A STUDY OF BIG DATA TECHNOLOGY ADOPTION IN THAILAND: ORGANIZATIONAL PERSPECTIVE
Keywords:
Technology acceptance model, Big data technology, Structural equation modelingAbstract
The purpose of this study was to investigate the acceptance of Big Data Technology (BDT) within the Thailand context, using Technology Acceptance Model (TAM). The informants of this study were 260 participants who were familiar with BDT. Questionnaires were used to collect the data. The structural equation model (SEM) was employed to test the hypotheses via AMOS software. The result indicated that the research model was consistent with the empirical data with the statistics GFI = 0.959, AFGI = 0.929, SRMR = 0.519, RMSEA = 0.054, NFI= 0.977, CFI = 0.990 and Normed Chi-Square = 1.747. The research model could explain behavior intention to use BDT for 50%. Perceived usefulness and perceived ease of use affect the behavior intention to use BDT at 0.71 and 0.45 at statistical significance level of 0.001. Furthermore, perceived usefulness might be a mediator between the perceived ease of use and behavior intention to use. However, the results also showed that people would actually use BDT only when it was easy to use and useful.
Downloads
References
Esteves, J., & Curto, J. (2013). A Risk and Benefits Behavioral Model to Assess Intentions to Adopt Big Data. Journal of Intelligence Studies in Business, 3, 37-46.
Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: big data and the changing context of strategy. Journal of Information Technology, 30(1), 44-57. DOI:/10.1057/jit.2014.17
Brandbuffet. (2018, February). Penetrate the BIG DATA situation in Thailand, "treasure" giant of the business world. Retrieved from https://www.brandbuffet.in.th
Brock, V., & Khan, H. U. (2017). Big data analytics: does organizational factor matters impact technology acceptance? Journal of Big Data, 4(1), 14-28. DOI:/10.1186/s40537-017-0081-8
Magni, M., & Pennarola, F. (2008). Intra-organizational relationships and technology acceptance. International Journal of Information Management, 28(6), 517-523. DOI:/10.1016/j.ijinfomgt.2008.01.002
Verma, S., Bhattacharyya, S. S., & Kumar, S. (2018). An extension of the technology acceptance model in the big data analytics system implementation environment. Information Processing & Management, 54(5), 791-806. DOI:/10.1016/j.ipm.2018.01.004
Soon, K. W. K., Lee, C. A., & Boursier, P. (2016). A study of the determinants affecting Adoption of big data using integrated Technology acceptance model (TAM) and Diffusion of innovation (DOI) in Malaysia. International Journal of Applied Business and Economic Research, 14(1), 17-47.
Erl, T., Khattak, W., & Buhler, P. (2015). Big Data Fundamentals Concepts, Drivers & Techniques. 1st ed. United States: Prentice Hall.
Oussous, A., Benjelloun, F.-Z., Ait Lahcen, A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University - Computer and Information Sciences, 30(4), 431-448. DOI:/10.1016/j.jksuci.2017.06.001
Simon, P. (2010). Why New Systems Fail: An Insider's Guide to Successful IT Projects. 1st ed. Boston: Cengage Learning PTR.
Surendran, P. (2012). Technology Acceptance Model: A Survey of Literature. International Journal of Business and Social Research (IJBSR), 2(4), 175-178.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: theory and results. Dissertation, Ph.D. (Management). Boston: Massachusetts Institute of Technology.
Davis, Fred D., Bagozzi, Richard P., & Warshaw, Paul R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982.
Chutur, M. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Sprouts: Working Papers on Information Systems, 9(37).
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. DOI:/10.2307/30036540
Shin, D.-H. (2016). Demystifying big data: Anatomy of big data developmental process. Telecommunications Policy, 40(9), 837-854. DOI:/10.1016/j.telpol.2015.03.007
Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. K. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250-1261. DOI:/10.1016/j.tele.2017.05.010
Jackson, D. L. (2003). Revisiting Sample Size and Number of Parameter Estimates: Some Support for the N:q Hypothesis. Structural Equation Modeling: A Multidisciplinary Journal, 10(1), 128-141. DOI:/10.1207/S15328007SEM1001_6
Hair, J. F., Black, J. W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. 7th ed. United States: Pearson.
Rangsungnoen, G. (2011). Factor analysis with SPSS and AMOS for research. Bangkok: se-education public company limited.
Kline, R. B. (2016). Principles and practice of structural equation modeling. New York: Guilford Press.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232. DOI:/10.1016/j.chb.2016.10.028
Malaquias, R. F., Malaquias, F. F. O., & Hwang, Y. (2018). Understanding technology acceptance features in learning through a serious game. Computers in Human Behavior, 87, 395-402. DOI:/10.1016/j.chb.2018.06.008
Downloads
Published
How to Cite
Issue
Section
License
Srinakharinwirot University Journal of Sciences and Technology is licensed Under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC-BY-NC-ND 4.0) License, Unless Otherwise Stated. Please Read Journal Policies Page for More Information on Open Access, Copyright and Permissions.
