IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v30y2019i3p912-926.html
   My bibliography  Save this article

Triadic Closure, Homophily, and Reciprocation: An Empirical Investigation of Social Ties Between Content Providers

Author

Listed:
  • Tingting Song

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China 200030)

  • Qian Tang

    (School of Information Systems, Singapore Management University, Singapore 178902)

  • Jinghua Huang

    (Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing, China 100084)

Abstract

In social media, a content provider can initiate outgoing ties to other providers to promote their content, thus inviting reciprocal promotion. We investigate how the reciprocation benefit for the initiating provider is affected by homophily and triadic closure, the two major mechanisms of tie formation. Specifically, we examine how the increase in subscribers and viewership of the initiating provider’s content attributable to the responding providers’ reciprocation is moderated by common ties and content similarity between the two linked providers. Using panel data on 27,356 YouTube video providers, we specify a switching regression model to estimate the influence of content similarity and common ties on reciprocation impact while correcting for their influence on reciprocation probability. Confirming that reciprocation is generally beneficial for the initiator, we find that although content similarity and common ties increase reciprocation probability, they reduce the reciprocation benefit for the initiator in terms of subscriber growth. We also find a positive interaction effect between content similarity and common ties on reciprocation impact, reducing their individual effects. Combining their respective influence on reciprocation probability and benefit, we further examine how content similarity and common ties affect the expected benefit for the initiator and derive practical implications for content providers and social media platforms.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:orisre:v:30:y:2019:i:3:p:912-926
    DOI: 10.1287/isre.2019.0838
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/isre.2019.0838
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2019.0838?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. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Sergio Currarini & Matthew O. Jackson & Paolo Pin, 2009. "An Economic Model of Friendship: Homophily, Minorities, and Segregation," Econometrica, Econometric Society, vol. 77(4), pages 1003-1045, July.
    3. David Godes & Dina Mayzlin, 2009. "Firm-Created Word-of-Mouth Communication: Evidence from a Field Test," Marketing Science, INFORMS, vol. 28(4), pages 721-739, 07-08.
    4. Simon Rodan & Charles Galunic, 2004. "More than network structure: how knowledge heterogeneity influences managerial performance and innovativeness," Strategic Management Journal, Wiley Blackwell, vol. 25(6), pages 541-562, June.
    5. Ricard V. Solé & Romualdo Pastor-Satorras & Eric Smith & Thomas B. Kepler, 2002. "A Model Of Large-Scale Proteome Evolution," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 43-54.
    6. Gaudeul, Alexia & Giannetti, Caterina, 2011. "The role of reciprocation in social network formation, with an application to blogging," MPRA Paper 34094, University Library of Munich, Germany.
    7. Deborah Gladstein Ancona & David F. Caldwell, 1992. "Demography and Design: Predictors of New Product Team Performance," Organization Science, INFORMS, vol. 3(3), pages 321-341, August.
    8. Blundell,Richard & Newey,Whitney & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521692106, October.
    9. Ray E. Reagans & Ezra W. Zuckerman, 2008. "Why knowledge does not equal power: the network redundancy trade-off," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 17(5), pages 903-944, October.
    10. Ravi Bapna & Akhmed Umyarov, 2015. "Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks," Management Science, INFORMS, vol. 61(8), pages 1902-1920, August.
    11. 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.
    12. Vella, Francis & Verbeek, Marno, 1999. "Two-step estimation of panel data models with censored endogenous variables and selection bias," Journal of Econometrics, Elsevier, vol. 90(2), pages 239-263, June.
    13. Cesare Fracassi, 2017. "Corporate Finance Policies and Social Networks," Management Science, INFORMS, vol. 63(8), pages 2420-2438, August.
    14. Brian S. Butler, 2001. "Membership Size, Communication Activity, and Sustainability: A Resource-Based Model of Online Social Structures," Information Systems Research, INFORMS, vol. 12(4), pages 346-362, December.
    15. Blundell,Richard & Newey,Whitney & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521871549, October.
    16. Ray Reagans & Ezra W. Zuckerman, 2001. "Networks, Diversity, and Productivity: The Social Capital of Corporate R&D Teams," Organization Science, INFORMS, vol. 12(4), pages 502-517, August.
    17. 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.
    18. Berg Joyce & Dickhaut John & McCabe Kevin, 1995. "Trust, Reciprocity, and Social History," Games and Economic Behavior, Elsevier, vol. 10(1), pages 122-142, July.
    19. Bin Gu & Prabhudev Konana & Rajagopal Raghunathan & Hsuanwei Michelle Chen, 2014. "Research Note —The Allure of Homophily in Social Media: Evidence from Investor Responses on Virtual Communities," Information Systems Research, INFORMS, vol. 25(3), pages 604-617, September.
    20. Blundell,Richard & Newey,Whitney K. & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521871532, October.
    21. 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.
    22. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
    23. Murtazashvili, Irina & Wooldridge, Jeffrey M., 2016. "A control function approach to estimating switching regression models with endogenous explanatory variables and endogenous switching," Journal of Econometrics, Elsevier, vol. 190(2), pages 252-266.
    24. Brown, Jacqueline Johnson & Reingen, Peter H, 1987. "Social Ties and Word-of-Mouth Referral Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 350-362, December.
    25. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, October.
    26. Blundell,Richard & Newey,Whitney K. & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521692090, October.
    27. Zsolt Katona & Miklos Sarvary, 2008. "Network Formation and the Structure of the Commercial World Wide Web," Marketing Science, INFORMS, vol. 27(5), pages 764-778, 09-10.
    28. Karen A. Bantel & Susan E. Jackson, 1989. "Top management and innovations in banking: Does the composition of the top team make a difference?," Strategic Management Journal, Wiley Blackwell, vol. 10(S1), pages 107-124, June.
    29. Xiaohua Zeng & Liyuan Wei, 2013. "Social Ties and User Content Generation: Evidence from Flickr," Information Systems Research, INFORMS, vol. 24(1), pages 71-87, March.
    30. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    31. 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.
    32. 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.
    33. Dina Mayzlin & Hema Yoganarasimhan, 2012. "Link to Success: How Blogs Build an Audience by Promoting Rivals," Management Science, INFORMS, vol. 58(9), pages 1651-1668, September.
    34. Daniel Z. Levin & Rob Cross, 2004. "The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer," Management Science, INFORMS, vol. 50(11), pages 1477-1490, 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. Sidorov, Sergei & Mironov, Sergei, 2021. "Growth network models with random number of attached links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(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. Tianshu Sun & Sean J. Taylor, 2020. "Displaying things in common to encourage friendship formation: A large randomized field experiment," Quantitative Marketing and Economics (QME), Springer, vol. 18(3), pages 237-271, September.
    2. Rishika Rishika & Jui Ramaprasad, 2019. "The Effects of Asymmetric Social Ties, Structural Embeddedness, and Tie Strength on Online Content Contribution Behavior," Management Science, INFORMS, vol. 65(7), pages 3398-3422, July.
    3. Fernández-Val, Iván & Savchenko, Yevgeniya & Vella, Francis, 2017. "Evaluating the role of income, state dependence and individual specific heterogeneity in the determination of subjective health assessments," Economics & Human Biology, Elsevier, vol. 25(C), pages 85-98.
    4. Wang, Le & Luo, Xin (Robert) & Li, Han, 2022. "Envy or conformity? An empirical investigation of peer influence on the purchase of non-functional items in mobile free-to-play games," Journal of Business Research, Elsevier, vol. 147(C), pages 308-324.
    5. de Marti, Joan & Zenou, Yves, 2009. "Social Networks," Working Paper Series 816, Research Institute of Industrial Economics.
    6. Liangfei Qiu & Arunima Chhikara & Asoo Vakharia, 2021. "Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Information Systems Research, INFORMS, vol. 32(3), pages 876-894, September.
    7. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 106, University of California, Davis, Department of Economics.
    8. Michela Verardo & Andrew Patton, 2009. "Does Beta Move with News? Systematic Risk and Firm-Specific Information Flows," FMG Discussion Papers dp630, Financial Markets Group.
    9. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    10. Fernández-Val, Iván, 2009. "Fixed effects estimation of structural parameters and marginal effects in panel probit models," Journal of Econometrics, Elsevier, vol. 150(1), pages 71-85, May.
    11. Deb Partha & Trivedi Pravin K., 2013. "Finite Mixture for Panels with Fixed Effects," Journal of Econometric Methods, De Gruyter, vol. 2(1), pages 35-51, July.
    12. Daniel Czarnowske & Amrei Stammann, 2019. "Fixed Effects Binary Choice Models: Estimation and Inference with Long Panels," Papers 1904.04217, arXiv.org, revised Oct 2020.
    13. John Bailey Jones & Minhee Kim & Byoung G. Park, 2020. "The Wage Penalty for Married Women of Career Interruptions: Evidence from the 1970s and the 1990s," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 783-807, August.
    14. Bryan S. Graham & James Powell, 2008. "Identification and Estimation of 'Irregular' Correlated Random Coefficient Models," NBER Working Papers 14469, National Bureau of Economic Research, Inc.
    15. Traferri, Alejandra, 2009. "Correcting the bias in the estimation of a dynamic ordered probit with fixed effects of self-assessed health status," UC3M Working papers. Economics we094021, Universidad Carlos III de Madrid. Departamento de Economía.
    16. Matteo Richiardi & Ambra Poggi, 2014. "Imputing Individual Effects in Dynamic Microsimulation Models. An application to household formation and labour market participation in Italy," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 3-39.
    17. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 318, University of California, Davis, Department of Economics.
    18. 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.
    19. Williams, Benjamin, 2020. "Nonparametric identification of discrete choice models with lagged dependent variables," Journal of Econometrics, Elsevier, vol. 215(1), pages 286-304.
    20. Chang, Jinyuan & Chen, Song Xi & Chen, Xiaohong, 2015. "High dimensional generalized empirical likelihood for moment restrictions with dependent data," Journal of Econometrics, Elsevier, vol. 185(1), pages 283-304.

    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:30:y:2019:i:3:p:912-926. 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.