Marketing Letters

, Volume 19, Issue 3–4, pp 287–304 | Cite as

Modeling social interactions: Identification, empirical methods and policy implications

  • Wesley R. HartmannEmail author
  • Puneet Manchanda
  • Harikesh Nair
  • Matthew Bothner
  • Peter Dodds
  • David Godes
  • Kartik Hosanagar
  • Catherine Tucker


Social interactions occur when agents in a network affect other agents’ choices directly, as opposed to via the intermediation of markets. The study of such interactions and the resultant outcomes has long been an area of interest across a wide variety of social sciences. With the advent of electronic media that facilitate and record such interactions, this interest has grown sharply in the business world as well. In this paper, we provide a brief summary of what is known so far, discuss the main challenges for researchers interested in this area, and provide a common vocabulary that will hopefully engender future (cross disciplinary) research. The paper considers the challenges of distinguishing actual causal social interactions from other phenomena that may lead to a false inference of causality. Further, we distinguish between two broadly defined types of social interactions that relate to how strongly interactions spread through a network. We also provide a very selective review of how insights from other disciplines can improve and inform modeling choices. Finally, we discuss how models of social interaction can be used to provide guidelines for marketing policy and conclude with thoughts on future research directions.


Social interactions Networking Social multiplier Peer effects 


  1. Ackerberg, D., & Gowrisankaran, G. (2006). Quantifying equilibrium network externalities in the ach banking industry. NBER working paper 12488. Cambridge: NBER.Google Scholar
  2. American Marketing Association. (2007). Social network ad spending up. Accessed at
  3. Anselin, L. (2001). Spatial econometrics. In B. Baltagi (Ed.), A Companion to Theoretical Econometrics (pp. 310–330). Oxford: Blackwell.Google Scholar
  4. Bajari, P., Benkard, L., & Levin, J. (2007). Estimating dynamic models of imperfect competition. Econometrica, 75(5), 1331–1370.CrossRefGoogle Scholar
  5. Bajari, P., Hong, H., & Ryan, S. (2006a). Identification and Estimation of a Discrete Game of Complete Information. working paper. Minneapolis: University of Minnesota.Google Scholar
  6. Bajari, P., Hong, H., Krainer, J., & Nekipelov, D. (2006b). Estimating Static Models of Strategic Interactions. working paper. Minneapolis: University of Minnesota.Google Scholar
  7. Bala, V., & Goyal, S. (2000). A non-cooperative model of network formation. Econometrica, 68, 1181–1229.CrossRefGoogle Scholar
  8. Bell, D., & Song, S. (2007). Neighborhood effects and trial on the internet: evidence from online grocery retailing. Quantitative Marketing and Economics, 5(4), 361–400.CrossRefGoogle Scholar
  9. Bothner, M. S., Kim, Y. K., & Lee, W. (2006). Primary Status, Complementary Status, and Capital Acquisition in the US Venture Capital Industry. Working Paper. Chicago: University of Chicago.Google Scholar
  10. Bresnahan, T., & Reiss, P. (1991). Empirical models of discrete games. Journal of Econometrics, 48, 57–81.CrossRefGoogle Scholar
  11. Brock, W. A., & Durlauf, S. N. (2001). Discrete choice with social interactions. Review of Economic Studies, 68(235), 235–260, (April).CrossRefGoogle Scholar
  12. Burt, R. S. (1987). Social contagion and innovation: cohesion versus structural equivalence. American Journal of Sociology, 92, 1287–1335, (May).CrossRefGoogle Scholar
  13. Coleman, J. S., Elihu, K., & Menzel, H. (1966). Medical Innovation. New York: Bobbs-Merrill.Google Scholar
  14. Conley, T., & Udry, C. (2003). Learning About a New Technology: Pineapple in Ghana. Working Paper. Chicago: University of Chicago.Google Scholar
  15. Dodds, P. S., & Watts, D. J. (2004). Universal behavior in a generalized model of contagion. Physical Review Letters, 92, 218701–218705.CrossRefGoogle Scholar
  16. Dodds, P. S., & Watts, D. J. (2005). A generalized model of social and biological contagion. Journal of Theoretical Biology, 232, 587–604.CrossRefGoogle Scholar
  17. Doraszelski, U., & Judd, K. (2007). Avoiding the curse of dimensionality in dynamic stochastic games, working paper. Stanford: Hoover Institution.Google Scholar
  18. Draganska, M., et al. (2008). Discrete choice models of firms’ strategic decisions. Marketing Letters, in press.Google Scholar
  19. Dubé, J. P., Günter, J. H., & Chintagunta, P. (2007). Dynamic standards competition and tipping: The case of 32/64 Bit Video Game Consoles, working paper. Chicago: University of Chicago.Google Scholar
  20. Duflo, E., & Saez, E. (2003). The role of information and social interactions in retirement plan decisions: evidence from a randomized experiment. Quarterly Journal of Economics, 118(3), 815–842.CrossRefGoogle Scholar
  21. Durrett, R. (1999). Stochastic spatial models. SIAM Review, 41, 677–718.CrossRefGoogle Scholar
  22. Economides, N., & Himmelberg, C. (1995). Critical mass and network size with application to the US fax market, working paper. New York: New York University.Google Scholar
  23. Fleder, D. M., & Hosanagar, K. (2007). Blockbuster cultures next rise or fall: the impact of recommender systems on sales diversity, working paper. Philadelphia: University of Pennsylvania.Google Scholar
  24. Gladwell, M. (2000). The tipping point: how little things can make a big difference. Lancaster: Little Brown.Google Scholar
  25. Glaeser, E., & Scheinkman, J. (2001). Measuring social interactions. In S. Durlauf, & P. Young (Eds.), Social dynamics. Cambridge: MIT.Google Scholar
  26. Glaeser, E. L., Sacerdote, B. I., & Scheinkman, J. A. (2003). The social multiplier.. Journal of the European Economic Association, 1(2, 3), 345–353.CrossRefGoogle Scholar
  27. Godes, D., & Mayzlin, D. (2004). Firm-created word-of-mouth communication: a field-based quasi-experiment. HBS Marketing Research Paper No. 04-03. Available at SSRN:
  28. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380, (May).CrossRefGoogle Scholar
  29. Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443.CrossRefGoogle Scholar
  30. Hartmann, W. (2008). Demand estimation with social interactions and the implications for targeted marketing, working paper. Stanford: Stanford University.Google Scholar
  31. Katz, L., Kling, A., & Liebman, J. (2001). Moving to opportunity in Boston: early results of a randomized mobility experiment. Quarterly Journal of Economics, CXVI, 607–654.CrossRefGoogle Scholar
  32. Lee, L. F. (1982). Some approaches to the correction of selectivity bias. The Review of Economic Studies, XLIX, 355–372.CrossRefGoogle Scholar
  33. Manchanda, P., Xie, Y., & Youn, N. (2004). The role of targeted communication and contagion in new product adoption, working paper. Chicago: University of Chicago.Google Scholar
  34. Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. Review of Economic Studies, 60, 531–542.CrossRefGoogle Scholar
  35. Manski, C. F. (2000). Economic analysis of social interactions. Journal of Economic Perspectives, 14(3), 115–136.CrossRefGoogle Scholar
  36. Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63, (January 5).CrossRefGoogle Scholar
  37. Moffitt, R. (2001). Policy interventions, low-level equilibria, and social interactions. In S. Durlauf, & P. Young (Eds.), Social Dynamics (pp. 45–82). Washington, D. C.: Brookings Institution Press and MIT.Google Scholar
  38. Nair, H., Manchanda, P., & Bhatia, T. (2006). Asymmetric peer effects in prescription behavior: the role of opinion leaders, working paper. Stanford: Stanford University.Google Scholar
  39. Nam, S., Manchanda, P., & Pradeep, K. C. (2006). The effects of service quality and word-of-mouth on customer acquisition, retention and usage, working paper. Chicago: University of Chicago.Google Scholar
  40. Narayan, V., & Yang, S. (2006). Trust between consumers in online communities: modeling the formation of dyadic relationships, working paper. New York: New York University.Google Scholar
  41. Podolny, J. (2005). Status signals: a sociological study of market competition. Princeton: Princeton University Press.Google Scholar
  42. Ryan, S., & Tucker, C. (2006). Heterogeneity and the dynamics of technology adoption, working paper. Cambridge: MIT.Google Scholar
  43. Sacerdote, B. (2001). Peer effects with random assignment: results for Dartmouth roommates. Quarterly Journal of Economics, 116, 681–704, (2 May).CrossRefGoogle Scholar
  44. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). An experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854–856.CrossRefGoogle Scholar
  45. Schelling, T. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.Google Scholar
  46. Trusov, M. (2006). Your agents are also your customers: marketing for internet social networks, working paper. Baltimore: University of Maryland.Google Scholar
  47. Tucker, C. (2006). Interactive, option-value and domino network effects in technology adoption, working paper. Cambridge: MIT.Google Scholar
  48. Van Den, B. C., & Wuyts, S. (2007). Social networks and marketing. Cambridge: Marketing Science Institute.Google Scholar
  49. Watts, D. J. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences, 99, 5766–5571.CrossRefGoogle Scholar
  50. Watts, D. J., et al. (2005). Multiscale, resurgent epidemics in a hierarchical metapopulation model. Proceedings of the National Academy of Sciences, 102(32), 11157–11162.CrossRefGoogle Scholar
  51. Yang, S., & Allenby, G. M. (2003). Modeling interdependent consumer preferences. Journal of Marketing Research, 40(3), 282–294.CrossRefGoogle Scholar
  52. Yang, S., Narayan, V., & Assael, H. (2006). Estimating the interdependence of television program viewership between spouses: a bayesian simultaneous equation model. Marketing Science, 3, 336–349.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Wesley R. Hartmann
    • 1
    Email author
  • Puneet Manchanda
    • 2
  • Harikesh Nair
    • 1
  • Matthew Bothner
    • 3
  • Peter Dodds
    • 4
  • David Godes
    • 5
  • Kartik Hosanagar
    • 6
  • Catherine Tucker
    • 7
  1. 1.Stanford UniversityStanfordUSA
  2. 2.University of MichiganAnn ArborUSA
  3. 3.University of ChicagoChicagoUSA
  4. 4.University of VermontBurlingtonUSA
  5. 5.Harvard UniversityCambridgeUSA
  6. 6.University of PennsylvaniaPhiladelphiaUSA
  7. 7.MITCambridgeUSA

Personalised recommendations