IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i3p394-d735465.html
   My bibliography  Save this article

Targeted Allocation of Marketing Resource in Networks Based on Opinion Dynamics

Author

Listed:
  • Ningning Lang

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

  • Lin Wang

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

  • Quanbo Zha

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

Abstract

Recent advances in information technology and the boom in social media provide firms with easy access to the data of consumers’ preferences and their social interactions. To characterize marketing resource allocation in networks, this paper develops a game theoretical model that allows for each firm’s own utility, action strategies of other firms and the inner state (self-belief and opinions) of consumers. In this model, firms can sway consumers’ opinions by spending marketing resources among consumers under budget and cost constraints. Each firm competes for the collective preference of consumers in a social network to maximize its utility. We derived the equilibrium strategies theoretically in a connected network and a dispersed network from the constructed model. These reveal that firms should allocate more marketing resources to some of consumers depending on their initial opinions, self-belief and positions in a network. We found that some structures of consumer networks may have an innate dominance for one firm, which can be retained in equilibrium results. This means that network structure can be as a tool for firms to improve their utilities. Furthermore, the sensitivities of budget and cost to the equilibria were analyzed. These results can provide some reference for resource allocation strategies in marketing competition.

Suggested Citation

  • Ningning Lang & Lin Wang & Quanbo Zha, 2022. "Targeted Allocation of Marketing Resource in Networks Based on Opinion Dynamics," Mathematics, MDPI, vol. 10(3), pages 1-21, January.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:394-:d:735465
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/3/394/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/3/394/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yin, Xicheng & Wang, Hongwei & Yin, Pei & Zhu, Hengmin, 2019. "Agent-based opinion formation modeling in social network: A perspective of social psychology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 532(C).
    2. Lawrence Friedman, 1958. "Game-Theory Models in the Allocation of Advertising Expenditures," Operations Research, INFORMS, vol. 6(5), pages 699-709, October.
    3. Kostas Bimpikis & Shayan Ehsani & Rahmi İlkılıç, 2019. "Cournot Competition in Networked Markets," Management Science, INFORMS, vol. 67(6), pages 2467-2481, June.
    4. Kostas Bimpikis & Asuman Ozdaglar & Ercan Yildiz, 2016. "Competitive Targeted Advertising Over Networks," Operations Research, INFORMS, vol. 64(3), pages 705-720, June.
    5. Tavasoli, Ali & Shakeri, Heman & Ardjmand, Ehsan & Young, William A., 2021. "Incentive rate determination in viral marketing," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1169-1187.
    6. Snyder, James M, 1989. "Election Goals and the Allocation of Campaign Resources," Econometrica, Econometric Society, vol. 57(3), pages 637-660, 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. Zenou, Yves & Xu, Jin & Zhou, Junjie, 2019. "Networks in Conflict: A Variational Inequality Approach," CEPR Discussion Papers 13647, C.E.P.R. Discussion Papers.
    2. Alexander Matros & David Rietzke, 2024. "Contests on networks," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 78(3), pages 815-841, November.
    3. Xu, Jin & Zenou, Yves & Zhou, Junjie, 2022. "Equilibrium characterization and shock propagation in conflict networks," Journal of Economic Theory, Elsevier, vol. 206(C).
    4. Deck, Cary & Hao, Li & Porter, David, 2015. "Do prediction markets aid defenders in a weak-link contest?," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 248-258.
    5. David Rietzke & Brian Roberson, 2013. "The robustness of ‘enemy-of-my-enemy-is-my-friend’ alliances," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 40(4), pages 937-956, April.
    6. Shakun D. Mago & Roman M. Sheremeta, 2019. "New Hampshire Effect: behavior in sequential and simultaneous multi-battle contests," Experimental Economics, Springer;Economic Science Association, vol. 22(2), pages 325-349, June.
    7. Subhasish M Chowdhury & Dan Kovenock & David Rojo Arjona & Nathaniel T Wilcox, 2021. "Focality and Asymmetry in Multi-Battle Contests," The Economic Journal, Royal Economic Society, vol. 131(636), pages 1593-1619.
    8. Duffy, John & Matros, Alexander, 2017. "Stochastic asymmetric Blotto games: An experimental study," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 88-105.
    9. Sudipta Sarangi & Dan Kovenock & Matt Wiser, 2012. "All-Pay Hex: A Multibattle Contest With Complementarities," Departmental Working Papers 2012-06, Department of Economics, Louisiana State University.
    10. Alex Robson, 2005. "Multi-Item Contests," ANU Working Papers in Economics and Econometrics 2005-446, Australian National University, College of Business and Economics, School of Economics.
    11. Brett R. Gordon & Wesley R. Hartmann, 2016. "Advertising competition in presidential elections," Quantitative Marketing and Economics (QME), Springer, vol. 14(1), pages 1-40, March.
    12. Kimbrough, Erik O. & Laughren, Kevin & Sheremeta, Roman, 2020. "War and conflict in economics: Theories, applications, and recent trends," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 998-1013.
    13. Derek J. Clark & Kai A. Konrad, 2007. "Asymmetric Conflict," Journal of Conflict Resolution, Peace Science Society (International), vol. 51(3), pages 457-469, June.
    14. Qiang Fu & Ganesh Iyer, 2019. "Multimarket Value Creation and Competition," Marketing Science, INFORMS, vol. 38(1), pages 129-149, January.
    15. Kjell Hausken, 2014. "Individual versus overarching protection and attack of assets," 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. 22(1), pages 89-112, March.
    16. John Duffy & Alexander Matros, 2013. "Stochastic Asymmetric Blotto Games: Theory and Experimental Evidence," Working Paper 509, Department of Economics, University of Pittsburgh, revised Nov 2013.
    17. Osório, António (António Miguel), 2018. "Conflict and Competition over Multi-Issues," Working Papers 2072/306550, Universitat Rovira i Virgili, Department of Economics.
    18. Dan Kovenock & Sudipta Sarangi & Matt Wiser, 2015. "All-pay 2 $$\times $$ × 2 Hex: a multibattle contest with complementarities," International Journal of Game Theory, Springer;Game Theory Society, vol. 44(3), pages 571-597, August.
    19. Dziubiński, M. & Goyal, S. & Zhou, J., 2024. "Interconnected Conflict," Cambridge Working Papers in Economics 2408, Faculty of Economics, University of Cambridge.
    20. Dziubiński, M. & Goyal, S. & Zhou, J., 2024. "Interconnected Conflict," Janeway Institute Working Papers 2403, Faculty of Economics, University of Cambridge.

    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:gam:jmathe:v:10:y:2022:i:3:p:394-:d:735465. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.