Applying Technology Acceptance Model (TAM) to explore Users’ Behavioral Intention to Adopt a Performance Assessment System for E-book Production

Shin Liao; Jon-Chao Hong; Ming-Hui Wen; Yi-Chen Pan; Yun- Wu

doi:10.29333/ejmste/93575

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Shin Liao ¹, Jon-Chao Hong ¹, Ming-Hui Wen ² ^* , Yi-Chen Pan ¹, Yun- Wu ³

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¹ National Taiwan Normal University, Taipei, TAIWAN
² National Taipei University of Business, Taipei, TAIWAN
³ China University of Technology, Taipei, TAIWAN
^* Corresponding Author

Abstract

With rapidly rising popularity of digital reading coupled with advancement in electronic book technology, there is a sense of urgency to cultivate qualified talent for the digital publishing industry. Based on results of an exploration identifying technical skills needed for the industry to produce electronic books, this study developed a web-based performance assessment system with 35 questions in its item bank regarding four dimensions of full-text e-book production. The study applied technology acceptance model (TAM) to explore the behavioral intention of students in technological colleges and universities and use a web-based performance assessment system as a tool to evaluate their technical proficiency in e-book production. This study also applied structural equation model as a vehicle to test the hypotheses and relationships in the research to verify external effects of “computer self-efficacy”. This research concludes that the technology acceptance model can be applied to explain users’ willingness to adopt a web-based assessment system.

Keywords

digital publishing industry
Technology Acceptance Model
web-based assessment
electronic book
e-book production

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.29333/ejmste/93575

EURASIA J Math Sci Tech Ed, 2018 - Volume 14 Issue 10, Article No: em1601

Publication date: 19 Jul 2018

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Article Downloads: 775

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References

Agarwal, R., Sambamurthy, V., & Stair, R. M. (2000). The evolving relationship between general and specific computer self-efficacy—An empirical assessment. Information systems research, 11(4), 418-430. https://doi.org/10.1287/isre.11.4.418.11876.
Ajzen, I. (1985).From Intentions to Actions: A Theory of Planned Behavior, in Kuhl, J. and Bechkmann, J. (Eds). Action Control: From Cognition to Behavior, New York: Springer-verlag. https://doi.org/10.1007/978-3-642-69746-3_2.
Ajzen, I. (1991).The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T.
Alenezi, A. R., Abdul karim, A. M., & Veloo, A. (2010). An Empirical Investigation the role of enjoyment, Computer anxiety, Computer Self-Efficacy and internet experience in influencing the students’ intention to use E-learning: A case study from Saudi Arabian governmental universities. The Turkish Online Journal lf Educational Technology, 9(4), 22-34.
Alsofyani, M. M., Eynon, R., & Majid, N. A. (2012). A preliminary evaluation of short blended online training workshop for TPACK development using technology acceptance model. TOJET: The Turkish Online Journal of Educational Technology, 11(3).
Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of management education, 24(1), 32-54. https://doi.org/10.1177/105256290002400104.
Bagozzi, R. P., & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Academy of Marking Science, (16), 76-94. https://doi.org/10.1007/BF02723327.
Bandura, A. (1977). Social Learning Theory. Prentice Hall, Englewood Cliffs, NJ.
Bandura, A., & Cervone, D. (1986). Differential Engagement of Self-Reactive Influences in Cognitive Motivation. Organizational Behavior and Human Decision Processes, 138, 92-113. https://doi.org/10.1016/0749-5978(86)90028-2.
Chang, C. S., Liu, E. Z. F., Sung, H. Y., Lin, C. H., Chen, N. S., & Cheng, S. S. (2014). Effects of online college student’s Internet self-efficacy on learning motivation and performance. Innovations in education and teaching international, 51(4), 366-377. https://doi.org/10.1080/14703297.2013.771429.
Chen, I. S. (2017). Computer self-efficacy, learning performance, and the mediating role of learning engagement. Computers in Human Behavior, 72, 362-370. https://doi.org/10.1016/j.chb.2017.02.059.
Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation), Massachusetts Institute of Technology.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theorical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982.
Draft, P. C. (2000). A Framework for the E-publishing Ecology.
Dunbar, S. B., Koretz, D. M., & Hoover, H. D. (1991). Quality control in the development and use of performance assessments. Applied measurement in education, 4(4), 289-303. https://doi.org/10.1207/s15324818ame0404_3.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18(2), 39-50. https://doi.org/10.2307/3151312.
Hatlevik, O. E., Throndsen, I., Loi, M., & Gudmundsdottir, G. B. (2018). Students’ ICT self-efficacy and computer and information literacy: Determinants and relationships. Computers & Education, 118, 107-119. https://doi.org/10.1016/j.compedu.2017.11.011.
Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of efficacy expectations inpredicting the decision to use advanced technologies: The case of computers. Journal of Applied Psychology, 72, 307-313. https://doi.org/10.1037/0021-9010.72.2.307.
Johnson, R., Thatcher, J., & Gerow, J. (2017, January). A Meta-Analytic Review of Computer Self-Efficacy and Agenda for Future Research. In Academy of Management Proceedings (Vol. 2017, No. 1, p. 13982). Academy of Management. https://doi.org/10.5465/ambpp.2017.13982abstract.
Kim, B. (2010). An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Systems with Applications, 37(10), 7033-7039. https://doi.org/10.1016/j.eswa.2010.03.015.
Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.
Li, Y., Yang, H. H., Cai, J., & MacLeod, J. (2017, June). College Students’ Computer Self-efficacy, Intrinsic Motivation, Attitude, and Satisfaction in Blended Learning Environments. In International Conference on Blended Learning (pp. 65-73). Springer, Cham. https://doi.org/10.1007/978-3-319-59360-9_6.
Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in human behavior, 25(1), 29-39. https://doi.org/10.1016/j.chb.2008.06.002.
Marsh, H. W., & Hocevar, D. (1988). A New More Powerful Approach to Multitrait-Multimethod Analysis: Application of Second-Order Confirmatory Analysis. Journal of Applied Psychology, 73(1), 107-117. https://doi.org/10.1037/0021-9010.73.1.107.
Martocchio, J. J. (1994). Effects of Conceptions of Ability on Anxiety, Self-Efficacy, and Learning in Training. Journal of Applied Psychology, 79(6), 819-825. https://doi.org/10.1037/0021-9010.79.6.819.
Martocchio, J. J., & Dulebohn, J. (1994). Performance feedback effects in training: The role of perceived controllability. Personnel Psychology, 47(2), 357-373. https://doi.org/10.1111/j.1744-6570.1994.tb01729.x.
Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605. https://doi.org/10.1111/j.1467-8535.2011.01229.x.
Pierson, C. A., & Beck, S. S. (1993). Performance assessment: The realities that will influence the rewards. Childhood Education, 70(1), 29-32. https://doi.org/10.1080/00094056.1993.10520978.
Romano, F. J. (2006). The Future of Global Markets for Digital Printing to 2015. Pira International Limited.
Sanchez-Franco, M. J. (2010). WebCT–The quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers & Education, 54(1), 37-46. https://doi.org/10.1016/j.compedu.2009.07.005.
Schmidt, J. (2008). Advisory services & international co-operation federal institute for vocational education and training (BIBB) Germany. In International Technological and Vocational Education Conference.
Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032-1044. https://doi.org/10.1016/j.compedu.2010.11.017.
Tian, X. (2007, June). Developments in publishing: The potential of digital publishing. In ELPUB (pp. 471-472).
Tribute, A. (2006). Digital Goes International. Retrieved October, 13, 2008.
Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 115-139. https://doi.org/10.2307/3250981.
Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS quarterly, 201-226. https://doi.org/10.2307/249576.
Webster, J., & Martocchio, J. J. (1995). The differential effects of software training previews on training outcomes. Journal of Management, 21(4), 757-787. https://doi.org/10.1177/014920639502100409.
Wright, J. H., Brown, G. K., Thase, M. E., & Basco, M. R. (2017). Learning cognitive-behavior therapy: An illustrated guide. American Psychiatric Pub.

How to cite this article

Vancouver

Liao S, Hong J, Wen M, Pan Y, Wu Y. Applying Technology Acceptance Model (TAM) to explore Users’ Behavioral Intention to Adopt a Performance Assessment System for E-book Production. EURASIA J Math Sci Tech Ed. 2018;14(10):em1601. https://doi.org/10.29333/ejmste/93575

APA

Liao, S., Hong, J., Wen, M., Pan, Y., & Wu, Y. (2018). Applying Technology Acceptance Model (TAM) to explore Users’ Behavioral Intention to Adopt a Performance Assessment System for E-book Production. Eurasia Journal of Mathematics, Science and Technology Education, 14(10), em1601. https://doi.org/10.29333/ejmste/93575

AMA

Liao S, Hong J, Wen M, Pan Y, Wu Y. Applying Technology Acceptance Model (TAM) to explore Users’ Behavioral Intention to Adopt a Performance Assessment System for E-book Production. EURASIA J Math Sci Tech Ed. 2018;14(10), em1601. https://doi.org/10.29333/ejmste/93575

Chicago

Liao, Shin, Jon-Chao Hong, Ming-Hui Wen, Yi-Chen Pan, and Yun- Wu. "Applying Technology Acceptance Model (TAM) to explore Users’ Behavioral Intention to Adopt a Performance Assessment System for E-book Production". Eurasia Journal of Mathematics, Science and Technology Education 2018 14 no. 10 (2018): em1601. https://doi.org/10.29333/ejmste/93575

Harvard

Liao, S., Hong, J., Wen, M., Pan, Y., and Wu, Y. (2018). Applying Technology Acceptance Model (TAM) to explore Users’ Behavioral Intention to Adopt a Performance Assessment System for E-book Production. Eurasia Journal of Mathematics, Science and Technology Education, 14(10), em1601. https://doi.org/10.29333/ejmste/93575

MLA

Liao, Shin et al. "Applying Technology Acceptance Model (TAM) to explore Users’ Behavioral Intention to Adopt a Performance Assessment System for E-book Production". Eurasia Journal of Mathematics, Science and Technology Education, vol. 14, no. 10, 2018, em1601. https://doi.org/10.29333/ejmste/93575