Social Influence Theory

= Kelman's Social Influence Theory =
== Acronym ==
== Alternate name(s) ==
Kelman's three process theory
== Concise description of theory ==
The central theme of social influence theory, as proposed by Kelman (1958), is that an individual’s attitudes, beliefs, and subsequent actions or behaviors are influenced by referent others through three processes: compliance, identification, and internalization. Kelman (1958) posited that social influence brings about changes in attitude and actions, and that changes may occur at different “levels.” This difference in the level of changes can be attributed by the differences in the processes through which individuals accept influence. Kelman (1958) delineated three primary processes of influence as described below:
* Compliance is assumed to occur when individuals accept influence and adopt the induced behavior to gain rewards (or, approval) and avoid punishments (or, disapproval). Hence, “the satisfaction derived from compliance is due to the social effect of accepting influence.” (p. 53)
* Identification is said to happen when individuals adopt the induced behavior in order to create or maintain a desired and beneficial relationship to another person or a group. Hence, the satisfaction occurs due to “the act of conforming.” (p. 53)
* Internalization is assumed to occur when individuals accept influence after perceiving the content of the induced behavior is rewarding in which the content indicates the opinions and actions of others. It is also stated that individuals adopt the induced behavior realizing that it is congruent with their value system. In this case, therefore, the satisfaction occurs due to “the content of the new behavior.” (p. 53)
Each of the three processes can be represented by a function of the following three determinants of influence: (a) the relative importance of the anticipated effect, (b) the relative power of the influencing agent, and (c) the prepotency of the induced response (Kelman 1958). However, for each process, these determinants are qualitatively different. So each process has a distinctive set of antecedent conditions; similarly each process leads to a distinctive set of consequent conditions.
Since social influence can shape an individual’s attitudes, beliefs and actions, the impact of social influence on information systems (IS) acceptance and usage has been studied extensively. However, the initial theorizing on IS adoption and use (Lewis et al. 2003; Venkatesh and Davis 2000; Venkatesh et al. 2003) is contended to consider the perspective of social normative compliance, thereby overlooking the identification and internalization processes of social influence (Malhotra and Galletta 2005). It is argued that the subjective norm is the dominant conceptualization of social influence and the way it is operationalized that typically emphasizes compliance (Wang et al. 2013). Subjective norm is theorized in several behavioral models such as the theory of planned behavior (TPB) and the theory of reasoned action (TRA) (Ajzen 1991; Fishbein and Ajzen 1975). Technology-related subjective norm appears in different IS-specific models, including TAM2 and UTAUT (Venkatesh and Davis 2000; Venkatesh et al. 2003).
Realizing that only one aspect of social influence i.e., compliance may not predict the true relationship between the system users’ belief and behavior, and IS use, other researchers attempt to bring the perspectives of all three processes of social influence to provide the integrated impact of them (Malhotra and Galletta 2005; Wang et al. 2013). They believe that the effect of compliance-based social influence may reduce over time, whereas the effects of identification and internalization would persist over longer periods. Therefore, studies that theorize all three processes of social influence indicate that social influence may differ significantly across groups in organizations (Wang et al. 2013). Such conceptualization helps us to understand how the system users’ own beliefs and judgments also influence their commitment to adopt and use technology, complementing the understanding of previous studies that focus on how the system users comply and conform to the beliefs of salient others.
== Diagram/schematic of theory ==
N/A
== Originating author(s) ==
* Kelman (1958)
== Seminal articles ==
Kelman, H. C. 1958. “Compliance, Identification, and Internalization: Three Processes of Attitude Change,” Journal of Conflict Resolution (2:1), pp. 51-60.
== Originating area ==
* Psychology
== Level of analysis ==
* Individual 
== Links to WWW sites describing theory ==
* https://en.wikipedia.org/wiki/Social_influence
== Links from this theory to other theories ==
Technology Acceptance Model 2 (TAM2), TAM3, Unified theory of acceptance and use of technology, Self-determination theory, Organizational commitment
== IS articles that use the theory ==
Cheung, C. M., Chiu, P. Y., and Lee, M. K. 2011. “Online social networks: Why do students use facebook?,” Computers in Human Behavior, (27:4), pp. 1337-1343.
Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. 1989. “User acceptance of computer technology: a comparison of two theoretical models,” Management science, (35:8), pp. 982-1003.
Karahanna, E., Straub, D. W., & Chervany, N. L. 1999. “Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs,” MIS quarterly, pp. 183-213.
Lewis, W., Agarwal, R., and Sambamurthy, V. 2003. “Sources of Influence on Beliefs about Information Technology Use: An Empirical Study of Knowledge Workers,” MIS Quarterly, (27:4), pp. 657-678.
Li, H., Zhang, J., and Sarathy, R. 2010. "Understanding compliance with internet use policy from the perspective of rational choice theory," Decision Support Systems, (48:4), pp. 635-645.
Malhotra, Y., and Galletta, D. 2005. “A Multidimensional Commitment Model of Volitional Systems Adoption and Usage Behavior,” Journal of Management Information Systems (22:1), pp. 117-151.
Mun, Y. Y., Jackson, J. D., Park, J. S., and Probst, J. C. 2006. “Understanding information technology acceptance by individual professionals: Toward an integrative view,” Information & Management, (43:3), pp. 350-363.
Srite, M., and Karahanna, E. 2006. “The role of espoused national cultural values in technology acceptance,” MIS quarterly, pp. 679-704.
Venkatesh, V., and Bala, H. 2008. “Technology acceptance model 3 and a research agenda on interventions,” Decision sciences, (39:2), pp. 273-315.
Venkatesh, V., and Davis, F. D. 2000. “A theoretical extension of the technology acceptance model: Four longitudinal field studies,” Management science, (46:2), pp. 186-204.
Wang, Y., Meister, D. B., and Gray, P. H. 2013. “Social Influence and Knowledge Management Systems Use: Evidence from Panel Data,” MIS Quarterly, (37:1), pp. 299-313.
== Contributor(s) ==
* Anupriya Khan
== Date last updated ==
*28 Feb 2017
== References ==
Ajzen, I. 1991. “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes (50:2), pp. 179-211.
Fishbein, M., and Ajzen, I. 1975. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.
Kelman, H. C. 1958. “Compliance, Identification, and Internalization: Three Processes of Attitude Change,” Journal of Conflict Resolution (2:1), pp. 51-60.
Lewis, W., Agarwal, R., and Sambamurthy, V. 2003. “Sources of Influence on Beliefs about Information Technology Use: An Empirical Study of Knowledge Workers,” MIS Quarterly, (27:4), pp. 657-678.
Malhotra, Y., and Galletta, D. 2005. “A Multidimensional Commitment Model of Volitional Systems Adoption and Usage Behavior,” Journal of Management Information Systems (22:1), pp. 117-151.
Venkatesh, V., and Davis, F. D. 2000. “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” Management Science (46:2), pp. 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. 2003. “User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly (27:3), pp. 425-478.
Wang, Y., Meister, D. B., and Gray, P. H. 2013. “Social Influence and Knowledge Management Systems Use: Evidence from Panel Data,” MIS Quarterly, (37:1), pp. 299-313.