Technology Acceptance Model, Social Influence and Perceived Risk in Using Mobile Applications: Empirical Evidence in Online Transportation in Indonesia

Khairul Ikhsan

Abstract

The purpose is to examine the impact of technology acceptance model consisting of usefulness and ease of use on the intention to use, social influence and perceived risk consisting of physical and psychological risk on the intention to use, and examine its impact on behavior using mobile applications. Benefit, ease of use and social factors were considered as the critical factors in accepting and using a technology. However, mobile applications has limitation in consuming an online product or service. A total of 1383 questionnaires were obtained from respondents of online transportation service users in Jakarta, Bandung, Yogyakarta and Surabaya. Using PLS-SEM, it is found that the there are significant relationships between technology acceptance model, physical risk, intention and behavior using mobile applications, but no relationship between psychological risk and intention. This study also found that the influence of intention on behavior using mobile applications is dependent on perceived usefulness, perceived ease of use and perceived physical risk rather than perceived psychological risks.

Keywords

Technology acceptance model (TAM), social influence, perceived risk, mobile applications

Full Text:

PDF

References

Amaro, S., & Duarte, P. (2015). An Integrative Model of Consumers’ Intentions to Purchase Travel Online. Tourism Management, 46(1), 64-79.

APJII. (2016). Penetrasi & Profil Perilaku Pengguna Internet Indonesia 2016. Available at: https://apjii.or.id/content/read/39/264/Survei-Internet-APJII-2016. 20 December 2019.

APJII. (2017). Penetrasi & Profil Perilaku Pengguna Internet Indonesia 2017. Available at: https://apjii.or.id/content/read/39/264/Survei-Internet-APJII-2017. 2 January 2017.

Bagozzi, R. P. (1982). A Field Investigation of Causal Relations among Cognitions, Affect, Intentions, and Behavior. Journal of Marketing Research, 19(4), 562-583.

Bettman, J. R. (1973). Perceived Risk and Its Components: a Model and Empirical Test. Journal of Marketing Research, 10(2), 184-190.

Carroll, M. S., Connaughton, D. P., Spengler, J. O., & Byon, K. K. (2014). A Multi Dimensional Model of Perceived Risk in Spectator Sport. Marketing Management Journal, 24(1), 80-95.

Chaouali, W., Ben Yahia, I., & Souiden, N. (2016). The Interplay of Counter-Conformity Motivation, Social Influence, and Trust in Customers’ Intention to Adopt Internet Banking Services: the Case of an Emerging Country. Journal of Retailing and Consumer Services, 28(January), 209-218.

Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods (12Th Edition). New York: McGraw Hill.

Cox, D. F., & Rich, S. U. (1964). Perceived Risk and Consumer Decision-Making: the Case of Telephone Shopping. Journal of Marketing Research, 1(4), 32-39.

Cunningham, L. F., Gerlach, J. H., Harper, M. D., & Young, C. E. (2005). Perceived Risk and the Customer Buying Process: Internet Airline Reservations. International Journal of Service Industry Management, 16(4), 357-372.

Davis, F. D. (1989). Perceived Usefulness, Perceived, and User Acceptance. Management Information System Quarterly, 13(3), 319-339.

Davis, F. D. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts. International Journal of Man Machine Studies, 38(3), 475-487.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: a Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.

Featherman, M. S., & Hajli, N. (2016). Self-Service Technologies and E-Services Risks in Social Commerce Era. Journal of Business Ethics, 139(2), 251-269.

Forsythe, S. M., & Shi, B. (2003). Consumer Patronage and Risk Perceptions in Internet Shopping. Journal of Business Research, 56(11), 867-875.

French, Jr., Raven, B., & Cartwright, D. (1959). Classics of Organization Theory. Boston: Cengage Learning.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: an Integrated Model. Management Information System Quarterly, 27(1), 51-90.

Grob, M. (2016). Impediments to Mobile Shopping Continued Usage Intention: a Trust-Risk-Relationship. Journal of Retailing and Consumer Services, 33(1), 109-119.

Gupta, A., & Arora, N. (2017). Understanding Determinants and Barriers of Mobile Shopping Adoption Using Behavioral Reasoning Theory. Journal of Retailing and Consumer Services, 36(August), 1-7.

Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A Premier on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oak: Sage Publication.

Hsu, C. L., & Lin, J. C. C. (2016). Effect of Perceived Value and Social Influences on Mobile App Stickiness and in-App Purchase Intention. Technological Forecasting and Social Change, 108(July), 42-53.

Hu, P. J., Chau, P. Y. K., Liu Sheng, O. R., & Tam, K. Y. (1999). Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology. Journal of Management Information Systems, 16(2), 91-112.

Jacoby, J., & Kaplan, L. B. (1972). The Components of Perceived Risk. Proceedings. Presented at the Third Annual Convention of the Association for Consumer Research, 3-5 November, Chicago.

Jia, J., Dyer, J. S., & Butler, J. C. (1999). Measures of Perceived Risk. Management Science, 45(4), 519-532.

Kim, H. W., Kankanhalli, A., & Lee, H. L. (2016). Investigating Decision Factors in Mobile Application Purchase: a Mixed-Methods Approach. Information and Management, 53(6), 727-739.

Lim, N. (2003). Consumers’ Perceived Risk: Sources Versus Consequences. Electronic Commerce Research and Applications, 2(3), 216-228.

Mitchell, V. (1999). Consumer Perceived Risk: Conceptualisations and Models. European Journal of Marketing, 33(1/2), 163-195.

Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017). Understanding the Intention to Use Mobile Shopping Applications and its Influence on Price Sensitivity. Journal of Retailing and Consumer Services, 37(July), 8-22.

Pascual-Miguel, F. J., Agudo-Peregrina, Ã. F., & Chaparro-Peláez, J. (2015). Influences of Gender and Product Type on Online Purchasing. Journal of Business Research, 68(7), 1550-1556.

Peter, J. P., & Tarpey, Sr., L. X. (1975). A Comparative Analysis of Three Consumer Decision Strategies. Journal of Consumer Research, 2(1), 29.

Porter, M. E. (2001). Strategy and the Internet. Harvard Business Review, March, 62-78.

Stone, R. N., & Grønhaug, K. (1993). Perceived Risk: Further Considerations for the Marketing Discipline. European Journal of Marketing, 27(3), 39-50.

Taylor, J. W. (1974). The Role of Risk in Consumer Behavior. Journal of Marketing, 38(2), 54-60.

Van Raaij, E. M., & Schepers, J. J. L. (2008). The Acceptance and Use of a Virtual Learning Environment in China. Computers and Education, 50(3), 838-852.

Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. Management Information System Quarterly, 27(3), 425-478.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. Management Information System Quarterly, 36(1), 157-178.

Xu, C., Peak, D., & Prybutok, V. (2015). A Customer Value, Satisfaction, and Loyalty Perspective of Mobile Application Recommendations. Decision Support Systems, 79(November), 171-183.

Yang, L., Liu, Y., Li, H., & Yu, B. (2015). Understanding Perceived Risks in Mobile Payment Acceptance. Industrial Management & Data System, 115(2), 253-269.

View Counter: Abstract - 2128 and PDF - 1981

Refbacks

  • There are currently no refbacks.


Bookmark and Share