Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies

Behaviour and Information Technology

Volumn 33, Issue 1, 2014, Pages 51-64

  Hsia, Jung Wen ^a   Chang, Chia Chi ^b   Tseng, Ai Hua ^a,b  

^a CHUNG HUA UNIVERSITY   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

computer self efficacy; electronic learning (e learning); individual difference; locus of control; personality; technology acceptance model

Indexed keywords

COMPUTER SELF-EFFICACY; ELECTRONIC LEARNING (E-LEARNING); INDIVIDUAL DIFFERENCES; LOCUS OF CONTROL; PERSONALITY; TECHNOLOGY ACCEPTANCE MODEL;

E-LEARNING; LEARNING SYSTEMS;

PERSONNEL TRAINING;

EID: 84893061532     PISSN: 0144929X     EISSN: 13623001     Source Type: Journal    
DOI: 10.1080/0144929X.2012.702284     Document Type: Article

References (112)

1. AbuShanab, E., Pearson, J. M., Setterstrom, A. J., 2010. Internet banking and customers' acceptance in Jordan: The unified model's perspective. Communications of the Association for Information Systems, 26 (23), 493-524.
2. Ambient Insight., 2011. Ambient insight comprehensive report: The worldwide market for self-paced eLearning products and services: 2010-2015 forecast and analysis [online]. Available from: http://www.ambientinsight.com/ Resources/Documents/Ambient-Insight-2010-2015-Worldwide-eLearning-Market- Executive-Overview.pdf [Accessed 17 October 2011]
3. Arbaugh, J. B., 2000. Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of Management Education, 24 (1), 32-54.
4. Aspinwall, L. G., Taylor, S. E., 1992. Modeling cognitive adaptation: a longitudinal investigation of the impact of individual differences and coping on college adjustment and performance. Journal of Personality and Social Psychology, 63, 989-1003.
5. Bagozzi, R. P., 2007. The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8 (4), 244-254.
6. Bagozzi, R. P., Yi, Y., 1988. On the evaluation of structural equation models. Journal of Academy of Marketing Science, 16 (1), 74-94.
7. Bandura, A., 1986. Social foundations of thought and action: a social cognitive theory, Englewood, NJ: Prentice-Hall.
8. Bandura, A., 1997. Self-efficacy, New York: Freeman.
9. Bandura, A., Adams, N. E., Beyer, J., 1977. Cognitive processes mediating behavioral change. Journal of Personality and Social Psychology, 35 (3), 125-139.
10. Barling, J., Beattie, R., 1983. Self-efficacy beliefs and sales performance. Journal of Organizational Behavior Management, 5, 41-51.
11. Bar-Tal, D., Yaakov, B.-Z., 1977. The relationship between perception of locus of control and academic achievement. Contemporary Educational Psychology, 2, 181-199.
12. Bellman, L. M., 1998. Using personality traits to predict communication technology use in the home. Doctoral dissertation. Pace University. Abstract in Dissertation Abstracts International, 58 (11-B), 6268
13. Blum, K. D., 1999. Gender differences in asynchronous learning in higher education: learning styles, participation barriers and communication patterns. Journal of Asynchronous Learning Networks [online], 3 (1),. Available from: http://eec.edc.org/cwis-docs/NEWS-ARTICLES-JOURNALS/ gender%20differences%20in%20asynchronous%20learning.htm [Accessed 17 February 2012]
14. Bohmstedt, G. W., 1970. Reliability and validity assessment in attitude measurement. In G. F. Summers. (Ed.), Attitude measurement, (pp. 80-99). Chicago: Rand-McNally.
15. Charlton, J. P., 2005. Measuring perceptual and motivational facets of computer control: The development and validation of the computing control scale. Computers in Human Behavior, 21, 791-815.
16. Chen, H.-J., 2010. Linking employees' e-learning system use to their overall job outcomes: an empirical study based on the IS success model. Computers & Education, 55 (4), 1628-1639.
17. Cho, V., Cheng, T. C.E., Lai, W. M.J., 2009. The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53 (2), 216-227.
18. Christian, T., Chea, K. S., Bohn, R. E., 1999. An exploratory study of international product transfer and production ramp-up in the data storage industry [online], UC San Diego: Information Storage Industry Center. Available from: http://escholarship.org/uc/item/10m6g3ff [Accessed 21 June 2012]
19. Collins, C., Clark, K. D., 2003. Strategic human resource practices, top management team social networks, and firm performance: The role of human resource practices in creating organizational competitive advantage. Academy of Management Journal, 46 (6), 740-762.
20. Compeau, D. R., Higgins, C. A., 1995. Computer self-efficacy: development of a measure and initial test. MIS Quarterly, 19 (2), 26-33.
21. Coovert, M. D., Goldstein, M., 1980. Locus of control as a predictor of user' attitude toward computers. Psychological Reports, 47, 1167-1173.
22. Corporate University Xchange., 2000. Learning in the dot.com world: E-learners speak out, New York: Corporate University Xchange.
23. Davis, F. D., 1989. Perceived usefulness, perceived ease of use, and user accepance of information technology. MIS Quarterly, 13 (3), 319-340.
24. Davis, F. D., Bagozzi, R. P., Warshaw, P. R., 1992. Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22, 1111-1132.
25. Deci, E. L., 1975. Intrinsic motivation, New York: Plenum Press.
26. Deng, X., Doll, W. J., Truong, D., 2004. Computer self-efficacy in an ongoing use context. Behaviour & Information Technology, 23 (6), 395-412.
27. Dille, B., Mezack, M., 1991. Identifying predictors of high risk among community college telecourse students. The American Journal of Distance Education, 5 (1), 24-35.
28. Drennan, J., Kennedy, J., Pisarski, A., 2005. Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98 (6), 331-338.
29. Findley, M. J., Cooper, H. M., 1983. Locus of control and academic achievement: a literature review. Journal of Personality & Social Psychology, 44 (2), 419-427.
30. Fornell, C., Lacker, D. F., 1981. Evaluating structural equation models with unobserved variables and measurement error. Journal of Marketing Research, 19, 440-452.
31. 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.
32. Green, D. T., Pearson, J. M., 2011. Integrating websites usability with electronic commerce acceptance model. Behaviour & Information Technology, 30 (2), 181-199.
33. Hair, J. J., et al., 1998. Multivariate data analysis 5th ed, Upper Saddle River, NJ: Prentice-Hall.
34. Harpaz, I., Meshoulam, I., 2004. Differences in the meaning of work in Israel: workers in high-tech vs. traditional work industries. The Journal of High Technology Management Research, 15, 163-182.
35. Hayashi, A., et al., 2004. The role of social presence and moderating role of computer self efficacy in predicting the continuance usage of e-Learning systems. Journal of Information Systems Education, 15 (2), 139-154.
36. Hodson, R., 1985. Working in High-Tech': research issues and opportunities for the industrial sociologist. The Sociologist Quarterly, 26, 351-364.
37. Hoffman, D. L., Novak, T. P., Schlosser, A. E., 2003. Locus of control, web use, and consumer attitudes toward Internet regulation. Journal of Public Policy & Marketing, 22 (1), 41-57.
38. Hsu, C. L., Lu, H. P., 2004. Why do people play online games? An extended TAM with social influences and flow experience. Information & Management, 41, 853-868.
39. Hu, P. J.H., Clark, T. H.K., Ma, W. W., 2003. Examining technology acceptance by school teachers: a longitudinal study. Information & Management, 41, 227-241.
40. Hwang, Y., 2005. Investigating enterprise systems adoption: uncertainty avoidance, intrinsic motivation, and the technology acceptance model. European Journal of Information Systems, 14, 150-161.
41. James, L. R., et al., 1979. Correlates of psychological influence: an illustration of the psychological climate approach to work environment perceptions. Personality Psychology, 32, 563-588.
42. Jashapara, A., Tai, W.-C., 2006. Understanding the complexity of human characteristics on e-learning systems: an integrated study of dynamic individual differences on user perceptions of ease of use. Knowledge Management Research & Practice, 4, 227-239.
43. Johnson, R. D., Gueutal, H., Falbe, C. M., 2009. Technology, trainees, metacognitive activity and e-learning effectiveness. Journal of Managerial Psychology, 24 (6), 545-566.
44. Jonas, G. A., Norman, C. S., 2011. Textbook websites: user technology acceptance behaviour. Behaviour & Information Technology, 30 (2), 147-159.
45. Jones, E., 2008. Predicting performance in first-semester college basic writers: revisiting the role of self-beliefs. Contemporary Educational Psychology, 33, 209-238.
46. Kalechstein, A. D., Nowicki, Jr. S., 1997. A meta-analytic examination of the relationship between control expectancies and academic achievement: an 11-year follow-up to Findley and Cooper. Genetic, Social, and General Psychology Monographs, 123, 27-56.
47. Karaali, D., Gumussoy, C. A., Calisir, F., 2011. Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior, 27 (1), 343-354.
48. Karahanna, E., et al., 2002. Individual differences and relative advantage: The case of GSS. Decision Support Systems, 32, 327-341.
49. Kay, R. H., 1990. The relation between locus of control and computer literacy. Journal of Research on Computing in Education, 22 (4), 464-474.
50. King, W. R., He, J., 2006. A meta-analysis of the technology acceptance model. Information & Management, 43 (6), 740-755.
51. Kirkpatrick, M. A., et al., 2008. Perceived locus of control and academic performance: broadening the construct's applicability. Journal of College Student Development, 49 (5), 486-496.
52. Korzaan, M., Morris, S. A., 2009. Individual characteristics and the intention to continue project escalation. Computers in Human Behavior, 25, 1320-1330.
53. Kovenklioglu, G., Greenhaus, J. H., 1978. Causal attributions, expectations, and task performance. Journal of Applied Psychology, 63, 698-705.
54. Kwon, O., Choi, K., Kim, M., 2007. User acceptance of context-aware services: self-efficacy, user innovativeness and perceived sensitivity on contextual pressure. Behaviour & Information Technology, 26 (6), 483-498.
55. Lee, J.-S., et al., 2003. Technology acceptance and social networking in distance learning. Educational Technology & Society, 6 (2), 50-61.
56. Lee, M.-C., 2010. Explaining and predicting users' continuance intention toward e-learning: an extension of the expectation-confirmation model. Computers & Education, 54 (2), 506-516.
57. Lee, M., Cheung, C., Chen, Z., 2005. Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42 (8), 1095-1104.
58. Lee, Y.-C., 2006. An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30 (5), 517-541.
59. Lee, Y.-H., Hsieh, Y.-C., Ma, C.-Y., 2011. A model of organizational employees' elearning systems acceptance. Knowledge-Based Systems, 24 (3), 355-366.
60. Legris, P. J., Ingham, P., Collerette, P., 2003. Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40 (3), 191-204.
61. Levy, Y., 2007. Comparing dropouts and persistence in e-learning courses. Computers & Education, 48, 185-204.
62. Lin, K.-M., 2011. e-Learning continuance intention: moderating effects of user e-learning experience. Computers & Education, 56 (2), 515-526.
63. Liu, Y., Li, H., Carlsson, C., 2010. Factors driving the adoption of m-learning: an empirical study. Computers & Education, 55 (3), 1211-1219.
64. Makri-Botsari, E., Paraskeva, F., 2010. Exploring the personality characteristics in information and digital age expertise. Procedia Social and Behavioral Sciences, 9, 1705-1716.
65. Marakas, G. M., Johnson, M. D., Palmer, J. W., 2000. A theoretical model of differential social attributions towards computing technology: when the metaphor becomes the model. International Journal of Human-Computer Studies, 45, 529-552.
66. Mathieson, K., 1991. Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2 (3), 173-191.
67. McCormick, M. J., Martinko, M. J., 2004. Identifying leader social cognitions: integrating the causal reasoning perspective into social cognitive theory. Journal of Leadership & Organizational Studies, 10 (4), 2-12.
68. Miles, E. R., Snow, C. C., 1984. Designing strategic human resources systems. Organizational dynamics, 16, 36-52.
69. Moon, J. W., Kim, Y. G., 2001. Extending the TAM for a World-Wide-Web context. Information & Management, 38, 217-230.
70. Morrow, P. C., Prell, E. R., McElroy, J. C., 1986. Attiudinal and behavioral correlates of computer anxiety. Psychological Reports, 59, 1199-1204.
71. Ngai, E. W.T., Poon, J. K.L., Chan, Y. H.C., 2007. Empirical examination of the adoption of Web CT using TAM. Computers and Education, 48 (2), 250-267.
72. Ong, C. -S., Lai, J. -Y., Wang, Y. -S., 2004. Factors affecting engineers acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41 (6), 795-804.
73. Ong, C.-S., Lai, J.-Y., 2006. Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22 (5), 816-829.
74. Padilla-Melendez, A., Garrido-Moreno, A., Del, Aguila-Obra, A., 2008. Factors affecting e-collaboration technology use among management students. Computers & Education, 51 (2), 609-623.
75. Parker, A., 1999. A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1 (2), 1-12.
76. Phares, E. J., 1976. Locus of control in personality, Morris-town, NJ: General Learning Press.
77. Pituch, K., Lee, Y., 2006. The influence of system characteristics on e-learning use. Computers & Education, 47 (2), 222-244.
78. Potosky, D., Bobko, P., 2001. A model for predicting computer experience from attitudes toward computers. Journal of Business and Psychology, 15 (3), 391-404.
79. Project of Industrial Development and Promotion of Digital Archives and E-learning., 2010. Taiwan e-learning industry status and market analysis [online]. Available from: http://idp.teldap.tw/epaper/20100430/430 [Accessed 16 September 2011]
80. Pugliese, R. R., 1994. Telecourse persistence and psychological variables. The American Journal of Distance Education, 8 (3), 22-39.
81. Qian, G., Li, L., 2003. Profitability of small-and medium-sized enterprises in high-tech industries: The case of the biotechnology industry. Strategic Management Journal, 24, 881-887.
82. Roca, J., Chiu, C., Martinez, F., 2006. Understanding e-learning continuance intention: an extension of the technology acceptance model. International Journal of Human-Computer Studies, 64 (8), 683-696.
83. Roca, J., Gagne, M., 2008. Understanding e-learning continuance intention in the workplace: a self-determination theory perspective. Computers in Human Behavior, 24 (4), 1585-1604.
84. Rotter, J. B., 1966. Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 80 (1), 1-28.
85. Rotter, J. B., 1990. Internal versus external control of reinforcement: a case history of a variable. American Psychologist, 45 (3), 489-493.
86. Saade, R., Bahli, B., 2005. The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & Management, 42 (2), 317-327.
87. Sanchez, R. A., Hueros, A. D., 2010. Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26 (6), 1632-1640.
88. Sanders, D. W., Morrison-Shetlar, A. I., 2001. Students attitudes towards web-enhanced instruction in an introductory Biology course. Journal of Research on Computing Education, 33, 251-262.
89. Seyal, A. H., Rahim, M. M., Rahman, M. N.A., 2002. A study of computer attitudes of non-computing students of technical colleges in Brunei Darussalam. Journal of End User Computing, 14 (2), 40-47.
90. Skinner, E. A., 1996. A guide to constructs of control. Journal of Personality and Social Psychology, 71, 549-570.
91. Spector, P. E., 1982. Behavior in organizations as a function of employee' locus of control. Psychological Bulletin, 91, 482-497.
92. Šumak, B., Heričko, M., Pušnik, M., 2011. A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27, 2067-2077.
93. Tao, Y.-H., Cheng, C.-J., Sun, S.-Y., 2009. What influences college students to continue using business simulation games? The Taiwan experience. Computers & Education, 53 (3), 929-939.
94. Terzis, V., Economides, A. A., 2011. The acceptance and use of computer based assessment. Computers & Education, 56, 1032-1044.
95. Thatcher, J. B., Perrewe, P. L., 2002. An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26 (4), 381-396.
96. Tung, F.-C., Chang, S. C., 2008. A new hybrid model for exploring the adoption of online nursing courses. Nurse Education Today, 28, 293-300.
97. Valecha, G. K., Ostrom, T. M., 1974. An abbreviated measure of internal-external locus of control. Journal of Personality Assessment, 38 (4), 369-376.
98. van Raaij, E. M., Schepers, J. J., 2008. The acceptance and use of a virtual learning environment in China. Computers & Education, 50 (3), 838-852.
99. Venkatesh, V., Davis, F. D., 1996. A model of the antecedents of perceived ease of use: development and test. Decision Sciences, 27 (3), 451-458.
100. Venkatesh, V., Morris, M. G., 2000. 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.
101. Wang, W.-T., Wang, C.-C., 2009. An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53 (3), 761-774.
102. Wang, Y.-S., 2002. The adoption of electronic tax filing systems: an empirical study. Government Information Quarterly, 20, 333-352.
103. Wagner, G. D., Flannery, D. D., 2004. A quantitative study of factors affecting learner acceptance of a computer-based training support tool. Journal of European Industrial Training, 28 (5), 383-399.
104. Waheed, M., 2010. Instructor's intention to accept online education: an extended TAM model. In: J. Sanchez, K. Zhang., eds. Proceedings of the world conference on e-learning in corporate, government, healthcare, and higher education. Chesapeake, VA: AACE, 1263-1271. Available from: http://www.editlib. org/p/35729.
105. Walczuch, R., Lemmink, J., Streukens, S., 2007. The effect of service employees' technology readiness on technology acceptance. Information & Management, 44, 206-215.
106. Xenos, M., Pierrakeas, C., Pintelas, P., 2002. A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education International Journal, 39, 361-377.
107. Zhang, D., Zhou, L., 2003. Enhancing e-learning with interactive multimedia. Information Resources Management Journal, 16 (4), 1-14.
108. Zhang, S., Zhao, J., Tan, W., 2008. Extending TAM for online learning systems: an intrinsic motivation perspective. Tsinghua Science & Technology, 13 (3), 312-317.
109. Zhang, W., Xu, P., 2011. Do I have to learn something new? Mental models and the acceptance of replacement technologies. Behaviour & Information Technology, 30 (2), 201-211.
110. Ziefle, M., Bay, S., Schwade, A., 2006. On keys' meanings and modes: The impact of different key solutions on children's efficiency using a mobile phone. Behaviour & Information Technology, 25 (5), 413-431.
111. Zimmerman, B. J., 2000. Self-efficacy: an essential motive to learn. Contemporary Educational Psychology, 25, 82-91.
112. Zmud, R. W., 1979. Individual differences and MIS success: a review of the empirical literature. Management Science, 25 (10), 966-979.