IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v65y2019i10p4771-4794.html
   My bibliography  Save this article

Understanding the Social Learning Effect in Contagious Switching Behavior

Author

Listed:
  • Mandy Mantian Hu

    (Chinese University of Hong Kong Business School, Shatin, Hong Kong)

  • Sha Yang

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Daniel Yi Xu

    (Department of Economics, Duke University, Durham, North Carolina 27708)

Abstract

We study the contagious switching behavior related to a consumer’s choice of wireless carriers, that is, that a consumer is more likely to switch wireless carriers if more of their contacts from the same carrier have switched. Contagious switching (or a positive network effect) can be driven by information-based social learning, as well as other mechanisms related to network size. Although previous marketing literature has documented the social-learning effect, most of the applications studied involve products in which consumers usually do not enjoy any direct benefits from a large network other than from information-based social learning. We explore the importance of the social-learning effect relative to other mechanisms that may also lead to the network effect. We propose a dynamic structural model with interpersonal interactions. To model the social-learning effect, a consumer uses feedback from his or her contacts who have switched from a focal carrier to update his or her quality expectations of alternative carriers. Our model further accounts for two unique aspects of consumer strategic learning: (i) the individual’s perception on the signal of alternative carriers from contacts who switch is systematically different according to whether the signal comes from a loyal contact; and (ii) that the perceived noisiness of the signal on alternative carriers from a contact who has switched depends on the strength of the relationship between the individual and the contact. The remaining network effect not captured through social learning is modeled as a function of the size of the network. We solve the model with a two-step dynamic programming algorithm, with the assumption that a consumer is forward-looking and decides whether to stay with the same service carrier in each period by maximizing the total utility received from that day onward. We apply the proposed model to the data set of a mobile network operator in a European country. We find that churning/switching behavior is contagious in the network context and that one-third of general network effects can be attributed to social learning. We also detect strategic learning by consumers from their contacts in two ways: the experience signal on alternative carriers from a more loyal contact who has switched from the focal carrier is perceived to be more positive than that from a less loyal contact; and the social-learning effect is stronger from an individual’s closest contacts. The simulation analysis demonstrates the value of our model in helping a company prioritize its customer relationship management effort.

Suggested Citation

  • Mandy Mantian Hu & Sha Yang & Daniel Yi Xu, 2019. "Understanding the Social Learning Effect in Contagious Switching Behavior," Management Science, INFORMS, vol. 65(10), pages 4771-4794, October.
  • Handle: RePEc:inm:ormnsc:v:65:y:2019:i:10:p:4771-4794
    DOI: 10.1287/mnsc.2018.3173
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mnsc.2018.3173
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2018.3173?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Pradeep Chintagunta & Renna Jiang & Ginger Jin, 2009. "Information, learning, and drug diffusion: The case of Cox-2 inhibitors," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 399-443, December.
    2. Raghuram Iyengar & Christophe Van den Bulte & Jae Young Lee, 2015. "Social Contagion in New Product Trial and Repeat," Marketing Science, INFORMS, vol. 34(3), pages 408-429, May.
    3. Ariel Pakes & Paul McGuire, 1994. "Computing Markov-Perfect Nash Equilibria: Numerical Implications of a Dynamic Differentiated Product Model," RAND Journal of Economics, The RAND Corporation, vol. 25(4), pages 555-589, Winter.
    4. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    5. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2017. "Empirical Models of Learning Dynamics: A Survey of Recent Developments," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 223-257, Springer.
    6. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2007. "Estimating Dynamic Models of Imperfect Competition," Econometrica, Econometric Society, vol. 75(5), pages 1331-1370, September.
    7. Stephen Ryan & Catherine Tucker, 2012. "Heterogeneity and the dynamics of technology adoption," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 63-109, March.
    8. Tat Chan & Chakravarthi Narasimhan & Ying Xie, 2013. "Treatment Effectiveness and Side Effects: A Model of Physician Learning," Management Science, INFORMS, vol. 59(6), pages 1309-1325, June.
    9. Vishal Narayan & Vithala R. Rao & Carolyne Saunders, 2011. "How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism," Marketing Science, INFORMS, vol. 30(2), pages 368-384, 03-04.
    10. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 497-529.
    11. Goldenberg, Jacob & Libai, Barak & Muller, Eitan, 2010. "The chilling effects of network externalities," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 4-15.
    12. Timothy Dunne & Shawn D. Klimek & Mark J. Roberts & Daniel Yi Xu, 2013. "Entry, exit, and the determinants of market structure," RAND Journal of Economics, RAND Corporation, vol. 44(3), pages 462-487, September.
    13. Mark Israel, 2005. "Services as Experience Goods: An Empirical Examination of Consumer Learning in Automobile Insurance," American Economic Review, American Economic Association, vol. 95(5), pages 1444-1463, December.
    14. Goolsbee, Austan & Klenow, Peter J, 2002. "Evidence on Learning and Network Externalities in the Diffusion of Home Computers," Journal of Law and Economics, University of Chicago Press, vol. 45(2), pages 317-343, October.
    15. Ariel Pakes & Michael Ostrovsky & Steven Berry, 2007. "Simple estimators for the parameters of discrete dynamic games (with entry/exit examples)," RAND Journal of Economics, RAND Corporation, vol. 38(2), pages 373-399, June.
    16. Enrico Moretti, 2011. "Social Learning and Peer Effects in Consumption: Evidence from Movie Sales," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(1), pages 356-393.
    17. Lee, Lung-fei, 2007. "Identification and estimation of econometric models with group interactions, contextual factors and fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 333-374, October.
    18. Puneet Manchanda & Ying Xie & Nara Youn, 2008. "The Role of Targeted Communication and Contagion in Product Adoption," Marketing Science, INFORMS, vol. 27(6), pages 961-976, 11-12.
    19. Keane, Michael P & Wolpin, Kenneth I, 1997. "The Career Decisions of Young Men," Journal of Political Economy, University of Chicago Press, vol. 105(3), pages 473-522, June.
    20. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    21. Tülin Erdem & Michael P. Keane & Baohong Sun, 2008. "A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality," Marketing Science, INFORMS, vol. 27(6), pages 1111-1125, 11-12.
    22. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    23. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    24. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    25. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    26. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    27. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    28. Hongbin Cai & Yuyu Chen & Hanming Fang, 2009. "Observational Learning: Evidence from a Randomized Natural Field Experiment," American Economic Review, American Economic Association, vol. 99(3), pages 864-882, June.
    29. Victor Aguirregabiria & Pedro Mira, 2007. "Sequential Estimation of Dynamic Discrete Games," Econometrica, Econometric Society, vol. 75(1), pages 1-53, January.
    30. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    31. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
    32. Tülin Erdem & Michael Keane & T. Öncü & Judi Strebel, 2005. "Learning About Computers: An Analysis of Information Search and Technology Choice," Quantitative Marketing and Economics (QME), Springer, vol. 3(3), pages 207-247, September.
    33. Sinan Aral & Dylan Walker, 2014. "Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment," Management Science, INFORMS, vol. 60(6), pages 1352-1370, June.
    34. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
    35. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liang Chen & Pengxiang Zhang & Sali Li & Scott F. Turner, 2022. "Growing pains: The effect of generational product innovation on mobile games performance," Strategic Management Journal, Wiley Blackwell, vol. 43(4), pages 792-821, April.
    2. Johannes Loh, 2022. "Selection, Consumption, and New Music Exploration in an Online Social Network: A Dyadic Approach," CESifo Working Paper Series 10120, CESifo.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    2. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2018. "The Dynamic Spillovers of Entry: An Application to the Generic Drug Industry," Management Science, INFORMS, vol. 64(3), pages 1189-1211, March.
    3. Liangfei Qiu & Zhan (Michael) Shi & Andrew B. Whinston, 2018. "Learning from Your Friends’ Check-Ins: An Empirical Study of Location-Based Social Networks," Information Systems Research, INFORMS, vol. 29(4), pages 1044-1061, December.
    4. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    5. Jason R. Blevins & Ahmed Khwaja & Nathan Yang, 2018. "Firm Expansion, Size Spillovers, and Market Dominance in Retail Chain Dynamics," Management Science, INFORMS, vol. 64(9), pages 4070-4093.
    6. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    7. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2017. "Empirical Models of Learning Dynamics: A Survey of Recent Developments," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 223-257, Springer.
    8. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.
    9. Guofang Huang & Hong Luo & Jing Xia, 2019. "Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning," Management Science, INFORMS, vol. 65(12), pages 5556-5583, December.
    10. Limin Fang, 2022. "The Effects of Online Review Platforms on Restaurant Revenue, Consumer Learning, and Welfare," Management Science, INFORMS, vol. 68(11), pages 8116-8143, November.
    11. Andrew T. Ching & Hyunwoo Lim, 2020. "A Structural Model of Correlated Learning and Late-Mover Advantages: The Case of Statins," Management Science, INFORMS, vol. 66(3), pages 1095-1123, March.
    12. Hai Che & Tülin Erdem & T. Sabri Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    13. van Ewijk, Bernadette J. & Gijsbrechts, Els & Steenkamp, Jan-Benedict E.M., 2022. "The dark side of innovation: How new SKUs affect brand choice in the presence of consumer uncertainty and learning," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 967-987.
    14. Shan Huang & Sinan Aral & Yu Jeffrey Hu & Erik Brynjolfsson, 2020. "Social Advertising Effectiveness Across Products: A Large-Scale Field Experiment," Marketing Science, INFORMS, vol. 39(6), pages 1142-1165, November.
    15. Haijing Hao & Rema Padman & Baohong Sun & Rahul Telang, 2019. "Modeling social learning on consumers’ long-term usage of a mobile technology: a Bayesian estimation of a Bayesian learning model," Electronic Commerce Research, Springer, vol. 19(1), pages 1-21, March.
    16. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2007. "Estimating Dynamic Models of Imperfect Competition," Econometrica, Econometric Society, vol. 75(5), pages 1331-1370, September.
    17. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.
    18. Kohei Kawaguchi, 2021. "When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business," Management Science, INFORMS, vol. 67(3), pages 1670-1695, March.
    19. Daniel Björkegren, 2022. "Competition in network industries: Evidence from the Rwandan mobile phone network," RAND Journal of Economics, RAND Corporation, vol. 53(1), pages 200-225, March.
    20. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:65:y:2019:i:10:p:4771-4794. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.