DOI QR코드

DOI QR Code

An Empirical Study on Influencing Factors of Switching Intention from Online Shopping to Webrooming

온라인 쇼핑에서 웹루밍으로의 쇼핑전환 의도에 영향을 미치는 요인에 대한 연구

  • 최현승 (아주대학교 경영대학 e-Business학과) ;
  • 양성병 (경희대학교 경영대학 경영학과)
  • Received : 2015.11.23
  • Accepted : 2016.02.23
  • Published : 2016.03.31

Abstract

Recently, the proliferation of mobile devices such as smartphones and tablet personal computers and the development of information communication technologies (ICT) have led to a big trend of a shift from single-channel shopping to multi-channel shopping. With the emergence of a "smart" group of consumers who want to shop in more reasonable and convenient ways, the boundaries apparently dividing online and offline shopping have collapsed and blurred more than ever before. Thus, there is now fierce competition between online and offline channels. Ever since the emergence of online shopping, a major type of multi-channel shopping has been "showrooming," where consumers visit offline stores to examine products before buying them online. However, because of the growing use of smart devices and the counterattack of offline retailers represented by omni-channel marketing strategies, one of the latest huge trends of shopping is "webrooming," where consumers visit online stores to examine products before buying them offline. This has become a threat to online retailers. In this situation, although it is very important to examine the influencing factors for switching from online shopping to webrooming, most prior studies have mainly focused on a single- or multi-channel shopping pattern. Therefore, this study thoroughly investigated the influencing factors on customers switching from online shopping to webrooming in terms of both the "search" and "purchase" processes through the application of a push-pull-mooring (PPM) framework. In order to test the research model, 280 individual samples were gathered from undergraduate and graduate students who had actual experience with webrooming. The results of the structural equation model (SEM) test revealed that the "pull" effect is strongest on the webrooming intention rather than the "push" or "mooring" effects. This proves a significant relationship between "attractiveness of webrooming" and "webrooming intention." In addition, the results showed that both the "perceived risk of online search" and "perceived risk of online purchase" significantly affect "distrust of online shopping." Similarly, both "perceived benefit of multi-channel search" and "perceived benefit of offline purchase" were found to have significant effects on "attractiveness of webrooming" were also found. Furthermore, the results indicated that "online purchase habit" is the only influencing factor that leads to "online shopping lock-in." The theoretical implications of the study are as follows. First, by examining the multi-channel shopping phenomenon from the perspective of "shopping switching" from online shopping to webrooming, this study complements the limits of the "channel switching" perspective, represented by multi-channel freeriding studies that merely focused on customers' channel switching behaviors from one to another. While extant studies with a channel switching perspective have focused on only one type of multi-channel shopping, where consumers just move from one particular channel to different channels, a study with a shopping switching perspective has the advantage of comprehensively investigating how consumers choose and navigate among diverse types of single- or multi-channel shopping alternatives. In this study, only limited shopping switching behavior from online shopping to webrooming was examined; however, the results should explain various phenomena in a more comprehensive manner from the perspective of shopping switching. Second, this study extends the scope of application of the push-pull-mooring framework, which is quite commonly used in marketing research to explain consumers' product switching behaviors. Through the application of this framework, it is hoped that more diverse shopping switching behaviors can be examined in future research. This study can serve a stepping stone for future studies. One of the most important practical implications of the study is that it may help single- and multi-channel retailers develop more specific customer strategies by revealing the influencing factors of webrooming intention from online shopping. For example, online single-channel retailers can ease the distrust of online shopping to prevent consumers from churning by reducing the perceived risk in terms of online search and purchase. On the other hand, offline retailers can develop specific strategies to increase the attractiveness of webrooming by letting customers perceive the benefits of multi-channel search or offline purchase. Although this study focused only on customers switching from online shopping to webrooming, the results can be expanded to various types of shopping switching behaviors embedded in single- and multi-channel shopping environments, such as showrooming and mobile shopping.

Keywords

Webrooming;Shopping Switching Intention;Multi-Channel Shopping;Online Shopping;Push-Pull-Mooring (PPM) Theory

References

  1. Bagozzi, R. P., Y. Yi, and L. W. Phillips, "Assessing Construct Validity in Organizational Research," Administrative Science Quarterly, Vol.36, No.3(1991), 421-458. https://doi.org/10.2307/2393203
  2. Bansal, H. S., S. F. Taylor, and Y. S. James, "Migrating" to New Service Providers: Toward a Unifying Framework of Consumers' Switching Behaviors," Journal of the Academy of Marketing Science, Vol.33, No.1(2005), 96-115. https://doi.org/10.1177/0092070304267928
  3. Burnham, T. A., J. K. Frels, and V. Mahajan, "Consumer Switching Costs: A Typology, Antecedents, and Consequences." Journal of the Academy of Marketing Science, Vol. 31, No.2(2003), 109-126. https://doi.org/10.1177/0092070302250897
  4. Chae, S. H., J. I. Lim, and J. Kang, "A Comparative Analysis of Social Commerce and Open Market Using User Reviews in Korean Mobile Commerce," Journal of Intelligence and Information Systems, Vol.21, No.4(2015), 53-77.
  5. Chatterjee, P., "Multiple-Channel and Cross-Channel Shopping Behavior: Role of Consumer Shopping Orientations," Marketing Intelligence and Planning, Vol.28, No.1(2010), 9-24. https://doi.org/10.1108/02634501011014589
  6. Cheung, C. and M. K. Lee, "Trust in Internet Shopping: A Proposed Model and Measurement Instrument," Proceedings of Americas Conference on Information Systems, (2000), 681-686.
  7. Chiu, H.-C., Y.-C. Hsieh, J. Roan, K.-J. Tseng, and J.-K. Hsieh, "The Challenge for Multichannel Services: Cross-Channel Free-Riding Behavior," Electronic Commerce Research and Applications, Vol.10, No.2(2011), 268-277. https://doi.org/10.1016/j.elerap.2010.07.002
  8. Choi, J. Y., "Consumer Multichannel Choice Behavior in the Information Search and Purchasing Stages," Journal of Consumer Studies, Vol.15, No.4(2004), 103-120.
  9. Forsythe, S, M. and B. Shi, "Consumer Patronage and Risk Perceptions in Internet Shopping," Journal of Business Research, Vol.56, No.11(2003), 867-875. https://doi.org/10.1016/S0148-2963(01)00273-9
  10. Forsythe, S. M., C. Liu, and D. Shannon, "Development of a Scale to Measure the Perceived Benefits and Risks of Online Shopping," Journal of Interactive Marketing, Vol.20, No.2(2006), 55-75. https://doi.org/10.1002/dir.20061
  11. Garplid, D., Top 5 E-commerce Trends 2014 - 5th Place Goes to "ulti-Channel" 2014. Available at https://www.linkedin.com/pulse/20140424090903-26243586-top-5-e-commerce-trends-2014-5th-place-goes-to-multi-channel (Accessed 10 February, 2016).
  12. Heitz-Spahn, S., "Cross-Channel Free-Riding Consumer Behavior in a Multichannel Environment: An Investigation of Shopping Motives, Sociodemographics and Product Categories," Journal of Retailing and Consumer Services, Vol.20, No.6(2013), 570-578. https://doi.org/10.1016/j.jretconser.2013.07.006
  13. Hsieh, J.-K., Y.-C. Hsieh, H.-C. Chiu, and Y.-C. Feng, "Post-Adoption Switching Behavior for Online Service Substitutes: A perspective of the Push-Pull-Mooring Framework," Computers in Human Behavior, Vol.28, No.5(2012), 1912-1920. https://doi.org/10.1016/j.chb.2012.05.010
  14. Jones, M. A., D. L. Mothersbaugh, and S. E. Beatty, "Switching Barriers and Repurchase Intentions in Services," Journal of Retailing, Vol.76, No.2(2000), 259-274. https://doi.org/10.1016/S0022-4359(00)00024-5
  15. Khalifa, M. and V. Liu, "Online Consumer Retention Contingent Effects of Online Shopping Habit and Online Shopping Experience," European Journal of Information Systems, Vol.16, No.6(2007), 780-792. https://doi.org/10.1057/palgrave.ejis.3000711
  16. Kim, D. J., D. Ferrin, and H. R. Rao, "A Trust-Based Consumer Decision-Making Model in Electronic Commerce: The Role of Trust, Perceived Risk, and their Antecedents," Decision Support Systems, Vol.44, No.2(2008), 544-564. https://doi.org/10.1016/j.dss.2007.07.001
  17. Kim, E. and M. C. Park, "Antecedents of Cross-Channel Free-Riding Intention: The Moderating Effect of Product Categories Using Push-Pull-Mooring Framework," Proceedings of the Korea Society of Management Information Systems 2015, (2015).
  18. Kim, H. H. and J. Kim, "The Effect of Offline Brand Trust and Perceived Internet Confidence on Online Shopping Intention in the Integrated Multi-Channel Context," International Journal of Retail and Distribution Management, Vol.37, No.2(2009), 126-141. https://doi.org/10.1108/09590550910934272
  19. Kim, K. M., Unites States, 4 Keywords of Purchase Trend Change, KOTRA, 2009. Available at http://www.globalwindow.org/w/overmarket/GWOMAL020M.html?BBS_ID=10&MENU_CD=M10103&UPPER_MENU_CD=M10102&MENU_STEP=3&ARTICLE_ID=2112711 (Accessed 10 February, 2016).
  20. Kim, M. H., "Trend of Showrooming and Reverse-howrooming: Distribution Channels Need to Make a Synergy of Online and Offline," LG Business Insight, Vol.1328, No.3(2014), 16-24.
  21. Kim, S. H., K. Y. Park, and H. J. Park, "Factors Influencing Buyers' Choice of Online vs. Offline Channel at Information Search and Purchase Stages," Journal of Distribution Research, Vol.12, No.3(2007), 69-90.
  22. Lee, D., S. H. Park, and S. Moon, "Measuring the Economic Impact of Item Descriptions on Sales Performance," Journal of Intelligence and Information Systems, Vol.18, No.4(2012), 1-17.
  23. Lee, M. K. O. and E. Turban, "A Trust Model for Consumer Internet Shopping," International Journal of Electronic Commerce, Vol.6, No.1(2001), 75-91. https://doi.org/10.1080/10864415.2001.11044227
  24. Lu, Y., Y. Cao, B. Wang, and S. Yang, "A Study on Factors that Affect Users' Behavioral Intention to Transfer Usage from the Offline to the Online Channel," Computers in Human Behavior, Vol.27, No.1(2011), 355-364. https://doi.org/10.1016/j.chb.2010.08.013
  25. Nunnally, J. C. and I.H. Bernstein, Psychometric Theory, McGraw-Hill, New York, 1978.
  26. Park, H. K., "Showrooming vs Reverse-Showrooming," Excellence Marketing for Customer, Vol.48, No.10(2014), 46-52.
  27. Schoenbachler, D. D. and G. L. Gordon, "Multi-Channel Shopping: Understanding What Drives Channel Choice," Journal of Consumer Marketing, Vol.19, No.1(2002), 42-53. https://doi.org/10.1108/07363760210414943
  28. Sevitt, D. and A. Samuel, "How Pinterest Puts People in Stores," Harvard Business Review, Vol.91, No.7/8(2013), 26-27.
  29. Verhoef, P. C., S. A. Neslin, and B. Vroomen, "Multichannel Customer Management: Understanding the Research-Shopper Phenomenon," International Journal of Research in Marketing, Vol.24, No.2(2007), 129-148. https://doi.org/10.1016/j.ijresmar.2006.11.002
  30. Yang, C. G., E. B. Lee, and Y. Huang, "The Effect of the Context Awareness Value on the Smartphone Adopter's Advertising Attitude," Journal of Intelligence and Information Systems, Vol.19, No.3(2013), 73-91.

Cited by

  1. Mobile app Loyalty of Cross-over Shoppers: A Comparison of Korean and Chinese vol.20, pp.3, 2018, https://doi.org/10.5805/SFTI.2018.20.3.293