Journal of the Korean Society of Clothing and Textiles (한국의류학회지)

The Korean Society of Clothing and Textiles (한국의류학회)
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The Effect of Recommended Product Presentation on Consumers' Usage Intentions of a Website -Focusing on the Mediating Roles of Mental Simulation-

온라인 추천 상품의 제시방법이 웹사이트 이용의도에 미치는 영향 -심적 시뮬레이션의 매개효과를 중심으로-

  • Lee, Ha Kyung (Dept. of Business Administration, Seoul National University of Science and Technology) ;
  • Ahn, Sowon (Dept. of Business Administration, Seoul National University of Science and Technology)
  • 이하경 (서울과학기술대학교 경영학과) ;
  • 안서원 (서울과학기술대학교 경영학과)
  • Received : 2018.08.23
  • Accepted : 2018.10.27
  • Published : 2018.12.31
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Abstract

This study tests the effect of recommended product presentation on consumers' usage intentions of a website, mediated by mental simulation. Mental simulation refers to perceptual experience, a more automatic form of mental imagery, initiated by exposure to the representations of objects. This study expects that when compliments of clothes (coordination items) are vertically presented online, consumers are likely to feel as if they wear the outfits due to the activation of mental simulation. The survey was conducted on 147 women in an age group between 20 and 40 years in a panel of an online survey firm. Data are analyzed using exploratory factor analysis and bootstrapping analysis by SPSS 20.0. The results show that when compliments (vs. substitutes) of clothes are presented, participants perceive a greater mental simulation. When compliments of clothes are vertically presented (vs. horizontally presented), mental simulation is also highly driven. In addition, mental simulation mediates the effects of online product presentation on consumers' usage intentions of a website. The findings of this study contribute to marketing strategies of online retailers in terms of how product recommendation can be offered to consumers with more psychological benefits.

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References

  1. Areni, C. S., Duhan, D. F., & Kiecker, P. (1999). Point-of-purchase displays, product organization, and brand purchase likelihoods. Journal of the Academy of Marketing Science, 27(4), 428-441. doi:10.1177/0092070399274003
  2. Baier, D., & Stuber, E. (2010). Acceptance of recommendations to buy in online retailing. Journal of Retailing and Consumer Services, 17(3), 173-180. doi:10.1016/j.jretconser.2010.03.005
  3. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370. doi:10.1023/A:1021240730564
  4. Choi, J., & Lee, H. J. (2012). An integrated perspective of user evaluating personalized recommender systems: Performance- driven or user-centric. The Journal of Society for e- Business Studies, 17(3), 85-103. doi:10.7838/jsebs.2012.17.3.085
  5. Deng, X., Kahn, B. E., Unnava, H. R., & Lee, H. (2016). A 'Wide' variety: Effects of horizontal versus vertical display on assortment processing, perceived variety, and choice. Journal of Marketing Research, 53(5), 682-698. doi:10.1509/jmr.13.0151
  6. Diehl, K., Kornish, L. J., & Lynch Jr, J. G. (2003). Smart agents: When lower search costs for quality information increase price sensitivity. Journal of Consumer Research, 30(1), 56-71. doi:10.1086/374698
  7. Diehl, K., van Herpen, E., & Lamberton, C. (2015). Organizing products with complements versus substitutes: Effects on store preferences as a function of effort and assortment perceptions. Journal of Retailing, 91(1), 1-18. doi:10.1016/j.jretai.2014.10.003
  8. Elder, R. S., & Krishna, A. (2012). The 'Visual Depiction Effect' in advertising: Facilitating embodied mental simulation through product orientation. Journal of Consumer Research, 38(6), 988-1003. doi:10.1086/661531
  9. Hoch, S. J., Bradlow, E. T., & Wansink, B. (1999). The variety of an assortment. Marketing Science, 18(4), 527-546. doi:10.1287/mksc.18.4.527
  10. Huffman, C., & Kahn, B. E. (1998). Variety for sale: Mass customization or mass confusion? Journal of Retailing, 74(4), 491-513. doi:10.1016/S0022-4359(99)80105-5
  11. Jeannerod, M. (2001). Neural simulation of action: A unifying mechanism for motor cognition. Neuroimage, 14(1), S103-S109. doi:10.1006/nimg.2001.0832
  12. Jeong, S. Y., & Kim, H. J. (2017). A recommender system using factorization machine. Journal of Digital Contents Society, 18(4), 707-712. doi:10.9728/dcs.2017.18.4.707
  13. Keller, P. A., & McGill, A. L. (1994). Differences in the relative influence of product attributes under alternative processing conditions: Attribute importance versus attribute ease of imagability. Journal of Consumer Psychology, 3(1), 29-49. doi:10.1016/S1057-7408(08)80027-7
  14. Kim, J. I. (2009). A recommendation method of similar clothes on intelligent fashion coordination system. Journal of Korea Multimedia Society, 12(5), 688-698.
  15. Kim, S. O., Lee, S. Y., Lee, S. J., Lee, H. C., & Ji, S. S. (2013). A study of development for movie recommendation system algorithm using filtering. Journal of the Korean Data & Information Science Society, 24(4), 803-813. doi:10.7465/jkdi.2013.24.4.803
  16. Lamberton, C. P., & Diehl, K. (2013). Retail choice architecture: The effects of benefit-and attribute-based assortment organization on consumer perceptions and choice. Journal of Consumer Research, 40(3), 393-411. doi:10.1086/671103
  17. Lattin, J. M., & McAlister, L. (1985). Using a variety-seeking model to identify substitute and complementary relationships among competing products. Journal of Marketing Research, 22(3), 330-339. doi:10.2307/3151429
  18. Lee, D., Kim, U., & Yeom, K. (2017). Content recommendation system using user context-aware based knowledge filtering in smart environments. The Journal of Korean Institute of Next Generation Computing, 13(2), 35-48.
  19. Moon, H., & Lee, H. H. (2012). Product recommendation service in online mass customization: Consumers' cognitive and affective responses. Journal of the Korean Society of Clothing and Textiles, 36(11), 1222-1236. doi:10.5850/JKSCT.2012.36.11.1222
  20. Mukherjee, A., & Nath, P. (2007). Role of electronic trust in online retailing: A re-examination of the commitment-trust theory. European Journal of Marketing, 41(9/10), 1173-1202. doi:10.1108/03090560710773390
  21. Nah, F. F. H., Hong, W., Chen, L., & Lee, H. H. (2010). Information search patterns in e-commerce product comparison services. Journal of Database Management, 21(2), 26-40. doi:10.4018/jdm.2010040102
  22. O'Cass, A. (2000). Fashion clothing consumption: antecedents and consequences of fashion clothing involvement. European Journal of Marketing, 38(7), 869-882. doi:10.1108/03090560410539294
  23. Oh, D. H. (2018, February 9). NAVER, Advancement of personalization product recommendation system based on AI 'AiTEMS'. NEWSIS. Retrieved July 21, 2018, from http://www.newsis.com/view/?id=NISX20180209_0000226324
  24. Park, J. (2016). The effects of product recommendations on choice difficulty in online fashion shopping mall. Unpublished doctoral dissertation, Seoul National University, Seoul.
  25. Park, Y. J. (2016). An analysis of customer preferences of recommendation techniques and influencing factors: A comparative study of electronic goods and apparel products. Information Systems Review, 18(2), 59-77. doi:10.14329/isr.2016.18.2.059
  26. Park, J., & Nam, K. (2017). A user based collaborative filtering recommender system with recommendation quantity and repetitive recommendation considerations. Information Systems Review, 19(2), 71-94. doi:10.14329/isr.2017.19.2.071
  27. Phillips, D. M. (1996). Anticipating the future: The role of consumption visions in consumer behavior. Advances in Consumer Research, 23, 70-75.
  28. Shen, H., Zhang, M., & Krishna, A. (2016). Computer interfaces and the "Direct-Touch" effect: Can iPads increase the choice of hedonic food? Journal of Marketing Research, 53(5), 745-758. doi:10.1509/jmr.14.0563
  29. Shin, C. H., Lee, J. W., Yang, H. N., & Choi, I. Y. (2012). The research on recommender for new customers using collaborative filtering and social network analysis. Journal of Intelligence and Information Systems, 18(4), 19-42. doi:10.13088/jiis.2012.18.4.019
  30. Simonson, I., Nowlis, S., & Lemon, K. (1993). The effect of local consideration sets on global choice between lower price and higher quality. Marketing Science, 12(4), 357-377. doi:10.1287/mksc.12.4.357
  31. Tucker, M., & Ellis, R. (1998). On the relations between seen objects and components of potential actions. Journal of Experimental Psychology: Human Perception and Performance, 24(3), 830-846. doi:10.1037/0096-1523.24.3.830
  32. Walters, R. G. (1991). Assessing the impact of retail price promotions on product substitution, complementary purchase, and interstore sales displacement. Journal of Marketing, 55(2), 17-28. doi:10.2307/1252234
  33. Zhao, M., Hoeffler, S., & Zauberman, G. (2007). Mental simulation and preference consistency over time: The role of process-versus outcome-focused thoughts. Journal of Marketing Research, 44(3), 379-388. doi:10.1509/jmkr.44.3.379