Skip to main content
SpringerLink
Log in

Influence of individual characteristics on whether and how much consumers engage in showrooming behavior

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

This article investigates a set of individual characteristics that can explain whether and how much a consumer engages in showrooming behavior. The authors conceptualized and empirically examined certain variables’ impact on both showrooming probability as well as the extent of behavior. The variables under consideration include consumers’ involvement, prior knowledge, perceived risk, price consciousness, Internet usage, access device usage, and certain demographic variables. The results reveal that involvement and price consciousness significantly explain whether a consumer is a potential showroomer. Further, showrooming frequency is found to be affected by prior knowledge, perceived risk, price consciousness, Internet usage, access devise usage, and age. Some implications are discussed regarding how retailers can handle showrooming.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Note that our hypotheses imply that each \(\beta_{j}\) has the opposite sign with the respective \(\gamma_{j}\) for \(j \ge 1\).

  2. We find that the result is considerably sensitive to the exclusion of the outliers. The results of a likelihood ratio test revealed that the model’s accuracy significantly increased after we omitted the outliers from the analysis.

  3. This can be calculated using Eq. (5).

  4. We thank an anonymous reviewer for pointing out this policy.

  5. This can be accomplished by using the expected showrooming frequency conditional on customer characteristics, or \(E(y_{i} ) = (1 - p_{i} )\lambda_{i}\). The small and large values of \(E(y_{i} )\) correspond to occasional and frequent showroomers, respectively.

References

  1. Blattberg, R. C., Kim, B., & Neslin, S. A. (2008). Database marketing: analyzing and managing customers, international series in quantitative marketing. New York, NY: Springer.

    Book  Google Scholar 

  2. 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), 129–148.

    Article  Google Scholar 

  3. Quint, M., Rogers, D., & Ferguson, R. (2013). Showrooming and the rise of the mobile-assisted shopper. New York, NY: Columbia Business School Center on Global Brand Leadership.

    Google Scholar 

  4. Business Insider. (2013). BII Report: The mobile showrooming threat and its effects on retail industry, March 8. http://www.businessinsider.com/bii-report-mobile-showrooming-threat-2013-3.

  5. Rapp, A., Baker, T. L., Bachrach, D. G., Ogilvie, J., & Beitelspacher, L. S. (2015). Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance. Journal of Retailing, 91(2), 358–369.

    Article  Google Scholar 

  6. Brown, T. A., Friedman, D. C., & Taran, Z. (2013). Showrooming and the small retailer. Proceedings of marketing and management association, pp. 46–48.

  7. Mehra, A., Kumar, S., & Raju, J. S. (2013). Showrooming and the competition between store and online retailers. Available at: http://www.haas.berkeley.edu/groups/marketing/sics/pdf_2013/mkr.pdf.

  8. Zimmerman, A., & Mattioli, D. (2012). Retailers’ new odd couple. Wall Street Journal, July 11, B1.

  9. Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248–287.

    Article  Google Scholar 

  10. Herhausen, D., Binder, J., Schoegel, M., & Herrmann, A. (2015). Integrating bricks with clicks: Retailer-level and channel-level outcomes of online-offline channel integration. Journal of Retailing, 91(2), 309–325.

    Article  Google Scholar 

  11. Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., et al. (2006). Challenges and opportunities in multichannel customer management. Journal of Service Research, 9(2), 95–112.

    Article  Google Scholar 

  12. Ansari, A., Mela, C. F., & Neslin, S. A. (2008). Customer channel migration. Journal of Marketing Research, 45(1), 60–76.

    Article  Google Scholar 

  13. Thomas, J. S., & Sullivan, U. Y. (2005). Managing marketing communications with multichannel customers. Journal of Marketing, 69(4), 239–251.

    Article  Google Scholar 

  14. Montoya-Weiss, M. M., Voss, G. B., & Grewal, D. (2003). Determinants of online channel use and overall satisfaction with a relational, multichannel service provider. Journal of the Academy of Marketing Science, 31(4), 448–458.

    Article  Google Scholar 

  15. Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model—A critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8(1), 41–62.

    Google Scholar 

  16. Higgins, E. T. (2000). Making a good decision: Value from fit. American Psychologist, 55(11), 12–17.

    Article  Google Scholar 

  17. Higgins, E. T. (2005). Value from regulatory fit. Current Directions in Psychological Science, 14(4), 209–213.

    Article  Google Scholar 

  18. Clarke, K., & Belk, R. W. (1978). Product involvement and task definition on anticipated consumer effort. Advances of Consumer Research, 5, 313–318.

    Google Scholar 

  19. Bloch, P. H., & Richins, M. L. (1983). A theoretical model for the study of product importance perceptions. Journal of Marketing, 47, 69–81.

    Article  Google Scholar 

  20. Bloch, P. H., Sherrell, D. L., & Ridgway, N. M. (1986). Consumer search: An extended framework. Journal of Consumer Research, 13(1), 119–126.

    Article  Google Scholar 

  21. Moore, W. L., & Lehmann, D. R. (1980). Individual differences in search behavior for a nondurable. Journal of Consumer Research, 7(3), 296–307.

    Article  Google Scholar 

  22. Srinivasan, N., & Ratchford, B. T. (1991). An empirical test of a model of external search for automobiles. Journal of Consumer Research, 18(2), 233–242.

    Article  Google Scholar 

  23. Beatty, S. E., & Smith, S. M. (1987). External search effort: An investigation across several product categories. Journal of Consumer Research, 14(1), 83–95.

    Article  Google Scholar 

  24. Moorthy, S., Ratchford, B. T., & Talukdar, D. (1997). Consumer information search revisited: Theory and empirical analysis. Journal of Consumer Research, 23(4), 263–277.

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Rao, A. R., & Monroe, K. B. (1988). The moderating effect of prior knowledge on cue utilization in product evaluations. Journal of Consumer Research, 15(2), 253–264.

    Article  Google Scholar 

  27. Brucks, M. (1985). The effects of product class knowledge on information search behavior. Journal of Consumer Research, 12(1), 1–16.

    Article  Google Scholar 

  28. Johnson, E. J., & Russo, J. E. (1984). Product familiarity and learning new information. Journal of Consumer Research, 11(1), 542–550.

    Article  Google Scholar 

  29. Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(3), 350–362.

    Article  Google Scholar 

  30. Punj, G., & Staelin, R. (1983). A model of consumer information search behavior for new automobiles. Journal of Consumer Research, 9(4), 366–380.

    Article  Google Scholar 

  31. Urbany, J., Dickson, P., & Wilkie, W. (1989). Buyer uncertainty and information search. Journal of Consumer Research, 16(2), 208–215.

    Article  Google Scholar 

  32. Mitchell, A. A., & Dacin, P. A. (1996). The assessment of alternative measures of consumer expertise. Journal of Consumer Research, 23(3), 219–239.

    Article  Google Scholar 

  33. Forsythe, S. M., Kim, J. O., & Petee, T. (2002). Modeling consumer behavior in on-line environments. National Textile Center Annual Report, pp. 1–10.

  34. Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research, 56(11), 867–875.

    Article  Google Scholar 

  35. Lee, K. S., & Tan, S. J. (2003). E-retailing versus physical retailing: A theoretical model and empirical test of consumer choice. Journal of Business Research, 56(11), 877–885.

    Article  Google Scholar 

  36. Tan, S. J. (1999). Strategies for reducing consumers’ risk aversion in Internet shopping. The Journal of Consumer Marketing, 16(2), 163–178.

    Article  Google Scholar 

  37. Bhatnagar, A., Misra, S., & Rao, H. R. (2000). On risk, convenience, and Internet shopping behavior. Communications of the ACM, 43(11), 98–105.

    Article  Google Scholar 

  38. Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474.

    Article  Google Scholar 

  39. Donthu, N., & Garcia, A. (1999). The Internet shopper. Journal of Advertising Research, 39(3), 52–58.

    Google Scholar 

  40. Lichtenstein, D. R., Bloch, P. H., & Black, W. C. (1988). Correlates of price acceptability. Journal of Consumer Research, 15(2), 243–252.

    Article  Google Scholar 

  41. Lichtenstein, D. R., Ridgway, N. M., & Netemeyer, R. G. (1993). Price perceptions and consumer shopping behavior: A field study. Journal of Marketing Research, 30(May), 234–245.

    Article  Google Scholar 

  42. Kukar-Kinney, M. (2006). The role of price-matching characteristics in influencing store loyalty. Journal of Business Research, 59(4), 475–482.

    Article  Google Scholar 

  43. Citrin, A. V., Sprott, D. E., Silverman, S. N., & Stem, D. E., Jr. (2000). Adoption of Internet shopping: The role of consumer innovativeness. Industrial Management & Data Systems, 100(7), 294–300.

    Article  Google Scholar 

  44. Liao, Z., & Cheung, M. T. (2001). Internet-based e-shopping and consumer attitudes: An empirical study. Information & Management, 38(5), 299–306.

    Article  Google Scholar 

  45. Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19(1), 4–21.

    Article  Google Scholar 

  46. Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(July), 50–68.

    Article  Google Scholar 

  47. Huang, M. (2003). Modeling virtual exploratory and shopping dynamics: An environmental psychology approach. Information & Management, 41(1), 39–47.

    Article  Google Scholar 

  48. Lynch, P. D., Kent, R. J., & Srinivasan, S. S. (2001). The global Internet shopper: Evidence from shopping tasks in twelve countries. Journal of Advertising Research, 41(3), 15–24.

    Article  Google Scholar 

  49. Roper Center for Public Opinion Research. (1998). http://www.ropercenter.uconn.edu.

  50. 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 

  51. 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 

  52. Liebermann, Y., & Stashevsky, S. (2002). Perceived risks as barriers to Internet and e-commerce usage. Qualitative Market Research: An International Journal, 5(4), 291–300.

    Article  Google Scholar 

  53. Andreasen, A. R. (1984). Life status changes and changes in consumer preferences and satisfaction. Journal of Consumer Research, 11(3), 784–794.

    Article  Google Scholar 

  54. Shim, S., & Drake, M. F. (1990). Consumer intention to utilize electronic shopping: The Fishbein behavioral intention model. Journal of Direct Marketing, 4(3), 22–33.

    Article  Google Scholar 

  55. Cole, C. A., & Balasubramanian, S. K. (1993). Age differences in consumers’ search for information: Public policy implications. Journal of Consumer Research, 20(1), 157–169.

    Article  Google Scholar 

  56. Forsythe, S. M., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55–75.

    Article  Google Scholar 

  57. Lee, M. K. O., & Turban, E. (2001). A trust model for consumer Internet shopping. International Journal of Electronic Commerce, 6(1), 75–91.

    Article  Google Scholar 

  58. Wang, R. J., Malthouse, E. C., & Krishnamurthi, L. (2015). On the go: How mobile shopping affects customer purchase behavior. Journal of Retailing, 91(2), 217–234.

    Article  Google Scholar 

  59. Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.

    Article  Google Scholar 

  60. Lastovicka, J. L., & Gradner, D. M. (1979). Components of involvement. In J. C. Maloney & B. Silverman (Eds.), Attitude research plays for high stakes (pp. 53–73). Chicago, IL: American Marketing Association.

    Google Scholar 

  61. Laurent, G., & Kapferer, J. (1985). Measuring consumer involvement profiles. Journal of Marketing Research, 22, 41–53.

    Article  Google Scholar 

  62. Jain, K., & Srinivasan, N. (1990). An empirical assessment of multiple operationalizations of involvement. In M. Goldberg, G. Gorn, & R. Pollay (Eds.), Advances in consumer research (Vol. 17, pp. 594–602). Provo, UT: Association for Consumer Research.

    Google Scholar 

  63. Flynn, L. R., & Goldsmith, R. E. (1999). A short, reliable measure of subjective knowledge. Journal of Business Research, 46(1), 57–66.

    Article  Google Scholar 

  64. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society, 57(2), 307–333.

    Article  Google Scholar 

  65. Kline, P. (2000). The handbook of psychological testing (2nd ed.). London: Routledge.

    Google Scholar 

  66. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis with readings. Upper Saddle River, NJ: Prentice-Hall.

    Google Scholar 

  67. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  68. Smith, K. T. (2011). Digital marketing strategies that millennials find appealing, motivating, or just annoying. Journal of Strategic Marketing, 19(6), 489–499.

    Article  Google Scholar 

Download references

Acknowledgements

Funding was provided by Japan Society for the Promotion of Science (Grant No. 17H02573).

Author information

Authors and Affiliations

  1. Graduate School of Economics, Osaka University, 1-7 Machikaneyama, Toyonaka, Osaka, Japan

    Wirawan Dony Dahana, HeeJae Shin & Sotaro Katsumata

Authors
  1. Wirawan Dony Dahana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HeeJae Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sotaro Katsumata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wirawan Dony Dahana.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dahana, W.D., Shin, H. & Katsumata, S. Influence of individual characteristics on whether and how much consumers engage in showrooming behavior. Electron Commer Res 18, 665–692 (2018). https://doi.org/10.1007/s10660-017-9277-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-017-9277-4

Keywords

Search

Navigation