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Modeling social learning on consumers’ long-term usage of a mobile technology: a Bayesian estimation of a Bayesian learning model

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  • Haijing Hao

    (University of Massachusetts - Boston)

  • Rema Padman

    (Carnegie Mellon University)

  • Baohong Sun

    (Cheung Kong Graduate School of Business)

  • Rahul Telang

    (Carnegie Mellon University)

Abstract

Studies on how social influence impacts individuals’ social learning during the technology adoption process have increased over the last few decades. However, few studies have examined the social learning effects on individual consumers’ learning at the post-adoption stage, or long-term usage. The present study intends to fill this gap. We construct a Bayesian learning model to investigate consumers’ learning process at the post-adoption stage and how social learning effects influence individuals’ learning at this stage. The model result shows that, among the two social learning effects, influential peer effects (early adopters) are not significantly different from general peer effects at the post-adoption stage; i.e., users no longer treated early adopters differently from general peers. To the best of our knowledge, this is one of the first studies that investigates social learning effects on consumers’ learning at the post-adoption stage by using a Bayesian learning model, which uncovers the underlying mechanism of people’s long-term use of technology.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:elcore:v:19:y:2019:i:1:d:10.1007_s10660-018-09324-5
    DOI: 10.1007/s10660-018-09324-5
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    1. Alan T. Sorensen, 2006. "Social learning and health plan choice," RAND Journal of Economics, RAND Corporation, vol. 37(4), pages 929-945, December.
    2. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, vol. 34(2), pages 167-185, February.
    3. 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.
    4. Andrew Ching & Masakazu Ishihara, 2010. "The effects of detailing on prescribing decisions under quality uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 8(2), pages 123-165, June.
    5. 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.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    7. Alan T. Sorensen, 2006. "Social learning and health plan choice," RAND Journal of Economics, The RAND Corporation, vol. 37(4), pages 929-945, December.
    8. Sridhar Narayanan & Pradeep Chintagunta & Eugenio Miravete, 2007. "The role of self selection, usage uncertainty and learning in the demand for local telephone service," Quantitative Marketing and Economics (QME), Springer, vol. 5(1), pages 1-34, March.
    9. 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.
    10. 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.
    11. 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.
    12. Haijing Hao & Rema Padman & Baohong Sun & Rahul Telang, 2018. "Quantifying the Impact of Social Influence on the Information Technology Implementation Process by Physicians: A Hierarchical Bayesian Learning Approach," Information Systems Research, INFORMS, vol. 29(1), pages 25-41, March.
    13. 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.
    14. 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.
    15. Christoph Riedl & Victor P. Seidel, 2018. "Learning from Mixed Signals in Online Innovation Communities," Organization Science, INFORMS, vol. 29(6), pages 1010-1032, December.
    16. 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.
    17. Lifang Peng & Qinyu Liao & Xiaorong Wang & Xuanfang He, 2016. "Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites," Electronic Commerce Research, Springer, vol. 16(2), pages 145-169, June.
    18. Inseong Song & Pradeep Chintagunta, 2003. "A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category," Quantitative Marketing and Economics (QME), Springer, vol. 1(4), pages 371-407, December.
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    2. Pei Yee Chin & Nina Evans & Charles Zhechao Liu & Kim-Kwang Raymond Choo, 0. "Understanding Factors Influencing Employees’ Consumptive and Contributive Use of Enterprise Social Networks," Information Systems Frontiers, Springer, vol. 0, pages 1-20.

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