ABSTRACT
This research aims to examine the moderating effect of artificial intelligence phobia (AI-phobia) on the relationship between perceived trust and product promotion effectiveness in a recommendation system. The perceived trust consists of three subtypes of trusts: competence trust, benevolence trust and integrity trust. The experimental result shows that the moderating effect is nonsignificant.
- Li, S.S. and E. Karahanna, Online recommendation systems in a B2C E-commerce context: a review and future directions. Journal of the Association for Information Systems, 2015. 16(2): p. 72--72.Google Scholar
- Komiak and Benbasat, The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly, 2006: p. 941--960.Google Scholar
- Gomez-Uribe, C.A. and N. Hunt, The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 2016. 6(4): p. 13.Google ScholarDigital Library
- Nguyen, T.T., et al., Multi-Label Classification via Incremental Clustering on Evolving Data Stream. Pattern Recognition, 2019.Google Scholar
- Komiak, S.Y.X. and I. Benbasat, A two-process view of trust and distrust building in recommendation agents: A process-tracing study. Journal of the Association for Information Systems, 2008. 9(12): p. 2--2.Google Scholar
- Qiu, L. and I. Benbasat, Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems. Journal of Management Information Systems, 2009. 25(4): p. 145--182.Google Scholar
- Benbasat, I. and W. Wang, Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 2005. 6(3): p. 4--4.Google Scholar
- Komiak and Benbasat, A two-process view of trust and distrust building in recommendation agents: A process-tracing study. Journal of the Association for Information Systems, 2008. 9(12): p. 2.Google ScholarCross Ref
- Sztompka, P., Trust: A sociological theory. 1999: Cambridge University Press.Google Scholar
- Reeves, B. and C.I. Nass, The media equation: How people treat computers, television, and new media like real people and places. 1996: Cambridge university press.Google Scholar
- Cassell, J. and T. Bickmore, External manifestations of trustworthiness in the interface. Communications of the ACM, 2000. 43(12): p. 50--56.Google Scholar
- Jian, J.-Y., A.M. Bisantz, and C.G. Drury, Foundations for an empirically determined scale of trust in automated systems. International Journal of Cognitive Ergonomics, 2000. 4(1): p. 53--71.Google Scholar
- Xiao, S. and I. Benbasat, The impact of internalization and familiarity on trust and adoption of recommendation agents. AIS SIG-HCI, 2002.Google Scholar
- McKnight, D.H., V. Choudhury, and C. Kacmar, Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 2002. 13(3): p. 334--359.Google Scholar
- Mayer, R.C., J.H. Davis, and F.D. Schoorman, An integrative model of organizational trust. Academy of Management Review, 1995. 20(3): p. 709--734.Google Scholar
- Komiak, S.X. and I. Benbasat, Understanding customer trust in agent-mediated electronic commerce, web-mediated electronic commerce, and traditional commerce. Information Technology and Management, 2004. 5(1-2): p. 181--207.Google Scholar
- Doney, P.M. and J.P. Cannon, An examination of the nature of trust in buyer-seller relationships. Journal of Marketing, 1997. 61(2): p. 35--51.Google Scholar
- Chopra, K. and W.A. Wallace. Trust in electronic environments. in 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the. 2003. IEEE.Google ScholarCross Ref
- Slonim, J., et al. An extensible, human-centric framework that promotes universal access to electronic commerce. in International Symposium on Electronic Commerce. 2001. Springer.Google ScholarCross Ref
- Hultink, E.J., et al., Industrial new product launch strategies and product development performance. Journal of Product Innovation Management, 1997. 14(4): p. 243--257.Google Scholar
- Hostler, R.E., et al., Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior. Information & Management, 2011. 48(8): p. 336--343.Google Scholar
- Hu, X., Z. Lin, and H. Zhang, Trust promoting seals in electronic markets: an exploratory study of their effectiveness for online sales promotion. Journal of Promotion Management, 2002. 9(1-2): p. 163--180.Google Scholar
- Luk, S.T. and L.S. Yip, The moderator effect of monetary sales promotion on the relationship between brand trust and purchase behaviour. Journal of Brand Management, 2008. 15(6): p. 452--464.Google Scholar
- Jay, T.B., Computerphobia: What to do about it. Educational Technology, 1981. 21(1): p. 47--48.Google Scholar
- Rosen, L.D., D.C. Sears, and M.M. Weil, Computerphobia. Behavior Research Methods, Instruments, & Computers, 1987. 19(2): p. 167--179.Google Scholar
- Osiceanu, M.-E., Psychological implications of modern technologies:"technofobia" versus "technophilia". Procedia-Social and Behavioral Sciences, 2015. 180: p. 1137--1144.Google ScholarCross Ref
- Khasawneh, O.Y., Technophobia: Examining its hidden factors and defining it. Technology, 2018. 13: p. 60.Google Scholar
- Lohmöller, J.-B., Predictive vs. structural modeling: PLS vs. ML, in Latent variable path modeling with partial least squares. 1989, Springer. p. 199--226.Google ScholarCross Ref
- Hair Jr, J.F., et al., A primer on partial least squares structural equation modeling (PLS-SEM). 2016: Sage publications.Google Scholar
Index Terms
- The Moderating Effect of Artificial Intelligence Phobia on the Relationship between Trust and Product Promotion Effectiveness: An Exploratory Study
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