IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v32y2013i6p977-997.html
   My bibliography  Save this article

Modeling Choice Interdependence in a Social Network

Author

Listed:
  • Jing Wang

    (McKinsey & Company, Shanghai 200020, China)

  • Anocha Aribarg

    (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

  • Yves F. Atchadé

    (Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

This paper investigates how individuals' product choices are influenced by the product choices of their connected others and how the influence mechanism may differ for fashion- versus technology-related products. We conduct a novel field experiment to induce and observe choice interdependence in a closed social network. In our experiment, we conceptualize individuals' choices to be driven by multiattribute utilities, and we measure their initial attribute preferences prior to observing their choice interdependence and collecting network information. These design elements help alleviate concerns in identifying social interaction effects from other confounds. Given that we have complete information on choices and their sequence, we use a discrete-time Markov chain model. Nonetheless, we also use a Markov random field (MRF) model as an alternative when the information on choice sequence is missing. We find significant social interaction effects. Our findings show that whereas experts exert asymmetrically greater influence on a technology-related product, popular individuals exert greater influence on a fashion-related product. In addition, we find choices made by early decision makers to be more influential than choices made later for the technology-related product. Finally, using the MRF with snapshot data can also provide good out-of-sample predictions for a technology-related product.

Suggested Citation

  • Jing Wang & Anocha Aribarg & Yves F. Atchadé, 2013. "Modeling Choice Interdependence in a Social Network," Marketing Science, INFORMS, vol. 32(6), pages 977-997, November.
    • Handle: RePEc:inm:ormksc:v:32:y:2013:i:6:p:977-997
    DOI: 10.1287/mksc.2013.0811
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2013.0811
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2013.0811?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. René Algesheimer & Sharad Borle & Utpal M. Dholakia & Siddharth S. Singh, 2010. "The Impact of Customer Community Participation on Customer Behaviors: An Empirical Investigation," Marketing Science, INFORMS, vol. 29(4), pages 756-769, 07-08.
    2. Stephen Leider & Markus M. Möbius & Tanya Rosenblat & Quoc-Anh Do, 2009. "Directed Altruism and Enforced Reciprocity in Social Networks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(4), pages 1815-1851.
    3. Childers, Terry L & Rao, Akshay R, 1992. "The Influence of Familial and Peer-Based Reference Groups on Consumer Decisions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(2), pages 198-211, September.
    4. repec:hal:spmain:info:hdl:2441/14otokka698nb83lk2n7bhqbo2 is not listed on IDEAS
    5. 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.
    6. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    7. Keeney,Ralph L. & Raiffa,Howard, 1993. "Decisions with Multiple Objectives," Cambridge Books, Cambridge University Press, number 9780521438834.
    8. Bearden, William O & Rose, Randall L, 1990. "Attention to Social Comparison Information: An Individual Difference Factor Affecting Consumer Conformity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 461-471, March.
    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. Belk, Russell W, 1988. "Possessions and the Extended Self," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(2), pages 139-168, September.
    11. Reingen, Peter H, et al, 1984. "Brand Congruence in Interpersonal Relations: A Social Network Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 11(3), pages 771-783, December.
    12. William A. Brock & Steven N. Durlauf, 2001. "Discrete Choice with Social Interactions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 68(2), pages 235-260.
    13. De Bruyn, Arnaud & Lilien, Gary L., 2008. "A multi-stage model of word-of-mouth influence through viral marketing," International Journal of Research in Marketing, Elsevier, vol. 25(3), pages 151-163.
    14. Rinus Haaijer & Michel Wedel, 2007. "Conjoint Choice Experiments: General Characteristics and Alternative Model Specifications," Springer Books, in: Anders Gustafsson & Andreas Herrmann & Frank Huber (ed.), Conjoint Measurement, edition 0, chapter 11, pages 199-229, Springer.
    15. Sangkil Moon & Gary J. Russell, 2008. "Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach," Management Science, INFORMS, vol. 54(1), pages 71-82, January.
    16. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    17. Sinan Aral & Dylan Walker, 2011. "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, INFORMS, vol. 57(9), pages 1623-1639, February.
    18. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    19. David Godes & Dina Mayzlin & Yubo Chen & Sanjiv Das & Chrysanthos Dellarocas & Bruce Pfeiffer & Barak Libai & Subrata Sen & Mengze Shi & Peeter Verlegh, 2005. "The Firm's Management of Social Interactions," Marketing Letters, Springer, vol. 16(3), pages 415-428, December.
    20. Bearden, William O & Netemeyer, Richard G & Teel, Jesse E, 1989. "Measurement of Consumer Susceptibility to Interpersonal Influence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(4), pages 473-481, March.
    21. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 115-136, Summer.
    22. Inseong Song & Pradeep K. Chintagunta, 2006. "Measuring Cross-Category Price Effects with Aggregate Store Data," Management Science, INFORMS, vol. 52(10), pages 1594-1609, October.
    23. Bearden, William O & Etzel, Michael J, 1982. "Reference Group Influence on Product and Brand Purchase Decisions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(2), pages 183-194, September.
    24. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    25. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    26. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 361-400, December.
    27. Herbert C. Kelman, 1958. "Compliance, identification, and internalization three processes of attitude change," Journal of Conflict Resolution, Peace Science Society (International), vol. 2(1), pages 51-60, March.
    28. Park, C Whan & Lessig, V Parker, 1977. "Students and Housewives: Differences in Susceptibility to Reference Group Influence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 4(2), pages 102-110, Se.
    29. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    30. Rakesh Niraj & V. Padmanabhan & P. B. Seetharaman, 2008. "Research Note—A Cross-Category Model of Households' Incidence and Quantity Decisions," Marketing Science, INFORMS, vol. 27(2), pages 225-235, 03-04.
    31. Wesley R. Hartmann, 2010. "Demand Estimation with Social Interactions and the Implications for Targeted Marketing," Marketing Science, INFORMS, vol. 29(4), pages 585-601, 07-08.
    32. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    33. Anocha Aribarg & Neeraj Arora & Moon Young Kang, 2010. "Predicting Joint Choice Using Individual Data," Marketing Science, INFORMS, vol. 29(1), pages 139-157, 01-02.
    34. 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.
    35. Burnkrant, Robert E & Cousineau, Alain, 1975. "Informational and Normative Social Influence in Buyer Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(3), pages 206-215, December.
    36. van den Bulte, C. & Stremersch, S., 2003. "Contagion and heterogeneity in new product diffusion: An emperical test," ERIM Report Series Research in Management ERS-2003-077-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    37. Midgley, David F, 1976. "A Simple Mathematical Theory of Innovative Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 3(1), pages 31-41, June.
    38. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    39. 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.
    40. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
    41. Cosma Rohilla Shalizi & Andrew C. Thomas, 2011. "Homophily and Contagion Are Generically Confounded in Observational Social Network Studies," Sociological Methods & Research, , vol. 40(2), pages 211-239, May.
    42. Sungjoon Nam & Puneet Manchanda & Pradeep K. Chintagunta, 2010. "The Effect of Signal Quality and Contiguous Word of Mouth on Customer Acquisition for a Video-on-Demand Service," Marketing Science, INFORMS, vol. 29(4), pages 690-700, 07-08.
    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. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    2. Yuho Chung & Yiwei Li & Jianmin Jia, 2021. "Exploring embeddedness, centrality, and social influence on backer behavior: the role of backer networks in crowdfunding," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 925-946, September.
    3. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    4. Nick Huntington‐Klein, 2018. "College Choice As A Collective Decision," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1202-1219, April.
    5. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2019. "Individual-level social influence identification in social media: A learning-simulation coordinated method," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1005-1015.
    6. Fred Feinberg & Elizabeth Bruch & Michael Braun & Brett Hemenway Falk & Nina Fefferman & Elea McDonnell Feit & John Helveston & Daniel Larremore & Blakeley B. McShane & Alice Patania & Mario L. Small, 2020. "Choices in networks: a research framework," Marketing Letters, Springer, vol. 31(4), pages 349-359, December.
    7. Johannes Loh, 2022. "Selection, Consumption, and New Music Exploration in an Online Social Network: A Dyadic Approach," CESifo Working Paper Series 10120, CESifo.
    8. Tuck Siong Chung & Michel Wedel & Roland T. Rust, 2016. "Adaptive personalization using social networks," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 66-87, January.
    9. Sheng, Jie, 2019. "Being Active in Online Communications: Firm Responsiveness and Customer Engagement Behaviour," Journal of Interactive Marketing, Elsevier, vol. 46(C), pages 40-51.
    10. Chul Kim & P. K. Kannan & Michael Trusov & Andrea Ordanini, 2020. "Modeling Dynamics in Crowdfunding," Marketing Science, INFORMS, vol. 39(2), pages 339-365, March.
    11. Yan Chen & Youran Qi & Qing Liu & Peter Chien, 2018. "Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks," Quantitative Marketing and Economics (QME), Springer, vol. 16(4), pages 409-440, December.
    12. Kim, Junghun & Lee, Hyunjoo & Lee, Jongsu, 2020. "Smartphone preferences and brand loyalty: A discrete choice model reflecting the reference point and peer effect," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).

    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. Florian Probst & Laura Grosswiele & Regina Pfleger, 2013. "Who will lead and who will follow: Identifying Influential Users in Online Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(3), pages 179-193, June.
    2. Landsman, Vardit & Nitzan, Irit, 2020. "Cross-decision social effects in product adoption and defection decisions," International Journal of Research in Marketing, Elsevier, vol. 37(2), pages 213-235.
    3. 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.
    4. Grant Miller & A. Mushfiq Mobarak, 2015. "Learning About New Technologies Through Social Networks: Experimental Evidence on Nontraditional Stoves in Bangladesh," Marketing Science, INFORMS, vol. 34(4), pages 480-499, July.
    5. Liye Ma & Ramayya Krishnan & Alan L. Montgomery, 2015. "Latent Homophily or Social Influence? An Empirical Analysis of Purchase Within a Social Network," Management Science, INFORMS, vol. 61(2), pages 454-473, February.
    6. 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.
    7. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    8. Sinan Aral & Dylan Walker, 2011. "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, INFORMS, vol. 57(9), pages 1623-1639, February.
    9. Bhatia, Tulikaa & Wang, Lei, 2011. "Identifying physician peer-to-peer effects using patient movement data," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 51-61.
    10. 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.
    11. Jonathan Z. Zhang, 2019. "Dynamic customer interdependence," Journal of the Academy of Marketing Science, Springer, vol. 47(4), pages 723-746, July.
    12. Alfis S. Gayazov & Gulnara F. Zamaletdinova & Artur F. Amirov & Andrey V. Kostryukov & Evgeniya I. Tikhomirova, 2016. "Modern Teaching Tendencies of Critical Thinking Forming of University Students," International Review of Management and Marketing, Econjournals, vol. 6(2), pages 358-363.
    13. Chong (Alex) Wang & Xiaoquan (Michael) Zhang & Il-Horn Hann, 2018. "Socially Nudged: A Quasi-Experimental Study of Friends’ Social Influence in Online Product Ratings," Information Systems Research, INFORMS, vol. 29(3), pages 641-655, September.
    14. Bonan, Jacopo & Battiston, Pietro & Bleck, Jaimie & LeMay-Boucher, Philippe & Pareglio, Stefano & Sarr, Bassirou & Tavoni, Massimo, 2021. "Social interaction and technology adoption: Experimental evidence from improved cookstoves in Mali," World Development, Elsevier, vol. 144(C).
    15. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    16. Wesley R. Hartmann, 2010. "Demand Estimation with Social Interactions and the Implications for Targeted Marketing," Marketing Science, INFORMS, vol. 29(4), pages 585-601, 07-08.
    17. Ashutosh Tiwari & Timothy J. Richards, 2016. "Social Networks and Restaurant Ratings," Agribusiness, John Wiley & Sons, Ltd., vol. 32(2), pages 153-174, April.
    18. 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.
    19. Mina Ameri & Elisabeth Honka & Ying Xie, 2019. "Word of Mouth, Observed Adoptions, and Anime-Watching Decisions: The Role of the Personal vs. the Community Network," Marketing Science, INFORMS, vol. 38(4), pages 567-583, July.
    20. Xiao Fang & Paul Jen-Hwa Hu & Zhepeng (Lionel) Li & Weiyu Tsai, 2013. "Predicting Adoption Probabilities in Social Networks," Information Systems Research, INFORMS, vol. 24(1), pages 128-145, March.

    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:ormksc:v:32:y:2013:i:6:p:977-997. 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.