바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Inconsistency between Information Search and Purchase Channels: Focusing on the "Showrooming Phenomenon"

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2015, v.13 no.9, pp.81-93
https://doi.org/https://doi.org/10.15722/jds.13.9.201509.81
Yeom, Min-Sun
  • Downloaded
  • Viewed

Abstract

Purpose - "Showrooming" refers to the phenomenon where a shopper visits a store to see and compare products but makes the purchase online at a lower price. Surveys on showrooming activities at home and abroad indicate that a significant number of consumers pursue showrooming activities. The advent of "showroomers," who engage in buying activities, hovering both on and offline, while selectively choosing sales channels to suit their needs, is powerful enough to erode the borders between channels and bring about seismic changes in the distribution industry. However, surprisingly, there has been no in-depth discussion on showrooming. This study seeks to theoretically investigate what impact personal characteristics have on showrooming preferences and attitudes in a multi-channel environment. Specifically, assumptions have been made that price perception, perceived performance risk, and trust in online shopping not only have a direct impact on showrooming attitudes but also indirectly affect it through the means of contact motivation. Research design, data, and methodology - To test the hypotheses, this study conducted a survey of male and female shoppers, ages 20 through 40s, who live in metropolitan areas, and have actively showroomed fashion items in the last six months. A clothing item usually purchased after a careful decision-making process was chosen as the target product of the study. The survey was conducted between October and November 2014, using a professional survey service provider. A total of 200 surveys were collected, of which 198 were used for analysis. Conceptual model Structural Equation Modeling (SEM) and Amos 18.0 were employed for data analysis and model verification. In addition, following the confirmatory factor analysis and measurement model analysis, the theoretical model that corresponds to the research model was analyzed. Results - Analysis results show that price perception, perceived performance risk, and trust in online shopping have a statistically significant and positive (+) impact on showrooming attitudes. In addition, in terms of the indirect influence of price perception and perceived performance risk on showrooming attitudes through means of contact motivation, price perception had a statistically significant and positive impact on means of contact motivation, whereas perceived performance risk did not have a statistically significant impact on it, with the relevant hypothesis rejected. Conclusions - These analysis results imply that the ultimate goal of consumers is to maximize their shopping benefits by selectively and strategically taking advantage of different channels in a complementary manner. This study presents many implications for distributors to encourage a deep understanding of showrooming consumers who have complicated consumption behaviors and to build channel integration strategies. This study has limitations in theoretical and practical implications. Therefore, subsequent studies need to focus on verifying that showrooming activities are based on reasonable and planned decisions by applying the theory of reasoned or planned behavior. In addition, the scope of the study should expand to include web showrooming, where consumers conduct product research online and purchase offline.

keywords
Multi-channel, Showrooming, Performance Risk, Price Consciousness, Need for Touch

Reference

1.

Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value, Journal of Consumer Research, 20(4), 644-656.

2.

Badrinarayanan, V., Becerra, E. P., Kim, C. H., & Madhavaram, S. (2012). Transference and Congruence Effects on Purchase Intentions in Online Stores of Multi-Channel Retailers: Initial Evidence from the US and South Korea, Journal of the Academy of Marketing Science, 40(4), 539-557.

3.

Bai, B., Law, R., & Wen, I. (2008). The Impact of Website Quality on Customer Satisfaction and Purchase Intentions: Evidence from Chinese Online Visitors, International Journal of Hospitality Management, 27(3), 391-402.

4.

Bauer, R. A. (1960). Consumer Behavior as Risk Taking, In R. S. Hancock (Ed.), Dynamic Marketing for a Changing World. Proceedings of the 43rd National Conference of the American Marketing Association (pp.389–398). Chicago, America: AMA.

5.

Bloch, P. H., & Richins, M. L. (1983). Shopping without Purchase: An Investigation of Consumer Browsing Behavior, Advances in Consumer Research, 10(1), 389-393.

6.

Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating Attitudinal Theories to Understand and Predict Use of Technology-based Self-service: The Internet as an Illustration, International Journal of Service Industry.

7.

Büttner, O. B., & Göritz, A. S. (2008). Perceived of Online Shops. Journal of Consumer Behaviour, 7(1), 35-50.

8.

Chatterjee, P. (2010). Multiple-Channel and Cross-Channel Shopping Behavior: Role of Consumer Shopping Orientations, Marketing Intelligence & Planning, 28(1), 9-24.

9.

Citrin, A. V., Stem, D. E., Spangenberg, E. R., & Clark, M. J. (2003). Consumer Need for Tactile Input: An Internet Retailing Challenge, Journal of Business research, 56(11), 915-922.

10.

Donthu, N., & Garcia, A. (1999). The Internet Shopper, Journal of Advertising Research, 39, 52-58.

11.

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

12.

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research, 18(1), 39-50.

13.

Forsythe, S. M., & Shi, B. (2003). Consumer Patronage and Risk Perceptions in Internet Shopping, Journal of Business Research, 56(11), 867-875.

14.

Gefen, D. (2000). E-commerce: The Role of Familiarity and Trust, Omega, 28(6), 725-737.

15.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust TAM in Online Shopping: an Integrated Mode1, MIS Quarterly, 27(1), 51-90.

16.

Greenleaf, E. A., & Lehmann, D. R. (1995). Reasons for Substantial Delay in Consumer Decision Making, Journal of Consumer Research, 22(2), 186-199.

17.

Grohmann, B., Spangenberg, E. R., & Sprott, D. E. (2007). The Influence of Tactile Input on the Evaluation of Retail Product Offerings, Journal of Retailing, 83(2), 237-245.

18.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

19.

Hassan, A. M., Kunz, M. B., Pearson, A. W., & Mohamed, F. A. (2006). Conceptualization and Measurement of Perceived Risk in Online Shopping, Marketing Management Journal, 16(1), 138-147.

20.

Holbrook, M. B., & Hirschman, E. C. (1982). The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun, Journal of Consumer Research, 9(2), 132-140.

21.

Horton, R. L. (1976). The Structure of Perceived Risk: Some Further Progress, Journal of the Academy of Marketing Science, 4(4), 694-706.

22.

Huang, Y., & Oppewal, H. (2006). Why Consumers Hesitate to Shop Online: An Experimental Choice Analysis of Grocery Shopping and the Role of Delivery Fees. International Journal of Retail & Distribution Management, 34(4), 334-353.

23.

IBM (2013). Analysis of Consumer Shopping Trends in 2013. Press Release, Seoul, Korea. Retrieved August 27, 2014, from http://m.ibm.com/http/www-03.ibm.com/press/kr/ko/pressrelease/40438.wss

24.

Jacoby, J., & Kaplan, L. B. (1972). The Components of Perceived Risk, Advances in consumer research, 3(3), 382-383.

25.

Jarvenpaa, S. L., & Todd, P. A. (1997). Is There a Future for Retailing On the Internet, In Electronic Marketing and The Consumer (Ed. Peterson R. A.). Thousand Oaks, CA: Sage.

26.

Keeney, R. L. (1999). The Value of Internet Commerce to the Customer, Management science, 45(4), 533-542.

27.

Kim, Sang-Hoon, Park, Gye-Young, & Park, Hyun-Jung (2007). Factors Influencing Buyers' Choice of Online vs. Offline Channel at Information Search and Purchase Stages, Journal of Channel and Retailing, 12(3), 69-90.

28.

Korea Chamber of Commerce & Industry (2012a). Investigation of Consumer Behaviors Affected by a Rise in Prices. Press Release, Seoul, Korea. Retrieved August 27, 2014, from http://www.korcham.net/nCham/Service/Economy/appl/KcciReportDetail.asp?DATA_ID=20120327001&CHAM_CD=A001

29.

Korea Chamber of Commerce & Industry (2012b). Investigation of Consumption Patterns Following Integration of Online and Offline Shopping Channels. Press Release, Seoul, Korea. Retrieved August 27, 2014, from http://www. korcham.net/nCham/Service/Economy/appl/KcciReportDetail.asp?DATA_ID=20120803001&CHAM_CD=A001

30.

Kumar, V., & Venkatesan, R. (2005). Who are the Multichannel Shoppers and How Do They Perform?: Correlates of Multichannel Shopping Behavior, Journal of Interactive Marketing, 19(2), 44-62.

31.

Kushwaha, T., & Shankar, V. (2013). Are Multichannel Customers Really More Valuable? The Moderating Role of Product Category Characteristics, Journal of Marketing, 77(4), 67-85.

32.

Lee, Kwang-Keun, Ahn, Seong-Ho, Kim, Hyung-Deok, & Youn, Myoung-Kil (2014). Effects of the Flow of an Internet Shopping Mall upon Revisit Intention and Purchase Intention. The East Asian Journal of Business Management, 4(4), 27-38.

33.

Liang, T. P., & Huang, J. S. (1998). An Empirical Study on Consumer Acceptance of Products in Electronic Markets:A Transaction Cost Model, Decision Support Systems, 24(1), 29-43.

34.

Lichtenstein, D. R., Bloch, P. H., & Black, W. C. (1988). Correlates of Price Acceptability, Journal of Consumer Research, 15(2), 243-252.

35.

Lichtenstein, D. R., Ridgway, N. M., & Netemeyer, R. G. (1993). Price Perceptions and Consumer Shopping Behavior: A Field Study, Journal of Marketing Research, 30(2), 234-245.

36.

Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A Comparison of the Theory of Planned Behavior and the Theory of Reasoned Action, Personality and Social Psychology Bulletin, 18(1), 3-9.

37.

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative Model of Organizational Trust, Academy of Management Review, 20(3), 709-734.

38.

McCabe, D. B., & Nowlis, S. M. (2003). The Effect of Examining Actual Products or Product Descriptions on Consumer Preference, Journal of Consumer Psychology, 13 (4), 431–39.

39.

McGoldrick, P. J., & Collins, N. (2007). Multichannel Retailing:Profiling the Multichannel Shopper, International Review of Retail, Distribution and Consumer Research, 17(2), 139-158.

40.

McKnight, D. H., Choudhury, V., & Kacmar, C. (2000). Trust in E-Commerce Vendors: A Two-Stage Model. Proceedings of the Twenty First International Conference of ICIS (pp. 532-536). Brisbane, Australia: ICIS.

41.

Mitchell, V. W., Davies, F., Moutinho, L., & Vassos, V. (1999). Using Neural Networks to Understand Service Risk in the Holiday Product, Journal of Business Research, 46(2), 167–180.

42.

Morgan, R. M., & Hunt, S. D. (1994). The Commitment-Trust Theory of Relationship Marketing, Journal of Marketing, 58(3), 20-38.

43.

Murray, K. B. (1991). A Test of Services Marketing Theory:Consumer Information Acquisition Activities, Journal of Marketing, 55(1), 10-25.

44.

Neslin, S. A., & Shankar, V. (2009). Key Issues in Multichannel Customer Management: Current Knowledge and Future Directions. Journal of Interactive Marketing, 23(1), 70-81.

45.

Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., & Verhoef, P. C. (2006). Challenges and Opportunities in Multichannel Customer Management, Journal of Service Research, 9(2), 95-112.

46.

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.

47.

Peck, J. (2010). Does touch matter? Insights from haptic research in marketing. Sensory Marketing: A Confluence of Psychology, Neuroscience and Consumer Behavior Research. (Ed. Krishna A.). Psychology Press, Routledge, New York.

48.

Peck, J., & Childers, T. L. (2003a). To Have and to Hold: The Influence of Haptic Information on Product Judgments, Journal of Marketing, 67(2), 35-48

49.

Peck, J., & Childers, T. L. (2003b). Individual Differences in Haptic Information Processing: The “Need For Touch”Scale, Journal of Consumer Research, 30(3), 430-442.

50.

Peck, J., & Johnson, J. W. (2011). Autotelic Need for Touch, Haptics, and Persuasion: The Role of Involvement, Psychology & Marketing, 28(3), 222-239.

51.

Reichheld, F. F., & Schefter, P. (2000). E-loyalty, Harvard Business Review, 78(4), 105-113.

52.

Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not So Different After All: A Cross-Discipline View of Trust, Academy of Management Review, 23 (3), 393–404.

53.

Schlosser, A. E., White, T. B., & Lloyd, S. M. (2006). Converting Web Site Visitors into Buyers: How Web Site Investment Increases Consumer Trusting Beliefs and Online Purchase Intentions, Journal of Marketing, 70(2), 133-148.

54.

Sevitt, D., & Samuel, A. (2013). How Pinterest Puts People in Stores, Harvard Business Review, 91(7), 26-27.

55.

Sheth, J. N., & Venkatesan, M. (1968). Risk-Reduction Processes in Repetitive Consumer Behavior, Journal of Marketing Research, 5(3), 307-310.

56.

Shimp, T. A., & Kavas, A. (1984). The Theory of Reasoned Action Applied to Coupon Usage. Journal of Consumer Research, 11(3), 795-809.

57.

Simpson, L., & Lakner, H. B. (1993). Perceived Risk and Mail Order Shopping for Apparel, Journal of Consumer Studies & Home Economics 17(4), 377-389.

58.

Singh, D. P. (2014). Online Shopping Motivations, Information Search, and Shopping Intentions in an Emerging Economy, The East Asian Journal of Business Management, 4(3), 5-12.

59.

Spence, H. E., Engel, J. F., & Blackwell, R. D. (1970). Perceived Risk in Mail-Order and Retail Store Buying, Journal of Marketing Research, 7(3), 364-369.

60.

Stone, R. N., & Grønhaug, K. (1993). Perceived Risk: Further Considerations for the Marketing Discipline, European Journal of Marketing, 27(3), 39–50.

61.

Sweeney, J. C., & Soutar, G. N. (2001). Consumer Perceived Value: The Development of a Multiple Item Scale, Journal of Retailing, 77(2), 203-220.

62.

Tan, S. J. (1999). Strategies for Reducing Consumers' Risk Aversion in Internet Shopping, Journal of Consumer Marketing, 16(2), 163-180.

63.

Tsiros, M., & Heilman, C. M. (2005). The Effect of Expiration Dates and Perceived Risk on Purchasing Behavior in Grocery Store Perishable Categories, Journal of Marketing, 69(2), 114-129.

64.

Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-Channel Retailing to Omni-Channel Retailing:Introduction to the Special Issue on Multi-Channel Retailing, Journal of Retailing, 91(2), 174-181.

65.

Verhoef, P. C., Neslin, S. A., & Vroomen, B. (2007). Multichannel Customer Management: Understanding the Research-shopper Phenomenon, International Journal of Research in Marketing, 24(2).

66.

Wallace, D. W., Giese, J. L., & Johnson, J. L. (2004). Customer Retailer Loyalty in the Context of Multiple Channel Strategies, Journal of Retailing, 80(4), 249-263.

67.

Wind, Y., & Mahajan, V. (2002). Convergence Marketing, Journal of Interactive Marketing, 16(2), 64-79.

68.

Zhang, J., Farris, P. W., Irvin, J. W., Kushwaha, T., Steenburgh, T. J., & Weitz, B. A. (2010). Crafting Integrated Multichannel Retailing Strategies, Journal of Interactive Marketing, 24(2), 168-180.

The Journal of Distribution Science