M-commerce: Examining the Adoption of Mobile Commerce in Bangkok, Thailand: The Moderating Effect of Education Level
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Published:
Apr 11, 2020
Keywords:
M-commerce, Partial Least Square, Theory of Planned Behavior
Main Article Content
Abstract
This research examined the adoption of mobile commerce in retail mobile shopping service in Bangkok, Thailand. Theory of planned behavior (TPB) was selected as a conceptual model to investigate the mobile user’s adoption in purchasing retail products via their smart devices. This study enhanced an existing TPB model by investigating how user’s education level affect their adoption of m-commerce by proposing the education level as a moderator between intention to adopt mobile commerce and behavioral of adoption mobile commerce. The questionnaire survey data from 168 respondents who live in Bangkok were collected. The results from a partial least squares regression analysis statistically supported seven of eight hypotheses of the proposed theoretical model. The finding illustrated that attitude, subjective norm toward m-commerce influenced the user’s behavior of adoption through their intention to use m-commerce. Moreover, the results also found that users who have a higher education level tend to adopt m-commerce more than who possessed a lower level of education. This research suggested the company should concerned users’ attitude toward m-commerce by not only considering the ease of use of their m-commerce’s user interface but also the security issue, and focusing in influencers who were able to influence new users.
Article Details
How to Cite
Suthatorn, P. . (2020). M-commerce: Examining the Adoption of Mobile Commerce in Bangkok, Thailand: The Moderating Effect of Education Level. Dusit Thani College Journal, 13(2), 307–329. Retrieved from https://so01.tci-thaijo.org/index.php/journaldtc/article/view/241074
Section
Research Article
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681-694.
2. Abu-Shanab, E. A. (2011). Education level as a technology adoption moderator. Paper presented at the 2011 3rd International Conference on Computer Research and Development.
3. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
4. Antoniadis, I., Saprikis, V., & Poltitis, K. (2014). Investigating internet users’ perceptions towards online shopping: An empirical study on Greek university students. Paper presented at the Proceedings of the International Conference on Contemporary Marketing Issues, (ICCMI).
5. Bermudez, M. E. (2002). A mobile commerce challenge model. Proceedings of the Scuola Superiore.
6. Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and humans, 30(4), 411-420.
7. Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of cross-cultural psychology, 1(3), 185-216.
8. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
9 Delcourt, M., & Kinzie, M. (1993). Computer technologies in teacher education: the measurement of attitudes and self-efficacy. Journal of research and development in education, 27(1), 35-41.
10. Efron, B., Rogosa, D., & Tibshirani, R. (2004). Resampling methods of estimation. . International Encyclopedia of the Social & Behavioral Sciences, 13216-13220.
11. Fornell, C., & Larker, D. (1981). Structural equation modeling and regression: guidelines for research practice. Journal of Marketing Research, 18(1), 39-50.
12. Hair, J., Black, W., Babin, B., & Anderson, R. (2009). Multivariate Data Analysis 7th Edition Pearson Prentice Hall: JOUR.
13. Hernández, B., Jiménez, J., & Martín, M. J. (2010). Customer behavior in electronic commerce: The moderating effect of e-purchasing experience. Journal of Business Research, 63(9-10), 964-971.
14. Hootsuite. (2019, February 1). Digital 2019 Thailand. Retrieved from https://www.twfdigital.com/blog/2019/02/thailand-digital-usage-stats-2019/.
15. Hyman, L., Lamb, J., & Bulmer, M. (2006). The use of pre-existing survey questions: Implications for data quality. Paper presented at the Proceedings of the European Conference on Quality in Survey Statistics.
16. Insa-Ciriza, R. (2001). ECommerce and mCommerce in Southern Europe. European Retail Digest, 23-25.
17 Kalinic, Z., & Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and e-Business Management, 14(2), 367-387.
18. Katawetawaraks, C., & Wang, C. (2011). Online shopper behavior: Influences of online shopping decision. Asian Journal of Business Research, 1(2).
19. Khalifa, M., & Shen, K. N. (2008). Drivers for transactional B2C m-commerce adoption: Extended theory of planned behavior. Journal of Computer Information Systems, 48(3), 111-117.
20. Kini, R. B. (2009). Adoption and Evaluation of Mobile Commerce in Chile. Electronic Journal of Information Systems Evaluation, 12(1).
21. Kline, R. (2005). Methodology in the social sciences: Principles and practice of structural equation modeling (2nd ed.). New York ….
22. Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7).
23. Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.
24. Meadows, K. A. (2003). So you want to do research? 5: Questionnaire design. British journal of community nursing, 8(12), 562-570.
25. Mishra, S. (2014). Adoption of M-commerce in India: applying theory of planned behaviour model. The Journal of Internet Banking and Commerce, 19(1), 1-17.
26. Nunnally, J. (1978). Psychometric methods: New York: McGraw-Hill.
27. Pascual-Miguel, F. J., Agudo-Peregrina, Á. F., & Chaparro-Peláez, J. (2015). Influences of gender and product type on online purchasing. Journal of Business Research, 68(7), 1550-1556.
28. Petter, S., Straub, D. W., & Rai, A. (2007). Specifying formative constructs in information systems research.
29. Rammstedt, B., & Rammsayer, T. H. (2002). Self-estimated intelligence: Gender differences, relationship to psychometric intelligence and moderating effects of level of education. European Psychologist, 7(4), 275.
30. Shao Yeh, Y., & Li, Y.-M. (2009). Building trust in m-commerce: contributions from quality and satisfaction. Online Information Review, 33(6), 1066-1086.
31. Sundström, M., Balkow, J., Florhed, J., Tjernström, M., & Wadenfors, P. (2013). Inpulsive Buying Behaviour: The Role of Feelings When Shopping for Online Fashion. Paper presented at the 17th The European Association for Education and Research in Commercial Distribution.
32. Thongpapanl, N., Ashraf, A. R., Lapa, L., & Venkatesh, V. (2018). Differential Effects of Customers’ Regulatory Fit on Trust, Perceived Value, and M-Commerce Use among Developing and Developed Countries. Journal of International Marketing, 26(3), 22-44.
33. Troutman, M., & Timpson, S. (2008). Effective Optimization of Web Sites for Mobile Access: the transition from eCommerce to mCommerce. Journal of Interactive Advertising, 9(1), 65-70.
34. Wu, W.-Y., Quyen, P. T. P., & Rivas, A. A. A. (2017). How e-servicescapes affect customer online shopping intention: the moderating effects of gender and online purchasing experience. Information Systems and e-Business Management, 15(3), 689-715.