Examining Consumers Adoption and Continuance Intention of Online Group-Buying from User Experience Perspective

Abstract:

Online group-buying (OGB) has attracted a lot of interest among academics and practitioners. Although there is an abundance of studies on OGB, most studies on OGB aims on how to enhance the consumers adoption, the studies related to the determinants of consumers continuance intention and discontinue using anomalies are still insufficient. To identify the determinants that influence consumers continuance intention in OGB and establish generalize ability is crucial due to an empirical study measuring continuance intention would enable OGB platforms providers and consumers to improve their adoption and continuance intention. Emerging design approach: User eXperience Design (UXD) could provide learner with great user experience to alleviate the above issues. This paper synthesizes the Information System Success Model (ISSM) and the ExpectationConfirmation Theory (ECT) to establish an extension model based-on user experience perspective to discovery what critical factors affected the users benefit and users intentions to continue using OGB. Initial results of this study have shown our questionnaire reached good convergent and discriminate validities. In next steps, the promising model will be empirically tested with wider areas.

• Periodical: Applied Mechanics and Materials (Volumes 519-520)
• Pages: 397-400
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.519-520.397
• Online since: February 2014
• Authors: Yu Ling Liu, Chia Lin Wu, Po Yin Chang*
• Keywords: Expectance-Confirmation Theory, Online Group-Buying, Technology-Acceptance Model, User Experience

References

[1] Anderson, J. C., D. W. Gerbing, Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin. 103(3) (1988) 411-423.

DOI: 10.1037/0033-2909.103.3.411

[2] Bearden, W. O., J. E. Teel, Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research (1983) 21-28.

DOI: 10.1177/002224378302000103

[3] Bhattacherjee, A., An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems. 32(2) (2001) 201-214.

DOI: 10.1016/s0167-9236(01)00111-7

[4] Bhattacherjee, A., Understanding information systems continuance: an expectation-confirmation model. MIS quarterly. 25(3) (2001) 351-370.

DOI: 10.2307/3250921

[5] Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly (1989) 319-340.

DOI: 10.2307/249008

[6] Davis, F. D., R. P. Bagozzi, P. R. Warshaw, User acceptance of computer technology: a comparison of two theoretical models. Management Science. 35(8) (1989) 982-1003.

DOI: 10.1287/mnsc.35.8.982

[7] Fishbein, M., I. Ajzen. Belief, attitude, intention and behavior: An introduction to theory and research. (1975).

[8] Gefen, D., D. W. Straub, Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS quarterly (1997) 389-400.

DOI: 10.2307/249720

[9] Kauffman, R. J., H. Lai, C. -T. Ho, Incentive mechanisms, fairness and participation in online group-buying auctions. Electronic Commerce Research and Applications. 9(3) (2010) 249-262.

DOI: 10.1016/j.elerap.2008.11.009

[10] Liao, S. -h., P. -h. Chu, Y. -j. Chen, C. -C. Chang, Mining customer knowledge for exploring online group buying behavior. Expert Systems with Applications. 39(3) (2012) 3708-3716.

DOI: 10.1016/j.eswa.2011.09.066

[11] Lu, Y., T. Zhou, B. Wang, 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) (2009) 29-39.

DOI: 10.1016/j.chb.2008.06.002

[12] Moore, G. C., I. Benbasat, Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research. 2(3) (1991) 192-222.

DOI: 10.1287/isre.2.3.192

[13] Oliver, R. L., Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research (1993) 418-430.

[14] Oliver, R. L., W. S. DeSarbo, Response determinants in satisfaction judgments. Journal of Consumer Research (1988) 495-507.

[15] Shiau, W. -L., M. M. Luo, Factors affecting online group buying intention and satisfaction: A social exchange theory perspective. Computers in Human Behavior (2012).

DOI: 10.1016/j.chb.2012.07.030

[16] Szymanski, D. M., D. H. Henard, Customer satisfaction: a meta-analysis of the empirical evidence. Journal of the academy of marketing science. 29(1) (2001) 16-35.

DOI: 10.1177/009207030102900102

[17] Taylor, S., P. Todd, Assessing IT usage: The role of prior experience. MIS quarterly (1995) 561-570.

DOI: 10.2307/249633

[18] Taylor, S., P. A. Todd, Understanding information technology usage: A test of competing models. Information Systems Research. 6(2) (1995) 144-176.

DOI: 10.1287/isre.6.2.144

[19] Tsai, M. -T., N. -C. Cheng, K. -S. Chen, Understanding online group buying intention: the roles of sense of virtual community and technology acceptance factors. Total Quality Management & Business Excellence. 22(10) (2011) 1091-1104.

DOI: 10.1080/14783363.2011.614870

[20] Venkatesh, V., Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research. 11(4) (2000) 342-365.

DOI: 10.1287/isre.11.4.342.11872

[21] Venkatesh, V., F. D. Davis, A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science. 46(2) (2000) 186-204.

DOI: 10.1287/mnsc.46.2.186.11926

[22] Wu, I. -L., J. -L. Chen, An extension of trust and TAM model with TPB in the initial adoption of on-line tax: an empirical study. International Journal of Human-Computer Studies. 62(6) (2005) 784-808.

DOI: 10.1016/j.ijhcs.2005.03.003