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Beaming market simulation to the future by combining agent-based modeling with scenario analysis

Author

Listed:
  • Christian Stummer

    (Bielefeld University)

  • Lars Lüpke

    (Bielefeld University)

  • Markus Günther

    (Bielefeld University)

Abstract

Agent-based simulation has become an established method for innovation and technology diffusion research. It extends traditional approaches by modeling diffusion processes from a micro-level perspective, which enables the consideration of various heterogeneous stakeholders and their diverse interactions. While such a simulation is well suited to capture the complex behavior of markets, its application is challenging when it comes to modeling future markets. Therefore, we propose a multi-method approach that combines scenario analysis that generates multiple “pictures of the future” with an agent-based market simulation that offers insight into the potential outcomes of today’s strategic (technological) decisions in each of these futures. Thus, simulation results can provide valuable decision support for corporate planners and industrial engineers when they are engaged in technology planning. This paper describes the novel approach and illustrates it through a sample application that is based on an industry-related research project on the development and market introduction of smart products.

Suggested Citation

  • Christian Stummer & Lars Lüpke & Markus Günther, 2021. "Beaming market simulation to the future by combining agent-based modeling with scenario analysis," Journal of Business Economics, Springer, vol. 91(9), pages 1469-1497, November.
    • Handle: RePEc:spr:jbecon:v:91:y:2021:i:9:d:10.1007_s11573-021-01046-9
    DOI: 10.1007/s11573-021-01046-9
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    References listed on IDEAS

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    1. 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.
    2. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    3. Bearden, William O & Rose, Randall L, 1990. "Attention to Social Comparison Information: An Individual Difference Factor Affecting Consumer Conformity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 461-471, March.
    4. Federico Bianchi & Andreas Flache & Flaminio Squazzoni, 2020. "Solidarity in collaboration networks when everyone competes for the strongest partner: a stochastic actor-based simulation model," The Journal of Mathematical Sociology, Taylor & Francis Journals, vol. 44(4), pages 249-266, October.
    5. Márcia Baptista & Carlos Roque Martinho & Francisco Lima & Pedro A. Santos & Helmut Prendinger, 2014. "Improving Learning in Business Simulations with an Agent-Based Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(3), pages 1-7.
    6. Sven Banisch & Tanya Araújo & Jorge Louçã, 2010. "Opinion Dynamics And Communication Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 95-111.
    7. Pawel Sobkowicz, 2018. "Opinion Dynamics Model Based on Cognitive Biases of Complex Agents," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(4), pages 1-8.
    8. 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.
    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. Sven Banisch & Eckehard Olbrich, 2017. "The Coconut Model with Heterogeneous Strategies and Learning," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-14.
    11. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    12. Lea Sonderegger-Wakolbinger & Christian Stummer, 2015. "An agent-based simulation of customer multi-channel choice behavior," 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. 23(2), pages 459-477, June.
    13. Sabrina Backs & Markus Günther & Christian Stummer, 2019. "Stimulating academic patenting in a university ecosystem: an agent-based simulation approach," The Journal of Technology Transfer, Springer, vol. 44(2), pages 434-461, April.
    14. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    15. Lea M. Wakolbinger & Christian Stummer & Markus Günther, 2013. "Market Introduction And Diffusion Of New Products: Recent Developments In Agent-Based Modeling," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 10(05), pages 1-19.
    16. Derbyshire, James & Wright, George, 2017. "Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation," International Journal of Forecasting, Elsevier, vol. 33(1), pages 254-266.
    17. Sinan Aral & Dylan Walker, 2014. "Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment," Management Science, INFORMS, vol. 60(6), pages 1352-1370, June.
    18. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    19. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    20. William Rand & Roland T. Rust & Min Kim, 2018. "Complex systems: marketing’s new frontier," AMS Review, Springer;Academy of Marketing Science, vol. 8(3), pages 111-127, December.
    21. Peter N. Golder & Gerard J. Tellis, 2004. "Growing, Growing, Gone: Cascades, Diffusion, and Turning Points in the Product Life Cycle," Marketing Science, INFORMS, vol. 23(2), pages 207-218, December.
    22. Anja Schwering, 2017. "The influence of peer honesty and anonymity on managerial reporting," Journal of Business Economics, Springer, vol. 87(9), pages 1151-1172, December.
    23. 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.
    24. 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.
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    Cited by:

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    2. Bilstein, Nicola & Stummer, Christian, 2023. "Editorial – Special Issue: Managing Smart Services and Smart Service Systems," SMR - Journal of Service Management Research, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 7(1), pages 2-6.
    3. Kai Fischbach & Johannes Marx & Tim Weitzel, 2021. "Agent-based modeling in social sciences," Journal of Business Economics, Springer, vol. 91(9), pages 1263-1270, November.

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    More about this item

    Keywords

    Agent-based modeling; Scenario analysis; Technology planning; Smart products;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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