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Strategies for the Diffusion of Innovations on Social Networks

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  • Floortje Alkemade
  • Carolina Castaldi

Abstract

We investigate the spread of innovations on a social network. The network consists of agents that are exposed to the introduction of a new product. Consumers decide whether or not to buy the product based on their own preferences and the decisions of their neighbors in the social network. We use and extend concepts from the literature on epidemics and herd behavior to study this problem. The central question of this paper is whether firms can learn about the network structure and consumer characteristics when only limited information is available, and use this information to evolve a successful directed-advertising strategy. In order to do so, we extend existing models to allow for heterogeneous agents and both positive and negative externalities. The firm can learn a directed-advertising strategy that takes into account both the topology of the social consumer network and the characteristics of the consumer. Such directed-advertising strategies outperform random advertising. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Floortje Alkemade & Carolina Castaldi, 2005. "Strategies for the Diffusion of Innovations on Social Networks," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 3-23, February.
    • Handle: RePEc:kap:compec:v:25:y:2005:i:1:p:3-23
    DOI: 10.1007/s10614-005-6245-1
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    1. van Rijnsoever, Frank J. & van den Berg, Jesse & Koch, Joost & Hekkert, Marko P., 2015. "Smart innovation policy: How network position and project composition affect the diversity of an emerging technology," Research Policy, Elsevier, vol. 44(5), pages 1094-1107.
    2. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    3. Floris J. Huétink & Alexander van der Vooren & Floortje Alkemade, 2009. "Initial infrastructure development strategies for the transition to sustainable mobility," Innovation Studies Utrecht (ISU) working paper series 09-05, Utrecht University, Department of Innovation Studies, revised Mar 2009.
    4. Lea F. Stöber & Marius Boesino & Andreas Pyka & Franziska Schuenemann, 2023. "Bioeconomy Innovation Networks in Urban Regions: The Case of Stuttgart," Land, MDPI, vol. 12(4), pages 1-22, April.
    5. Ana Teresa Santos & Sandro Mendonça, 2022. "The small world of innovation studies: an “editormetrics” perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7471-7486, December.
    6. Andreas Klein, 2011. "Die Entwicklung eines agentenbasierten Basismodells zur Bestimmung der deckungsbeitragsmaximierenden Anzahl von Außendienstmitarbeitern," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 21(2), pages 189-210, January.
    7. 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.
    8. Pedro Campos & Pavel Brazdil & Isabel Mota, 2013. "Comparing Strategies of Collaborative Networks for R&D: An Agent-Based Study," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 1-22, June.
    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. Sorda, G. & Sunak, Y. & Madlener, R., 2013. "An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany," Ecological Economics, Elsevier, vol. 89(C), pages 43-60.
    11. Chiara Marradi & Ingrid Mulder, 2022. "Scaling Local Bottom-Up Innovations through Value Co-Creation," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    12. M Günther & C Stummer & L M Wakolbinger & M Wildpaner, 2011. "An agent-based simulation approach for the new product diffusion of a novel biomass fuel," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 12-20, January.
    13. Praveen Kumar & Nishant Tiwary, 2020. "Role of Social Enterprises in Addressing Energy Poverty: Making the Case for Refined Understanding through Theory of Co-Production of Knowledge and Theory of Social Capital," Sustainability, MDPI, vol. 12(20), pages 1-13, October.
    14. Angela Barbuto & Antonio Lopolito & Fabio Gaetano Santeramo, 2019. "Improving diffusion in agriculture: an agent-based model to find the predictors for efficient early adopters," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-12, December.
    15. Justyna Przychodzen & Fernando Gómez-Bezares & Wojciech Przychodzen & Mikel Larreina, 2016. "ESG Issues among Fund Managers—Factors and Motives," Sustainability, MDPI, vol. 8(10), pages 1-19, October.
    16. Gadi Fibich & Ro'i Gibori, 2010. "Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure," Operations Research, INFORMS, vol. 58(5), pages 1450-1468, October.
    17. Natalie Svarcova & Petr Svarc, 2008. "Technology adoption and herding behavior in complex social networks," Working Papers IES 2008/07, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2008.

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