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Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach

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  • Stummer, Christian
  • Kiesling, Elmar
  • Günther, Markus
  • Vetschera, Rudolf

Abstract

When introducing a new product into market, substantial amounts of resources are put at stake. Innovation managers therefore seek for reliable predictions of the respective innovation diffusion process. Making such predictions, however, is challenging, because the diffusion trajectory is affected by various factors such as the type of innovation, its perceived attributes, marketing activities and their impact, or consumers’ individual communication and adoption behaviors. Modeling the diffusion of innovations accordingly is of interest for both practitioners and management scholars.

Suggested Citation

  • Stummer, Christian & Kiesling, Elmar & Günther, Markus & Vetschera, Rudolf, 2015. "Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach," European Journal of Operational Research, Elsevier, vol. 245(1), pages 157-167.
  • Handle: RePEc:eee:ejores:v:245:y:2015:i:1:p:157-167
    DOI: 10.1016/j.ejor.2015.03.008
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    1. Adam G. Dunn & Blanca Gallego, 2010. "Diffusion of Competing Innovations: The Effects of Network Structure on the Provision of Healthcare," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(4), pages 1-8.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. 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.
    4. Garcia, Rosanna & Rummel, Paul & Hauser, John, 2007. "Validating agent-based marketing models through conjoint analysis," Journal of Business Research, Elsevier, vol. 60(8), pages 848-857, August.
    5. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    6. Vag, Andras, 2007. "Simulating changing consumer preferences: A dynamic conjoint model," Journal of Business Research, Elsevier, vol. 60(8), pages 904-911, August.
    7. Zhengrui Jiang & Dipak C. Jain, 2012. "A Generalized Norton-Bass Model for Multigeneration Diffusion," Management Science, INFORMS, vol. 58(10), pages 1887-1897, October.
    8. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    9. Naik, S.N. & Goud, Vaibhav V. & Rout, Prasant K. & Dalai, Ajay K., 2010. "Production of first and second generation biofuels: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(2), pages 578-597, February.
    10. Jager, Wander, 2007. "The four P's in social simulation, a perspective on how marketing could benefit from the use of social simulation," Journal of Business Research, Elsevier, vol. 60(8), pages 868-875, August.
    11. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
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