Skip to main content
Log in

Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Dellarocas, C. (2003). The digitization of word of mouth: promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.

    Article  Google Scholar 

  2. 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.

    Article  Google Scholar 

  3. Sevitt, D., & Samuel, A. (2013). How pinterest puts people in stores. Harvard Business Review, 91(7), 26–27.

    Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. Bae, S., & Lee, T. (2011). Gender differences in consumers’ perception of online consumer reviews. Electronic Commerce Research, 11(2), 201–214.

    Article  Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. Allen, D. (2001). E-Marketer: women on the web. http://www.ebusinessforum.com/analysis/ecommerce_b2c/20010228_b2c.html. Accessed 22 August 2015.

  9. Elkin, N. (2001). E-marketer: online apparel and footwear sales on the rise. http://ebusinessforum.com. Accessed 23 February 2006.

  10. iResearch. (2015). China online shopping user behavior research report [R/OL]. http://www.iresearchchina.com/samplereports/6405.html. Accessed 18 November 2015 (Ch).

  11. 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.

  12. 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.

  13. 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.

    Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. 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.

    Google Scholar 

  16. 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.

    Article  Google Scholar 

  17. 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.

    Google Scholar 

  18. Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1(1), 5–17.

    Article  Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. 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.

    Article  Google Scholar 

  21. 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.

    Article  Google Scholar 

  22. 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.

    Article  Google Scholar 

  23. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.

    Article  Google Scholar 

  24. 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.

    Article  Google Scholar 

  25. 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.

    Article  Google Scholar 

  26. 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.

    Article  Google Scholar 

  27. 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.

    Article  Google Scholar 

  28. 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.

    Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151.

    Article  Google Scholar 

  31. 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.

    Article  Google Scholar 

  32. 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.

    Article  Google Scholar 

  33. Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.

    Article  Google Scholar 

  34. 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.

    Article  Google Scholar 

  35. 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.

    Article  Google Scholar 

  36. 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.

    Article  Google Scholar 

  37. 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.

  38. 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.

    Article  Google Scholar 

  39. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. New York: Springer.

    Google Scholar 

  40. 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.

    Article  Google Scholar 

  41. 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.

    Article  Google Scholar 

  42. 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.

    Article  Google Scholar 

  43. 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.

    Chapter  Google Scholar 

  44. Davison, R. (1997). An instrument for measuring meeting success. Information & Management, 32(4), 163–176.

    Article  Google Scholar 

  45. Rodgers, S., & Harris, M. A. (2003). Gender and e-commerce: An exploratory study. Journal of Advertising Research, 43(03), 322–329.

    Article  Google Scholar 

  46. 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.

    Article  Google Scholar 

  47. Miyazaki, A. D., & Fernandez, A. (2001). Consumer perceptions of privacy and security risks for online shopping. Journal of Consumer Affairs, 35(1), 27–44.

    Article  Google Scholar 

  48. 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.

    Article  Google Scholar 

  49. 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.

    Article  Google Scholar 

  50. Wolin, L. D., & Korgaonkar, P. (2003). Web advertising: gender differences in beliefs, attitudes and behavior. Internet Research, 13(5), 375–385.

    Article  Google Scholar 

  51. 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.

    Article  Google Scholar 

  52. 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.

    Article  Google Scholar 

  53. 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.

  54. Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Social Psychology, 34(2), 243–281.

    Article  Google Scholar 

  55. 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.

    Article  Google Scholar 

  56. 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.

    Article  Google Scholar 

  57. 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.

    Article  Google Scholar 

  58. Sternthal, B., Phillips, L. W., & Dholakia, R. (1978). The persuasive effect of scarce credibility: A situational analysis. Public Opinion Quarterly, 42(3), 285–314.

    Article  Google Scholar 

  59. 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.

    Article  Google Scholar 

  60. Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(4), 411–454.

    Article  Google Scholar 

  61. 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.

    Article  Google Scholar 

  62. Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.

    Google Scholar 

  63. 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.

    Article  Google Scholar 

  64. 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.

  65. 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.

    Article  Google Scholar 

  66. Stamm, K., & Dube, R. (1994). The relationship of attitudinal components to trust in media. Communication Research, 21(1), 105–123.

    Article  Google Scholar 

  67. 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.

    Article  Google Scholar 

  68. Janiszewski, C. (1998). The influence of display characteristics on visual exploratory search behavior. Journal of Consumer Research, 25(3), 290–301.

    Article  Google Scholar 

  69. Suh, K. S., & Lee, Y. E. (2005). The effects of virtual reality on consumer learning: An empirical investigation. MIS Quarterly, 29(4), 673–697.

    Google Scholar 

  70. Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545.

    Article  Google Scholar 

  71. 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.

    Article  Google Scholar 

  72. 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.

    Article  Google Scholar 

  73. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.

    Article  Google Scholar 

  74. 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.

    Google Scholar 

  75. Nunnally, J. C., Bernstein, I. H., & Berge, J. M. T. (1967). Psychometric theory (Vol. 226). New York: McGraw-Hill.

    Google Scholar 

  76. 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.

    Article  Google Scholar 

  77. 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.

    Google Scholar 

  78. Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487.

    Article  Google Scholar 

  79. Jung, T. (2011). AMOS and research method. Taipei: Wunan Books Inc.

    Google Scholar 

  80. 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.

    Google Scholar 

  81. Doran, K. (2002). Lessons learned in cross-cultural research of Chinese and North American consumers. Journal of Business Research, 55(10), 823–829.

    Article  Google Scholar 

  82. Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qinyu Liao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-016-9213-z

Keywords

Navigation