The Research Journal of the Costume Culture (복식문화연구)

The Costume Culture Association (복식문화학회)
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Perceived challenges in fashion shopping online: Scale development and validation

온라인 패션 쇼핑 시 도전감의 척도 개발 및 타당성 연구

  • Shim, Soo In (Dept. of Fashion Design, Chonbuk National University/Research Institute of Human Ecology, Chonbuk National University)
  • 심수인 (전북대학교 의류학과/전북대학교 인간생활과학연구소)
  • Received : 2016.10.04
  • Accepted : 2016.10.24
  • Published : 2016.12.31
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Abstract

The purpose of this study is to develop a multi-dimensional scale measuring consumers' perceived challenge in shopping fashion products online, and to verify its validity and reliability. Relevant literature is first reviewed to identify possible dimensions of perceived challenge. Next, Study 1 is conducted in order to explore the dimensions empirically and to see whether the dimensions that emerged were consistent with prior findings. A total of 190 responses to an open-ended question was qualitatively analyzed by using content analysis. The findings of Study 1 generate 26 items reflecting four dimensions (i.e., product knowledge, previous experience, website functionality, and product availability), which correspond to the dimensions suggested in literature review. Study 2 is subsequently conducted to refine the items so that the perceived challenge scale establishes cross-validation, convergent validity, discriminant validity, reliability, and predictive validity. A total of 238 responses is quantitatively analyzed by using exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. In the results of Study 2, the perceived challenge scale is found to consist of a total of 16 items reflecting three dimensions: E-commerce Challenge (corresponding to Previous Experience reported in Study 1), Retailer Challenge (corresponding to Website Functionality), and Product Challenge (corresponding to Product Knowledge); all Product Availability items have been eliminated through the item refinement process. Specifically, E-commerce Challenge and Retailer Challenge are found to predict flow, supporting flow theory, while Product Challenge fails to lead to flow significantly. Implications, limitations, and suggestions for future studies are also discussed.

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References

  1. Baldus, B. J., Voorhees, C., & Calantone, R. (2015). Online brand community engagement: Scale development and validation. Journal of Business Research, 68(5), 978-985. doi:10.1016/j.jbusres.2014.09.035
  2. Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64-73. doi:10.2307/3150876
  3. Conway, J. M., & Huffcutt, A. I. (2003). A review and evaluation of exploratory factor analysis practices in organizational research. Organizational Research Methods, 6(2), 147-168. doi:10.1177/1094428103-251541
  4. Csikszentmihalyi, M. (1991). Flow: The psychology of optimal experience. New York: Harper Perennial.
  5. Fan, Q., Lee, J. Y., & Kim, J. I. (2013). The impact of web site quality on flow-related online shopping behaviors in C2C e-marketplaces: A crossnational study. Managing Service Quality: An International Journal, 23(5), 364-387. doi:10.1108/MSQ-11-2012-0150
  6. Farrell, A. M. (2010). Insufficient discriminant validity:A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63(3), 324-327. doi:10.1016/j.jbusres.2009.05.003
  7. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi:10.2307/3151312
  8. Gao, J., Zhang, C., Wang, K., & Ba, S. (2012). Understanding online purchase decision making: The effects of unconscious thought, information quality, and information quantity. Decision Support Systems, 53(4), 772-781. doi:10.1016/j.dss.2012.05.011
  9. Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(2), 186-192. doi:10.2307/3172650
  10. Guo, Y. M., & Poole, M. S. (2009). Antecedents of flow in online shopping: A test of alternative models. Information Systems Journal, 19(4), 369-390. doi:10.1111/j.1365-2575.2007.00292.x
  11. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
  12. Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments:Conceptual foundations. Journal of Marketing, 60(3), 50-68. doi:10.2307/1251841
  13. Hoffman, D. L., & Novak, T. P. (2009). Flow online:Lessons learned and future prospects. Journal of Interactive Marketing, 23(1), 23-34. doi:10.1016/j.intmar.2008.10.003
  14. Hsu, C.-L., Wu, C.-C., & Chen, M.-C. (2013). An empirical analysis of the antecedents of e-satisfaction and e-loyalty: Focusing on the role of flow and its antecedents. Information Systems and e-Business Management, 11(2), 287-311. doi:10.1007/s10257-012-0194-8
  15. Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. doi:10.1080/10705519909540118
  16. Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience:The flow state scale. Journal of Sport & Exercise Psychology, 18(1), 17-35. https://doi.org/10.1123/jsep.18.1.17
  17. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. doi:10.1007/bf0229-1575
  18. Klein, L. R. (2003). Creating virtual product experiences:The role of telepresence. Journal of Interactive Marketing, 17(1), 41-55. doi:10.1002/dir.10046
  19. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York:Guilford publications.
  20. Lee, Y. J., & Rhee, Y. J. (2014). A study on the effects of the characteristics of fashion brand's mobile SNS on brand attachment and brand loyalty. The Research Journal of the Costume Culture, 22(5), 848-861. doi:10.7741/rjcc.2014.22.5.848
  21. Li, D., & Browne, G. J. (2006). The role of need for cognition and mood in online flow experience. Journal of Computer Information Systems, 46(3), 11-17.
  22. Lian, J.-W., & Yen, D. C. (2013). To buy or not to buy experience goods online: Perspective of innovation adoption barriers. Computers in Human Behavior, 29(3), 665-672. doi:10.1016/j.chb.2012.10.009
  23. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705-717. doi:10.1037/0003-066X.57.9.705
  24. Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320-341. doi:10.1207/s15328007sem1103_2
  25. Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks, CA: Sage Publications.
  26. Mitchell, V.-W., & Papavassiliou, V. (1999). Marketing causes and implications of consumer confusion. Journal of Product & Brand Management, 8(4), 319-342. doi:10.1108/10610429910284300
  27. Mitchell, V.-W., Walsh, G., & Yamin, M. (2005). Towards a conceptual model of consumer confusion. Advances in Consumer Research, 32, 143-150.
  28. Nah, F. F.-H., Eschenbrenner, B., & DeWester, D. (2011). Enhancing brand equity through flow and telepresence: A comparison of 2D and 3D virtual worlds. MIS Quarterly, 35(3), 731-748. https://doi.org/10.2307/23042806
  29. Nepomuceno, M. V., Laroche, M., & Richard, M.-O. (2014). How to reduce perceived risk when buying online: The interactions between intangibility, product knowledge, brand familiarity, privacy and security concerns. Journal of Retailing and Consumer Services, 21(4), 619-629. doi:10.1016/j.jretconser.2013.11.006
  30. Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments:A structural modeling approach. Marketing Science, 19(1), 22-42. doi:10.1287/mksc.19.1.22.15184
  31. Nunnally, J. C. (1978). Psychometric methods (2nd ed.). New York: McGraw-Hill.
  32. Pappas, N. (2015). Effect of marketing activities, benefits, risks, confusion due to over-choice, price, quality and consumer trust on online tourism purchasing. Journal of Marketing Communications, 1-24. doi:10.1080/13527266.2015.1061037
  33. Park, E. J., Kim, E. Y., Funches, V. M., & Foxx, W. (2012). Apparel product attributes, web browsing, and e-impulse buying on shopping websites. Journal of Business Research, 65(11), 1583-1589. doi:10.1016/j.jbusres.2011.02.043
  34. Roman, S., & Riquelme, I. P. (2014). Personal determinants of online shopping frustration and its influence on consumers' positive word of mouth. Journal of Electronic Commerce Research, 15(2), 87-103.
  35. Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in e-retailing: An empirical model of antecedents and outcomes. Journal of Retailing, 88(2), 308-322. doi:10.1016/j.jretai.2012.03.001
  36. Schweizer, M., Kotouc, A. J., & Wagner, T. (2006). Scale development for consumer confusion. Advances in Consumer Research, 33, 184-190.
  37. Shim, S. I. (2016). Themes of perceived challenge in online shopping and their difference between Korean and American consumers: A cross-cultural study. Korea Science & Art Forum, 23, 203-215. doi:10.17548/ksaf.2016.03.23.203
  38. Shim, S. I., Forsythe, S., & Kwon, W.-S. (2015). Impact of online flow on brand experience and loyalty. Journal of Electronic Commerce Research, 16(1), 56-71.
  39. Shim, S. I., & Kwon, W.-S. (2016). Online flow:Effects of perceived challenges measured before and after a shopping task. Fashion, Industry and Education, 14(1), 10-17. doi:10.7741/fie.2016.14.1.010
  40. Solomon, M. R. (2014). Consumer behavior: Buying, having, and being (11th ed.). Engelwood Cliffs, NJ:Prentice Hall.
  41. Suresh, K. P. (2011). An overview of randomization techniques: An unbiased assessment of outcome in clinical research. Journal of Human Reproductive Sciences, 4(1), 8-11. doi:10.4103/0974-1208.82352
  42. Varma Citrin, A., Sprott, D. E., Silverman, S. N., & Stem, D. E. (2000). Adoption of internet shopping:The role of consumer innovativeness. Industrial Management & Data Systems, 100(7), 294-300. doi:10.1108/02635570010304806
  43. Voelkl, J. E., & Ellis, G. D. (1998). Measuring flow experiences in daily life: An examination of the items used to measure challenge and skill. Journal of Leisure Research, 30(3), 380-389. https://doi.org/10.1080/00222216.1998.11949839
  44. Walsh, G., & Mitchell, V.-W. (2010). The effect of consumer confusion proneness on word of mouth, trust, and customer satisfaction. European Journal of Marketing, 44(6), 838-859. doi:10.1108/03090-561011032739
  45. Wang, C.-C., & Hsu, M.-C. (2013, June). Flow experience and challenge-skill balance in e-learning. Paper presented at the Pacific Asia Conference on Information Systems, Jeju Island, Korea.
  46. Wang, L. C., & Hsiao, D. F. (2012). Antecedents of flow in retail store shopping. Journal of Retailing and Consumer Services, 19(4), 381-389. doi:10.1016/j.jretconser.2012.03.002
  47. Webster, J., Trevino, L. K., & Ryan, L. (1993). The dimensionality and correlates of flow in humancomputer interactions. Computers in Human Behavior, 9(4), 411-426. doi:10.1016/0747-5632(93)90032-N
  48. Yu, U.-J., Lee, H.-H., & Damhorst, M. L. (2012). Exploring multidimensions of product performance risk in the online apparel shopping context: Visual, tactile, and trial risks. Clothing and Textiles Research Journal, 30(4), 251-266. doi:10.1177/088-7302x12462059