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Research Report: The Role of Behavioral Modeling in Computer Skills Acquisition: Toward Refinement of the Model
Open accessAbstract
Recent empirical work by Compeau and Higgins (1995) investigated the role of behavioral modeling training in the development of computer skills. Their efforts have provided insight into our understanding of the role of computer self-efficacy (CSE) and behavioral modeling (BM) techniques with regard to training effectiveness. Contrary to their expectations, however, several of the hypothesized relationships were not supported, especially those relating to outcome expectancy. In this paper, an empirically derived model of the CSE construct proposed by Marakas, Yi, and Johnson (1998) is offered to highlight potential theoretical, methodological, and measurement issues which may have contributed to or exacerbated the unexpected results obtained in the Compeau and Higgins study. The empirical work contained herein is intended to both replicate and extend the work of Compeau and Higgins and to assist in resolving several key issues left unsettled by their seminal work in this area.
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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
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.191 · Cited by 43,925
Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation.
Bandura, A., & Schunk, D. H. (1981) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.41.3.586 · Cited by 2,675
Modeling and attributional effects on children's achievement: A self-efficacy analysis.
Schunk, D. H. (1981) · Journal of Educational Psychology · doi:10.1037/0022-0663.73.1.93 · Cited by 781
Reliability of Subjective Probability Forecasts of Precipitation and Temperature
Murphy, A. H., & Winkler, R. L. (1977) · Applied Statistics · doi:10.2307/2346866 · Cited by 386
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van Dinther, M., Dochy, F., & Segers, M. (2011) · Educational Research Review · doi:10.1016/j.edurev.2010.10.003 · Cited by 967
Shu, Q., Tu, Q., & Wang, K. (2011) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2011.555313 · Cited by 434
Research Note—Gamification of Technology-Mediated Training: Not All Competitions Are the Same
Santhanam, R., Liu, D., & Shen, W.-C. M. (2016) · Information Systems Research · doi:10.1287/isre.2016.0630 · Cited by 213
Exploring the influence of perceptual factors in the success of web-based spatial DSS
Jarupathirun, S., & Zahedi, F. “. (2007) · Decision Support Systems · doi:10.1016/j.dss.2005.05.024 · Cited by 145
Self-efficacy as an antecedent of cognition and affect in technology acceptance
Kulviwat, S., C. Bruner II, G., & P. Neelankavil, J. (2014) · Journal of Consumer Marketing · doi:10.1108/jcm-10-2013-0727 · Cited by 144
Metadata
- Authors
- Johnson, R. D., & Marakas, G. M.
- Venue
- Information Systems Research
- Published
- 2000-12
- Volume
- 11
- Issue
- 4
- Pages
- 402-417
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2097426753
- Cited by
- 321
- References
- 5
- FWCI
- 18.23
- Type
- journal article
- Language
- en
- Open access
- green
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