IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v29y2018i2p292-314.html
   My bibliography  Save this article

On Direct vs. Indirect Peer Influence in Large Social Networks

Author

Listed:
  • Bin Zhang

    (Eller College of Management, University of Arizona, Tucson, Arizona 85721)

  • Paul A. Pavlou

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • Ramayya Krishnan

    (Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

With the availability of large-scale network data, peer influence in social networks can be more rigorously examined and understood than before. Peer influence can arise from immediate neighbors in the network (formally defined as cohesion or direct ties with one-hop neighbors) and from indirect peers who share common neighbors (formally defined as structural equivalence or indirect ties with two-hop neighbors). While the literature examined the role of each peer influence (direct or indirect) separately, the study of both peer network effects acting simultaneously was ignored, largely because of methodological constraints. This paper attempts to fill this gap by evaluating the simultaneous effect of both direct and indirect peer influences in technology adoption in the context of caller ringback tone (CRBT) in a cellular telephone network, using data from 200 million calls by 1.4 million users. Given that such a large-scale network makes traditional social network analysis intractable, we extract many densely connected and self-contained subpopulations from the network. We find a regularity in these subpopulations in that they consist either of about 200 nodes or about 500 nodes. Using these subpopulations and panel data, we analyze direct and indirect peer influences using a novel autoprobit model with multiple network terms (direct and indirect peer influence, with homophily as a control variable). Our identification strategy relies on a newly designed spatial autoregressive model, allowing us to identify the direct and indirect peer influences on each of the extracted subpopulations. We use meta-analysis to summarize the estimated parameters from all subpopulations. The results show CRBT adoption to be simultaneously determined by both direct and indirect peer influence (while controlling for homophily and centrality). Robustness checks show model fit to improve when both peer influences are included. The size and direction of the two peer influences, however, differ by group size. Interestingly, indirect peer influence (structural equivalence) plays a negative role in diffusion when group size is about 200, but a positive role when group size is about 500. The role of direct peer influence (cohesion), on the other hand, is always positive, irrespective of group size. Our findings imply that businesses must design different target strategies for large versus small groups: for large groups, businesses should focus on consumers with both multiple one-hop and two-hop neighbors; for small groups, businesses should only focus on consumers with multiple one-hop neighbors. The online appendix is available at https://doi.org/10.1287/isre.2017.0753 .

Suggested Citation

  • Bin Zhang & Paul A. Pavlou & Ramayya Krishnan, 2018. "On Direct vs. Indirect Peer Influence in Large Social Networks," Information Systems Research, INFORMS, vol. 29(2), pages 292-314, June.
    • Handle: RePEc:inm:orisre:v:29:y:2018:i:2:p:292-314
    DOI: isre.2017.0753
    as

    Download full text from publisher

    File URL: https://doi.org/isre.2017.0753
    Download Restriction: no
    File URL: https://libkey.io/isre.2017.0753?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. 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.
    2. Ritu Agarwal & Anil K. Gupta & Robert Kraut, 2008. "Editorial Overview ---The Interplay Between Digital and Social Networks," Information Systems Research, INFORMS, vol. 19(3), pages 243-252, September.
    3. Yann Bramoullé & Bernard Fortin, 2009. "The Econometrics of Social Networks," Cahiers de recherche 0913, CIRPEE.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Scott K. Shriver & Harikesh S. Nair & Reto Hofstetter, 2013. "Social Ties and User-Generated Content: Evidence from an Online Social Network," Management Science, INFORMS, vol. 59(6), pages 1425-1443, June.
    6. Anjana Susarla & Jeong-Ha Oh & Yong Tan, 2012. "Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube," Information Systems Research, INFORMS, vol. 23(1), pages 23-41, March.
    7. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 115-136, Summer.
    8. 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.
    9. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
    10. Bernheim, B Douglas, 1994. "A Theory of Conformity," Journal of Political Economy, University of Chicago Press, vol. 102(5), pages 841-877, October.
    11. Fujimoto, Kayo & Valente, Thomas W., 2012. "Social network influences on adolescent substance use: Disentangling structural equivalence from cohesion," Social Science & Medicine, Elsevier, vol. 74(12), pages 1952-1960.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. James C. Brancheau & James C. Wetherbe, 1990. "The Adoption of Spreadsheet Software: Testing Innovation Diffusion Theory in the Context of End-User Computing," Information Systems Research, INFORMS, vol. 1(2), pages 115-143, June.
    17. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
    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. 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.
    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. Johannes Loh, 2022. "Selection, Consumption, and New Music Exploration in an Online Social Network: A Dyadic Approach," CESifo Working Paper Series 10120, CESifo.
    4. Tingting Song & Qian Tang & Jinghua Huang, 2019. "Triadic Closure, Homophily, and Reciprocation: An Empirical Investigation of Social Ties Between Content Providers," Information Systems Research, INFORMS, vol. 30(3), pages 912-926, September.
    5. Gimpel, Henner & Graf, Vanessa & Graf-Drasch, Valerie, 2020. "A comprehensive model for individuals’ acceptance of smart energy technology – A meta-analysis," Energy Policy, Elsevier, vol. 138(C).
    6. Terence J. V. Saldanha & Arvin Sahaym & Sunil Mithas & Mariana Giovanna Andrade-Rojas & Abhishek Kathuria & Hsiao-Hui Lee, 2020. "Turning Liabilities of Global Operations into Assets: IT-Enabled Social Integration Capacity and Exploratory Innovation," Information Systems Research, INFORMS, vol. 31(2), pages 361-382, June.
    7. Pei Yee Chin & Nina Evans & Charles Zhechao Liu & Kim-Kwang Raymond Choo, 2020. "Understanding Factors Influencing Employees’ Consumptive and Contributive Use of Enterprise Social Networks," Information Systems Frontiers, Springer, vol. 22(6), pages 1357-1376, December.
    8. S. Raghavan & Rui Zhang, 2022. "Influence Maximization with Latency Requirements on Social Networks," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 710-728, March.
    9. S. Raghavan & Rui Zhang, 2022. "Rapid Influence Maximization on Social Networks: The Positive Influence Dominating Set Problem," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1345-1365, May.
    10. Yun Young Hur & Fujie Jin & Xitong Li & Yuan Cheng & Yu Jeffrey Hu, 2023. "Does Social Influence Change with Other Information Sources? A Large-Scale Randomized Experiment in Medical Crowdfunding," Information Systems Research, INFORMS, vol. 34(4), pages 1476-1492, December.
    11. Haibing Gao & Subodha Kumar & Yinliang (Ricky) Tan & Huazhong Zhao, 2022. "Socialize More, Pay Less: Randomized Field Experiments on Social Pricing," Information Systems Research, INFORMS, vol. 33(3), pages 935-953, September.
    12. Liyi Zhang & Daomeng Guo & Xuan Wen & Yiran Li, 2022. "Effect of other visible reviews’ votes and personality on review helpfulness evaluation: an event-related potentials study," Electronic Commerce Research, Springer, vol. 22(2), pages 351-375, June.
    13. 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.
    14. Fan Zhou & Kunpeng Zhang & Bangying Wu & Yi Yang & Harry Jiannan Wang, 2021. "Unifying Online and Offline Preference for Social Link Prediction," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1400-1418, October.
    15. Kathrin Eismann, 2021. "Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter," Journal of Business Economics, Springer, vol. 91(9), pages 1299-1329, November.

    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. 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.
    2. 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.
    3. 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.
    4. Kexin Zhao & Bin Zhang & Xue Bai, 2018. "Estimating Contextual Motivating Factors in Virtual Interorganizational Communities of Practice: Peer Effects and Organizational Influences," Information Systems Research, INFORMS, vol. 29(4), pages 910-927, December.
    5. Zaiyan Wei & Mo Xiao & Rong Rong, 2021. "Network Size and Content Generation on Social Media Platforms," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1406-1426, May.
    6. Jörg Claussen & Tobias Kretschmer & Philip Mayrhofer, 2013. "The Effects of Rewarding User Engagement: The Case of Facebook Apps," Information Systems Research, INFORMS, vol. 24(1), pages 186-200, March.
    7. Steven N. Durlauf & Yannis M. Ioannides, 2010. "Social Interactions," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 451-478, September.
    8. 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).
    9. Xiong, Hang & Payne, Diane & Kinsella, Stephen, 2016. "Peer effects in the diffusion of innovations: Theory and simulation," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 63(C), pages 1-13.
    10. Qingliang Wang & Fred Miao & Giri Kumar Tayi & En Xie, 2019. "What makes online content viral? The contingent effects of hub users versus non–hub users on social media platforms," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 1005-1026, November.
    11. Arun Sundararajan & Foster Provost & Gal Oestreicher-Singer & Sinan Aral, 2013. "Research Commentary ---Information in Digital, Economic, and Social Networks," Information Systems Research, INFORMS, vol. 24(4), pages 883-905, December.
    12. Tolciu, Andreia, 2008. "Is unemployment a consequence of social interactions? Seeking for a common research framework for economists and other social scientists," HWWI Research Papers 1-15, Hamburg Institute of International Economics (HWWI).
    13. Topa, Giorgio & Zenou, Yves, 2015. "Neighborhood and Network Effects," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 561-624, Elsevier.
    14. Matteo Ploner, 2013. "Peer effects at campus cafeterias," Journal of Evolutionary Economics, Springer, vol. 23(1), pages 61-76, January.
    15. 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.
    16. Michael R. Ward, 2022. "Network engagement from learning friends’ preferences: evidence from a video gaming social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1239-1255, September.
    17. Anjana Susarla & Jeong-Ha Oh & Yong Tan, 2012. "Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube," Information Systems Research, INFORMS, vol. 23(1), pages 23-41, March.
    18. ÖZGÜR, Onur & BISIN, Alberto, 2011. "Dynamic Linear Economies with Social Interactions," Cahiers de recherche 04-2011, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    19. 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.
    20. Tianshu Sun & Siva Viswanathan & Elena Zheleva, 2021. "Creating Social Contagion Through Firm-Mediated Message Design: Evidence from a Randomized Field Experiment," Management Science, INFORMS, vol. 67(2), pages 808-827, February.

    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:orisre:v:29:y:2018:i:2:p:292-314. 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.