IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v5y2022i1d10.1007_s42001-021-00127-7.html
   My bibliography  Save this article

Battle of positioning: exploring the role of bridges in competitive diffusion

Author

Listed:
  • Jie Gu

    (Shanghai Academy of Social Sciences)

  • Yunjie Xu

    (Fudan University)

Abstract

While social media facilitate product diffusion, the co-existence of competing products makes the diffusion process complex. This study employs an agent-based model to simulate competitive diffusion on social networks and examines the role of a special network position, network bridges, in influencing the diffusion process. The simulation experiments show that targeting bridges can help the weak product with an initially decreasing diffusion curve to increase its market share. The effect of bridges in competitive diffusion increases with the intensity of market competition. This study also reveals that the effect of bridges is larger when the degree distribution of a network has a lower variation. Overall, bridges can be effective alternatives to network hubs in winning market share. Our analysis based on a large-scale real social network further reveals that bridges enhance the offensive and defensive power of a product. This study offers a systematical exploration of the impact of bridges in competitive diffusion under various conditions and the underlying mechanism. It provides guidance for firms competing in social media regarding whom to target (i.e., bridges vs. hubs) and how effective the targeting strategy is.

Suggested Citation

  • Jie Gu & Yunjie Xu, 2022. "Battle of positioning: exploring the role of bridges in competitive diffusion," Journal of Computational Social Science, Springer, vol. 5(1), pages 319-350, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00127-7
    DOI: 10.1007/s42001-021-00127-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-021-00127-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-021-00127-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Honghong & Fam, Kim-Shyan & Goh, Tiong-Thye & Dai, Xin, 2018. "When are influentials equally influenceable? The strength of strong ties in new product adoption," Journal of Business Research, Elsevier, vol. 82(C), pages 160-170.
    2. Hou, Rui & Wu, Jiawen & Du, Helen S., 2017. "Customer social network affects marketing strategy: A simulation analysis based on competitive diffusion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 644-653.
    3. Pradeep Dubey & Rahul Garg & Bernard De Meyer, 2006. "Competing for Customers in a Social Network," Cowles Foundation Discussion Papers 1591, Cowles Foundation for Research in Economics, Yale University.
    4. Hinz, Oliver & Skiera, Bernd & Barrot, Christian & Becker, Jan, 2011. "Seeding Strategies for Viral Marketing: An Empirical Comparison," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56543, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Bernard de Meyer & Pradeep K. Dubey & Rahul Garg, 2006. "Competing for Customers in a Social Network: The Quasi-linear Case," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00367866, HAL.
    6. Hu, Hai-hua & Lin, Jun & Qian, Yanjun & Sun, Jian, 2018. "Strategies for new product diffusion: Whom and how to target?," Journal of Business Research, Elsevier, vol. 83(C), pages 111-119.
    7. Carl T. Bergstrom & Joseph B. Bak-Coleman, 2019. "Information gerrymandering in social networks skews collective decision-making," Nature, Nature, vol. 573(7772), pages 40-41, September.
    8. Baum, Daniela & Spann, Martin & Füller, Johann & Thürridl, Carina, 2019. "The impact of social media campaigns on the success of new product introductions," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 289-297.
    9. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    10. Frank M. Bass, 2004. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 50(12_supple), pages 1825-1832, December.
    11. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    12. Kostas Bimpikis & Asuman Ozdaglar & Ercan Yildiz, 2016. "Competitive Targeted Advertising Over Networks," Operations Research, INFORMS, vol. 64(3), pages 705-720, June.
    13. Jiang, Guoyin & Tadikamalla, Pandu R. & Shang, Jennifer & Zhao, Ling, 2016. "Impacts of knowledge on online brand success: an agent-based model for online market share enhancement," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1093-1103.
    14. Delre, S.A. & Jager, W. & Bijmolt, T.H.A. & Janssen, M.A., 2007. "Targeting and timing promotional activities: An agent-based model for the takeoff of new products," Journal of Business Research, Elsevier, vol. 60(8), pages 826-835, August.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    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. Nejad, Mohammad G. & Amini, Mehdi & Babakus, Emin, 2015. "Success Factors in Product Seeding: The Role of Homophily," Journal of Retailing, Elsevier, vol. 91(1), pages 68-88.
    2. Lixin Zhou & Jie Lin & Yanfeng Li & Zhenyu Zhang, 2020. "Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    3. Bigdellou, Saeide & Aslani, Shirin & Modarres, Mohammad, 2022. "Optimal promotion planning for a product launch in the presence of word-of-mouth," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    4. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    5. Hu, Hai-hua & Lin, Jun & Qian, Yanjun & Sun, Jian, 2018. "Strategies for new product diffusion: Whom and how to target?," Journal of Business Research, Elsevier, vol. 83(C), pages 111-119.
    6. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    7. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2018. "IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level," Contributions of the Institute for Infrastructure and Resources Management 02/2018, University of Leipzig, Institute for Infrastructure and Resources Management.
    8. Xingyu Chen & Xing Li & Dai Yao & Zhimin Zhou, 2019. "Seeking the support of the silent majority: are lurking users valuable to UGC platforms?," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 986-1004, November.
    9. Li, Feng & Du, Timon Chih-ting & Wei, Ying, 2019. "Offensive pricing strategies for online platforms," International Journal of Production Economics, Elsevier, vol. 216(C), pages 287-304.
    10. Nejad, Mohammad G. & Amini, Mehdi & Sherrell, Daniel L., 2016. "The profit impact of revenue heterogeneity and assortativity in the presence of negative word-of-mouth," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 656-673.
    11. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.
    12. Christian Fieseler & Matthes Fleck, 2013. "The Pursuit of Empowerment through Social Media: Structural Social Capital Dynamics in CSR-Blogging," Journal of Business Ethics, Springer, vol. 118(4), pages 759-775, December.
    13. William Rand & Christian Stummer, 2021. "Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms," Annals of Operations Research, Springer, vol. 305(1), pages 425-447, October.
    14. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    15. Nicole Tabasso, 2014. "Diffusion of Multiple Information," School of Economics Discussion Papers 0914, School of Economics, University of Surrey.
    16. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.
    17. Goyal, Sanjeev & Heidari, Hoda & Kearns, Michael, 2019. "Competitive contagion in networks," Games and Economic Behavior, Elsevier, vol. 113(C), pages 58-79.
    18. Inyoung Chae & Andrew T. Stephen & Yakov Bart & Dai Yao, 2017. "Spillover Effects in Seeded Word-of-Mouth Marketing Campaigns," Marketing Science, INFORMS, vol. 36(1), pages 89-104, January.
    19. Bernd Frick & Franziska Prockl, 2018. "Information Precision In Online Communities: Player Valuations On Www.Transfermarkt.De," Working Papers Dissertations 37, Paderborn University, Faculty of Business Administration and Economics.
    20. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.

    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:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00127-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.