Information Credibility Evaluation in Presence of Users’ Safety in New Retailing
DOI:
https://doi.org/10.13052/jwe1540-9589.2034Keywords:
Information credibility, perceived information quality, user judgement motivation, safety preference, new retailingAbstract
Understanding users’ safety perception of the credibility of web-based information has become increasingly important in the context of new retailing. This study extends the existing literature by exploring the factors influencing information credibility in the context of new retailing. Based on the technology acceptance model and the rational behavior theory, a theoretical model for the assessment of information credibility in new retailing was developed. We analyzed the factors influencing users’ safety preference toward information communication procedures and information credibility in new retailing based on two aspects: perceived information quality and user judgment motivation. The reliability and validity of the model measure were analyzed, and structural equation modeling was used to test the model hypotheses. The following results were obtained: (1) Authenticity, accuracy, and practicability positively affected the perceived information quality of new retailing information; (2) User judgment motivation had a positive impact on information users’ safety preference and information credibility; (3) Users’ safety preference positively affected information credibility; (4) Information acquisition, social interaction, and self-identity positively affected the perceived credibility of new retailing information.
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References
Guille, A. Information diffusion in online social networks. in Proceedings of the 2013 Sigmod/PODS Ph. D. symposium on PhD symposium. 2013: ACM.
Tseng, S., & Fogg, B. J. (1999). Credibility and Computing Technology. Commun. ACM, 42(5), 39–44. doi:10.1145/301353.301402.
Metzger, M. J., Flanagin, A. J., Eyal, K., Lemus, D. R., & McCann, R. M. (2003). Credibility for the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment. Communication Yearbook, 27, 293–336.
Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on twitter. In Proceedings of the 20th international conference on World wide web (pp. 675–684). New York, NY, USA: ACM. doi:10.1145/1963405.1963500.
Cha, M., et al. Measuring user influence in Twitter: the million follower fallacy. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. 2010. Washington.
Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010). TwitterRank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining (pp. 261–270). New York, NY, USA: ACM. doi:10.1145/1718487.1718520.
Flanagin, A. J., & Metzger, M. J. (2007). The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information. New Media & Society, 9(2), 319–342. doi:10.1177/1461444807075015.
Davis F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology [J]. MIS Quarterly, 1989, 13(3): 319–340.
Petty R.E., Cacioppo J.T. The Elaboration Likelihood Model of Persuasion. Advances in Experimental Social Psychology, 1986, 19(4): 123–205.
Yusriyah, K., Budiman, & Dharmayanti. (2017). Behavioral intention to use sharia cooperative application based on technology acceptance model. Advanced Science Letters, 23(9), 8603–8606.
Shareef, M. A., Kumar, V., Dwivedi, Y. K., & Kumar, U. (2016). Service delivery through mobile-government (MGOV): driving factors and cultural impacts. Information Systems Frontiers, 18(2), 315–332.
Clemons, E. K., Wilson, J., Matt, C., Hess, T., Ren, F., & Jin, F., et al. (2016). Global differences in online shopping behavior: understanding factors leading to trust. Journal of Management Information Systems, forthcoming (4), 1117–1148.
Xiong, W., Wu, Z., Li, B., & Gu, Q. (2017). A constrained learning approach to the prediction of reliability ranking for WSN services. International Journal of Web Services Research, 14(3), 33–52.
Chen, Yuangao & Wang, Shuo. (2017). Purchasing channel choice based on fuzzy TOPSIS method. International Journal of Services Technology and Management. 23. 237. 10.1504/IJSTM.2017.10006358.
Wang R.W., Strong D.M. Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 1996, 12(4):5–33.
Kim K.S., Sin S.C.J. Selecting quality sources: Bridging the gap between the perception and use of information sources. Journal of Information Science, 2011, 37(2):178–188.
Chaiken, S., & Trope, Y. (1999). Dual-process theories in social psychology. Guilford Press. Chen Y., Wu X., Jiang R., Integrating human omics data to prioritize candidate genes, BMC Medical Genomics, 6:57, 2013.
Gunther, A.C. (1992). Biased press or biased public? Attitudes toward media coverage of social groups. Public Opinion Quarterly, 56(2), 147–167.
Petty R.E, Cacioppo J.T., Sedikides C., et al. Affect and persuasion: A contemporary perspective. American Behavioral Scientist, 1988, 31(3): 355–371.
Hilligoss, B., & Rieh, S. Y. (2008). Developing a unifying framework of credibility assessment: Construct, heuristics, and interaction in context. Information Processing & Management, 44(4), 1467–1484.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. Mis Quarterly, 27(3), 425–478.
Hsu C.L., Lu H.P., Hsu H.H. Adoption of the mobile Internet: An empirical study of multimedia message service (MMS). Omega, 2007, 35(6): 715–726.
Wang, D. & Chen, Y. Neural Comput & Applic (2018). https://doi.org/10.1007/s00521-018-3734-4
Asimakopoulos, S., & Dix, A. (2017). Walking: a grounded theory of social engagement and experience. Interacting with Computers, 1–21.
Taylor R.S./M.J. Value Added Processes in Information Systems. Greenwood Publishing Group Inc. 1986.
Men, L.R., & Tsai, W.H.S. (2016). Public engagement with ceos on social media: motivations and relational outcomes. Public Relations Review, S0363811116301631.
Novello, S., & Fernandez, P. M. (2016). The influence of event authenticity and quality attributes on behavioral intentions. Journal of Hospitality & Tourism Research, 40(6), 685–714.
Liang H.G., Xue Y.J., Zhang Z.R. (2017). Understanding Online Health Information Use: The Case of People with Physical Disabilities. Journal of the Association for Information Systems, 18(6): 433–460.
Wangenheim F V, Bayón T. The effect of word of mouth on services switching. European Journal of Marketing, 2004, 38(9/10):1173–1185.
Choi, J., Chang, Y. K., Lee, K., & Chang, J. D. (2016). Effect of perceived warmth on positive judgment. Journal of Consumer Marketing, 33(4), 235–244.
Suki, & Mohd, N. (2015). Consumer environmental concern and green product purchase in Malaysia: structural effects of consumption values. Journal of Cleaner Production, S0959652615013153.
Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60(3), 413–439.
Eastin, M. S., Yang, M.-S., & Nathanson, A. I. (2006). Children of the net: An empirical exploration into the evaluation of Internet content. Journal of Broadcasting & Electronic Media, 50(2), 211–230.
Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. Digital Media, Youth, and Credibility, 73–100.
Rieh, S. Y. (2002). Judgment of information quality and cognitive authority in the Web. Journal of the American Society for Information Science and Technology, 53(2), 145–161.
Wathen, C. N., & Burkell, J. (2002). Believe it or not: Factors influencing credibility on the Web. Journal of the American Society for Information Science and Technology, 53(2), 134–144.
