The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation
Abstract
The Technology Acceptance Model (TAM) represents an important theoretical contribution toward understanding IS usage and IS acceptance behaviors. However, as noted by several IS researchers, TAM is incomplete in one important respect: it doesn't account for social influence in the adoption and utilization of new information systems. Davis (1986) and Davis et al. (1989) noted that it is important to account for subjective norm (SN), the construct denoting social influence. However, they observed that the conceptualization of SN based on TRA (Theory of Reasoned Action) has theoretical and psychometric problems. Specifically, they observed that it is difficult to distinguish if usage behavior is caused by the influence of referents on one's intent or by one's own attitude. They suggested that this problem may be circumvented by using an alternative theoretical basis for conceptualizing SN, specifically in terms of Kelman's (1958, 1961) processes of social influence (compliance, identification and internalization). Within the context of organizational enterprisewide implementation and adoption of collaboration and communication technologies, this study establishes theoretical and empirical bases for the above conceptualization originally suggested by Davis and his colleagues. The construct of social influence is operationalized in terms of Kelman's processes of internalization, identification and compliance. Analyses of field study data provide evidence of the reliability and validity of the proposed constructs, factor structures and measures. The findings enable future researchers to account for social influence in further investigating TAM.
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Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
Understanding information technology usage: a test of competing models
Taylor, S., & Todd, P. A. (1995) · Information Systems Research · doi:10.1287/isre.6.2.144 · Cited by 8,815
Attitude-behavior relations: A theoretical analysis and review of empirical research
Ajzen, I., & Fishbein, M. (1977) · Psychological Bulletin · doi:10.1037/0033-2909.84.5.888 · Cited by 7,611
Mathieson, K. (1991) · Information Systems Research · doi:10.1287/isre.2.3.173 · Cited by 4,216
The Challenge of Innovation Implementation
Klein, K. J., & Sorra, J. S. (1996) · The Academy of Management Review · doi:10.2307/259164 · Cited by 2,113
Cited by
The Technology Acceptance Model: Past, Present, and Future
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003) · Communications of the Association for Information Systems · doi:10.17705/1cais.01250 · Cited by 2,572
Does the technology acceptance model predict actual use? A systematic literature review
Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010) · Information and Software Technology · doi:10.1016/j.infsof.2009.11.005 · Cited by 851
Customer experiences in the age of artificial intelligence
Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106548 · Cited by 833
The Technology Acceptance Model: A Meta-Analysis of Empirical Findings
Ma, Q., & Liu, L. (2004) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2004010104 · Cited by 660
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2017.08.026 · Cited by 576
Metadata
- Authors
- Malhotra, Y., & Galletta, D. F.
- Venue
- Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers
- Publisher
- IEEE Comput. Soc
- Published
- 2003-01-20
- Pages
- 14
- ISSN
- 1530-1605 (ISSN-L)
- OpenAlex
- W2144283632
- Cited by
- 869
- References
- 6
- Type
- conference abstract
- Language
- en
- Open access
- closed
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- Malhotra, Y., & Galletta, D. F. (2003). Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation. Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers. https://doi.org/10.1109/hicss.1999.772658
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