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Pricing in Information Orchestrators and Maximizing Stable Networks

    In innovation networks based on information exchange, an orchestrating actor, or hub, captures information from peripheral actors, promotes innovation and then distributes it to the network in the form of added value. This paper identifies the pricing options proposed by the orchestrating hub that would result in the network’s stability and efficiency. Since all the companies in this ecosystem can be seen as rational agents, game theory is an appropriate framework for studying pricing as a mechanism to promote network stability. We analyze the equilibrium conditions in this context and conclude that the Nash equilibrium entails the network’s stability. Our findings indicate that, in order to maximize the innovation power of the network, the agents should be charged a price proportional to the financial benefit obtained by the net innovation. This study fills relevant gaps in the literature on monopolistic orchestrated innovation and the pricing structures of network connections.

    References

    • Anshelevich, E., Dasgupta, A., Kleinberg, J., Tardos, E., Wexler, T. and Roughgarden, T. [2008] The price of stability for network design with fair cost allocation, SIAM J. Comput. 38(4), 1602–1623. Crossref, ISIGoogle Scholar
    • Avrachenkov, K., Elias, J., Martignon, F., Neglia, G. and Petrosyan, L. [2015] Cooperative network design: A Nash bargaining solution approach, Comput. Netw. 83, 265–279. CrossrefGoogle Scholar
    • Bala, V. and Goyal, S. [2000] A noncooperative model of network formation, Econometrica 68(5), 1181–1229. Google Scholar
    • Bartelings, J., Goedee, J., Raab, J. and Bijl, R. [2017] The nature of orchestrational work, Pub. Manage. Rev. 19(3), 342–360. CrossrefGoogle Scholar
    • Bloch, F. and Quérou, N. [2013] Pricing in social networks, Games Econ. Behav. 80, 243–261. CrossrefGoogle Scholar
    • Delgado, R. [2010] State space collapse and stability of queueing networks, Math. Methods Oper. Res. 72(3), 477–499. CrossrefGoogle Scholar
    • Dhanaraj, C. and Parkhe, A. [2006] Orchestrating innovation networks, Acad. Manage. Rev. 31(3), 659–669. Crossref, ISIGoogle Scholar
    • Dutta, B. and Mutuswami, S. [1997] Stable networks, J. Econ. Theory 76(2), 322–344. CrossrefGoogle Scholar
    • Fainmesser, I. and Galeotti, A. [2016] Pricing network effects, Rev. Econ. Studies 83(1), 165–198. CrossrefGoogle Scholar
    • Freeman, C. [1991] Networks of innovators: A synthesis of research issues, Res. Policy 20(5), 499–514. Crossref, ISIGoogle Scholar
    • Gibbons, R. [1992] Game Theory for Applied Economists (Princeton University Press, New Jersey). CrossrefGoogle Scholar
    • Goyal, S. and Vega-Redondo, F. [2005] Network formation and social coordination, Games Econ. Behav. 50(2), 178–207. Crossref, ISIGoogle Scholar
    • Hong, S. and Chun, Y. [2010] Efficiency and stability in a model of wireless communication networks, Soc. Choice Welfare 34(3), 441–454. CrossrefGoogle Scholar
    • Jackson, M. and Van den Nouweland, A. [2005] Strongly stable networks, Games Econ. Behav. 51(2), 420–444. Crossref, ISIGoogle Scholar
    • Katz, M. and Shapiro, C. [1985] Network externalities, competition, and compatibility, Am. Econ. Rev. 75(3), 424–440. ISIGoogle Scholar
    • König, M., Battiston, S., Napoletano, M. and Schweitzer, F. [2012] The efficiency and stability of R&D networks, Games Econ. Behav. 75(2), 694–713. CrossrefGoogle Scholar
    • Landsperger, J. and Spieth, P. [2011] Managing innovation networks in the industrial goods sector, Int. J. Innov. Manage. 15(6), 1209–1241. LinkGoogle Scholar
    • Marcon, M. and Moinet, N. [2000] La Stratégie-réseau (Zéro Heure, Paris). Google Scholar
    • Mas-Colell, A., Whinston, M. and Green, J. [1995] Microeconomic Theory (Oxford University Press, New York). Google Scholar
    • Meyer, J. and Rowan, B. [1977] Institutionalized organizations: Formal structure as myth and ceremony, Am. J. Sociol. 83(2), 340–363. Crossref, ISIGoogle Scholar
    • Nash, J. [1951] Non-cooperative games, Ann. Math. 54(2), 286–295. Crossref, ISIGoogle Scholar
    • Nilsen, E. and Gausdal, A. [2017] The multifaceted role of the network orchestrator – A longitudinal case study, Int. J. Innov. Manage. 21(6). LinkGoogle Scholar
    • Ostrovsky, M. [2008] Stability in supply chain networks, Am. Econ. Rev. 98(3), 897–923. CrossrefGoogle Scholar
    • Ozkan-Canbolat, E. and Beraha, A. [2016] Evolutionary knowledge games in social networks, J. Bus. Res. 69, 1807–1811. CrossrefGoogle Scholar
    • Pagano, M. and Jappelli, T. [1993] Information sharing in credit markets, J. Finance 48(5), 1693–1718. CrossrefGoogle Scholar
    • Papadimitriou, C. [2001] Algorithms, games, and the internet, Proc. Thirty-Third Annual ACM Symp. Theory of Computing, pp. 749–753. CrossrefGoogle Scholar
    • Rowley, T. [1997] Moving beyond dyadic ties: A network theory of stakeholder influences Acad. Manage. Rev. 22(4), 887–910. Crossref, ISIGoogle Scholar
    • Von Neumann, J. and Morgenstern, O. [1944] Theory of Games and Economic Behavior (Princeton University Press, New Jersey). Google Scholar
    Published: 26 October 2018