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Understanding the Social Learning Effect in Contagious Switching Behavior This article was downloaded by: [152.3.10.128] On: 13 October 2019, At: 10:22 Publisher: Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA Management Science Publication details, including instructions for authors and subscription information: http://pubsonline.informs.org Understanding the Social Learning Effect in Contagious Switching Behavior Mandy Mantian Hu, Sha Yang, Daniel Yi Xu To cite this article: Mandy Mantian Hu, Sha Yang, Daniel Yi Xu (2019) Understanding the Social Learning Effect in Contagious Switching Behavior. Management Science 65(10):4771-4794. https://doi.org/10.1287/mnsc.2018.3173 Full terms and conditions of use: https://pubsonline.informs.org/Publications/Librarians-Portal/PubsOnLine-Terms-and- Conditions This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact [email protected]. The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. Copyright © 2019, INFORMS Please scroll down for article—it is on subsequent pages With 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org MANAGEMENT SCIENCE Vol. 65, No. 10, October 2019, pp. 4771–4794 http://pubsonline.informs.org/journal/mnsc ISSN 0025-1909 (print), ISSN 1526-5501 (online) Understanding the Social Learning Effect in Contagious Switching Behavior Mandy Mantian Hu,a Sha Yang,b Daniel Yi Xuc a Chinese University of Hong Kong Business School, Shatin, Hong Kong; b Marshall School of Business, University of Southern California, Los Angeles, California 90089; c Department of Economics, Duke University, Durham, North Carolina 27708 Contact: [email protected], http://orcid.org/0000-0001-6168-4600 (MMH); [email protected], http://orcid.org/0000-0003-4190-5722 (SY); [email protected] (DYX) Received: August 26, 2015 Abstract. We study the contagious switching behavior related to a consumer’s choice of Revised: April 15, 2017; April 9, 2018; wireless carriers, that is, that a consumer is more likely to switch wireless carriers if more of July 4, 2018 their contacts from the same carrier have switched. Contagious switching (or a positive Accepted: July 5, 2018 network effect) can be driven by information-based social learning, as well as other Published Online in Articles in Advance: mechanisms related to network size. Although previous marketing literature has docu- July 19, 2019 mented the social-learning effect, most of the applications studied involve products in https://doi.org/10.1287/mnsc.2018.3173 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 Copyright: © 2019 INFORMS 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 per- ception 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 al- gorithm, 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. History: Accepted by Matthew Shum, marketing. Funding: The project was supported by Hong Kong General Research Fund [Grant 14521716]. Keywords: social learning • network effect • contagious switching behavior • customer relationship management 1. Introduction to update their own expectations of product quality When people are connected by a network, they in- (Moretti 2011). Social learning encompasses a wide 1 evitably influence each other. The network effect, re- range of activities, including “word-of-mouth” learning ferring to particular forms of externalities such that (Narayan et al. 2011,Chandrasekharetal.2012,Maetal. actions of peers affect an individual’s preference (Katz 2014), influence from expert opinions in movie reviews, and Shapiro 1985), has attracted great interest both in magazine articles, and news coverage (Erdem et al. 2005, practice and research (e.g., Tucker 2008, Goldenberg 2008; Chintagunta et al. 2009), reading online reviews et al. 2010, Ryan and Tucker 2012). Social learning is from other consumers (Zhao et al. 2013), observa- one of the mechanisms by which network effect in- tional learning (Cai et al. 2009,Zhang2010), and peer- fluences consumer behavior and is defined as the pro- based learning (Chan et al. 2014a, b). In addition to social cess by which individuals use feedback from their peers learning, the network effect also manifests through other 4771 Hu, Yang, and Xu: Social Learning and Network Effect 4772 Management Science, 2019, vol. 65, no. 10, pp. 4771–4794, © 2019 INFORMS mechanisms (e.g., Goolsbee and Klenow 2002,Moretti customer relationship management (CRM) efforts can 2011). For example, it could be due to “conformism,” be given to those customers who have a higher number because people tend to behave like the peers in their of closely connected switching contacts. From a com- group, to earn a potential social benefit. petitor’s perspective, marketing efforts should be tar- Unlike typical consumer products, many high-tech geted toward the influential customers whose experience products (such as mobile service) enjoy a strong pos- signals are viewed to be more credible. A competitor can itive network effect; that is, the utility from the product offer referral programs to incentivize information sharing or service increases with the number of others in a and acquire more customers. network also using it. The network externality in such Our empirical context pertains to an individual’s contexts leads to one important behavioral pattern: stay/switch decision with one focal wireless carrier. contagious switching behavior (i.e., contacts in one’s Individual consumers in our model are assumed to be network that leave will positively affect an individual forward-looking and to make the stay/switch decision customer’s switching probability). Contagious switching by maximizing their expected future utility from al- behavior related to consumer choice of wireless carriers ternative decision outcomes. After controlling for plan has attracted a great deal of attention in the telecom- details and individual characteristics, we focus our munications industry in recent years (e.g., Richter et al. attention on two factors that explain the stay/switch 2010). According to a comprehensive 2007 survey con- decision of individual consumers. First is the learning- ducted by ComScore Networks on the behavior and from-self mechanism. The service quality of the current attitudes of U.S. wireless phone subscribers, 13% of re- carrier directly affects the consumer’s utility of staying spondents considered the influence of friends and family (Iyengar et al. 2007). Because of service variability, a to be the primary reason for switching carriers, which consumer faces uncertainty and learns about the mean was nearly as important as the second-most-important service quality of the carrier to whom he or she sub- reason: lower prices (14%). The literature in multiple scribes on the
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