, focused immersion, and temporal dissociation. CA is construct that is grounded in the seminal flow literature, yet ironically CA has traditionally been used as a static construct, as if all five of its subconstructs occur at the same time—in direct contradiction to the flow literature. Thus, part of HMSAM’s contribution is to return CA closer to its flow roots by re-ordering these CA subconstructs into more natural process-variance order as predicted by flow. Empirical data collection along with mediation tests further support this modeling approach. Figure 1 overviews HMSAM.<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:HMSAM_overview.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of HMSAM, from Lowry et al. (2013)<br />
<br />
== Originating author(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry], James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts.<br />
<br />
== Seminal articles ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
== Originating area ==<br />
<br />
Information Systems (native IS theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
Yichuan Wang, Pramod Rajan, Chetan S. Sankar, P. K. Raju (2014). “Relationships between Goal Clarity, Concentration and Learning Effectiveness When Playing Serious Games,” Proceeding of Twentieth Americas Conference on Information Systems, Savannah, Georgia.<br />
<br />
Tobias Kowatsch and Flavius Kehr (2014). “Towards a Design Theory for IS Services Enabling Incentive-based Health Promotion in Organizations,” Wirtschaftsinformatik (MKWI 2014)<br />
<br />
Mark Keith, Greg Anderson, Douglas Dean, and James Eric Gaskin (2014). “The Effects of Team Flow on Performance: A Video Game Experiment,” SIGHCI 2014 Proceedings.<br />
<br />
Mosiane, Segomotso; Brown, Irwin (2014). “Exploring antecedents of game-based learning effectiveness,” Proceedings of the 25th Australasian Conference on Information Systems, 8th - 10th December, Auckland, New Zealand<br />
<br />
Thomas Wiegand and Stefan Stieglitz (2015) “Serious Fun - Effects of Gamification on Knowledge Exchange in Enterprises” Lecture Notes in Informatics Proceedings<br />
<br />
Lisa-Maria Putz and Horst Treiblmaier (2015). “Creating a Theory-Based Research Agenda for Gamification,” Proceeding of Twenty-First Americas Conference on Information Systems, Puerto Rico.<br />
== Links from this theory to other theories ==<br />
<br />
[[Multi-motive information_systems continuance model (MISC)]]<br><br />
<br />
[[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Expectation_confirmation_theory&diff=907Expectation confirmation theory2015-11-21T09:30:50Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Expectation confirmation theory''' ==<br />
----<br />
== Acronym ==<br />
ECT<br />
== Alternate name(s)==<br />
Expectation disconfirmation theory (EDT)<br />
== Main dependent construct(s)/factor(s)==<br />
Satisfaction<br />
== Main independent construct(s)/factor(s) ==<br />
Expectations, Perceived performance, Disconfirmation<br />
== Concise description of theory ==<br />
Expectations-confirmation theory posits that expectations, coupled with perceived performance, lead to post-purchase satisfaction. This effect is mediated through positive or negative disconfirmation between expectations and performance. If a product outperforms expectations (positive disconfirmation) post-purchase satisfaction will result. If a product falls short of expectations (negative disconfirmation) the consumer is likely to be dissatisfied (Oliver, 1980; Spreng et al. 1996). <br />
<br />
The four main constructs in the model are: expectations, performance, disconfirmation, and satisfaction. Expectations reflect anticipated behavior (Churchill and Suprenant, 1982). They are predictive, indicating expected product attributes at some point in the future (Spreng et al. 1996). Expectations serve as the comparison standard in ECT – what consumers use to evaluate performance and form a disconfirmation judgment (Halstead, 1999). Disconfirmation is hypothesized to affect satisfaction, with positive disconfirmation leading to satisfaction and negative disconfirmation leading to dissatisfaction. <br />
<br />
A major debate within the marketing literature concerns the nature of the effect of disconfirmation on satisfaction. The root of the problem lies in the definition of predictive expectations as the comparison standard for perceived performance. In such case, the confirmation of negative expectations is not likely to lead to satisfaction (Santos and Boote 2003). To overcome this problem, researchers have proposed other comparison standards such as desires, ideals, equity, or past product and brand experience (see reviews by Halstead, 1999; Yi 1990 and analysis by Tse and Wilton, 1988. Also see Spreng et al. 1996; Woodruff et al., 1983). <br />
== Diagram/schematic of theory ==<br />
[[Image:Ect.JPG]]<br />
== Originating author(s) ==<br />
Oliver (1977, 1980)<br />
== Seminal articles ==<br />
Oliver R. L, 1977, "Effect of Expectation and Disconfirmation on Postexposure Product Evaluations - an Alternative Interpretation," Journal of Applied Psychology, 62(4), p. 480. <br />
<br />
Oliver R. L, 1980, "A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions," JMR, Journal of Marketing Research, 17(4), p. 460. <br />
<br />
Spreng R. A, S.B. MacKenzie and R.W. Olshavsky, 1996, "A reexamination of the determinants of consumer satisfaction," Journal of Marketing, 60(3), p. 15.<br />
== Originating area ==<br />
Marketing, Consumer behavior<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Au, N., Ngai, E., Cheng, E. "A Critical Review of End-User Information System Satisfaction Research and a New Research Framework," Omega (30), 2002, pp. 451-478. <br />
<br />
Bhattacherjee, A. (2001a). [http://inn.colorado.edu/Details/Paper/137 Understanding information systems continuance: An expectation-confirmation model]. MIS Quarterly, 25(3), 351.<br />
<br />
Bhattacherjee, A. (2001b). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32, 201.<br />
<br />
Erevelles, S., Srinivasan, S., & Rangel, S. (2003). Consumer satisfaction for internet service providers: An analysis of underlying processes. Information Technology and Management, 4(1), 69.<br />
<br />
Hsu, M.H., Chiu, C.M., & Ju, T.L. (2004). Determinants of continued use of the WWW: An integration of two theoretical models. Industrial Management & Data Systems, 104 (9), 766.<br />
<br />
Khalifa M. and V. Liu, 2004, "The State of Research on Information System Satisfaction," JITTA : Journal of Information Technology Theory and Application, 5(4), p. 37.<br />
<br />
Lin, C.S., Wu, S., & Tsai, R.J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management 42, 683.<br />
<br />
McKinney, V., Yoon, K., & Zahedi, F. (2002). [http://inn.colorado.edu/Details/Paper/6802 The measurement of web-customer satisfaction: An expectation and disconfirmation approach.] Information Systems Research, 13(3), 296.<br />
<br />
Nevo, D. and Wade, M., “How to Avoid Disappointment by Design”, The Communications of the ACM, Vol. 50, No. 4, pp. 43-48, 2007.<br />
<br />
Piccoli, G., M.K. Brohman, R. Watson, and A. Parasuraman, “Net-Based Customer Service Systems: Evolution and Revolution in Website Functionalities”, Decision Sciences. 35(3), Summer 2004, pp. 423-455.<br />
<br />
Staples, D.S., Wong, I., & Seddon, P.B. (2002). Having expectations of information systems benefits that match received benefits: Does it really matter?. Information & Management, 40, 115.<br />
<br />
Susarla, A., Barua, A., & Whinston, A. B. (2003).[http://inn.colorado.edu/Details/Paper/127 Understanding the service component of application service provision: An empirical analysis of satisfaction with ASP services.] MIS Quarterly, 27(1), 91.<br />
<br />
Thong, J.Y.L., Hong S.-J., & Tam, K.Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. ''International Journal of Human-Computer Studies, 64'', 799-810.<br />
<br />
== Links from this theory to other theories ==<br />
[[Multi-motive information_systems continuance model (MISC)]]<br><br />
[[SERVQUAL]], [[Cognitive dissonance theory]], [[Social exchange theory]], [[Equity theory]], adaptation theory<br />
<br />
== External links ==<br />
N/A<br />
<br />
== Original Contributor(s) ==<br />
Dorit Nevo<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Delone_and_McLean_IS_success_model&diff=906Delone and McLean IS success model2015-11-21T09:28:57Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Delone and McLean IS success model''' ==<br />
----<br />
== Acronym ==<br />
n/a<br />
<br />
== Alternate name(s)==<br />
DeLone & McLean Information Systems Success Model, DeLone & McLean IS Success Model, D&M IS Success Model<br />
<br />
== Main dependent construct(s)/factor(s)==<br />
Net Benefits, (Intention to) Use, User Satisfaction<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
System Quality, Information Quality, Service Quality<br />
<br />
== Concise description of theory ==<br />
In order to provide a general and comprehensive definition of IS success that covers different perspectives of evaluating information systems, DeLone and McLean reviewed the existing definitions of IS success and their corresponding measures, and classified them into six major categories. Thus, they created a multidimensional measuring model with interdependencies between the different success categories (DeLone & McLean 1992). <br><br />
<br><br />
Motivated by DeLone and McLean’s call for further development and validation of their model, many researchers have attempted to extend or respecify the original model. Ten years after the publication of their first model and based on the evaluation of the many contributions to it, DeLone and McLean proposed an updated IS success model (DeLone & McLean 2002, 2003).<br><br />
<br><br />
The updated model consists of six interrelated dimensions of IS success: information, system and service quality, (intention to) use, user satisfaction, and net benefits. The arrows demonstrate proposed associations between the success dimensions. The model can be interpreted as follows: A system can be evaluated in terms of information, system, and service quality; these characteristics affect the subsequent use or intention to use and user satisfaction. As a result of using the system, certain benefits will be achieved. The net benefits will (positively or negatively) influence user satisfaction and the further use of the information system.<br />
<br />
== Diagram/schematic of theory ==<br />
[[Image:D&M1992.jpg]]<br><br />
Information Systems Success Model (DeLone & McLean 1992)<br><br />
<br><br />
[[Image:D&M2002.jpg]]<br><br />
Updated Information Systems Success Model (DeLone & McLean 2002, 2003)<br><br />
<br />
== Originating author(s) ==<br />
DeLone & McLean (1992); DeLone & McLean (2002); DeLone & McLean (2003)<br />
<br />
== Seminal articles ==<br />
DeLone, W.H., and McLean, E.R. 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research (3:1), pp 60-95.<br />
<br />
DeLone, W.H., and McLean, E.R. 2002. "Information Systems Success Revisited," in: Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS 02). Big Island, Hawaii: pp. 238-249.<br />
<br />
DeLone, W.H., and McLean, E.R. 2003. "The DeLone and McLean Model of Information Systems Success: A Ten-Year Update," Journal of Management Information Systems (19:4), Spring, pp 9-30.<br />
<br />
== Originating area ==<br />
Information Systems<br />
<br />
== Level of analysis ==<br />
Individual, Organization<br />
<br />
== IS articles that use the theory ==<br />
Almutairi, H., and Subramanian, G.H. 2005. "An Empirical Application of the DeLone and McLean Model in the Kuwaiti Private Sector," Journal of Computer Information Systems (45:3), Spring, pp 113-122.<br />
<br />
Bharati, P. and Chaudhury, A. (2006), “Product Customization on the Web: An Empirical Study of Factors Impacting Choiceboard User Satisfaction,” Information Resources Management Journal, Vol. 19, No. 2, pp. 69 - 81. <br />
<br />
Bharati, P. and Berg, D. (2005), “Service Quality from the Other Side: Information Systems Management at Duquesne Light”, International Journal of Information Management, Vol. 25, No. 4, pp. 367-380.<br />
<br />
Bharati, P. and Chaudhury, A. (2004), “An Empirical Investigation of Decision-Making Satisfaction in Web-Based Decision Support Systems”, Decision Support Systems, Vol. 37, No. 2, pp. 187-197. <br />
<br />
Bharati, P. and Berg, D. (2003), “Managing Information Technology for Service Quality: A Study from the Other Side”, IT and People, Vol. 16, No. 2, pp. 183-202. <br />
<br />
Bharati, P. (2002-2003), “People and Information Matter: Task Support Satisfaction from the Other Side”, Journal of Computer Information Systems, Winter.<br />
<br />
Chae, H.-C.M. 2007. "Is Success Model and Perceived It Value," in: Proceedings of the 13th Americas Conference on Information Systems (AMCIS 07). Keystone, CO, USA.<br />
<br />
DeLone, W.H., and McLean, E.R. 2004. "Measuring E-Commerce Success: Applying the DeLone & McLean Information Systems Success Model," International Journal of Electronic Commerce (9:1), Fall, pp 31-47.<br />
<br />
Halawi,L.A, McCarthy,R. V and Aronson, J. E. 2007-2008. "An Empirical Investigation of Knowledge Management Systems Success”. Journal of Computer Information Systems (JCIS) Winter, 121-135.<br />
<br />
Hu, P.J.-H. 2003. "Evaluating Telemedicine Systems Success: A Revised Model," in: Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS 03). Big Island, Hawaii.<br />
<br />
Hwang, M., and McLean, E.R. 1996. "The Use of Meta-Analysis in Validating the DeLone and McLean Information Systems Success Model," in: Proceedings of the 29th Hawaii International Conference on System Sciences (HICSS 96). Big Island, Hawaii.<br />
<br />
Iivari, J. 2005. "An Empirical Test of the DeLone-McLean Model of Information System Success," The DATA BASE for Advances in Information Systems (26:2), pp 8-27.<br />
<br />
Jennex, M., Olfman, L., Panthawi, P., and Park, Y.-T. 1998. "An Organizational Memory Information Systems Success Model: An Extension of DeLone and McLean's I/S Success Model " in: Proceedings of the 31st Hawaii International Conference on System Sciences (HICSS 98). Big Island, Hawaii.<br />
<br />
Jennex, M., and Olfman, L. 2003. "A Knowledge Management Success Model: An Extension of DeLone and McLean’s Is Success Model," in: Proceedings of the 9th Americas Conference on Information Systems (AMCIS 03). Tampa, Florida.<br />
<br />
Kulkarni, U.R., Ravindran, S., and Freeze, R. 2006. "A Knowledge Management Success Model: Theoretical Development and Empirical Validation," Journal of Management Information Systems (23:3), 12, pp 309-347.<br />
<br />
Mao, E., and Ambrose, P. 2004. "A Theoretical and Empirical Validation of Is Success Models in a Temporal and Quasi Volitional Technology Usage Context," in: Proceedings of the 10th Americas Conference on Information Systems (AMCIS 04). New York City, New York.<br />
<br />
McGill, T., Hobbs, V., and Klobas, J. 2003. "User-Developed Applications and Information Systems Success: A Test of DeLone and McLean's Model," Information Resources Management Journal (16:1), p 24.<br />
<br />
Molla, A., and Licker, P.S. 2001. "E-Commerce Systems Success: An Attempt to Extend and Respecify the DeLone and MacLean Model of Is Success," Journal of Electronic Commerce Research (2:4), pp 131-141.<br />
<br />
Pare, G., Aubry, D., Lepanto, L., and Sicotte, C. 2005. "Evaluating Pacs Success: A Multidimensional Model," in: Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS 05). Big Island, Hawaii.<br />
<br />
Qian, Z., and Bock, G.-W. 2005. "An Empirical Study on Measuring the Success of Knowledge Repository Systems," in: Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS 05). Big Island, Hawaii.<br />
<br />
Rai, A., Lang, S.S., and Welker, R.B. 2002. "Assessing the Validity of Is Success Models: An Empirical Test and Theoretical Analysis," Information Systems Research (13:1), pp 50-69.<br />
<br />
Roldán, J.L., and Leal, A. 2003. "A Validation Test of an Adaptation of the DeLone and McLean’s Model in the Spanish EIS Field," in: Critical Reflections on Information Systems: A Systemic Approach, J.J. Cano (ed.). Hershey, PA, USA: Idea Group Publishing, pp. 66-84.<br />
<br />
Rosemann, M., and Vessey, I. 2005. "Linking Theory and Practice: Performing a Reality Check on a Model of Is Success," in: Proceedings of the 13th European Conference on Information Systems (ECIS 05). Regensburg, Germany.<br />
<br />
Seddon, P.B. 1997. "A Respecification and Extension of the DeLone and McLean Model of Is Success," Information Systems Research (8:3), pp 240-253.<br />
<br />
Seddon, P.B., and Kiew, M.-Y. 1994. "A Partial Test and Development of the DeLone and McLean Model of Is Success," in: Proceedings of the 15th International Conference on Information Systems (ICIS 94). Vancouver, Canada: pp. 99-110.<br />
<br />
Seddon, P.B., Staples, S., Patnayakuni, R., and Bowtell, M. 1999. "Dimensions of Information Systems Success," Communication of the AIS (2), pp 1-60.<br />
<br />
Sedera, D. 2006. "An Empirical Investigation of the Salient Characteristics of Is-Success Models," in: Proceedings of the 12th Americas Conference on Information Systems (AMCIS 06). Acapulco, Mexico.<br />
<br />
Seen, M., Rouse, A.C., and Beaumont, N. 2007. "Explaining and Predicting Information Systems Acceptance and Success: An Integrative Model," in: Proceedings of the 15th European Conference on Information Systems (ECIS 07). St Gallen, Switzerland.<br />
<br />
Skok, W., Kophamel, A., and Richardson, I. 2001. "Diagnosing Information Systems Success: Importance-Performance Maps in the Health Club Industry," Information & Management (38:7), pp 409-419.<br />
<br />
Thomas, P. 2006. "Information Systems Success and Technology Acceptance within Government Organization," in: Proceedings of the 12th Americas Conference on Information Systems (AMCIS 06). Acapulco, Mexico.<br />
<br />
[http://ssrn.com/abstract=1612176 Trkman,M., Trkman,P.2009."A Wiki as Intranet – a Critical Analysis Using the DeLone & McLean Model," Online Information Review, 33(6), pp 1087-1102.]<br />
<br />
Urbach, N., Smolnik, S., and Riempp, G. 2008. "A Methodological Examination of Empirical Research on Information Systems Success: 2003 to 2007," in: Proceedings of the 14th Americas Conference on Information Systems (AMCIS 2008). Toronto, Ontario, Canada.<br />
<br />
Wu, J.-H., and Wang, Y.-M. 2006. "Measuring Kms Success: A Respecification of the DeLone and McLean's Model," Information & Management (43:6), pp 728-739.<br />
<br />
== Links from this theory to other theories ==<br />
[[Multi-motive information systems continuance model (MISC)]]<br><br />
[[Technology acceptance model]]<br><br />
[[Unified theory of acceptance and use of technology]]<br />
<br />
== External links ==<br />
[http://business.clemson.edu/ise/index.html] Information Systems Effectiveness Home Page<br />
<br />
== Original Contributor(s) ==<br />
Nils Urbach & Benjamin Müller<br />
<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Technology_acceptance_model&diff=905Technology acceptance model2015-11-21T09:27:35Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
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<br />
== '''Technology acceptance model''' ==<br />
----<br />
== Acronym ==<br />
TAM<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention to use, System usage<br />
== Main independent construct(s)/factor(s) ==<br />
Perceived usefulness, Perceived ease of use<br />
== Concise description of theory ==<br />
TAM is an adaptation of the Theory of Reasoned Action (TRA) to the field of IS. TAM posits that perceived usefulness and perceived ease of use determine an individual's intention to use a system with intention to use serving as a mediator of actual system use. Perceived usefulness is also seen as being directly impacted by perceived ease of use. Researchers have simplified TAM by removing the attitude construct found in TRA from the current specification (Venkatesh et. al., 2003). Attempts to extend TAM have generally taken one of three approaches: by introducing factors from related models, by introducing additional or alternative belief factors, and by examining antecedents and moderators of perceived usefulness and perceived ease of use (Wixom and Todd, 2005).<br />
<br />
TRA and TAM, both of which have strong behavioural elements, assume that when someone forms an intention to act, that they will be free to act without limitation. In practice constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act.<br />
== Diagram/schematic of theory ==<br />
[[Image:Tam.JPG]]<br />
== Originating author(s) ==<br />
Davis (1986); Davis (1989)<br />
== Seminal articles ==<br />
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology). <br />
<br />
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. <br />
<br />
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. <br />
== Originating area ==<br />
Information Systems, Technology Adoption<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information: A replication. MIS Quarterly, 16(2), 227-247. <br />
<br />
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391. <br />
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Al-Gahtani, S. (2001). The applicability of TAM outside north america: An empirical test in the united kingdom. Information Resources Management Journal, 14(3), 37-46. <br />
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Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745. <br />
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Brosnan, M. J. (1999). Modeling technophobia: A case for word processing. Computers in Human Behavior, 15(2), 105-121. <br />
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Brown, S. A., Massey, A. P., Montoya-Weiss, M. M., & Burkman, J. R. (2002). Do I really have to? user acceptance of mandated technology. European Journal of Information Systems, 11(4), 283-295. <br />
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Chan, S., & Lu, M. (2004). Understanding internet banking adoption and use behavior: A hong kong perspective. Journal of Global Information Management, 12(3), 21-43. <br />
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Chau, P. K. Y. (1996). [http://inn.colorado.edu/Details/Paper/8150 An empirical assessment of a modified technology acceptance model]. Journal of Management Information Systems, 13(2), 185-204. <br />
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Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311. <br />
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Chau, P. Y. K., & Hu, P. J. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719. <br />
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Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. <br />
<br />
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology). <br />
<br />
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). [http://inn.colorado.edu/Details/Paper/199 User acceptance of computer technology: A comparison of two theoretical models]. Management Science, 35(8), 982-1003. <br />
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Devaraj, S., Fan, M., & Kohli, R. (2002). [http://inn.colorado.edu/Details/Paper/6798 Antecedents of b2C channel satisfaction and preference: Validation e-commerce metrics]. Information Systems Research, 13(3), 316-333. <br />
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Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9-21. <br />
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Elwood, S., Changchit, C. & Cutshall, R. (2006). Investigating students' perceptions on laptop initiative in higher education: An extension of the technology acceptance model. Campus Wide Information Systems, 23(5), 336-349.<br />
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Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1-13. <br />
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Gefen, D., Karahanna, E., & Straub, D. W. (2003). [http://inn.colorado.edu/Details/Paper/126 Trust and TAM in online shopping: An integrated model]. MIS Quarterly, 27(1), 51-90. <br />
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Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of E-commerce adoption. Journal of the Association for Information Systems, 1(8), 1-28. <br />
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Gefen, D., & Straub, D. W. (1997). [http://inn.colorado.edu/Details/Paper/146 Gender differences in the perception and use of E-mail: An extension to the technology acceptance model]. MIS Quarterly, 21(4), 389-400. <br />
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Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
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Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365-374. <br />
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Hsu, C. L. and Lin, J. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation, Information & Management, 45, 65-74.<br />
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Hsu, C. L. and Lu, H. P. (2007). Consumer behavior in on-line game communities: a motivational factor perspective Computers in Human Behavior, 23, 1642-1659. <br />
<br />
Hsu, C. L. and Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience, Information & Management, 41(7), 853-868.<br />
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Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). [http://inn.colorado.edu/Details/Paper/8650 Testing the determinants of microcomputer usage via a structural equation model]. Journal of Management Information Systems, 11(4), 87-114. <br />
<br />
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). [http://inn.colorado.edu/Details/Paper/144 Personal computing acceptance factors in small firms: A structural equation model]. MIS Quarterly, 21(3), 279-305. <br />
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Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2), 357-389. <br />
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Kamel, S., & Hassan, A. (2003). Assessing the introduction of electronic banking in egypt using the technology acceptance model. Annals of Cases on Information Technology, 5, 1-25. <br />
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Kamis, A. and Stohr, E. (2006), Parametric Search Engines: What Makes them Effective when Shopping Online for Differentiated Products? Information & Management, 43(7): 904-918.<br />
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Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741-755. <br />
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Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer E-commerce. Information Technology, Learning, and Performance Journal, 22(1), 35-48. <br />
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Koufaris, M. (2002). [http://inn.colorado.edu/Details/Paper/6795 Applying the technology acceptance model and flow theory to online consumer behavior]. Information Systems Research, 13(2), 205-223. <br />
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Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29(3), 269-282. <br />
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Lowry, Paul Benjamin; James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
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Lim, J. (2003). A conceptual framework on the adoption of negotiation support systems. Information and Software Technology, 45(8), 469-477. <br />
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Lu, H., Hsu, C., & Hsu, H. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2/3), 106-120. <br />
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Lucas, H. C.,Jr, & Spitler, V. K. (1999). Technology use and performance: A field study of broker workstations. Decision Sciences, 30(2), 291-311. <br />
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Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-72. <br />
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Mathieson, K. (1991). [http://inn.colorado.edu/Details/Paper/6663 Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior]. Information Systems Research, 2(3), 173-191. <br />
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McCloskey, D. (2003). Evaluating electronic commerce acceptance with the technology acceptance model. The Journal of Computer Information Systems, 44(2), 49-57. <br />
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McCoy, S., Everard, A., & Jones, B. M. (2005). An examination of the technology acceptance model in uruguay and the US: A focus on culture. Journal of Global Information Technology Management, 8(2), 27-45. <br />
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Ndubisi, N. O., Gupta, O. K., & Ndubisi, G. C. (2005). The moguls' model of computing: Integrating the moderating impact of users' persona into the technology acceptance model. Journal of Global Information Technology Management, 8(1), 27-47. <br />
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Ndubisi, N. O., & Jantan, M. (2003). Evaluating IS usage in malaysian small and medium-sized firms using the technology acceptance model. Logistics Information Management, 16(6), 440-450. <br />
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Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224-235. <br />
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Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). [http://inn.colorado.edu/Details/Paper/6788 Research report: Richness versus parsimony in modeling technology adoption decisions - understanding merchant adoption of a smart card-based payment system]. Information Systems Research, 12(2), 208-222. <br />
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Riemenschneider, C. K., & Hardgrave, B. C. (2001). Explaining software development tool use with the technology acceptance model. The Journal of Computer Information Systems, 41(4), 1-8. <br />
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Riemenschneider, C. K., Harrison, D. A., & Mykytn, P. P.,Jr. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information & Management, 40(4), 269-285. <br />
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Roberts, P., & Henderson, R. (2000). Information technology acceptance in a sample of government employees: A test of the technology acceptance model. Interacting with Computers, 12(5), 427-443. <br />
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Shih, H. (2004). Extended technology acceptance model of internet utilization behavior. Information & Management, 41(6), 719-729. <br />
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Spacey, R., Goulding, A., & Murray, I. (2004). Exploring the attitudes of public library staff to the internet using the TAM. Journal of Documentation, 60(5), 550-564. <br />
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Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92. <br />
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Szajna, B. (1994). [http://inn.colorado.edu/Details/Paper/161 Software evaluation and choice: Predictive validation of the technology acceptance instrument]. MIS Quarterly, 18(3), 319-324. <br />
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Taylor, S., & Todd, P. (1995). [http://inn.colorado.edu/Details/Paper/213 Assessing IT usage: The role of prior experience]. MIS Quarterly, 19(4), 561-570. <br />
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Taylor, S., & Todd, P. A. (1995). [http://inn.colorado.edu/Details/Paper/6885 Understanding information technology usage: A test of competing models]. Information Systems Research, 6(2), 144-176. <br />
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Venkatesh, V. (2000). [http://inn.colorado.edu/Details/Paper/6588 Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model]. Information Systems Research, 11(4), 342-365. <br />
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Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. <br />
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Venkatesh, V., & Morris, M. G. (2000). [http://inn.colorado.edu/Details/Paper/79 Why don't men ever stop to ask for dirrections? gender, social influence, and their role in technology acceptance and usage behavior]. MIS Quarterly, 24(1), 115-139. <br />
<br />
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). [http://inn.colorado.edu/Details/Paper/75 User acceptance of information technology: Toward a unified view]. MIS Quarterly, 27(3), 425-478. <br />
<br />
Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297-316. <br />
<br />
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41(6), 747-762. <br />
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Wang, W., & Benbasat, I. (2005). Trust in and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72-101.<br />
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Wang, C., Hsu, Y., & Fang, W. (2004). Acceptance of technology with network externalities: An empirical study of internet instant messaging services. JITTA : Journal of Information Technology Theory and Application, 6(4), 15-28. <br />
<br />
Wang, Y., Wang, Y., Lin, H., & Tang, T. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501-519. <br />
<br />
Wixom, B. H., & Todd, P. A. (2005). [http://inn.colorado.edu/Details/Paper/6818 A theoretical integration of user satisfaction and technology acceptance]. Information Systems Research, 16(1), 85-102. <br />
<br />
Yu, J. L. C., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless internet. Internet Research, 13(3), 206-222.<br />
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== Links from this theory to other theories ==<br />
[[Hedonic-motivation system adoption model (HMSAM)]], [[Multi-motive information systems continuance model (MISC)]], [[Theory of planned behavior]], [[Theory of reasoned action]], [[Unified theory of acceptance and use of technology]], [[Delone and McLean IS success model]]<br />
<br />
== External links ==<br />
http://en.wikipedia.org/wiki/Technology_acceptance_model, Wikipedia entry for TAM<br />
<br />
http://www.guuspijpers.com/Research.htm#Technology%20Acceptance%20Model%20(TAM), Guus Pijpers presents an extensive list of TAM references up to December 2003<br />
== Original Contributor(s) ==<br />
Brent Furneaux<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=904Multi-motive information systems continuance model (MISC)2015-11-21T09:26:36Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:MISC_figure_1.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=903Multi-motive information systems continuance model (MISC)2015-11-21T09:25:34Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:MISC_figure_1.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), [[Hedonic-motivation system adoption model (HMSAM)]], [[Technology Acceptance Model]]<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=902Multi-motive information systems continuance model (MISC)2015-11-21T09:23:30Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:MISC_figure_1.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT (or [[expectation confirmation theory]]), HMSAM<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=901Multi-motive information systems continuance model (MISC)2015-11-21T09:19:33Z<p>Pblowry: /* Motivation for MISC / Why IS researchers can benefit from using it */</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. <br />
<br />
Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
* “'''''Expectations'''''” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. <br />
* “'''''Disconfirmation'''''” is the extent to which an event is evaluated as either exceeding or falling short of expectations. <br />
* “'''''Positive disconfirmation'''''” results when perceived performance exceeds expectations, thereby causing satisfaction. <br />
* Whereas, “'''''negative disconfirmation'''''” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
# '''<u>''DEF''</u>''' is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
# '''<u>''Ease of use''</u>''' is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
# '''<u>''Design Aesthetics refers''</u>''' to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:MISC_figure_1.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT, HMSAM<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Multi-motive_information_systems_continuance_model_(MISC)&diff=900Multi-motive information systems continuance model (MISC)2015-11-21T09:12:48Z<p>Pblowry: Created page with "== Acronym == MISC == Alternate name(s) == MISC model or The MISC == Main dependent construct(s)/factor(s) == Intention to continue / system continuance intention == Ma..."</p>
<hr />
<div>== Acronym ==<br />
<br />
MISC <br />
<br />
== Alternate name(s) ==<br />
<br />
MISC model or The MISC<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Intention to continue / system continuance intention<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Hedonic expectations, intrinsic expectations, extrinsic expectations, hedonic disconfirmation, intrinsic disconfirmation, extrinsic disconfirmation, attitude, satisfaction, hedonic performance, intrinsic performance, extrinsic performance, design expectations fit, ease of use, design aesthetics.<br />
== Motivation for MISC / Why IS researchers can benefit from using it ==<br />
Motivation for use of the theory: Information systems designers and publishers are keenly interested in how to retain users. Accordingly, information systems researchers are eager to supply theories to explain and predict users’ intentions to continue to use information systems. Now, many different theoretical approaches have been taken to predict continuance intentions, however, these existing models often focus on users’ extrinsic motivations, such as desires for productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and extrinsic motivations that influence continuance intentions. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user outcomes, such as continuance intentions, as well as satisfaction, and perceived performance Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfill these motives—is particularly relevant for encouraging positive user interactions. These ideas are also highly pertinent to the newer idea of gamification that is starting revolutionize systems design. To identify key differences between intrinsic and extrinsic motivators, several studies have extended extrinsic motivation models or created new models to address users’ intrinsic motivations. However, models predicting intrinsic motives of system use often ignore extrinsic motives. As far as we know, no study has proposed a model that can account for the effects that these normally conflicting motives have on a user’s satisfaction, continuance intentions, and evaluations of system performance. Additionally, most studies do not conceptualize the different types of intrinsic motivation – for example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not measure the successful fulfillment of intrinsic motivations independently of that of extrinsic motivations. Existing models also often fail to account for user expectations, which are a key component of all interactions and are directly predicted by motivations. So, to address the issue of nomological completeness, researchers must also consider the role of expectations in system interactions. The existing underdeveloped constructs and models potentially confound research on system use and thus make such studies difficult to interpret or at least difficult to generalize across various types of systems and interactions. This gap in the literature also holds back the theoretical and empirical advancement of gamification and information systems design.<br />
As a result, the MISC model is a comprehensive model for explaining and predicting how a range of motives and expectations influences user satisfaction and continuance intentions for multiple types of information systems that have been designed with various intents. Among many other findings, initial empirical research by Lowry et al. (2015) reveals that design-related constructs affect performance beliefs differently depending on system intent and user motives and expectations. This suggests that system designers can leverage the MISC model to learn where to focus their efforts as they design specific systems with specific intents. Nevertheless, they show that a user’s motives do not always match the intent of a system’s design, which increases the need for systems to be designed to accommodate multiple motives. 36. Additionally, many findings are consistent across all types of systems, suggesting that certain design constructs are universally essential. The MISC model also provides a foundation for extending a wide range of research in human-computer interaction and for revisiting prior research to examine the effects of multiple types of motivation in established systems-use theories.<br />
<br />
== Concise description of theory ==<br />
The multi-motive information systems continuance model (or MISC), explains and predicts the discrete cognitive processes through which systems fulfill a range of motives and expectations and how this fulfillment leads to continuance intentions. The MISC model also accounts for design-related constructs that have the potential to contribute to or confound any study on system use, namely: design aesthetics, perceived ease of use, and design-expectations fit.<br />
The MISC model is built upon expectation disconfirmation theory, or EDT, and the Bhattacherjee and Premkumar (2004) model. Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations, expectancy violation, or expectation-disconfirmation, these theoretical models concern whether an experience conforms to one’s expectations or if those expectations are disconfirmed or violated. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue that an individual’s expectations largely determine his or her overall satisfaction with something, such as a person, service, or product – and in our context, an online interaction with a user interface. While accounting for satisfaction, the MISC focuses on system continuance intention as the primary phenomenon of interest. <br />
“Expectations” refers to one’s beliefs about future events. By nature, the human mind projects and considers future scenarios to anticipate required actions, for both physical and social survival. “Disconfirmation” is the extent to which an event is evaluated as either exceeding or falling short of expectations. “Positive disconfirmation” results when perceived performance exceeds expectations, thereby causing satisfaction. Whereas, “negative disconfirmation” occurs when performance falls below expectations, causing dissatisfaction.<br />
To address several limitations of using EDT for systems use, MISC adds three more expectations as predictors of disconfirmation, and by including multiple motivation-related factors to the model. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics as additional predictors of Disconfirmation. <br />
DEF is the extent to which the design of the software matches the expected interaction. For example, if, prior to using some software, you expect to be able to have fun interactions with it, but the software is designed for productivity rather than fun, then the DEF is low. In this case, the DEF would be much higher if you had expected to interact productively with the software. Applications with designs that match the expectations of the user will be preferred to those which do not match the users’ expectations.<br />
Ease of use is a common construct in information systems research that represents the degree to which you believe using a system will be free of effort. Applications that are easy to use will be preferred over those that require more effort.<br />
Design Aesthetics refers to the appropriateness and professionalism of the user interface. Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.<br />
Beyond these three new predictors of disconfirmation, the MISC further addresses issues with EDT by splitting beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic, Intrinsic, and Extrinsic components. When people learn about a new software or information system, they may have expectations or beliefs that fit into one of these three categories. For example, they may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to some extent, useful for accomplishing something.<br />
In all existing models, these three different components are conceptually combined or ignored, resulting in the observed poor ability to predict disconfirmation. Since MISC separates them, it is able to predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict evaluations of the interactions far better, as well as the user’s intention to continue using the software. Initial empirical tests by Lowry et al. (2015) indicate this is the case.<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:MISC_figure_1.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of the Multimotive Information Systems Continuance Model (MISC), from Lowry et al. 2015, p. 525.<br />
<br />
== Video companion on the theory ==<br />
The following video companion on Youtube describes the development of MISC, empirical results of testing the MISC, and its several potential contributions to research and practice: https://www.youtube.com/watch?v=-1FsG0o0pOc&feature=youtu.be&hd=1 <br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Gregory D. Moody<br />
<br />
== Seminal articles ==<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Originating area ==<br />
<br />
Information systems (native theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, and Gregory D. Moody (2015). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Proposing the multimotive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions],” Journal of the Association for Information Systems (JAIS), vol. 16(7), pp. 515–579 (http://aisel.aisnet.org/jais/vol16/iss7/3/). <br />
<br />
== Links from this theory to other theories ==<br />
<br />
EDT, HMSAM<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Main_Page&diff=899Main Page2015-11-21T09:11:49Z<p>Pblowry: /* Theories */</p>
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<br />
== Theories ==<br />
<br />
*[[Absorptive capacity theory]]<br />
*[[Actor network theory]]<br />
*[[Accountability theory|Accountability theory (NEW entry!)]]<br />
*[[Adaptive structuration theory]]<br />
*[[Administrative behavior, theory of]]<br />
*[[Agency theory]] <br />
*[[Argumentation theory]]<br />
*[[Behavioral decision theory]]<br />
*[[Boundary object theory]]<br />
*[[Chaos theory]]<br />
*[[Cognitive dissonance theory]]<br />
*[[Cognitive fit theory]]<br />
*[[Cognitive load theory]]<br />
*[[Competitive strategy (Porter)]]<br />
*[[Complexity theory]]<br />
*[[Contingency theory]]<br />
*[[Critical realism theory]]<br />
*[[Critical social theory]] <br />
*[[Critical success factors, theory of]]<br />
*[[Customer Focus Theory]]<br />
*[[Deferred action, theory of]] <br />
*[[Delone and McLean IS success model]]<br />
*[[Diffusion of innovations theory]]<br />
*[[Dynamic capabilities]]<br />
*[[Elaboration likelihood model]]<br />
*[[Embodied social presence theory]]<br />
*[[Equity theory]] <br />
*[[Evolutionary theory]]<br />
*[[Expectation confirmation theory]] <br />
*[[Feminism theory]]<br />
*[[Fit-Viability theory]]<br />
*[[Flow theory]]<br />
*[[Game theory]]<br />
*[[Garbage can theory]] <br />
*[[General systems theory]]<br />
*[[General deterrence theory]]<br />
*[[Hedonic-motivation system adoption model (HMSAM)|Hedonic-motivation system adoption model (HMSAM) (NEW Entry!)]]<br />
*[[Hermeneutics]]<br />
*[[Illusion of control]]<br />
*[[Impression management, theory of]]<br />
*[[Information processing theory]]<br />
*[[Institutional theory]]<br />
*[[International information systems theory]]<br />
*[[Kellers Motivational Model |Keller's Motivational Model]]<br />
*[[Knowledge-based theory of the firm]]<br />
*[[Language action perspective]] <br />
*[http://istheory.byu.edu/wiki/Lemon_Market_Theory Information asymmetry theory (lemon market)]<br />
*[[Management fashion theory]]<br />
*[[Media richness theory]]<br />
*[[Media synchronicity theory]]<br />
*[[Modal aspects, theory of]]<br />
*[[Multi-attribute utility theory]] <br />
*[[Multi-motive information systems continuance model (MISC)]]<br />
*[[Organizational culture theory]] <br />
*[[Organizational information processing theory]]<br />
*[[Organizational knowledge creation]]<br />
*[[Organizational learning theory]]<br />
*[[Portfolio theory]] <br />
*[[Process virtualization theory]]<br />
*[[Prospect theory]] <br />
*[[Protection motivation theory (NEW entry!)]]<br />
*[[Punctuated equilibrium theory]]<br />
*[[Real options theory]]<br />
*[[Resource-based view of the firm]]<br />
*[[Resource dependency theory]]<br />
*[[Self-efficacy theory]]<br />
*[[SERVQUAL]]<br />
*[http://is.theorizeit.org/wiki/Signaling Signaling theory]<br />
*[[Social capital theory]]<br />
*[[Social cognitive theory]]<br />
*[[Social exchange theory]]<br />
*[[Social learning theory]]<br />
*[[Social network theory]]<br />
*[[Social shaping of technology]]<br />
*[[Socio-technical theory]]<br />
*[[Soft systems theory]]<br />
*[[Stakeholder theory]] <br />
*[[Structuration theory]]<br />
*[[Task closure theory]] <br />
*[[Task-technology fit]]<br />
*[[Technological frames of reference]]<br />
*[[Technology acceptance model]] <br />
*[[Technology dominance, theory of]] <br />
*[[Technology-organization-environment framework]]<br />
*[[Theory of collective action]]<br />
*[[Theory of planned behavior]]<br />
*[[Theory of reasoned action]]<br />
*[[Transaction cost economics]] <br />
*[[Transactive memory theory]] <br />
*[[Unified theory of acceptance and use of technology]]<br />
*[[Usage control model]]<br />
*[[Work systems theory]]<br />
*[[Yield shift theory of satisfaction]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Theory_of_reasoned_action&diff=892Theory of reasoned action2015-11-09T10:42:18Z<p>Pblowry: /* Links from this theory to other theories */</p>
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<br />
== '''Theory of reasoned action''' ==<br />
----<br />
== Acronym ==<br />
TRA<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention, Behavior<br />
== Main independent construct(s)/factor(s) ==<br />
Attitude toward behavior, Subjective norm,<br />
== Concise description of theory ==<br />
TRA posits that individual behavior is driven by behavioral intentions where behavioural intentions are a function of an individual's attitude toward the behaviour and subjective norms surrounding the performance of the behavior. Attitude toward the behavior is defined as the individual's positive or negative feelings about performing a behaviour. It is determined through an assessment of one's beliefs regarding the consequences arising from a behavior and an evaluation of the desirability of these consequences. Formally, overall attitude can be assessed as the sum of the individual consequence x desirability assessments for all expected consequences of the behavior. Subjective norm is defined as an individual's perception of whether people important to the individual think the behavior should be performed. The contribution of the opinion of any given referent is weighted by the motivation that an individual has to comply with the wishes of that referent. Hence, overall subjective norm can be expressed as the sum of the individual perception x motivation assessments for all relevant referents. Algebraically TRA can be represented as B ≈ BI = w1AB + w2SN where B is behavior, BI is behavioral intention, AB is attitude toward behavior, SN is subjective norm, and w1 and w2 are weights representing the importance of each term.<br />
<br />
The model has some limitations including a significant risk of confounding between attitudes and norms since attitudes can often be reframed as norms and vice versa. A second limitation is the assumption that when someone forms an intention to act, they will be free to act without limitation. In practice, constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act. The theory of planned behavior (TPB) attempts to resolve this limitation. <br />
<br />
Sources:<br />
<br />
http://en.wikipedia.org/wiki/Technology_acceptance_model <br><br />
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth: Harcourt Brace Jovanovich College Publishers.<br />
<br />
== Diagram/schematic of theory ==<br />
[[Image:Tra.JPG]]<br />
<br />
Source: Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior : An introduction to theory and research. Reading, Mass. ; Don Mills, Ontario: Addison-Wesley Pub. Co. <br />
== Originating author(s) ==<br />
Fishbein (1967); Ajzen and Fishbein (1973); Fishbein and Ajzen (1975)<br />
== Seminal articles ==<br />
Ajzen, I., & Fishbein, M. (1973). Attitudinal and normative variables as predictors of specific behavior. Journal of Personality and Social Psychology, 27(1), 41-57. <br />
<br />
Fishbein, M. (1967). Attitude and the prediction of behavior. In M. Fishbein (Ed.), Readings in attitude theory and measurement (pp. 477-492). New York: Wiley. <br />
<br />
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior : An introduction to theory and research. Reading, Mass. ; Don Mills, Ontario: Addison-Wesley Pub. Co. <br />
== Originating area ==<br />
Social psychology<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Bagchi, S., Kanungo, S., & Dasgupta, S. (2003). Modeling use of enterprise resource planning systems: A path analytic study. European Journal of Information Systems, 12(2), 142-158. <br />
<br />
Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service: The internet as an illustration. International Journal of Service Industry Management, 12(5), 423-450. <br />
<br />
Celuch, K., Taylor, S. A., & Goodwin, S. (2004). Understanding insurance salesperson internet information management intentions: A test of competing models. Journal of Insurance Issues, 27(1), 22-40. <br />
<br />
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). [http://inn.colorado.edu/Details/Paper/199 User acceptance of computer technology: A comparison of two theoretical models]. Management Science, 35(8), 982-1003. <br />
<br />
Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
<br />
Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior. International Journal of Information Management, 24(6), 539-550. <br />
<br />
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440-465. <br />
<br />
Hebert, M., & Benbasat, I. (1994). Adopting information technology in hospitals: The relationship between attitudes/expectations and behavior. Hospital & Health Services Administration, 39(3), 369-383. <br />
<br />
Jae-Nam, L., & Young-Gul, K. (2005). Understanding outsourcing partnership: A comparison of three theoretical perspectives. IEEE Transactions on Engineering Management, 52(1), 43-58. <br />
<br />
Jeffrey, A. C., & Fawzy, S. (1999). A graphical method for assessing knowledge-based systems investments. Logistics Information Management, 12(1/2), 63-77. <br />
<br />
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). [http://inn.colorado.edu/Details/Paper/83 Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs]. MIS Quarterly, 23(2), 183-213. <br />
<br />
Leonard, L. N. K., Cronan, T. P., & Kreie, J. (2004). What influences IT ethical behavior intentions-planned behavior, reasoned action, perceived importance, or individual characteristics? Information & Management, 42(1), 143-158. <br />
<br />
Liker, J. K., & Sindi, A. A. (1997). User acceptance of expert systems: A test of the theory of reasoned action. Journal of Engineering and Technology Management, 14(2), 147-173. <br />
<br />
Lowry, Paul Benjamin, Gaskin, James, Twyman, Nathan W., Hammer, Bryan, and Roberts, Tom L. (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br />
Lowry, Paul Benjamin, Jenkins, Jeffrey L., Twyman, Nathan W., Hammer, Bryan, Gaskin, James, and Hassell, Martin (2008). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287401 Proposing the hedonic affect model (HAM) to explain how stimuli and performance expectations predict affect in individual and group hedonic systems use],” Proceedings of the Journal of the Association for Information Systems Theory Development Workshop at the International Conference on Systems Sciences, Paris, France, December 13. All Sprouts Content, vol. 8(24), paper 230, pp. 1–51.<br />
<br />
Lowry, Paul Benjamin, Twyman, Nathan W., Gaskin, James, Hammer, Bryan, Bailey, Aaron, and Roberts, Tom L. (2007). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1482844 Proposing the interactivity-stimulus-attention model (ISAM) to explain and predict enjoyment, immersion, and adoption of purely hedonic systems],” Proceedings of the Special Interest Group on Human-Computer Interaction 2007 Pre-ICIS Workshop at the International Conference on System Sciences, Montréal, Canada, December 8, paper 11, pp. 72–76 (best-paper nomination) http://aisel.aisnet.org/sighci2007/11/.<br />
<br />
Mykytyn, P. P. J., & Harrison, D. A. (1993). The application of the theory of reasoned action to senior management and strategic information systems. Information Resources Management Journal, 6(2), 15-26. <br />
<br />
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15(3), 325-343. <br />
<br />
Shih, Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study internet banking in taiwan. Internet Research, 14(3), 213-223. <br />
<br />
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). [http://inn.colorado.edu/Details/Paper/75 User acceptance of information technology: Toward a unified view]. MIS Quarterly, 27(3), 425-478. <br />
<br />
Yoh, E., Damhorst, M. L., Sapp, S., & Laczniak, R. (2003). Consumer adoption of the internet: The case of apparel shopping. Psychology & Marketing, 20(12), 1095-1118.<br />
<br />
== Links from this theory to other theories ==<br />
[[Hedonic-motivation system adoption model (HMSAM)]], [[Theory of planned behavior]], [[Technology acceptance model]], [[Unified theory of acceptance and use of technology]]<br />
<br />
== External links ==<br />
http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Health%20Communication/theory_planned_behavior.doc/, The University of Twente in the Netherlands presents an overview of the theory of planned behavior and the theory of reasoned action including references.<br />
<br />
== Original Contributor(s) ==<br />
Brent Furneaux<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Theory_of_reasoned_action&diff=891Theory of reasoned action2015-11-09T10:41:47Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Theory of reasoned action''' ==<br />
----<br />
== Acronym ==<br />
TRA<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention, Behavior<br />
== Main independent construct(s)/factor(s) ==<br />
Attitude toward behavior, Subjective norm,<br />
== Concise description of theory ==<br />
TRA posits that individual behavior is driven by behavioral intentions where behavioural intentions are a function of an individual's attitude toward the behaviour and subjective norms surrounding the performance of the behavior. Attitude toward the behavior is defined as the individual's positive or negative feelings about performing a behaviour. It is determined through an assessment of one's beliefs regarding the consequences arising from a behavior and an evaluation of the desirability of these consequences. Formally, overall attitude can be assessed as the sum of the individual consequence x desirability assessments for all expected consequences of the behavior. Subjective norm is defined as an individual's perception of whether people important to the individual think the behavior should be performed. The contribution of the opinion of any given referent is weighted by the motivation that an individual has to comply with the wishes of that referent. Hence, overall subjective norm can be expressed as the sum of the individual perception x motivation assessments for all relevant referents. Algebraically TRA can be represented as B ≈ BI = w1AB + w2SN where B is behavior, BI is behavioral intention, AB is attitude toward behavior, SN is subjective norm, and w1 and w2 are weights representing the importance of each term.<br />
<br />
The model has some limitations including a significant risk of confounding between attitudes and norms since attitudes can often be reframed as norms and vice versa. A second limitation is the assumption that when someone forms an intention to act, they will be free to act without limitation. In practice, constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act. The theory of planned behavior (TPB) attempts to resolve this limitation. <br />
<br />
Sources:<br />
<br />
http://en.wikipedia.org/wiki/Technology_acceptance_model <br><br />
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth: Harcourt Brace Jovanovich College Publishers.<br />
<br />
== Diagram/schematic of theory ==<br />
[[Image:Tra.JPG]]<br />
<br />
Source: Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior : An introduction to theory and research. Reading, Mass. ; Don Mills, Ontario: Addison-Wesley Pub. Co. <br />
== Originating author(s) ==<br />
Fishbein (1967); Ajzen and Fishbein (1973); Fishbein and Ajzen (1975)<br />
== Seminal articles ==<br />
Ajzen, I., & Fishbein, M. (1973). Attitudinal and normative variables as predictors of specific behavior. Journal of Personality and Social Psychology, 27(1), 41-57. <br />
<br />
Fishbein, M. (1967). Attitude and the prediction of behavior. In M. Fishbein (Ed.), Readings in attitude theory and measurement (pp. 477-492). New York: Wiley. <br />
<br />
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior : An introduction to theory and research. Reading, Mass. ; Don Mills, Ontario: Addison-Wesley Pub. Co. <br />
== Originating area ==<br />
Social psychology<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Bagchi, S., Kanungo, S., & Dasgupta, S. (2003). Modeling use of enterprise resource planning systems: A path analytic study. European Journal of Information Systems, 12(2), 142-158. <br />
<br />
Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service: The internet as an illustration. International Journal of Service Industry Management, 12(5), 423-450. <br />
<br />
Celuch, K., Taylor, S. A., & Goodwin, S. (2004). Understanding insurance salesperson internet information management intentions: A test of competing models. Journal of Insurance Issues, 27(1), 22-40. <br />
<br />
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). [http://inn.colorado.edu/Details/Paper/199 User acceptance of computer technology: A comparison of two theoretical models]. Management Science, 35(8), 982-1003. <br />
<br />
Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
<br />
Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior. International Journal of Information Management, 24(6), 539-550. <br />
<br />
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440-465. <br />
<br />
Hebert, M., & Benbasat, I. (1994). Adopting information technology in hospitals: The relationship between attitudes/expectations and behavior. Hospital & Health Services Administration, 39(3), 369-383. <br />
<br />
Jae-Nam, L., & Young-Gul, K. (2005). Understanding outsourcing partnership: A comparison of three theoretical perspectives. IEEE Transactions on Engineering Management, 52(1), 43-58. <br />
<br />
Jeffrey, A. C., & Fawzy, S. (1999). A graphical method for assessing knowledge-based systems investments. Logistics Information Management, 12(1/2), 63-77. <br />
<br />
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). [http://inn.colorado.edu/Details/Paper/83 Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs]. MIS Quarterly, 23(2), 183-213. <br />
<br />
Leonard, L. N. K., Cronan, T. P., & Kreie, J. (2004). What influences IT ethical behavior intentions-planned behavior, reasoned action, perceived importance, or individual characteristics? Information & Management, 42(1), 143-158. <br />
<br />
Liker, J. K., & Sindi, A. A. (1997). User acceptance of expert systems: A test of the theory of reasoned action. Journal of Engineering and Technology Management, 14(2), 147-173. <br />
<br />
Lowry, Paul Benjamin, Gaskin, James, Twyman, Nathan W., Hammer, Bryan, and Roberts, Tom L. (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br />
Lowry, Paul Benjamin, Jenkins, Jeffrey L., Twyman, Nathan W., Hammer, Bryan, Gaskin, James, and Hassell, Martin (2008). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287401 Proposing the hedonic affect model (HAM) to explain how stimuli and performance expectations predict affect in individual and group hedonic systems use],” Proceedings of the Journal of the Association for Information Systems Theory Development Workshop at the International Conference on Systems Sciences, Paris, France, December 13. All Sprouts Content, vol. 8(24), paper 230, pp. 1–51.<br />
<br />
Lowry, Paul Benjamin, Twyman, Nathan W., Gaskin, James, Hammer, Bryan, Bailey, Aaron, and Roberts, Tom L. (2007). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1482844 Proposing the interactivity-stimulus-attention model (ISAM) to explain and predict enjoyment, immersion, and adoption of purely hedonic systems],” Proceedings of the Special Interest Group on Human-Computer Interaction 2007 Pre-ICIS Workshop at the International Conference on System Sciences, Montréal, Canada, December 8, paper 11, pp. 72–76 (best-paper nomination) http://aisel.aisnet.org/sighci2007/11/.<br />
<br />
Mykytyn, P. P. J., & Harrison, D. A. (1993). The application of the theory of reasoned action to senior management and strategic information systems. Information Resources Management Journal, 6(2), 15-26. <br />
<br />
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15(3), 325-343. <br />
<br />
Shih, Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study internet banking in taiwan. Internet Research, 14(3), 213-223. <br />
<br />
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). [http://inn.colorado.edu/Details/Paper/75 User acceptance of information technology: Toward a unified view]. MIS Quarterly, 27(3), 425-478. <br />
<br />
Yoh, E., Damhorst, M. L., Sapp, S., & Laczniak, R. (2003). Consumer adoption of the internet: The case of apparel shopping. Psychology & Marketing, 20(12), 1095-1118.<br />
<br />
== Links from this theory to other theories ==<br />
[[Theory of planned behavior]], [[Technology acceptance model]], [[Unified theory of acceptance and use of technology]]<br />
<br />
== External links ==<br />
http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Health%20Communication/theory_planned_behavior.doc/, The University of Twente in the Netherlands presents an overview of the theory of planned behavior and the theory of reasoned action including references.<br />
<br />
== Original Contributor(s) ==<br />
Brent Furneaux<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Theory_of_planned_behavior&diff=890Theory of planned behavior2015-11-09T10:40:07Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Theory of planned behavior''' ==<br />
----<br />
== Acronym ==<br />
TPB<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention, Behavior<br />
== Main independent construct(s)/factor(s) ==<br />
Attitude toward behavior, Subjective norm, Perceived behavioral control<br />
== Concise description of theory ==<br />
TPB posits that individual behavior is driven by behavioral intentions where behavioural intentions are a function of an individual's attitude toward the behaviour, the subjective norms surrounding the performance of the behavior, and the individual's perception of the ease with which the behavior can be performed (behavioral control). Attitude toward the behavior is defined as the individual's positive or negative feelings about performing a behaviour. It is determined through an assessment of one's beliefs regarding the consequences arising from a behavior and an evaluation of the desirability of these consequences. Formally, overall attitude can be assessed as the sum of the individual consequence x desirability assessments for all expected consequences of the behavior. Subjective norm is defined as an individual's perception of whether people important to the individual think the behavior should be performed. The contribution of the opinion of any given referent is weighted by the motivation that an individual has to comply with the wishes of that referent. Hence, overall subjective norm can be expressed as the sum of the individual perception x motivation assessments for all relevant referents. Behavioral control is defined as one's perception of the difficulty of performing a behavior. TPB views the control that people have over their behavior as lying on a continuum from behaviors that are easily performed to those requiring considerable effort, resources, etc. Although Ajzen has suggested that the link between behavior and behavioral control outlined in the model should be between behavior and actual behavioural control rather than perceived behavioural control, the difficulty of assessing actual control has led to the use of perceived control as a proxy. <br />
<br />
Source: Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth: Harcourt Brace Jovanovich College Publishers.<br />
<br />
== Diagram/schematic of theory ==<br />
[[Image:Tpb.JPG]]<br />
== Originating author(s) ==<br />
Ajzen (1985); Ajzen (1991)<br />
== Seminal articles ==<br />
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Springer series in social psychology (pp. 11-39). Berlin: Springer. <br />
<br />
Ajzen, I. (1991). [http://inn.colorado.edu/Details/Paper/211 The theory of planned behavior]. Organizational Behavior and Human Decision Processes, 50(2), 179-211.<br />
<br />
== Originating area ==<br />
Social psychology<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service: The internet as an illustration. International Journal of Service Industry Management, 12(5), 423-450. <br />
<br />
Bosnjak, M., Tuten, T. L., & Wittmann, W. W. (2005). Unit (non)response in web-based access panel surveys: An extended planned-behavior approach. Psychology & Marketing, 22(6), 489-505. <br />
<br />
Brown, S. A., & Venkatesh, V. (2005).[http://inn.colorado.edu/Details/Paper/12082 Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle]. MIS Quarterly, 29(3), 399-426. <br />
<br />
Celuch, K., Taylor, S. A., & Goodwin, S. (2004). Understanding insurance salesperson internet information management intentions: A test of competing models. Journal of Insurance Issues, 27(1), 22-40. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719. <br />
<br />
d'Astous, A., Colbert, F., & Montpetit, D. (2005). Music piracy on the web - how effective are anti-piracy arguments? evidence from the theory of planned behaviour. Journal of Consumer Policy, 28(3), 289-310. <br />
<br />
Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
<br />
George, J. F. (2004). The theory of planned behavior and internet purchasing. Internet Research, 14(3), 198-212. <br />
<br />
Grandon, E. E., & Mykytyn, P. P.,Jr. (2004). Theory-based instrumentation to measure the intention to use electronic commerce in small and medium sized businesses. The Journal of Computer Information Systems, 44(3), 44-57. <br />
<br />
Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior. International Journal of Information Management, 24(6), 539-550. <br />
<br />
Harrison, D. A., Mykytyn, P. P., & Riemenschneider, C. K. (1997). [http://inn.colorado.edu/Details/Paper/6940 Executive decisions about adoption of information technology in small business: Theory and empirical tests]. Information Systems Research, 8(2), 171-195. <br />
<br />
Hsu, M., & Chiu, C. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems, 38(3), 369-381. <br />
<br />
Leonard, L. N. K., Cronan, T. P., & Kreie, J. (2004). What influences IT ethical behavior intentions-planned behavior, reasoned action, perceived importance, or individual characteristics? Information & Management, 42(1), 143-158. <br />
<br />
Liao, S., Shao, Y. P., Wang, H., & Chen, A. (1999). The adoption of virtual banking: An empirical study. International Journal of Information Management, 19(1), 63-74. <br />
<br />
Lim, J. (2003). A conceptual framework on the adoption of negotiation support systems. Information and Software Technology, 45(8), 469-477. <br />
<br />
Limayem, M., & Hirt, S. G. (2003). Force of habit and information systems usage: Theory and initial validation. Journal of the Association for Information Systems, 4, 65-97. <br />
<br />
Lowry, Paul Benjamin, Gaskin, James, Twyman, Nathan W., Hammer, Bryan, and Roberts, Tom L. (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br />
Lowry, Paul Benjamin, Jenkins, Jeffrey L., Twyman, Nathan W., Hammer, Bryan, Gaskin, James, and Hassell, Martin (2008). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287401 Proposing the hedonic affect model (HAM) to explain how stimuli and performance expectations predict affect in individual and group hedonic systems use],” Proceedings of the Journal of the Association for Information Systems Theory Development Workshop at the International Conference on Systems Sciences, Paris, France, December 13. All Sprouts Content, vol. 8(24), paper 230, pp. 1–51.<br />
<br />
Lowry, Paul Benjamin, Twyman, Nathan W., Gaskin, James, Hammer, Bryan, Bailey, Aaron, and Roberts, Tom L. (2007). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1482844 Proposing the interactivity-stimulus-attention model (ISAM) to explain and predict enjoyment, immersion, and adoption of purely hedonic systems],” Proceedings of the Special Interest Group on Human-Computer Interaction 2007 Pre-ICIS Workshop at the International Conference on System Sciences, Montréal, Canada, December 8, paper 11, pp. 72–76 (best-paper nomination) http://aisel.aisnet.org/sighci2007/11/.<br />
<br />
Lwin, M. O., & Williams, J. D. (2003). A model integrating the multidimensional developmental theory of privacy and theory of planned behavior to examine fabrication of information online. Marketing Letters, 14(4), 257-272. <br />
<br />
Mathieson, K. (1991). [http://inn.colorado.edu/Details/Paper/6663 Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior]. Information Systems Research, 2(3), 173-191. <br />
<br />
Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behavior. IEEE Transactions on Engineering Management, 52(1), 69-84. <br />
<br />
Riemenschneider, C. K., Harrison, D. A., & Mykytn, P. P.,Jr. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information & Management, 40(4), 269-285. <br />
<br />
Riemenschneider, C. K., & McKinney, V. R. (2001). Assessing belief differences in small business adopters and non-adopters of web-based e-commerce. The Journal of Computer Information Systems, 42(2), 101-107. <br />
<br />
Shih, Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study internet banking in taiwan. Internet Research, 14(3), 213-223. <br />
<br />
Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of internet banking. Journal of the Association for Information Systems, 1(5), 1-42. <br />
<br />
Taylor, S., & Todd, P. A. (1995). [http://inn.colorado.edu/Details/Paper/6885 Understanding information technology usage: A test of competing models]. Information Systems Research, 6(2), 144-176. <br />
<br />
Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71-102. <br />
<br />
Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33-60. <br />
<br />
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. <br />
<br />
Workman, M. (2005). Expert decision support system use, disuse, and misuse: A study using the theory of planned behavior. Journal of Computers in Human Behavior, 21, 211-231.<br />
<br />
== Links from this theory to other theories ==<br />
[[Hedonic-motivation system adoption model (HMSAM)]], [[Technology acceptance model]], [[Theory of reasoned action]], [[Unified theory of acceptance and use of technology]]<br />
<br />
== External links ==<br />
http://en.wikipedia.org/wiki/Theory_of_planned_behaviour, Wikipedia presents an overview of the theory of planned behavior including references. <br />
<br />
http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Health%20Communication/theory_planned_behavior.doc/ , The University of Twente in the Netherlands presents an overview of the theory of planned behavior and the theory of reasoned action including references.<br />
<br />
http://www.people.umass.edu/aizen/tpb.html, Useful summary of TPB, including bibliography and survey items, from Icek Ajzen<br />
<br />
== Original Contributor(s) ==<br />
Brent Furneaux<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Theory_of_planned_behavior&diff=889Theory of planned behavior2015-11-09T10:39:51Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Theory of planned behavior''' ==<br />
----<br />
== Acronym ==<br />
TPB<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention, Behavior<br />
== Main independent construct(s)/factor(s) ==<br />
Attitude toward behavior, Subjective norm, Perceived behavioral control<br />
== Concise description of theory ==<br />
TPB posits that individual behavior is driven by behavioral intentions where behavioural intentions are a function of an individual's attitude toward the behaviour, the subjective norms surrounding the performance of the behavior, and the individual's perception of the ease with which the behavior can be performed (behavioral control). Attitude toward the behavior is defined as the individual's positive or negative feelings about performing a behaviour. It is determined through an assessment of one's beliefs regarding the consequences arising from a behavior and an evaluation of the desirability of these consequences. Formally, overall attitude can be assessed as the sum of the individual consequence x desirability assessments for all expected consequences of the behavior. Subjective norm is defined as an individual's perception of whether people important to the individual think the behavior should be performed. The contribution of the opinion of any given referent is weighted by the motivation that an individual has to comply with the wishes of that referent. Hence, overall subjective norm can be expressed as the sum of the individual perception x motivation assessments for all relevant referents. Behavioral control is defined as one's perception of the difficulty of performing a behavior. TPB views the control that people have over their behavior as lying on a continuum from behaviors that are easily performed to those requiring considerable effort, resources, etc. Although Ajzen has suggested that the link between behavior and behavioral control outlined in the model should be between behavior and actual behavioural control rather than perceived behavioural control, the difficulty of assessing actual control has led to the use of perceived control as a proxy. <br />
<br />
Source: Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth: Harcourt Brace Jovanovich College Publishers.<br />
<br />
== Diagram/schematic of theory ==<br />
[[Image:Tpb.JPG]]<br />
== Originating author(s) ==<br />
Ajzen (1985); Ajzen (1991)<br />
== Seminal articles ==<br />
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Springer series in social psychology (pp. 11-39). Berlin: Springer. <br />
<br />
Ajzen, I. (1991). [http://inn.colorado.edu/Details/Paper/211 The theory of planned behavior]. Organizational Behavior and Human Decision Processes, 50(2), 179-211.<br />
<br />
== Originating area ==<br />
Social psychology<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service: The internet as an illustration. International Journal of Service Industry Management, 12(5), 423-450. <br />
<br />
Bosnjak, M., Tuten, T. L., & Wittmann, W. W. (2005). Unit (non)response in web-based access panel surveys: An extended planned-behavior approach. Psychology & Marketing, 22(6), 489-505. <br />
<br />
Brown, S. A., & Venkatesh, V. (2005).[http://inn.colorado.edu/Details/Paper/12082 Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle]. MIS Quarterly, 29(3), 399-426. <br />
<br />
Celuch, K., Taylor, S. A., & Goodwin, S. (2004). Understanding insurance salesperson internet information management intentions: A test of competing models. Journal of Insurance Issues, 27(1), 22-40. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719. <br />
<br />
d'Astous, A., Colbert, F., & Montpetit, D. (2005). Music piracy on the web - how effective are anti-piracy arguments? evidence from the theory of planned behaviour. Journal of Consumer Policy, 28(3), 289-310. <br />
<br />
Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
<br />
George, J. F. (2004). The theory of planned behavior and internet purchasing. Internet Research, 14(3), 198-212. <br />
<br />
Grandon, E. E., & Mykytyn, P. P.,Jr. (2004). Theory-based instrumentation to measure the intention to use electronic commerce in small and medium sized businesses. The Journal of Computer Information Systems, 44(3), 44-57. <br />
<br />
Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior. International Journal of Information Management, 24(6), 539-550. <br />
<br />
Harrison, D. A., Mykytyn, P. P., & Riemenschneider, C. K. (1997). [http://inn.colorado.edu/Details/Paper/6940 Executive decisions about adoption of information technology in small business: Theory and empirical tests]. Information Systems Research, 8(2), 171-195. <br />
<br />
Hsu, M., & Chiu, C. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems, 38(3), 369-381. <br />
<br />
Leonard, L. N. K., Cronan, T. P., & Kreie, J. (2004). What influences IT ethical behavior intentions-planned behavior, reasoned action, perceived importance, or individual characteristics? Information & Management, 42(1), 143-158. <br />
<br />
Liao, S., Shao, Y. P., Wang, H., & Chen, A. (1999). The adoption of virtual banking: An empirical study. International Journal of Information Management, 19(1), 63-74. <br />
<br />
Lim, J. (2003). A conceptual framework on the adoption of negotiation support systems. Information and Software Technology, 45(8), 469-477. <br />
<br />
Limayem, M., & Hirt, S. G. (2003). Force of habit and information systems usage: Theory and initial validation. Journal of the Association for Information Systems, 4, 65-97. <br />
<br />
Lowry, Paul Benjamin, Gaskin, James, Twyman, Nathan W., Hammer, Bryan, and Roberts, Tom L. (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br />
Lowry, Paul Benjamin, Jenkins, Jeffrey L., Twyman, Nathan W., Hammer, Bryan, Gaskin, James, and Hassell, Martin (2008). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287401 Proposing the hedonic affect model (HAM) to explain how stimuli and performance expectations predict affect in individual and group hedonic systems use],” Proceedings of the Journal of the Association for Information Systems Theory Development Workshop at the International Conference on Systems Sciences, Paris, France, December 13. All Sprouts Content, vol. 8(24), paper 230, pp. 1–51.<br />
<br />
Lowry, Paul Benjamin, Twyman, Nathan W., Gaskin, James, Hammer, Bryan, Bailey, Aaron, and Roberts, Tom L. (2007). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1482844 Proposing the interactivity-stimulus-attention model (ISAM) to explain and predict enjoyment, immersion, and adoption of purely hedonic systems],” Proceedings of the Special Interest Group on Human-Computer Interaction 2007 Pre-ICIS Workshop at the International Conference on System Sciences, Montréal, Canada, December 8, paper 11, pp. 72–76 (best-paper nomination) http://aisel.aisnet.org/sighci2007/11/.<br />
<br />
Lwin, M. O., & Williams, J. D. (2003). A model integrating the multidimensional developmental theory of privacy and theory of planned behavior to examine fabrication of information online. Marketing Letters, 14(4), 257-272. <br />
<br />
Mathieson, K. (1991). [http://inn.colorado.edu/Details/Paper/6663 Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior]. Information Systems Research, 2(3), 173-191. <br />
<br />
Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behavior. IEEE Transactions on Engineering Management, 52(1), 69-84. <br />
<br />
Riemenschneider, C. K., Harrison, D. A., & Mykytn, P. P.,Jr. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information & Management, 40(4), 269-285. <br />
<br />
Riemenschneider, C. K., & McKinney, V. R. (2001). Assessing belief differences in small business adopters and non-adopters of web-based e-commerce. The Journal of Computer Information Systems, 42(2), 101-107. <br />
<br />
Shih, Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study internet banking in taiwan. Internet Research, 14(3), 213-223. <br />
<br />
Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of internet banking. Journal of the Association for Information Systems, 1(5), 1-42. <br />
<br />
Taylor, S., & Todd, P. A. (1995). [http://inn.colorado.edu/Details/Paper/6885 Understanding information technology usage: A test of competing models]. Information Systems Research, 6(2), 144-176. <br />
<br />
Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71-102. <br />
<br />
Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33-60. <br />
<br />
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. <br />
<br />
Workman, M. (2005). Expert decision support system use, disuse, and misuse: A study using the theory of planned behavior. Journal of Computers in Human Behavior, 21, 211-231.<br />
<br />
== Links from this theory to other theories ==<br />
[[Hedonic-motivation system adoption model (HMSAM)]][[Technology acceptance model]], [[Theory of reasoned action]], [[Unified theory of acceptance and use of technology]]<br />
<br />
== External links ==<br />
http://en.wikipedia.org/wiki/Theory_of_planned_behaviour, Wikipedia presents an overview of the theory of planned behavior including references. <br />
<br />
http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Health%20Communication/theory_planned_behavior.doc/ , The University of Twente in the Netherlands presents an overview of the theory of planned behavior and the theory of reasoned action including references.<br />
<br />
http://www.people.umass.edu/aizen/tpb.html, Useful summary of TPB, including bibliography and survey items, from Icek Ajzen<br />
<br />
== Original Contributor(s) ==<br />
Brent Furneaux<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Theory_of_planned_behavior&diff=888Theory of planned behavior2015-11-09T10:39:08Z<p>Pblowry: /* IS articles that use the theory */</p>
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<br />
== '''Theory of planned behavior''' ==<br />
----<br />
== Acronym ==<br />
TPB<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention, Behavior<br />
== Main independent construct(s)/factor(s) ==<br />
Attitude toward behavior, Subjective norm, Perceived behavioral control<br />
== Concise description of theory ==<br />
TPB posits that individual behavior is driven by behavioral intentions where behavioural intentions are a function of an individual's attitude toward the behaviour, the subjective norms surrounding the performance of the behavior, and the individual's perception of the ease with which the behavior can be performed (behavioral control). Attitude toward the behavior is defined as the individual's positive or negative feelings about performing a behaviour. It is determined through an assessment of one's beliefs regarding the consequences arising from a behavior and an evaluation of the desirability of these consequences. Formally, overall attitude can be assessed as the sum of the individual consequence x desirability assessments for all expected consequences of the behavior. Subjective norm is defined as an individual's perception of whether people important to the individual think the behavior should be performed. The contribution of the opinion of any given referent is weighted by the motivation that an individual has to comply with the wishes of that referent. Hence, overall subjective norm can be expressed as the sum of the individual perception x motivation assessments for all relevant referents. Behavioral control is defined as one's perception of the difficulty of performing a behavior. TPB views the control that people have over their behavior as lying on a continuum from behaviors that are easily performed to those requiring considerable effort, resources, etc. Although Ajzen has suggested that the link between behavior and behavioral control outlined in the model should be between behavior and actual behavioural control rather than perceived behavioural control, the difficulty of assessing actual control has led to the use of perceived control as a proxy. <br />
<br />
Source: Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth: Harcourt Brace Jovanovich College Publishers.<br />
<br />
== Diagram/schematic of theory ==<br />
[[Image:Tpb.JPG]]<br />
== Originating author(s) ==<br />
Ajzen (1985); Ajzen (1991)<br />
== Seminal articles ==<br />
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Springer series in social psychology (pp. 11-39). Berlin: Springer. <br />
<br />
Ajzen, I. (1991). [http://inn.colorado.edu/Details/Paper/211 The theory of planned behavior]. Organizational Behavior and Human Decision Processes, 50(2), 179-211.<br />
<br />
== Originating area ==<br />
Social psychology<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service: The internet as an illustration. International Journal of Service Industry Management, 12(5), 423-450. <br />
<br />
Bosnjak, M., Tuten, T. L., & Wittmann, W. W. (2005). Unit (non)response in web-based access panel surveys: An extended planned-behavior approach. Psychology & Marketing, 22(6), 489-505. <br />
<br />
Brown, S. A., & Venkatesh, V. (2005).[http://inn.colorado.edu/Details/Paper/12082 Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle]. MIS Quarterly, 29(3), 399-426. <br />
<br />
Celuch, K., Taylor, S. A., & Goodwin, S. (2004). Understanding insurance salesperson internet information management intentions: A test of competing models. Journal of Insurance Issues, 27(1), 22-40. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719. <br />
<br />
d'Astous, A., Colbert, F., & Montpetit, D. (2005). Music piracy on the web - how effective are anti-piracy arguments? evidence from the theory of planned behaviour. Journal of Consumer Policy, 28(3), 289-310. <br />
<br />
Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
<br />
George, J. F. (2004). The theory of planned behavior and internet purchasing. Internet Research, 14(3), 198-212. <br />
<br />
Grandon, E. E., & Mykytyn, P. P.,Jr. (2004). Theory-based instrumentation to measure the intention to use electronic commerce in small and medium sized businesses. The Journal of Computer Information Systems, 44(3), 44-57. <br />
<br />
Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior. International Journal of Information Management, 24(6), 539-550. <br />
<br />
Harrison, D. A., Mykytyn, P. P., & Riemenschneider, C. K. (1997). [http://inn.colorado.edu/Details/Paper/6940 Executive decisions about adoption of information technology in small business: Theory and empirical tests]. Information Systems Research, 8(2), 171-195. <br />
<br />
Hsu, M., & Chiu, C. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems, 38(3), 369-381. <br />
<br />
Leonard, L. N. K., Cronan, T. P., & Kreie, J. (2004). What influences IT ethical behavior intentions-planned behavior, reasoned action, perceived importance, or individual characteristics? Information & Management, 42(1), 143-158. <br />
<br />
Liao, S., Shao, Y. P., Wang, H., & Chen, A. (1999). The adoption of virtual banking: An empirical study. International Journal of Information Management, 19(1), 63-74. <br />
<br />
Lim, J. (2003). A conceptual framework on the adoption of negotiation support systems. Information and Software Technology, 45(8), 469-477. <br />
<br />
Limayem, M., & Hirt, S. G. (2003). Force of habit and information systems usage: Theory and initial validation. Journal of the Association for Information Systems, 4, 65-97. <br />
<br />
Lowry, Paul Benjamin, Gaskin, James, Twyman, Nathan W., Hammer, Bryan, and Roberts, Tom L. (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br />
Lowry, Paul Benjamin, Jenkins, Jeffrey L., Twyman, Nathan W., Hammer, Bryan, Gaskin, James, and Hassell, Martin (2008). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287401 Proposing the hedonic affect model (HAM) to explain how stimuli and performance expectations predict affect in individual and group hedonic systems use],” Proceedings of the Journal of the Association for Information Systems Theory Development Workshop at the International Conference on Systems Sciences, Paris, France, December 13. All Sprouts Content, vol. 8(24), paper 230, pp. 1–51.<br />
<br />
Lowry, Paul Benjamin, Twyman, Nathan W., Gaskin, James, Hammer, Bryan, Bailey, Aaron, and Roberts, Tom L. (2007). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1482844 Proposing the interactivity-stimulus-attention model (ISAM) to explain and predict enjoyment, immersion, and adoption of purely hedonic systems],” Proceedings of the Special Interest Group on Human-Computer Interaction 2007 Pre-ICIS Workshop at the International Conference on System Sciences, Montréal, Canada, December 8, paper 11, pp. 72–76 (best-paper nomination) http://aisel.aisnet.org/sighci2007/11/.<br />
<br />
Lwin, M. O., & Williams, J. D. (2003). A model integrating the multidimensional developmental theory of privacy and theory of planned behavior to examine fabrication of information online. Marketing Letters, 14(4), 257-272. <br />
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Mathieson, K. (1991). [http://inn.colorado.edu/Details/Paper/6663 Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior]. Information Systems Research, 2(3), 173-191. <br />
<br />
Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behavior. IEEE Transactions on Engineering Management, 52(1), 69-84. <br />
<br />
Riemenschneider, C. K., Harrison, D. A., & Mykytn, P. P.,Jr. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information & Management, 40(4), 269-285. <br />
<br />
Riemenschneider, C. K., & McKinney, V. R. (2001). Assessing belief differences in small business adopters and non-adopters of web-based e-commerce. The Journal of Computer Information Systems, 42(2), 101-107. <br />
<br />
Shih, Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study internet banking in taiwan. Internet Research, 14(3), 213-223. <br />
<br />
Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of internet banking. Journal of the Association for Information Systems, 1(5), 1-42. <br />
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Taylor, S., & Todd, P. A. (1995). [http://inn.colorado.edu/Details/Paper/6885 Understanding information technology usage: A test of competing models]. Information Systems Research, 6(2), 144-176. <br />
<br />
Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71-102. <br />
<br />
Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33-60. <br />
<br />
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. <br />
<br />
Workman, M. (2005). Expert decision support system use, disuse, and misuse: A study using the theory of planned behavior. Journal of Computers in Human Behavior, 21, 211-231.<br />
<br />
== Links from this theory to other theories ==<br />
[[Technology acceptance model]], [[Theory of reasoned action]], [[Unified theory of acceptance and use of technology]]<br />
<br />
== External links ==<br />
http://en.wikipedia.org/wiki/Theory_of_planned_behaviour, Wikipedia presents an overview of the theory of planned behavior including references. <br />
<br />
http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Health%20Communication/theory_planned_behavior.doc/ , The University of Twente in the Netherlands presents an overview of the theory of planned behavior and the theory of reasoned action including references.<br />
<br />
http://www.people.umass.edu/aizen/tpb.html, Useful summary of TPB, including bibliography and survey items, from Icek Ajzen<br />
<br />
== Original Contributor(s) ==<br />
Brent Furneaux<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
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[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Hedonic-motivation_system_adoption_model_(HMSAM)&diff=887Hedonic-motivation system adoption model (HMSAM)2015-11-09T10:02:01Z<p>Pblowry: /* Originating author(s) */</p>
<hr />
<div>== Acronym ==<br />
<br />
HMSAM<br />
<br />
== Alternate name(s) ==<br />
<br />
n/a<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Behavioral intention to use, Immersion<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Perceived ease of use, perceived usefulness, curiosity, joy, control<br />
<br />
== Concise description of theory ==<br />
<br />
The hedonic-motivation system adoption model (HMSAM) is a native information systems theory to improve the understanding of hedonic-motivation systems (HMS) adoption. HMS are systems used primarily to fulfill users’ intrinsic motivations, such for online gaming, virtual worlds, online shopping, learning/education, online dating, digital music repositories, social networking, only pornography, gamified systems, and for general gamification. Instead of a minor, general technology acceptance model (TAM) extension, HMSAM is an HMS-specific system acceptance model based on an alternative theoretical perspective, which is in turn grounded in flow-based cognitive absorption (CA). The HMSAM further builds on van der Heijden’s (2004) model of hedonic system adoption by including CA as a key mediator of perceived ease of use (PEOU) and of behavioral intentions to use (BIU) hedonic-motivation systems. Typically, models simplistically represent “intrinsic motivations” by mere perceived enjoyed. Instead, HMSAM uses the more complex, rich construct of CA, which includes joy, control, curiosity, focused immersion, and temporal dissociation. CA is construct that is grounded in the seminal flow literature, yet ironically CA has traditionally been used as a static construct, as if all five of its subconstructs occur at the same time—in direct contradiction to the flow literature. Thus, part of HMSAM’s contribution is to return CA closer to its flow roots by re-ordering these CA subconstructs into more natural process-variance order as predicted by flow. Empirical data collection along with mediation tests further support this modeling approach. Figure 1 overviews HMSAM.<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:HMSAM_overview.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of HMSAM, from Lowry et al. (2013)<br />
<br />
== Originating author(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry], James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts.<br />
<br />
== Seminal articles ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
== Originating area ==<br />
<br />
Information Systems (native IS theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
Yichuan Wang, Pramod Rajan, Chetan S. Sankar, P. K. Raju (2014). “Relationships between Goal Clarity, Concentration and Learning Effectiveness When Playing Serious Games,” Proceeding of Twentieth Americas Conference on Information Systems, Savannah, Georgia.<br />
<br />
Tobias Kowatsch and Flavius Kehr (2014). “Towards a Design Theory for IS Services Enabling Incentive-based Health Promotion in Organizations,” Wirtschaftsinformatik (MKWI 2014)<br />
<br />
Mark Keith, Greg Anderson, Douglas Dean, and James Eric Gaskin (2014). “The Effects of Team Flow on Performance: A Video Game Experiment,” SIGHCI 2014 Proceedings.<br />
<br />
Mosiane, Segomotso; Brown, Irwin (2014). “Exploring antecedents of game-based learning effectiveness,” Proceedings of the 25th Australasian Conference on Information Systems, 8th - 10th December, Auckland, New Zealand<br />
<br />
Thomas Wiegand and Stefan Stieglitz (2015) “Serious Fun - Effects of Gamification on Knowledge Exchange in Enterprises” Lecture Notes in Informatics Proceedings<br />
<br />
Lisa-Maria Putz and Horst Treiblmaier (2015). “Creating a Theory-Based Research Agenda for Gamification,” Proceeding of Twenty-First Americas Conference on Information Systems, Puerto Rico.<br />
== Links from this theory to other theories ==<br />
<br />
Multimotive Information Systems Continuance Model (MISC)<br />
<br />
Technology Acceptance Model<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Flow_theory&diff=886Flow theory2015-11-09T09:54:15Z<p>Pblowry: /* Links from this theory to other theories */</p>
<hr />
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<br />
<br />
== '''Flow theory''' ==<br />
----<br />
== Acronym ==<br />
<br />
n/a<br />
<br />
== Alternate name(s)==<br />
<br />
Flow<br />
<br />
== Main dependent construct(s)/factor(s)==<br />
<br />
a mental state of complete absorption<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
situation, activity, high level of challenge, focused attention, high level of skill<br />
<br />
== Concise description of theory ==<br />
<br />
a mental state of complete absorption with the activity at hand, a feeling of total engagement and immersion, a harmonious blend of high level of challenge, focused attention and high level of skill<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
== Originating author(s) ==<br />
<br />
Mihaly Csikszentmihalyi<br />
<br />
== Seminal articles ==<br />
<br />
Csikszentmihalyi, M. (1975). Plan and Intrinsic Rewards. Journal of Humanistic Psychology, 15(3), 41-63.<br />
<br><br><br />
Montgomery, H., Sharafi, P., & Hedman, L. R. (2004). Engaging in Activities Involving Information Technology: Dimensions, Modes, and Flow. Human Factors, 46(2), 334-348.<br />
<br><br><br />
Qiu, L., & Benbasat, I. (2005). An Investigation into the Effects of Text-to-Speech Voice and 3D Avatars on the Perception of Presence and Flow of Live Help in Electronic Commerce. ACM Transactions on Computer-Human Interaction, 12(4), 329–355.<br />
<br><br><br />
Hoffman, D. L. and T. P. Novak (2009). "Flow Online: Lessons Learned and Future Prospects." Journal of Interactive Marketing 23(1): 23-34.<br />
<br />
== Originating area ==<br />
<br />
Positive psychology<br />
<br />
== Level of analysis ==<br />
<br />
individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Agarwal, R., & Karahanna, E. (2000). [http://inn.colorado.edu/Details/Paper/81 Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage.] MIS Quarterly, 24(4), 665-694.<br />
<br><br><br />
Koufaris, M. (2002). [http://inn.colorado.edu/Details/Paper/6795 Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior.] Information Systems Research, 13(2), 205-223.<br />
<br><br><br />
Hsu, C.-L. and H.-P. Lu (2003). "Why Do People Play On-Line Games? An Extended TAM with Social Influences and Flow Experience." Information and Management 41(7): 853–868.<br />
<br><br><br />
Kamis, A., Koufaris, M., & Stern, T. (2008). [http://inn.colorado.edu/Details/Paper/6357 Using an Attribute-Based DSS for User-Customized Products Online: An Experimental Investigation.] MIS Quarterly, 32(1), 159-177.<br />
Lowry, Paul Benjamin, Gaskin, James, Twyman, Nathan W., Hammer, Bryan, and Roberts, Tom L. (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br><br><br />
Lowry, Paul Benjamin, Jenkins, Jeffrey L., Twyman, Nathan W., Hammer, Bryan, Gaskin, James, and Hassell, Martin (2008). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287401 Proposing the hedonic affect model (HAM) to explain how stimuli and performance expectations predict affect in individual and group hedonic systems use],” Proceedings of the Journal of the Association for Information Systems Theory Development Workshop at the International Conference on Systems Sciences, Paris, France, December 13. All Sprouts Content, vol. 8(24), paper 230, pp. 1–51.<br />
<br><br><br />
Lowry, Paul Benjamin, Twyman, Nathan W., Gaskin, James, Hammer, Bryan, Bailey, Aaron, and Roberts, Tom L. (2007). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1482844 Proposing the interactivity-stimulus-attention model (ISAM) to explain and predict enjoyment, immersion, and adoption of purely hedonic systems],” Proceedings of the Special Interest Group on Human-Computer Interaction 2007 Pre-ICIS Workshop at the International Conference on System Sciences, Montréal, Canada, December 8, paper 11, pp. 72–76 (best-paper nomination) http://aisel.aisnet.org/sighci2007/11/.<br />
<br />
== Links from this theory to other theories ==<br />
[[Hedonic-motivation system adoption model (HMSAM)]]<br />
<br />
== External links ==<br />
<br />
[http://en.wikipedia.org/wiki/Mihaly_Csikszentmihalyi#Flow Flow]<br />
<br />
== Original Contributor(s) ==<br />
<br />
Arnold Kamis<br />
<br><br><br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Flow_theory&diff=885Flow theory2015-11-09T09:52:51Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Flow theory''' ==<br />
----<br />
== Acronym ==<br />
<br />
n/a<br />
<br />
== Alternate name(s)==<br />
<br />
Flow<br />
<br />
== Main dependent construct(s)/factor(s)==<br />
<br />
a mental state of complete absorption<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
situation, activity, high level of challenge, focused attention, high level of skill<br />
<br />
== Concise description of theory ==<br />
<br />
a mental state of complete absorption with the activity at hand, a feeling of total engagement and immersion, a harmonious blend of high level of challenge, focused attention and high level of skill<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
== Originating author(s) ==<br />
<br />
Mihaly Csikszentmihalyi<br />
<br />
== Seminal articles ==<br />
<br />
Csikszentmihalyi, M. (1975). Plan and Intrinsic Rewards. Journal of Humanistic Psychology, 15(3), 41-63.<br />
<br><br><br />
Montgomery, H., Sharafi, P., & Hedman, L. R. (2004). Engaging in Activities Involving Information Technology: Dimensions, Modes, and Flow. Human Factors, 46(2), 334-348.<br />
<br><br><br />
Qiu, L., & Benbasat, I. (2005). An Investigation into the Effects of Text-to-Speech Voice and 3D Avatars on the Perception of Presence and Flow of Live Help in Electronic Commerce. ACM Transactions on Computer-Human Interaction, 12(4), 329–355.<br />
<br><br><br />
Hoffman, D. L. and T. P. Novak (2009). "Flow Online: Lessons Learned and Future Prospects." Journal of Interactive Marketing 23(1): 23-34.<br />
<br />
== Originating area ==<br />
<br />
Positive psychology<br />
<br />
== Level of analysis ==<br />
<br />
individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Agarwal, R., & Karahanna, E. (2000). [http://inn.colorado.edu/Details/Paper/81 Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage.] MIS Quarterly, 24(4), 665-694.<br />
<br><br><br />
Koufaris, M. (2002). [http://inn.colorado.edu/Details/Paper/6795 Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior.] Information Systems Research, 13(2), 205-223.<br />
<br><br><br />
Hsu, C.-L. and H.-P. Lu (2003). "Why Do People Play On-Line Games? An Extended TAM with Social Influences and Flow Experience." Information and Management 41(7): 853–868.<br />
<br><br><br />
Kamis, A., Koufaris, M., & Stern, T. (2008). [http://inn.colorado.edu/Details/Paper/6357 Using an Attribute-Based DSS for User-Customized Products Online: An Experimental Investigation.] MIS Quarterly, 32(1), 159-177.<br />
Lowry, Paul Benjamin, Gaskin, James, Twyman, Nathan W., Hammer, Bryan, and Roberts, Tom L. (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br><br><br />
Lowry, Paul Benjamin, Jenkins, Jeffrey L., Twyman, Nathan W., Hammer, Bryan, Gaskin, James, and Hassell, Martin (2008). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287401 Proposing the hedonic affect model (HAM) to explain how stimuli and performance expectations predict affect in individual and group hedonic systems use],” Proceedings of the Journal of the Association for Information Systems Theory Development Workshop at the International Conference on Systems Sciences, Paris, France, December 13. All Sprouts Content, vol. 8(24), paper 230, pp. 1–51.<br />
<br><br><br />
Lowry, Paul Benjamin, Twyman, Nathan W., Gaskin, James, Hammer, Bryan, Bailey, Aaron, and Roberts, Tom L. (2007). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1482844 Proposing the interactivity-stimulus-attention model (ISAM) to explain and predict enjoyment, immersion, and adoption of purely hedonic systems],” Proceedings of the Special Interest Group on Human-Computer Interaction 2007 Pre-ICIS Workshop at the International Conference on System Sciences, Montréal, Canada, December 8, paper 11, pp. 72–76 (best-paper nomination) http://aisel.aisnet.org/sighci2007/11/.<br />
<br />
== Links from this theory to other theories ==<br />
<br />
== External links ==<br />
<br />
[http://en.wikipedia.org/wiki/Mihaly_Csikszentmihalyi#Flow Flow]<br />
<br />
== Original Contributor(s) ==<br />
<br />
Arnold Kamis<br />
<br><br><br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Technology_acceptance_model&diff=884Technology acceptance model2015-11-09T09:45:03Z<p>Pblowry: /* Links from this theory to other theories */</p>
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== '''Technology acceptance model''' ==<br />
----<br />
== Acronym ==<br />
TAM<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention to use, System usage<br />
== Main independent construct(s)/factor(s) ==<br />
Perceived usefulness, Perceived ease of use<br />
== Concise description of theory ==<br />
TAM is an adaptation of the Theory of Reasoned Action (TRA) to the field of IS. TAM posits that perceived usefulness and perceived ease of use determine an individual's intention to use a system with intention to use serving as a mediator of actual system use. Perceived usefulness is also seen as being directly impacted by perceived ease of use. Researchers have simplified TAM by removing the attitude construct found in TRA from the current specification (Venkatesh et. al., 2003). Attempts to extend TAM have generally taken one of three approaches: by introducing factors from related models, by introducing additional or alternative belief factors, and by examining antecedents and moderators of perceived usefulness and perceived ease of use (Wixom and Todd, 2005).<br />
<br />
TRA and TAM, both of which have strong behavioural elements, assume that when someone forms an intention to act, that they will be free to act without limitation. In practice constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act.<br />
== Diagram/schematic of theory ==<br />
[[Image:Tam.JPG]]<br />
== Originating author(s) ==<br />
Davis (1986); Davis (1989)<br />
== Seminal articles ==<br />
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology). <br />
<br />
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. <br />
<br />
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. <br />
== Originating area ==<br />
Information Systems, Technology Adoption<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information: A replication. MIS Quarterly, 16(2), 227-247. <br />
<br />
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391. <br />
<br />
Al-Gahtani, S. (2001). The applicability of TAM outside north america: An empirical test in the united kingdom. Information Resources Management Journal, 14(3), 37-46. <br />
<br />
Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745. <br />
<br />
Brosnan, M. J. (1999). Modeling technophobia: A case for word processing. Computers in Human Behavior, 15(2), 105-121. <br />
<br />
Brown, S. A., Massey, A. P., Montoya-Weiss, M. M., & Burkman, J. R. (2002). Do I really have to? user acceptance of mandated technology. European Journal of Information Systems, 11(4), 283-295. <br />
<br />
Chan, S., & Lu, M. (2004). Understanding internet banking adoption and use behavior: A hong kong perspective. Journal of Global Information Management, 12(3), 21-43. <br />
<br />
Chau, P. K. Y. (1996). [http://inn.colorado.edu/Details/Paper/8150 An empirical assessment of a modified technology acceptance model]. Journal of Management Information Systems, 13(2), 185-204. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719. <br />
<br />
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. <br />
<br />
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology). <br />
<br />
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). [http://inn.colorado.edu/Details/Paper/199 User acceptance of computer technology: A comparison of two theoretical models]. Management Science, 35(8), 982-1003. <br />
<br />
Devaraj, S., Fan, M., & Kohli, R. (2002). [http://inn.colorado.edu/Details/Paper/6798 Antecedents of b2C channel satisfaction and preference: Validation e-commerce metrics]. Information Systems Research, 13(3), 316-333. <br />
<br />
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9-21. <br />
<br />
Elwood, S., Changchit, C. & Cutshall, R. (2006). Investigating students' perceptions on laptop initiative in higher education: An extension of the technology acceptance model. Campus Wide Information Systems, 23(5), 336-349.<br />
<br />
Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1-13. <br />
<br />
Gefen, D., Karahanna, E., & Straub, D. W. (2003). [http://inn.colorado.edu/Details/Paper/126 Trust and TAM in online shopping: An integrated model]. MIS Quarterly, 27(1), 51-90. <br />
<br />
Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of E-commerce adoption. Journal of the Association for Information Systems, 1(8), 1-28. <br />
<br />
Gefen, D., & Straub, D. W. (1997). [http://inn.colorado.edu/Details/Paper/146 Gender differences in the perception and use of E-mail: An extension to the technology acceptance model]. MIS Quarterly, 21(4), 389-400. <br />
<br />
Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
<br />
Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365-374. <br />
<br />
Hsu, C. L. and Lin, J. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation, Information & Management, 45, 65-74.<br />
<br />
Hsu, C. L. and Lu, H. P. (2007). Consumer behavior in on-line game communities: a motivational factor perspective Computers in Human Behavior, 23, 1642-1659. <br />
<br />
Hsu, C. L. and Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience, Information & Management, 41(7), 853-868.<br />
<br />
Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). [http://inn.colorado.edu/Details/Paper/8650 Testing the determinants of microcomputer usage via a structural equation model]. Journal of Management Information Systems, 11(4), 87-114. <br />
<br />
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). [http://inn.colorado.edu/Details/Paper/144 Personal computing acceptance factors in small firms: A structural equation model]. MIS Quarterly, 21(3), 279-305. <br />
<br />
Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2), 357-389. <br />
<br />
Kamel, S., & Hassan, A. (2003). Assessing the introduction of electronic banking in egypt using the technology acceptance model. Annals of Cases on Information Technology, 5, 1-25. <br />
<br />
Kamis, A. and Stohr, E. (2006), Parametric Search Engines: What Makes them Effective when Shopping Online for Differentiated Products? Information & Management, 43(7): 904-918.<br />
<br />
Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741-755. <br />
<br />
Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer E-commerce. Information Technology, Learning, and Performance Journal, 22(1), 35-48. <br />
<br />
Koufaris, M. (2002). [http://inn.colorado.edu/Details/Paper/6795 Applying the technology acceptance model and flow theory to online consumer behavior]. Information Systems Research, 13(2), 205-223. <br />
<br />
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29(3), 269-282. <br />
<br />
Lowry, Paul Benjamin; James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br />
Lim, J. (2003). A conceptual framework on the adoption of negotiation support systems. Information and Software Technology, 45(8), 469-477. <br />
<br />
Lu, H., Hsu, C., & Hsu, H. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2/3), 106-120. <br />
<br />
Lucas, H. C.,Jr, & Spitler, V. K. (1999). Technology use and performance: A field study of broker workstations. Decision Sciences, 30(2), 291-311. <br />
<br />
Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-72. <br />
<br />
Mathieson, K. (1991). [http://inn.colorado.edu/Details/Paper/6663 Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior]. Information Systems Research, 2(3), 173-191. <br />
<br />
McCloskey, D. (2003). Evaluating electronic commerce acceptance with the technology acceptance model. The Journal of Computer Information Systems, 44(2), 49-57. <br />
<br />
McCoy, S., Everard, A., & Jones, B. M. (2005). An examination of the technology acceptance model in uruguay and the US: A focus on culture. Journal of Global Information Technology Management, 8(2), 27-45. <br />
<br />
Ndubisi, N. O., Gupta, O. K., & Ndubisi, G. C. (2005). The moguls' model of computing: Integrating the moderating impact of users' persona into the technology acceptance model. Journal of Global Information Technology Management, 8(1), 27-47. <br />
<br />
Ndubisi, N. O., & Jantan, M. (2003). Evaluating IS usage in malaysian small and medium-sized firms using the technology acceptance model. Logistics Information Management, 16(6), 440-450. <br />
<br />
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224-235. <br />
<br />
Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). [http://inn.colorado.edu/Details/Paper/6788 Research report: Richness versus parsimony in modeling technology adoption decisions - understanding merchant adoption of a smart card-based payment system]. Information Systems Research, 12(2), 208-222. <br />
<br />
Riemenschneider, C. K., & Hardgrave, B. C. (2001). Explaining software development tool use with the technology acceptance model. The Journal of Computer Information Systems, 41(4), 1-8. <br />
<br />
Riemenschneider, C. K., Harrison, D. A., & Mykytn, P. P.,Jr. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information & Management, 40(4), 269-285. <br />
<br />
Roberts, P., & Henderson, R. (2000). Information technology acceptance in a sample of government employees: A test of the technology acceptance model. Interacting with Computers, 12(5), 427-443. <br />
<br />
Shih, H. (2004). Extended technology acceptance model of internet utilization behavior. Information & Management, 41(6), 719-729. <br />
<br />
Spacey, R., Goulding, A., & Murray, I. (2004). Exploring the attitudes of public library staff to the internet using the TAM. Journal of Documentation, 60(5), 550-564. <br />
<br />
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92. <br />
<br />
Szajna, B. (1994). [http://inn.colorado.edu/Details/Paper/161 Software evaluation and choice: Predictive validation of the technology acceptance instrument]. MIS Quarterly, 18(3), 319-324. <br />
<br />
Taylor, S., & Todd, P. (1995). [http://inn.colorado.edu/Details/Paper/213 Assessing IT usage: The role of prior experience]. MIS Quarterly, 19(4), 561-570. <br />
<br />
Taylor, S., & Todd, P. A. (1995). [http://inn.colorado.edu/Details/Paper/6885 Understanding information technology usage: A test of competing models]. Information Systems Research, 6(2), 144-176. <br />
<br />
Venkatesh, V. (2000). [http://inn.colorado.edu/Details/Paper/6588 Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model]. Information Systems Research, 11(4), 342-365. <br />
<br />
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. <br />
<br />
Venkatesh, V., & Morris, M. G. (2000). [http://inn.colorado.edu/Details/Paper/79 Why don't men ever stop to ask for dirrections? gender, social influence, and their role in technology acceptance and usage behavior]. MIS Quarterly, 24(1), 115-139. <br />
<br />
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). [http://inn.colorado.edu/Details/Paper/75 User acceptance of information technology: Toward a unified view]. MIS Quarterly, 27(3), 425-478. <br />
<br />
Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297-316. <br />
<br />
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41(6), 747-762. <br />
<br />
Wang, W., & Benbasat, I. (2005). Trust in and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72-101.<br />
<br />
Wang, C., Hsu, Y., & Fang, W. (2004). Acceptance of technology with network externalities: An empirical study of internet instant messaging services. JITTA : Journal of Information Technology Theory and Application, 6(4), 15-28. <br />
<br />
Wang, Y., Wang, Y., Lin, H., & Tang, T. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501-519. <br />
<br />
Wixom, B. H., & Todd, P. A. (2005). [http://inn.colorado.edu/Details/Paper/6818 A theoretical integration of user satisfaction and technology acceptance]. Information Systems Research, 16(1), 85-102. <br />
<br />
Yu, J. L. C., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless internet. Internet Research, 13(3), 206-222.<br />
<br />
== Links from this theory to other theories ==<br />
[[Hedonic-motivation system adoption model (HMSAM)]], [[Theory of planned behavior]], [[Theory of reasoned action]], [[Unified theory of acceptance and use of technology]], [[Delone and McLean IS success model]]<br />
<br />
== External links ==<br />
http://en.wikipedia.org/wiki/Technology_acceptance_model, Wikipedia entry for TAM<br />
<br />
http://www.guuspijpers.com/Research.htm#Technology%20Acceptance%20Model%20(TAM), Guus Pijpers presents an extensive list of TAM references up to December 2003<br />
== Original Contributor(s) ==<br />
Brent Furneaux<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
<br><br />
<br><br />
[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Technology_acceptance_model&diff=883Technology acceptance model2015-11-09T09:44:10Z<p>Pblowry: /* IS articles that use the theory */</p>
<hr />
<div>{{Sponsor Thumbs}}<br />
<br />
<br />
== '''Technology acceptance model''' ==<br />
----<br />
== Acronym ==<br />
TAM<br />
== Alternate name(s)==<br />
N/A<br />
== Main dependent construct(s)/factor(s)==<br />
Behavioral intention to use, System usage<br />
== Main independent construct(s)/factor(s) ==<br />
Perceived usefulness, Perceived ease of use<br />
== Concise description of theory ==<br />
TAM is an adaptation of the Theory of Reasoned Action (TRA) to the field of IS. TAM posits that perceived usefulness and perceived ease of use determine an individual's intention to use a system with intention to use serving as a mediator of actual system use. Perceived usefulness is also seen as being directly impacted by perceived ease of use. Researchers have simplified TAM by removing the attitude construct found in TRA from the current specification (Venkatesh et. al., 2003). Attempts to extend TAM have generally taken one of three approaches: by introducing factors from related models, by introducing additional or alternative belief factors, and by examining antecedents and moderators of perceived usefulness and perceived ease of use (Wixom and Todd, 2005).<br />
<br />
TRA and TAM, both of which have strong behavioural elements, assume that when someone forms an intention to act, that they will be free to act without limitation. In practice constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act.<br />
== Diagram/schematic of theory ==<br />
[[Image:Tam.JPG]]<br />
== Originating author(s) ==<br />
Davis (1986); Davis (1989)<br />
== Seminal articles ==<br />
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology). <br />
<br />
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. <br />
<br />
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. <br />
== Originating area ==<br />
Information Systems, Technology Adoption<br />
== Level of analysis ==<br />
Individual<br />
== IS articles that use the theory ==<br />
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information: A replication. MIS Quarterly, 16(2), 227-247. <br />
<br />
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391. <br />
<br />
Al-Gahtani, S. (2001). The applicability of TAM outside north america: An empirical test in the united kingdom. Information Resources Management Journal, 14(3), 37-46. <br />
<br />
Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745. <br />
<br />
Brosnan, M. J. (1999). Modeling technophobia: A case for word processing. Computers in Human Behavior, 15(2), 105-121. <br />
<br />
Brown, S. A., Massey, A. P., Montoya-Weiss, M. M., & Burkman, J. R. (2002). Do I really have to? user acceptance of mandated technology. European Journal of Information Systems, 11(4), 283-295. <br />
<br />
Chan, S., & Lu, M. (2004). Understanding internet banking adoption and use behavior: A hong kong perspective. Journal of Global Information Management, 12(3), 21-43. <br />
<br />
Chau, P. K. Y. (1996). [http://inn.colorado.edu/Details/Paper/8150 An empirical assessment of a modified technology acceptance model]. Journal of Management Information Systems, 13(2), 185-204. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311. <br />
<br />
Chau, P. Y. K., & Hu, P. J. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719. <br />
<br />
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. <br />
<br />
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology). <br />
<br />
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). [http://inn.colorado.edu/Details/Paper/199 User acceptance of computer technology: A comparison of two theoretical models]. Management Science, 35(8), 982-1003. <br />
<br />
Devaraj, S., Fan, M., & Kohli, R. (2002). [http://inn.colorado.edu/Details/Paper/6798 Antecedents of b2C channel satisfaction and preference: Validation e-commerce metrics]. Information Systems Research, 13(3), 316-333. <br />
<br />
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9-21. <br />
<br />
Elwood, S., Changchit, C. & Cutshall, R. (2006). Investigating students' perceptions on laptop initiative in higher education: An extension of the technology acceptance model. Campus Wide Information Systems, 23(5), 336-349.<br />
<br />
Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1-13. <br />
<br />
Gefen, D., Karahanna, E., & Straub, D. W. (2003). [http://inn.colorado.edu/Details/Paper/126 Trust and TAM in online shopping: An integrated model]. MIS Quarterly, 27(1), 51-90. <br />
<br />
Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of E-commerce adoption. Journal of the Association for Information Systems, 1(8), 1-28. <br />
<br />
Gefen, D., & Straub, D. W. (1997). [http://inn.colorado.edu/Details/Paper/146 Gender differences in the perception and use of E-mail: An extension to the technology acceptance model]. MIS Quarterly, 21(4), 389-400. <br />
<br />
Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot use on the web. Psychology & Marketing, 19(11), 945-955. <br />
<br />
Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365-374. <br />
<br />
Hsu, C. L. and Lin, J. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation, Information & Management, 45, 65-74.<br />
<br />
Hsu, C. L. and Lu, H. P. (2007). Consumer behavior in on-line game communities: a motivational factor perspective Computers in Human Behavior, 23, 1642-1659. <br />
<br />
Hsu, C. L. and Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience, Information & Management, 41(7), 853-868.<br />
<br />
Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). [http://inn.colorado.edu/Details/Paper/8650 Testing the determinants of microcomputer usage via a structural equation model]. Journal of Management Information Systems, 11(4), 87-114. <br />
<br />
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). [http://inn.colorado.edu/Details/Paper/144 Personal computing acceptance factors in small firms: A structural equation model]. MIS Quarterly, 21(3), 279-305. <br />
<br />
Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2), 357-389. <br />
<br />
Kamel, S., & Hassan, A. (2003). Assessing the introduction of electronic banking in egypt using the technology acceptance model. Annals of Cases on Information Technology, 5, 1-25. <br />
<br />
Kamis, A. and Stohr, E. (2006), Parametric Search Engines: What Makes them Effective when Shopping Online for Differentiated Products? Information & Management, 43(7): 904-918.<br />
<br />
Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741-755. <br />
<br />
Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer E-commerce. Information Technology, Learning, and Performance Journal, 22(1), 35-48. <br />
<br />
Koufaris, M. (2002). [http://inn.colorado.edu/Details/Paper/6795 Applying the technology acceptance model and flow theory to online consumer behavior]. Information Systems Research, 13(2), 205-223. <br />
<br />
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29(3), 269-282. <br />
<br />
Lowry, Paul Benjamin; James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671.<br />
<br />
Lim, J. (2003). A conceptual framework on the adoption of negotiation support systems. Information and Software Technology, 45(8), 469-477. <br />
<br />
Lu, H., Hsu, C., & Hsu, H. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2/3), 106-120. <br />
<br />
Lucas, H. C.,Jr, & Spitler, V. K. (1999). Technology use and performance: A field study of broker workstations. Decision Sciences, 30(2), 291-311. <br />
<br />
Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-72. <br />
<br />
Mathieson, K. (1991). [http://inn.colorado.edu/Details/Paper/6663 Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior]. Information Systems Research, 2(3), 173-191. <br />
<br />
McCloskey, D. (2003). Evaluating electronic commerce acceptance with the technology acceptance model. The Journal of Computer Information Systems, 44(2), 49-57. <br />
<br />
McCoy, S., Everard, A., & Jones, B. M. (2005). An examination of the technology acceptance model in uruguay and the US: A focus on culture. Journal of Global Information Technology Management, 8(2), 27-45. <br />
<br />
Ndubisi, N. O., Gupta, O. K., & Ndubisi, G. C. (2005). The moguls' model of computing: Integrating the moderating impact of users' persona into the technology acceptance model. Journal of Global Information Technology Management, 8(1), 27-47. <br />
<br />
Ndubisi, N. O., & Jantan, M. (2003). Evaluating IS usage in malaysian small and medium-sized firms using the technology acceptance model. Logistics Information Management, 16(6), 440-450. <br />
<br />
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224-235. <br />
<br />
Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). [http://inn.colorado.edu/Details/Paper/6788 Research report: Richness versus parsimony in modeling technology adoption decisions - understanding merchant adoption of a smart card-based payment system]. Information Systems Research, 12(2), 208-222. <br />
<br />
Riemenschneider, C. K., & Hardgrave, B. C. (2001). Explaining software development tool use with the technology acceptance model. The Journal of Computer Information Systems, 41(4), 1-8. <br />
<br />
Riemenschneider, C. K., Harrison, D. A., & Mykytn, P. P.,Jr. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information & Management, 40(4), 269-285. <br />
<br />
Roberts, P., & Henderson, R. (2000). Information technology acceptance in a sample of government employees: A test of the technology acceptance model. Interacting with Computers, 12(5), 427-443. <br />
<br />
Shih, H. (2004). Extended technology acceptance model of internet utilization behavior. Information & Management, 41(6), 719-729. <br />
<br />
Spacey, R., Goulding, A., & Murray, I. (2004). Exploring the attitudes of public library staff to the internet using the TAM. Journal of Documentation, 60(5), 550-564. <br />
<br />
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92. <br />
<br />
Szajna, B. (1994). [http://inn.colorado.edu/Details/Paper/161 Software evaluation and choice: Predictive validation of the technology acceptance instrument]. MIS Quarterly, 18(3), 319-324. <br />
<br />
Taylor, S., & Todd, P. (1995). [http://inn.colorado.edu/Details/Paper/213 Assessing IT usage: The role of prior experience]. MIS Quarterly, 19(4), 561-570. <br />
<br />
Taylor, S., & Todd, P. A. (1995). [http://inn.colorado.edu/Details/Paper/6885 Understanding information technology usage: A test of competing models]. Information Systems Research, 6(2), 144-176. <br />
<br />
Venkatesh, V. (2000). [http://inn.colorado.edu/Details/Paper/6588 Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model]. Information Systems Research, 11(4), 342-365. <br />
<br />
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. <br />
<br />
Venkatesh, V., & Morris, M. G. (2000). [http://inn.colorado.edu/Details/Paper/79 Why don't men ever stop to ask for dirrections? gender, social influence, and their role in technology acceptance and usage behavior]. MIS Quarterly, 24(1), 115-139. <br />
<br />
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). [http://inn.colorado.edu/Details/Paper/75 User acceptance of information technology: Toward a unified view]. MIS Quarterly, 27(3), 425-478. <br />
<br />
Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297-316. <br />
<br />
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41(6), 747-762. <br />
<br />
Wang, W., & Benbasat, I. (2005). Trust in and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72-101.<br />
<br />
Wang, C., Hsu, Y., & Fang, W. (2004). Acceptance of technology with network externalities: An empirical study of internet instant messaging services. JITTA : Journal of Information Technology Theory and Application, 6(4), 15-28. <br />
<br />
Wang, Y., Wang, Y., Lin, H., & Tang, T. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501-519. <br />
<br />
Wixom, B. H., & Todd, P. A. (2005). [http://inn.colorado.edu/Details/Paper/6818 A theoretical integration of user satisfaction and technology acceptance]. Information Systems Research, 16(1), 85-102. <br />
<br />
Yu, J. L. C., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless internet. Internet Research, 13(3), 206-222.<br />
<br />
== Links from this theory to other theories ==<br />
[[Theory of planned behavior]], [[Theory of reasoned action]], [[Unified theory of acceptance and use of technology]], [[Delone and McLean IS success model]]<br />
<br />
== External links ==<br />
http://en.wikipedia.org/wiki/Technology_acceptance_model, Wikipedia entry for TAM<br />
<br />
http://www.guuspijpers.com/Research.htm#Technology%20Acceptance%20Model%20(TAM), Guus Pijpers presents an extensive list of TAM references up to December 2003<br />
== Original Contributor(s) ==<br />
Brent Furneaux<br />
<br><br />
<br><br />
Please feel free to make modifications to this site. In order to do so, you must register.<br />
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[[Main Page | Return to Theories Used in IS Research]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Hedonic-motivation_system_adoption_model_(HMSAM)&diff=875Hedonic-motivation system adoption model (HMSAM)2015-11-07T12:07:54Z<p>Pblowry: Created page with "== Acronym == HMSAM == Alternate name(s) == n/a == Main dependent construct(s)/factor(s) == Behavioral intention to use, Immersion == Main independent construct(s)/facto..."</p>
<hr />
<div>== Acronym ==<br />
<br />
HMSAM<br />
<br />
== Alternate name(s) ==<br />
<br />
n/a<br />
<br />
== Main dependent construct(s)/factor(s) ==<br />
<br />
Behavioral intention to use, Immersion<br />
<br />
== Main independent construct(s)/factor(s) ==<br />
<br />
Perceived ease of use, perceived usefulness, curiosity, joy, control<br />
<br />
== Concise description of theory ==<br />
<br />
The hedonic-motivation system adoption model (HMSAM) is a native information systems theory to improve the understanding of hedonic-motivation systems (HMS) adoption. HMS are systems used primarily to fulfill users’ intrinsic motivations, such for online gaming, virtual worlds, online shopping, learning/education, online dating, digital music repositories, social networking, only pornography, gamified systems, and for general gamification. Instead of a minor, general technology acceptance model (TAM) extension, HMSAM is an HMS-specific system acceptance model based on an alternative theoretical perspective, which is in turn grounded in flow-based cognitive absorption (CA). The HMSAM further builds on van der Heijden’s (2004) model of hedonic system adoption by including CA as a key mediator of perceived ease of use (PEOU) and of behavioral intentions to use (BIU) hedonic-motivation systems. Typically, models simplistically represent “intrinsic motivations” by mere perceived enjoyed. Instead, HMSAM uses the more complex, rich construct of CA, which includes joy, control, curiosity, focused immersion, and temporal dissociation. CA is construct that is grounded in the seminal flow literature, yet ironically CA has traditionally been used as a static construct, as if all five of its subconstructs occur at the same time—in direct contradiction to the flow literature. Thus, part of HMSAM’s contribution is to return CA closer to its flow roots by re-ordering these CA subconstructs into more natural process-variance order as predicted by flow. Empirical data collection along with mediation tests further support this modeling approach. Figure 1 overviews HMSAM.<br />
<br />
== Diagram/schematic of theory ==<br />
<br />
<gallery><br />
File:HMSAM_overview.jpg<br />
</gallery><br />
<br />
Figure 1. Overview of HMSAM, from Lowry et al. (2013)<br />
<br />
== Originating author(s) ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts.<br />
<br />
== Seminal articles ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
== Originating area ==<br />
<br />
Information Systems (native IS theory)<br />
<br />
== Level of analysis ==<br />
<br />
Individual<br />
<br />
== IS articles that use the theory ==<br />
<br />
Paul Benjamin Lowry, James Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2177442 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
Paul Benjamin Lowry, James Eric Gaskin, Nathan W. Twyman, Bryan Hammer, and Tom L. Roberts (2013). “[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534937 Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM)],” Journal of the Association for Information Systems (JAIS), vol. 14(11), 617–671. <br />
<br />
Yichuan Wang, Pramod Rajan, Chetan S. Sankar, P. K. Raju (2014). “Relationships between Goal Clarity, Concentration and Learning Effectiveness When Playing Serious Games,” Proceeding of Twentieth Americas Conference on Information Systems, Savannah, Georgia.<br />
<br />
Tobias Kowatsch and Flavius Kehr (2014). “Towards a Design Theory for IS Services Enabling Incentive-based Health Promotion in Organizations,” Wirtschaftsinformatik (MKWI 2014)<br />
<br />
Mark Keith, Greg Anderson, Douglas Dean, and James Eric Gaskin (2014). “The Effects of Team Flow on Performance: A Video Game Experiment,” SIGHCI 2014 Proceedings.<br />
<br />
Mosiane, Segomotso; Brown, Irwin (2014). “Exploring antecedents of game-based learning effectiveness,” Proceedings of the 25th Australasian Conference on Information Systems, 8th - 10th December, Auckland, New Zealand<br />
<br />
Thomas Wiegand and Stefan Stieglitz (2015) “Serious Fun - Effects of Gamification on Knowledge Exchange in Enterprises” Lecture Notes in Informatics Proceedings<br />
<br />
Lisa-Maria Putz and Horst Treiblmaier (2015). “Creating a Theory-Based Research Agenda for Gamification,” Proceeding of Twenty-First Americas Conference on Information Systems, Puerto Rico.<br />
== Links from this theory to other theories ==<br />
<br />
Multimotive Information Systems Continuance Model (MISC)<br />
<br />
Technology Acceptance Model<br />
<br />
== External links ==<br />
<br />
n/a<br />
<br />
== Original Contributor(s) ==<br />
<br />
[http://www.cb.cityu.edu.hk/staff/pblowry Paul Benjamin Lowry]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Main_Page&diff=874Main Page2015-11-07T12:05:22Z<p>Pblowry: </p>
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== Theories ==<br />
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*[[Absorptive capacity theory]]<br />
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*[[Competitive strategy (Porter)]]<br />
*[[Complexity theory]]<br />
*[[Contingency theory]]<br />
*[[Critical realism theory]]<br />
*[[Critical social theory]] <br />
*[[Critical success factors, theory of]]<br />
*[[Customer Focus Theory]]<br />
*[[Deferred action, theory of]] <br />
*[[Delone and McLean IS success model]]<br />
*[[Diffusion of innovations theory]]<br />
*[[Dynamic capabilities]]<br />
*[[Elaboration likelihood model]]<br />
*[[Embodied social presence theory]]<br />
*[[Equity theory]] <br />
*[[Evolutionary theory]]<br />
*[[Expectation confirmation theory]] <br />
*[[Feminism theory]]<br />
*[[Fit-Viability theory]]<br />
*[[Flow theory]]<br />
*[[Game theory]]<br />
*[[Garbage can theory]] <br />
*[[General systems theory]]<br />
*[[General deterrence theory]]<br />
*[[Hedonic-motivation system adoption model (HMSAM) (NEW Entry!)]]<br />
*[[Hermeneutics]]<br />
*[[Illusion of control]]<br />
*[[Impression management, theory of]]<br />
*[[Information processing theory]]<br />
*[[Institutional theory]]<br />
*[[International information systems theory]]<br />
*[[Kellers Motivational Model |Keller's Motivational Model]]<br />
*[[Knowledge-based theory of the firm]]<br />
*[[Language action perspective]] <br />
*[http://istheory.byu.edu/wiki/Lemon_Market_Theory Information asymmetry theory (lemon market)]<br />
*[[Management fashion theory]]<br />
*[[Media richness theory]]<br />
*[[Media synchronicity theory]]<br />
*[[Modal aspects, theory of]]<br />
*[[Multi-attribute utility theory]] <br />
*[[Organizational culture theory]] <br />
*[[Organizational information processing theory]]<br />
*[[Organizational knowledge creation]]<br />
*[[Organizational learning theory]]<br />
*[[Portfolio theory]] <br />
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*[[Prospect theory]] <br />
*[[Protection motivation theory (NEW entry!)]]<br />
*[[Punctuated equilibrium theory]]<br />
*[[Real options theory]]<br />
*[[Resource-based view of the firm]]<br />
*[[Resource dependency theory]]<br />
*[[Self-efficacy theory]]<br />
*[[SERVQUAL]]<br />
*[http://is.theorizeit.org/wiki/Signaling Signaling theory]<br />
*[[Social capital theory]]<br />
*[[Social cognitive theory]]<br />
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*[[Social learning theory]]<br />
*[[Social network theory]]<br />
*[[Social shaping of technology]]<br />
*[[Socio-technical theory]]<br />
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*[[Stakeholder theory]] <br />
*[[Structuration theory]]<br />
*[[Task closure theory]] <br />
*[[Task-technology fit]]<br />
*[[Technological frames of reference]]<br />
*[[Technology acceptance model]] <br />
*[[Technology dominance, theory of]] <br />
*[[Technology-organization-environment framework]]<br />
*[[Theory of collective action]]<br />
*[[Theory of planned behavior]]<br />
*[[Theory of reasoned action]]<br />
*[[Transaction cost economics]] <br />
*[[Transactive memory theory]] <br />
*[[Unified theory of acceptance and use of technology]]<br />
*[[Usage control model]]<br />
*[[Work systems theory]]<br />
*[[Yield shift theory of satisfaction]]</div>Pblowryhttps://is.theorizeit.org/w/index.php?title=Main_Page&diff=873Main Page2015-11-07T12:01:52Z<p>Pblowry: /* Theories */</p>
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<br />
== Theories ==<br />
<br />
*[[Absorptive capacity theory]]<br />
*[[Actor network theory]]<br />
*[[Accountability theory (NEW entry!)]]<br />
*[[Adaptive structuration theory]]<br />
*[[Administrative behavior, theory of]]<br />
*[[Agency theory]] <br />
*[[Argumentation theory]]<br />
*[[Behavioral decision theory]]<br />
*[[Boundary object theory]]<br />
*[[Chaos theory]]<br />
*[[Cognitive dissonance theory]]<br />
*[[Cognitive fit theory]]<br />
*[[Cognitive load theory]]<br />
*[[Competitive strategy (Porter)]]<br />
*[[Complexity theory]]<br />
*[[Contingency theory]]<br />
*[[Critical realism theory]]<br />
*[[Critical social theory]] <br />
*[[Critical success factors, theory of]]<br />
*[[Customer Focus Theory]]<br />
*[[Deferred action, theory of]] <br />
*[[Delone and McLean IS success model]]<br />
*[[Diffusion of innovations theory]]<br />
*[[Dynamic capabilities]]<br />
*[[Elaboration likelihood model]]<br />
*[[Embodied social presence theory]]<br />
*[[Equity theory]] <br />
*[[Evolutionary theory]]<br />
*[[Expectation confirmation theory]] <br />
*[[Feminism theory]]<br />
*[[Fit-Viability theory]]<br />
*[[Flow theory]]<br />
*[[Game theory]]<br />
*[[Garbage can theory]] <br />
*[[General systems theory]]<br />
*[[General deterrence theory]]<br />
*[[Hedonic-motivation system adoption model (NEW entry!]]<br />
*[[Hermeneutics]]<br />
*[[Illusion of control]]<br />
*[[Impression management, theory of]]<br />
*[[Information processing theory]]<br />
*[[Institutional theory]]<br />
*[[International information systems theory]]<br />
*[[Kellers Motivational Model |Keller's Motivational Model]]<br />
*[[Knowledge-based theory of the firm]]<br />
*[[Language action perspective]] <br />
*[http://istheory.byu.edu/wiki/Lemon_Market_Theory Information asymmetry theory (lemon market)]<br />
*[[Management fashion theory]]<br />
*[[Media richness theory]]<br />
*[[Media synchronicity theory]]<br />
*[[Modal aspects, theory of]]<br />
*[[Multi-attribute utility theory]] <br />
*[[Organizational culture theory]] <br />
*[[Organizational information processing theory]]<br />
*[[Organizational knowledge creation]]<br />
*[[Organizational learning theory]]<br />
*[[Portfolio theory]] <br />
*[[Process virtualization theory]]<br />
*[[Prospect theory]] <br />
*[[Protection motivation theory (NEW entry!)]]<br />
*[[Punctuated equilibrium theory]]<br />
*[[Real options theory]]<br />
*[[Resource-based view of the firm]]<br />
*[[Resource dependency theory]]<br />
*[[Self-efficacy theory]]<br />
*[[SERVQUAL]]<br />
*[http://is.theorizeit.org/wiki/Signaling Signaling theory]<br />
*[[Social capital theory]]<br />
*[[Social cognitive theory]]<br />
*[[Social exchange theory]]<br />
*[[Social learning theory]]<br />
*[[Social network theory]]<br />
*[[Social shaping of technology]]<br />
*[[Socio-technical theory]]<br />
*[[Soft systems theory]]<br />
*[[Stakeholder theory]] <br />
*[[Structuration theory]]<br />
*[[Task closure theory]] <br />
*[[Task-technology fit]]<br />
*[[Technological frames of reference]]<br />
*[[Technology acceptance model]] <br />
*[[Technology dominance, theory of]] <br />
*[[Technology-organization-environment framework]]<br />
*[[Theory of collective action]]<br />
*[[Theory of planned behavior]]<br />
*[[Theory of reasoned action]]<br />
*[[Transaction cost economics]] <br />
*[[Transactive memory theory]] <br />
*[[Unified theory of acceptance and use of technology]]<br />
*[[Usage control model]]<br />
*[[Work systems theory]]<br />
*[[Yield shift theory of satisfaction]]</div>Pblowry