The Impact of Technology Readiness on the Characteristics of Perceived Innovation and Intention to Use QR Code by Users in the Lower Northern Region
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Abstract
This research aimed to study the readiness of technology, characteristics, perception, innovation and intention to use QR code, and the perception of innovation as a link between technology readiness and the intention of users to use QR code in the lower north region. The quantitative research was conducted by questionnaires from the sample group. Descriptive statistics were used to analyze mean, percentage, standard deviation and inferential statistics. The structural equation model (SEM) was analyzed with AMOS program. The qualitative research was conducted by interviews the users, scholars and those involved in the use of QR Code in the lower north region.
The quantitative research revealed that the first hypothesis, technology readiness had a statistically significant positive influence on the perceived innovation characteristics. Path coefficient was 0.278 (β = 0.278, t = 8.064, p < 0.001). For the second hypothesis, empirical data did not support that technology readiness influenced the usage intention. Path coefficient was 0.023 (β = 0.023, t = 0.589) . And the third hypothesis, the perceived innovation characteristics had a statistically significant influence on the usage intention. The path coefficient was 1.140 (β= 1.140, t = 8.811, p <0.001). The qualitative findings from in-depth interviews were consistent with the first and second hypothesizes, but were inconsistent with the second hypothesis.
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Academic articles, research articles, and book reviews in the Ph.D. in Social Sciences Journal are author’s opinions, and not the publisher’s, and is not the responsibility of the Ph.D. in Social Sciences Journal Philosophy Association, Ramkhamhaeng University. (In the case that research is done on human, the researcher has to be trained in Ethics for Doing Research on Human Training and has to produce the evidence of the training).
References
Aggarwal, P., Cha, T., & Wilemon, W. (1998). Barriers to the adoption of really-new products and the role of surrogate buyers. Journal of Consumer Marketing, 15(4), 358-371.
Amornrattanakij, T. (2018). Factors affecting customer intension to use standardized quick response codes payment on mobile banking in Bangkok Metropolitan area. Business Informatics Journal, 4(4), 46-63. [In Thai]
Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: A technology acceptance perspective. Information & Management, 39(8), 705-719.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175.
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Lawrence Erlbaum Associates.
David, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319-340.
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(14), 1111-1132.
Elliott, K., Meng, G., & Hall, M. (2012). The influence of technology readiness on the evaluation of self-service technology attributes and resulting attitude toward technology usage. Services Marketing Quarterly, 33(4), 311-329.
Fishbein, M. A., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Addison-Wesley.
Igbaria, M., Iivari, J., & Maragahh, H. (1995). Why do individuals use computer technology?: A Finnish case study. Information and Management, 29(5), 227-238.
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal computing acceptance factors in small firms: A structural equation model. Management Information Systems Quarterly, 21(3), 279-305.
Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management, 36(6), 1350-1359.
Faqih, K. M. S. (2016). An empirical analysis of factors predicting the behavioral intention to adopt Internet shopping technology among non-shoppers in a developing country context: Does gender matter?. Journal of Retailing and Consumer Services, 30, 140-164.
Flight, R., D’Souza, G., & Allaway, A. (2011). Characteristics-based innovation adoption: Scale and model validation. Journal of Product & Brand Management, 20(5), 343-355.
Im, H., & Ha, Y. (2014). Determinants of mobile coupon service adoption: Assessment of gender difference. International Journal of Retail & Distribution Management, 42(5), 441-459.
Khaled, M. S. (2016). An empirical analysis of factors predicting the behavioural intention to adopt Internet shopping technology among non-shoppers in a developing country context: Does gender matter. Journal of Retailing and Consumer Services, 30,140-164.
Kulviwat, S., Bruner, G. C., Kumar, A., Nasco, S. A., & Clark, T. (2007). Toward a unified theory of consumer acceptance technology. Psychology & Marketing, 24(12), 1059-1084.
Kuo, R.-Z., & Lee, G.-G. (2009). KMS adoption: The effects of information quality. Management Decision, 47(10), 1633-1651.
Lai, J., & Chang, C. (2011). User attitudes toward dedicated e-book readers for reading: The effect of convenience, compatibility, and media richness. Online Information Review, 35(4), 558-580.
Lim, W. M. (2009). Alternative models framing UK independent hoteliers’ adoption of technology. International Journal of Contemporary Hospitality Management, 21, 610-618.
Lin, C. C., Wu, H. Y., & Chang, Y. F. (2011). The critical factors impact on online customer satisfaction. Procedia Computer Science, 3, 276-281.
Lu, H., & Su, P. Y. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research, 19(4), 442-458.
Obal, M. (2013). Why do incumbents sometimes succeed?: Investigating the role of interorganizational trust in the adoption of disruptive technology. Industrial Marketing Management, 42, 900-908.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224-235.
Parasuraman, A. P., & Colby, C. (2014). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18, 59-74.
Rattanayuenyong, P. (2017). Perceived attributes and attitude affecting the use of PROMPT PAY of people in Bangkok. Master’s Independent Study of Business Administration, Bangkok University. [In Thai]
Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). Free Press
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Shih, Y., & Fan, S. (2013). Adoption of instant messaging by travel agency workers in Taiwan: Integrating technology readiness with the theory of planned behavior. International Journal of Business and Information, 8, 120-136.
Thongkham, P. (2013). Factors affecting the adopting of entrepreneur on the payment services MPOS (Mobile Point-of-Sale) Kasikorn Bank casestudy: Jatujak Market. Master’s Independent Study of Science, Thammasat University. [In Thai]
Van der Heijden, H. (2004). User acceptance of hedonic information systems. Management Information Systems Quarterly, 28(4), 695-704.
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. Management Information Systems Quarterly, 24(1), 115-139.
Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.
Yoon, S.-B., & Cho, E. (2016). Convergence Adoption Model (CAM) in the context of a smart car service. Computers in Human Behavior, 60, 500-507.