Abstract
With the prosperity of online shopping platforms, similar or even the same products tend to have a large variety of sources to be purchased from. More and more consumers seek the product information from online review websites before making a purchase, as they are willing to provide reviews or share their purchase experience. These behaviors turn the online review websites into vertical and community-based sales channels. Based on the Information Adoption Model, this study conducted an empirical investigation to analyze female users’ information adoption process when using fashion shopping guide website. The results show that information quality and source credibility have significant impact on information usefulness, which in turn contributes to information adoption. In addition, users with different levels of purchasing motivation demonstrate different dependence on information quality and source credibility.
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References
Dellarocas, C. (2003). The digitization of word of mouth: promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52.
Sevitt, D., & Samuel, A. (2013). How pinterest puts people in stores. Harvard Business Review, 91(7), 26–27.
Awad, N. F., & Ragowsky, A. (2008). Establishing trust in electronic commerce through online word of mouth: An examination across genders. Journal of Management Information Systems, 24(4), 101–121.
Gefen, D., & Ridings, C. M. (2005). If you spoke as she does, sir, instead of the way you do: A sociolinguistics perspective of gender differences in virtual communities. ACM SIGMIS Database, 36(2), 78–92.
Bae, S., & Lee, T. (2011). Gender differences in consumers’ perception of online consumer reviews. Electronic Commerce Research, 11(2), 201–214.
Wei, P.-S., & Lu, H. P. (2013). An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior. Computers in Human Behavior, 29(1), 193–201.
Allen, D. (2001). E-Marketer: women on the web. http://www.ebusinessforum.com/analysis/ecommerce_b2c/20010228_b2c.html. Accessed 22 August 2015.
Elkin, N. (2001). E-marketer: online apparel and footwear sales on the rise. http://ebusinessforum.com. Accessed 23 February 2006.
iResearch. (2015). China online shopping user behavior research report [R/OL]. http://www.iresearchchina.com/samplereports/6405.html. Accessed 18 November 2015 (Ch).
NPD. (2013). The NPD group reports U.S. women’s apparel market grew 4 percent in 2013, https://www.npd.com/wps/portal/npd/us/news/press-releases/the-npd-group-reports-us-womens-apparel-market-grew-4-percent-in-2013/. Accessed 25 May 2015.
Enright, A. (2015). U.S. e-commerce sales could top $434 billion in 2017. https://www.internetretailer.com/2013/04/25/us-e-commerce-sales-could-top-434-billion-2017. Accessed 25 May 2015.
Cheung, M., Sia, C.-L., & Kuan, K. K. (2012). Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 13(8), 618–635.
Filieri, R., & McLeay, F. (2014). E-WOM and accommodation an analysis of the factors that influence travelers’ adoption of information from online reviews. Journal of Travel Research, 53(1), 44–57.
Zhang, T., Agarwal, R., & Lucas, H. C, Jr. (2011). The value of IT-enabled retailer learning: Personalized product recommendations and customer store loyalty in electronic markets. MIS Quarterly, 35(4), 859–881.
Gu, B., Park, J., & Konana, P. (2012). Research note-the impact of external word-of-mouth sources on retailer sales of high-involvement products. Information Systems Research, 23(1), 182–196.
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon. com. MIS Quarterly, 34(1), 185–200.
Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1(1), 5–17.
Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.
Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.
Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.
Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.
Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498–1512.
Wolny, J., & Mueller, C. (2013). Analysis of fashion consumers’ motives to engage in electronic word-of-mouth communication through social media platforms. Journal of Marketing Management, 29(5–6), 562–583.
Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H.-P. (2009). Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13(4), 9–38.
Lee, J., & Lee, J.-N. (2009). Understanding the product information inference process in electronic word-of-mouth: An objectivity–subjectivity dichotomy perspective. Information & Management, 46(5), 302–311.
Pavlou, P. A., Liang, H., & Xue, Y. (2006). Understanding and mitigating uncertainty in online environments: A principal-agent perspective. MIS Quarterly, 31(1), 105–136.
Hu, N., Liu, L., & Zhang, J. J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management, 9(3), 201–214.
Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151.
Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research, 18(3), 229–247.
Park, D.-H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148.
Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.
Xue, F., & Zhou, P. (2010). The effects of product involvement and prior experience on Chinese consumers’ responses to online word of mouth. Journal of International Consumer Marketing, 23(1), 45–58.
Cui, G., Lui, H. K., & Guo, X. (2012). The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 17(1), 39–58.
Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232.
Loureiro, S. M. C., & Ribeiro. L. (2012) The impact of online atmospheric cues on emotions and word-of-mouth: gender differentiation. In 5th Annual EuroMed Conference of the EuroMed Academy of Business.
Kuo, Y.-F., Hu, T.-L., & Yang, S.-C. (2013). Effects of inertia and satisfaction in female online shoppers on repeat-purchase intention: The moderating roles of word-of-mouth and alternative attraction. Managing Service Quality: An International Journal, 23(3), 168–187.
Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. New York: Springer.
Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a determinant of argument-based persuasion. Journal of Personality and Social Psychology, 41(5), 847.
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.
Luo, C., Luo, X. R., Schatzberg, L., & Sia, C. L. (2013). Impact of informational factors on online recommendation credibility: The moderating role of source credibility. Decision Support Systems, 56, 92–102.
Turban, E., Ling, D., Lee, J. K., Liang, T. P., & Turban, D. C. (2015). Social commerce: Foundations, social marketing, and advertising. In G. Schneider (Ed.), Electronic Commerce (pp. 309–365). New York: Springer.
Davison, R. (1997). An instrument for measuring meeting success. Information & Management, 32(4), 163–176.
Rodgers, S., & Harris, M. A. (2003). Gender and e-commerce: An exploratory study. Journal of Advertising Research, 43(03), 322–329.
Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57(7), 768–775.
Miyazaki, A. D., & Fernandez, A. (2001). Consumer perceptions of privacy and security risks for online shopping. Journal of Consumer Affairs, 35(1), 27–44.
Sheehan, K. B. (1999). An investigation of gender differences in on-line privacy concerns and resultant behaviors. Journal of Interactive Marketing, 13(4), 24–38.
Van Slyke, C., Comunale, C. L., & Belanger, F. (2002). Gender differences in perceptions of web-based shopping. Communications of the ACM, 45(8), 82–86.
Wolin, L. D., & Korgaonkar, P. (2003). Web advertising: gender differences in beliefs, attitudes and behavior. Internet Research, 13(5), 375–385.
Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752.
Sher, P. J., & Lee, S.-H. (2009). Consumer skepticism and online reviews: An elaboration likelihood model perspective. Social Behavior and Personality: An International Journal, 37(1), 137–143.
Ott, M., Choi, Y, -J., Cardie, C., & Hancock, J. T. (2011). Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (Vol. 1). Association for Computational Linguistics.
Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Social Psychology, 34(2), 243–281.
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.
Dellarocas, C., & Narayan, R. (2006). A statistical measure of a population’s propensity to engage in post-purchase online word-of-mouth. Statistical Science, 21(2), 277–285.
Jensen, M. L., Averbeck, J. M., Zhang, Z., & Wright, K. B. (2013). Credibility of anonymous online product reviews: A language expectancy perspective. Journal of Management Information Systems, 30(1), 293–324.
Sternthal, B., Phillips, L. W., & Dholakia, R. (1978). The persuasive effect of scarce credibility: A situational analysis. Public Opinion Quarterly, 42(3), 285–314.
Sanchez-Franco, M. J., Ramos, A. F. V., & Velicia, F. A. M. (2009). The moderating effect of gender on relationship quality and loyalty toward internet service providers. Information & Management, 46(3), 196–202.
Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(4), 411–454.
Ratneshwar, S., & Chaiken, S. (1991). Comprehension’s role in persuasion: The case of its moderating effect on the persuasive impact of source cues. Journal of Consumer Research, 18, 52–62.
Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.
Eagly, A. H., & Carli, L. L. (1981). Sex of researchers and sex-typed communications as determinants of sex differences in influenceability: A meta-analysis of social influence studies. Psychological Bulletin, 90(1), 1–20.
Carl, W. (2005). Word-of-mouth and gender. http://wom-study.blogspot.com/2005/06/word-of-mouth-and-gender.html. Accessed 22 June 2015.
Seock, Y. K., & Bailey, L. R. (2008). The influence of college students’ shopping orientations and gender differences on online information searches and purchase behaviours. International Journal of Consumer Studies, 32(2), 113–121.
Stamm, K., & Dube, R. (1994). The relationship of attitudinal components to trust in media. Communication Research, 21(1), 105–123.
Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1), 29–39.
Janiszewski, C. (1998). The influence of display characteristics on visual exploratory search behavior. Journal of Consumer Research, 25(3), 290–301.
Suh, K. S., & Lee, Y. E. (2005). The effects of virtual reality on consumer learning: An empirical investigation. MIS Quarterly, 29(4), 673–697.
Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545.
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33.
Berlo, D. K., Lemert, J. B., & Mertz, R. J. (1969). Dimensions for evaluating the acceptability of message sources. Public Opinion Quarterly, 33(4), 563–576.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.
Nunnally, J. C., Bernstein, I. H., & Berge, J. M. T. (1967). Psychometric theory (Vol. 226). New York: McGraw-Hill.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method bias in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
Liang, H. G., Saraf, N., Hu, Q., & Xue, Y. J. (2007). Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59–87.
Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487.
Jung, T. (2011). AMOS and research method. Taipei: Wunan Books Inc.
Hu, L.-T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling, concepts, issues, and applications (pp. 76–99). London: Sage.
Doran, K. (2002). Lessons learned in cross-cultural research of Chinese and North American consumers. Journal of Business Research, 55(10), 823–829.
Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329.
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Peng, L., Liao, Q., Wang, X. et al. Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites. Electron Commer Res 16, 145–169 (2016). https://doi.org/10.1007/s10660-016-9213-z
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DOI: https://doi.org/10.1007/s10660-016-9213-z