Skip to main content
Log in

Modeling the effects of dynamic group influence on shopper zone choice, purchase conversion, and spending

  • Original Empirical Research
  • Published:
Journal of the Academy of Marketing Science Aims and scope Submit manuscript

Abstract

In many retail contexts, social interaction plays an important role in the shopping process. We propose a three-stage dynamic linear model that captures the influence of group discussion on shopper behavior within a hierarchical Bayes framework. The model is tested using a video tracking and transaction dataset from a specialty apparel store. The research reveals that group conversations have a significant impact on the shopper’s department or “zone” choice, purchase likelihood, and spending over time. This group influence is magnified by the size of the group (particularly for zone penetration and purchase conversion), and is also moderated by group composition and cohesiveness. The conversations of mixed-age groups and groups who stay together while shopping have a significant influence on shopper behavior across all three stages, while discussions by adult groups exhibit a marginal carryover effect for purchase conversion. When shoppers have repeated discussions in a specific department, they are more likely to return to and buy from this department, while the cumulative number of discussions in the store drives higher spending levels. We also observe that group shoppers visit more departments than their solo counterparts; and mixed-age groups and solo shoppers are more likely to buy than adults-only or teen groups. This study has important implications for how retailers manage shopper engagement and group interaction in their stores.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Although we have shopper path and social influence (e.g., crowding) information, they are not zone-specific and hence will not impact the shopper’s zone choice (Cameron and Trivedi 2005). Therefore, we do not include these variables in the zone choice utility function. However, we do incorporate them into the shopper’s purchase conversion and amount equations.

  2. In order to avoid estimation of too many parameters in Eq. 2, we only allow the intercept and carryover effect (αji0, αji1) to be zone- and individual-specific and moderated by group composition and cohesiveness. We also tried the model with full zone and individual heterogeneity for all parameters in Eq. 2, but its performance was much worse and unstable.

  3. To show that the increased purchases are due to group conversations and not shopper self-selection, we also examined the effect of talk frequency by using propensity score matching on mixed-age groups (Rosenbaum and Rubin 1983). Results show that group conversations (from zero to nonzero) significantly increase sales in a zone by $4.92 on average from $0.12 to $5.04, confirming the causal effect of group conversations on sales (see details in Web Appendix 7). The average treatment effect (causal effect) is $4.88 (Std. error 1.45).

References

  • Aral, S., & Walker, D. (2014). Tie strength, embeddedness, and social influence: A large-scale networked experiment. Management Science, 60(6), 1352–1370.

    Article  Google Scholar 

  • Aribarg, A., Arora, N., & Onur Bodur, H. (2002). Understanding the role of preference revision and concession in group decisions. Journal of Marketing Research, 39(3), 336–349.

    Article  Google Scholar 

  • Aribarg, A., Arora, N., & Kang, M. Y. (2010). Predicting joint choice using individual data. Marketing Science, 29(1), 139–157.

    Article  Google Scholar 

  • Arnold, M. J., Reynolds, K. E., Ponder, N., & Lueg, J. E. (2005). Customer delight in a retail context: Investigating delightful and terrible shopping experiences. Journal of Business Research, 58(8), 1132–1145.

    Article  Google Scholar 

  • Arora, N., & Allenby, G. (1999). Measuring the influence of individual preference structures in group decision making. Journal of Marketing Research, 36(4), 476–487.

    Article  Google Scholar 

  • Ataman, M. B., Mela, C. F., & Van Heerde, H. J. (2008). Building brands. Marketing Science, 27(6), 1036–1054.

    Article  Google Scholar 

  • Ataman, M. B., Van Heerde, H. J., & Mela, C. F. (2010). The long-term effect of marketing strategy on brand sales. Journal of Marketing Research, 47(5), 866–882.

    Article  Google Scholar 

  • Atkin, C. K. (1978). Observation of parent-child interaction in supermarket decision making. Journal of Marketing, 42(4), 41–45.

    Article  Google Scholar 

  • Bell, G. (1967). Self-confidence and persuasion in car buying. Journal of Marketing Research, 4(1), 46–52.

    Article  Google Scholar 

  • Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York: Wiley.

    Book  Google Scholar 

  • Burke, R. R. (2005). The third wave of marketing intelligence. In M. Krafft & M. Mantrala (Eds.), Retailing in the 21 st century: Current and future trends (pp. 113–125). New York: Springer.

    Google Scholar 

  • Cameron, C., & Trivedi, P. (2005). Microeconometrics: Methods and applications. New York: Cambridge University Press.

    Book  Google Scholar 

  • Chandrashekaran, M., Walker, B. A., Ward, J. C., & Reingen, P. H. (1996). Modeling individual preference evolution and choice in a dynamic group setting. Journal of Marketing Research, 33(2), 211–223.

    Article  Google Scholar 

  • Chiang, J. (1991). A simultaneous approach to the whether, what and how much to buy questions. Marketing Science, 10(4), 297–315.

    Article  Google Scholar 

  • Childers, T. L., & Rao, A. R. (1992). The influence of familial and peer-based reference groups on consumer decisions. Journal of Consumer Research, 19(2), 198–211.

    Article  Google Scholar 

  • Coleman, J. S. (1988). Free riders and zealots: The role of social networks. Sociological Theory, 6(1), 52–57.

    Article  Google Scholar 

  • Corfman, K. P., & Lehmann, D. R. (1987). Models of cooperative group decision-making and relative influence: An experimental investigation of family purchase decisions. Journal of Consumer Research, 14(1), 1–13.

    Article  Google Scholar 

  • Darian, J. C. (1998). Parent-child decision making in children’s clothing stores. International Journal of Retail and Distribution, 26(11), 421–431.

    Article  Google Scholar 

  • Dion, K. L. (2000). Group cohesion: From ‘field of forces’ to multidimensional construct. Group Dynamics, 4, 7–26.

    Article  Google Scholar 

  • Evans, K. R., Christiansen, T., & Gill, J. D. (1996). The impact of social influence and role expectations on shopping center patronage intentions. Journal of the Academy of Marketing Science, 24(3), 208–218.

    Article  Google Scholar 

  • Flynn, L. R., & Goldsmith, R. E. (1993). Application of the personal involvement inventory in marketing. Psychology & Marketing, 10(4), 357–366.

    Article  Google Scholar 

  • Forsyth, D. R. (2006). Group dynamics. Belmont: Thomson Wadsworth.

    Google Scholar 

  • Furse, D. H., Punj, G. N., & Stewart, D. W. (1984). A typology of individual search strategies among purchasers of new automobiles. Journal of Consumer Research, 10, 417–431.

    Article  Google Scholar 

  • Goff, B. G., Bellenger, D. N., & Stojack, C. (1994). Cues to consumer susceptibility to salespeople influence: Implications for adaptive retail selling. Journal of Personal Selling & Sales Management, 14(2), 25–39.

    Google Scholar 

  • Gram, M. (2015). Buying food for the family: Negotiations in parent/child supermarket shopping: An observational study from Denmark and the United States. Journal of Contemporary Ethnography, 44(2), 169–195.

    Article  Google Scholar 

  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  • Hackman, J. R., & Morris, C. G. (1975). Group tasks, group interaction process, and group performance effectiveness: A review and proposed integration. Advances in Experimental Social Psychology, 8, 45–99.

    Article  Google Scholar 

  • Harmeling, C. M., Palmatier, R. W., Fang, E., & Wang, D. (2017). Group marketing: Theory, mechanisms, and dynamics. Journal of Marketing, 81(4), 1–24.

    Article  Google Scholar 

  • Harrell, G. D., Hutt, M. D., & Anderson, J. C. (1980). Path analysis of buyer behavior under conditions of crowding. Journal of Marketing Research, 17(1), 45–51.

    Article  Google Scholar 

  • Hartman, C. L., & Kiecker, P. L. (1991). Marketplace influencers at the point of purchase: The role of purchase pals in consumer decision making. In: 1991 AMA Summer Educators’ Conference Proceedings (pp. 461–469). Chicago: American Marketing Association.

  • Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161.

    Article  Google Scholar 

  • Hinsz, V. B., Tindale, R. S., & Vollrath, D. A. (1997). The emerging conceptualization of groups as information processors. Psychological Bulletin, 121, 43–64.

    Article  Google Scholar 

  • Hogg, M. A. (1992). The social psychology of group cohesiveness: From attraction to social identity. New York: New York University Press.

    Google Scholar 

  • Hui, S., Bradlow, E., & Fader, P. (2009). Testing behavioral hypotheses using an integrated model of grocery store shopping path and purchase behavior. Journal of Consumer Research, 36(3), 478–493.

    Article  Google Scholar 

  • Hui, S., Fader, P., & Bradlow, E. (2009a). Path data in marketing: An integrative framework and prospectus for model building. Marketing Science, 28(2), 320–335.

    Article  Google Scholar 

  • Hui, S., Fader, P., & Bradlow, E. (2009b). The traveling salesman goes shopping: The systematic deviations of grocery paths from TSP-optimality. Marketing Science, 28(3), 566–572.

    Article  Google Scholar 

  • Hui, S., Huang, Y., Suher, J., & Inman, J. (2013). Deconstructing the ‘first moment of truth’: Understanding unplanned consideration and purchase conversion using in-store video tracking. Journal of Marketing Research, 50(4), 445–462.

    Article  Google Scholar 

  • Inman, J. J., & Winer, R. S. (1998). Where the rubber meets the road: A model of in-store consumer decision-making. Marketing Science Institute Report No. 98-122.

  • Inman, J. J., Winer, R., & Ferraro, R. (2009). The interplay among category characteristics, customer characteristics, and customer activities on in-store decision making. Journal of Marketing, 73(5), 19–29.

    Article  Google Scholar 

  • Kahn, B. E., & McAlister, L. (1997). Grocery revolution: The new focus on the consumer. Reading: Addison-Wesley.

    Google Scholar 

  • Kiecker, P. L., & Hartman, C. L. (1993). Purchase pal use: Why buyers choose to shop with others. In: 1993 AMA Winter Educators’ Conference Proceedings (pp. 378–384). Chicago: American Marketing Association.

  • Kiecker, P. L., & Hartman, C. L. (1994). Predicting buyers’ selection of interpersonal sources: The role of strong and weak ties. In C. T. Allen & D. R. John (Eds.), Advances in Consumer Research (pp. 464–469). Provo: Association for Consumer Research.

    Google Scholar 

  • Kowert, P. A. (2012). Groupthink or deadlock: When do leaders learn from their advisors? Albany: State University of New York Press.

    Google Scholar 

  • Krackhardt, D. (1992). The strength of strong ties: The importance of philos in organizations. In N. Nohria & R. G. Eccles (Eds.), Networks and organization: Structure, form, and action (pp. 216–239). Boston: Harvard Business School Press.

    Google Scholar 

  • Lam, S. Y., Vandenbosch, M., Hulland, J., & Pearce, M. (2001). Evaluating promotions in shopping environments: Decomposing sales response into attraction, conversion, and spending effects. Marketing Science, 20(2), 194–215.

    Article  Google Scholar 

  • Larson, J. R., & Christensen, C. (1993). Groups as problem solving units: Toward a new meaning of social cognition. British Journal of Social Psychology, 32, 5–30.

    Article  Google Scholar 

  • Latané, B. (1981). The psychology of social impact. American Psychologist, 36(4), 343–356.

    Article  Google Scholar 

  • Latané, B., & Wolf, S. (1981). The social impact of majorities and minorities. Psychological Review, 88(5), 438–453.

    Article  Google Scholar 

  • Lewin, K. (1951). Field theory in social science. New York: Harper.

    Google Scholar 

  • Leykin, A., & Tuceryan, M. (2007). Detecting shopper groups in video sequences. IEEE Conference on Advanced Video and Signal based Surveillance (AVSS).

  • Luo, X. (2005). How does shopping with others influence impulsive purchasing? Journal of Consumer Psychology, 15(4), 288–294.

    Article  Google Scholar 

  • Manchanda, P., Ansari, A., & Gupta, S. (1999). The “shopping basket”: A model for multi-category purchase incidence decisions. Marketing Science, 18(2), 95–114.

    Article  Google Scholar 

  • Mangleburg, T. F., Doney, P. M., & Bristol, T. (2004). Shopping with friends and teens’ susceptibility to peer influence. Journal of Retailing, 80(2), 101–116.

    Article  Google Scholar 

  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.

    Article  Google Scholar 

  • Midgley, D. F. (1983). Patterns of interpersonal information seeking for the purchase of a symbolic product. Journal of Marketing Research, 20(1), 74–83.

    Article  Google Scholar 

  • Montgomery, A. L., Li, S., Srinivasan, K., & Liechty, J. C. (2004). Modeling online browsing and path analysis using clickstream data. Marketing Science, 23(4), 579–595.

    Article  Google Scholar 

  • Narayan, V., Rao, V. R., & Saunders, C. (2011). How peer influence affects attribute preferences: A Bayesian updating mechanism. Marketing Science, 30(2), 368–384.

    Article  Google Scholar 

  • Page, B., Sharp, A., Lockshin, L., & Sorensen, H. (2018). Parents and children in supermarkets: Incidence and influence. Journal of Retailing and Consumer Services, 40, 31–39.

    Article  Google Scholar 

  • POPAI (2011). Shopper influence study. POPAI: The Global Association for Marketing at Retail.

  • Propp, K. M. (1999). Collective information processing in groups. In L. Frey, D. S. Gouran, & M. S. Poole (Eds.), The handbook of group communication: Theory and research (pp. 225–250). Thousand Oaks: Sage.

    Google Scholar 

  • Puccinelli, N. M., Goodstein, R. C., Grewal, D., Price, R., Raghubir, P., & Stewart, D. (2009). Customer experience management in retailing: Understanding the buying process. Journal of Retailing, 85(1), 15–30.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  • Rust, L. (1993). Observations: Parents and children shopping together: A new approach to the qualitative analysis of observational data. Journal of Advertising Research, 33(4), 65–70.

    Google Scholar 

  • Seibold, D. R., Meyers, R. A., & Sunwolf. (1996). Communication and influence in group decision making. In R. Y. Hirokawa & M. S. Poole (Eds.), Communication and group decision-making (2nd ed., pp. 242–268). Thousand Oaks: Sage.

    Chapter  Google Scholar 

  • Stasser, G. (1988). Computer simulation as a research tool: The DISCUSS model of group decision making. Journal of Experimental Social Psychology, 24(5), 393–422.

    Article  Google Scholar 

  • Stasser, G., & Davis, J. H. (1981). Group decision making and social influence: A social interaction sequence model. Psychological Review, 88, 523–551.

    Article  Google Scholar 

  • Su, C., Fern, E. F., & Ye, K. (2003). A temporal dynamic model of spousal family purchase-decision behavior. Journal of Marketing Research, 40(3), 268–281.

    Article  Google Scholar 

  • Tootelian, D. H., & Gaedeke, R. M. (1992). The teen market: An exploratory analysis of income, spending, and shopping patterns. Journal of Consumer Marketing, 9(4), 35–44.

    Article  Google Scholar 

  • Underhill, P. (1999). Why we buy: The science of shopping. New York: Simon & Schuster.

    Google Scholar 

  • Wood, W. (1987). Meta-analytic review of sex differences in group performance. Psychological Bulletin, 102(1), 53–71.

    Article  Google Scholar 

  • Woodside, A., & Sims, J. T. (1976). Retail sales transactions and customer ‘purchase pal’ effects on buying behavior. Journal of Retailing, 52(3), 57–64.

    Google Scholar 

  • Yang, S., Narayan, V., & Assael, H. (2005). Estimating the interdependence of television program viewership between spouses: A Bayesian simultaneous equation model. Marketing Science, 25(4), 336–349.

    Article  Google Scholar 

  • Yang, S., Zhao, Y., Erdem, T., & Zhao, Y. (2010). Modeling the intrahousehold behavioral interaction. Journal of Marketing Research, 47(3), 470–484.

    Article  Google Scholar 

  • Yim, M. Y., Yoo, S., Sauer, P. L., & Seo, J. H. (2014). Hedonic shopping motivation and co-shopper influence on utilitarian grocery shopping in superstores. Journal of the Academy of Marketing Science, 42(5), 528–544.

    Article  Google Scholar 

  • Zhang, X., Li, S., Burke, R. R., & Leykin, A. (2014). An examination of social influence on shopper behavior using video tracking data. Journal of Marketing, 78(5), 24–41.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shibo Li.

Additional information

J. Andrew Petersen served as Area Editor for this article.

Electronic supplementary material

ESM 1

(DOCX 404 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Li, S. & Burke, R.R. Modeling the effects of dynamic group influence on shopper zone choice, purchase conversion, and spending. J. of the Acad. Mark. Sci. 46, 1089–1107 (2018). https://doi.org/10.1007/s11747-018-0590-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11747-018-0590-9

Keywords

Navigation