Skip to main content
Log in

Agent-based simulation applications in marketing research: an integrated review

  • Article
  • Published:
Journal of Simulation

Abstract

Agent-based simulation has been used to study a wide variety of complex and social systems. This paper presents the results of a comprehensive and multi-dimensional analysis of the application of agent-based modelling and simulation in marketing. Papers are evaluated with respect to types of interaction and communication channels, individual cognitive decision-making styles, social system structures, application area, goals of the simulation study, and other model properties. Furthermore, network analysis and text-mining methods are employed as two novel approaches to establish the current state of the literature and reveal literature trends, gaps, and strengths. Various emergent phenomena and important findings from the application of agent-based simulation in different areas within marketing are also discussed. Finally, the results of the literature analysis are used to make future research recommendations by providing an architecture for conceptual model design and general guidelines to increase the credibility of agent-based models in the marketing domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

References

  • Abernethy AM and Franke GR (1996). The information content of advertising: A meta-analysis. Journal of Advertising 25 (2): 1–17.

    Article  Google Scholar 

  • Arndt J (1967). Role of product-related conversations in the diffusion of a new product. Journal of Marketing Research 4 (3): 291–295.

    Article  Google Scholar 

  • Axelrod R (1997). Advancing the art of simulation in the social sciences. Complexity 3 (2): 16–22.

    Article  Google Scholar 

  • Balci O (1994). Validation, verification, and testing techniques throughout the life cycle of a simulation study. Annals of Operations Research 53 (1): 121–173.

    Article  Google Scholar 

  • Bankes SC (2002). Agent-based modeling: A revolution? Proceedings of the National Academy of Sciences of the United States of America 99 (3): 7199–7200.

    Article  Google Scholar 

  • Barabási A-L and Albert R (1999). Emergence of scaling in random networks. Science 286 (5439): 509–512.

    Article  Google Scholar 

  • Bass FM (1969). A new product growth for model consumer durables. Management Science 15 (5): 215–227.

    Article  Google Scholar 

  • Bass FM (2004). Comments on ‘a new product growth for model consumer durables’. Management Science 50 (12): 1833–1840.

    Article  Google Scholar 

  • Bettman JR, Johnson EJ and Payne JW (1991). Consumer decision making. In: Robertson T and Kassarjian H (eds). Handbook of Consumer Behavior. Prentice Hall, pp 50–84.

    Google Scholar 

  • Bonabeau E (2002). Agent-based modeling: Methods and techniques for simulating human systems. In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 99 (Suppl 3), National Center for Biotechnology Information (NCBI): Bethesda, MD, USA, pp 7280–7287.

  • Broekhuizen TLJ, Delre SA and Torres A (2011). Simulating the cinema market: How cross-cultural differences in social influence explain box office distributions. Journal of Product Innovation Management 28 (2): 204–217.

    Article  Google Scholar 

  • Brown LA (1981). Innovation Diffusion: A New Perspective. Methuen: New York.

    Google Scholar 

  • Brynjolfsson E and Smith MD (2000). Frictionless commerce? A comparison of internet and conventional retailers. Management Science 46 (4): 563–585.

    Article  Google Scholar 

  • Chang RM, Oh W, Pinsonneault A and Kwon D (2010). A network perspective of digital competition in online advertising industries: A simulation-based approach. Information Systems Research 21 (3): 571–593.

    Article  Google Scholar 

  • Chatterjee R and Eliashberg J (1990). The innovation diffusion process in a heterogeneous population: A micro-modeling approach. Management Science 36 (9): 1057–1079.

    Article  Google Scholar 

  • Crowder RM, Robinson MA, Hughes HPN and Sim Y-W (2012). The development of an agent-based modeling framework for simulating engineering team work. Systems, Man and Cybernetics, Part A: IEEE Transactions on Systems and Humans 42 (6): 1425–1439.

    Article  Google Scholar 

  • Dawid H and Neugart M (2011). Agent-based models for economic policy design. Eastern Economic Journal 37 (1): 44–50.

    Article  Google Scholar 

  • Dellarocas C (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science 49 (10): 1407–1424.

    Article  Google Scholar 

  • Delre SA, Jager W, Bijmolt THA and Janssen MA (2010). Will it spread or not? The effects of social influences and network topology on innovation diffusion. Journal of Product Innovation Management 27 (2): 267–282.

    Article  Google Scholar 

  • Garlick M and Chli M (2010). An agent-based simulation of lock-in dynamics in a duopoly. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, Vol. 1. International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC, USA, pp 1545–1546.

    Google Scholar 

  • Gilbert N (2007). Agent-Based Models: Quantitative Applications in the Social Sciences. Sage Publications: Thousand Oaks, CA.

    Google Scholar 

  • Gilbert N and Terna P (2000). How to build and use agent-based models in social science. Mind & Society 1 (1): 57–72.

    Article  Google Scholar 

  • Goldenberg J, Libai B and Muller E (2010). The chilling effects of network externalities. International Journal of Research in Marketing 27 (1): 4–15.

    Article  Google Scholar 

  • Heath B, Hill R and Ciarallo F (2009). A survey of agent-based modeling practices (January 1998 to July 2008). Journal of Artificial Societies and Social Simulation 12 (4): 9–43.

    Google Scholar 

  • Herbert D (2006). Agent-based models of innovation and technological change. In: Tesfatsion L and Judd KL (eds). Handbook of Computational Economics, Volume 2, Chapter 25. Elsevier, B.V.: Amsterdam, Netherlands, pp 1235–1272.

    Google Scholar 

  • Holland JH and Miller JH (1991). Artificial adaptive agents in economic theory. The American Economic Review 81 (2): 365–370.

    Google Scholar 

  • Horsky D and Simon LS (1983). Advertising and the diffusion of new products. Marketing Science 2 (1): 1–17.

    Article  Google Scholar 

  • Hughes HPN, Clegg CW, Robinson MA and Richard M (2012). Crowder. Agent-based modelling and simulation: The potential contribution to organizational psychology. Journal of Occupational and Organizational Psychology 85 (3): 487–502.

    Article  Google Scholar 

  • Jahangirian M, Eldabi T, Naseer A, Stergioulas LK and Young T (2010). Simulation in manufacturing and business: A review. European Journal of Operational Research 203 (1): 1–13.

    Article  Google Scholar 

  • Janssen MA and Jager W (1999). An integrated approach to simulating behavioural processes: A case study of the lock-in of consumption patterns. Journal of Artificial Societies and Social Simulation 2 (2): http://jasss.soc.surrey.ac.uk/2/2/2.html.

  • Janssen MA and Jager W (2001). Fashions, habits and changing preferences: Simulation of psychological factors affecting market dynamics. Journal of Economic Psychology 22 (6): 745–772.

    Article  Google Scholar 

  • Janssen MA and Ostrom E (2006). Empirically based agent-based models. Ecology and Society 11 (2): 37.

    Article  Google Scholar 

  • Jennings NR, Sycara K and Wooldridge M (1998). A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems 1 (1): 7–38.

    Article  Google Scholar 

  • Johnson PA and Sieber RE (2011). An agent-based approach to providing tourism planning support. Environment and Planning B: Planning and Design 38 (3): 486–504.

    Article  Google Scholar 

  • Katz E (1961). The social itinerary of technical change: Two studies on the diffusion of innovation. Human Organization 20 (2): 70–82.

    Article  Google Scholar 

  • Kiesling E, Gunther M, Stummer C and Wakolbinger L (2011). Agent-based simulation of innovation diffusion: A review. Central European Journal of Operations Research 20 (2): 1–48.

    Google Scholar 

  • LeBaron B (2006). Agent-based computational finance. In: Tesfatsion L and Judd KL (eds). Handbook of Computational Economics. Volume 2, Chapter 24. Elsevier, B.V.: Amsterdam, Netherlands, pp 1187–1233.

    Google Scholar 

  • Leombruni R and Richiardi M (2005). Why are economists sceptical about agent-based simulations? Physica A: Statistical Mechanics and its Applications 355 (1): 103–109.

    Article  Google Scholar 

  • Leong EKF, Xueu H and Stanners P-J (1998). Comparing the effectiveness of the web site with traditional media. Journal of Advertising Research 38 (5): 44–51.

    Google Scholar 

  • Leskovec J, Adamic LA and Huberman BA (2007). The dynamics of viral marketing. ACM Transactions on the Web 1 (1): 228–237.

    Article  Google Scholar 

  • Luke S, Cioffi-Revilla C, Panait L, Sullivan K and Balan G (2005). Mason: A multiagent simulation environment. Simulation 81 (7): 517–527.

    Article  Google Scholar 

  • Lytinen SL and Railsback SF (2012). The evolution of agent-based simulation platforms: A review of Netlogo 5.0 and Relogo. In: Proceedings of the 4th International Symposium on Agent-Based Modeling and Simulation. Austrian Society for Cybernetic Studies: Vienna, Austria.

    Google Scholar 

  • Macal CM and North MJ (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation 4 (3): 151–162.

    Article  Google Scholar 

  • Mahajan V, Muller E and Bass FM (1990). New product diffusion models in marketing: A review and directions for research. Journal of Marketing 54 (1): 1–26.

    Article  Google Scholar 

  • Malaga RA (2001). Consumer costs in electronic commerce: An empirical examination of electronic versus traditional markets. Journal of Organizational Computing and Electronic Commerce 11 (1): 47–58.

    Article  Google Scholar 

  • Mielczarek B and Uzialko-Mydlikowska J (2012). Application of computer simulation modeling in the health care sector: A survey. Simulation 88 (2): 197–216.

    Article  Google Scholar 

  • Minar N, Burkhart R, Langton C and Askenazi M (1996). The Swarm simulation system: A toolkit for building multi-agent simulations. Report No. 96-06-042, Santa Fe Institute.

  • Naseer A, Eldabi T and Jahangirian M (2009). Cross-sector analysis of simulation methods: A survey of defense and healthcare. Transforming Government: People, Process and Policy 3 (2): 181–189.

    Article  Google Scholar 

  • North MJ and Macal CM (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford University Press: New York.

    Book  Google Scholar 

  • North MJ, Collier NT and Vos JR (2006). Experiences creating three implementations of the repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation 16 (1): 1–25.

    Article  Google Scholar 

  • Olshavsky RW and Granbois DH (1979). Consumer decision making—Fact or fiction? Journal of Consumer Research 6 (2): 93–100.

    Article  Google Scholar 

  • Perrone A (2005). Agent-based environments: A review. In: Leskow J, Punzo LF and Anyul MP (eds). New Tools of Economic Dynamics, Volume 551 of Lecture Notes in Economics and Mathematical Systems. Springer: Berlin Heidelberg, pp 149–164.

    Google Scholar 

  • Poundstone W (1985). The Recursive Universe: Cosmic Complexity and the Limits of Scientific Knowledge. Contemporary Books: Chicago, IL.

    Google Scholar 

  • Railsback SF, Lytinen SL and Jackson SK (2006). Agent-based simulation platforms: Review and development recommendations. Simulation 82 (9): 609–623.

    Article  Google Scholar 

  • Rand W and Rust RT (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing 28 (3): 181–193.

    Article  Google Scholar 

  • Richins ML (1983). Negative word-of-mouth by dissatisfied consumers: A pilot study. Journal of Marketing 47 (1): 68–78.

    Article  Google Scholar 

  • Robinson WN and Ding Y (2010). A survey of customization support in agent-based business process simulation tools. ACM Transactions on Modeling and Computer Simulation 20 (October): 1–14.

    Article  Google Scholar 

  • Rogers EM (2003). Diffusion of Innovations. 5th edn, Free Press: New York.

    Google Scholar 

  • Rosser JB Jr. (1999). On the complexities of complex economic dynamics. The Journal of Economic Perspectives 13 (4): 169–192.

    Article  Google Scholar 

  • Samanidou E, Zschischang E, Stauffer D and Lux T (2007). Agent-based models of financial markets. Reports on Progress in Physics 70 (3): 409–450.

    Article  Google Scholar 

  • Sargent RG (2005). Verification and validation of simulation models. In: Proceedings of the 37th Winter Simulation Conference. Institute of Electrical and Electronics Engineers Inc. (IEEE): Washington, DC, USA, pp 130–143.

    Google Scholar 

  • Schelling TC (1978). Micromotives and Macrobehavior. W.W. Norton: New York.

    Google Scholar 

  • Smith JS (2003). Survey on the use of simulation for manufacturing system design and operation. Journal of Manufacturing Systems 22 (2): 157–171.

    Article  Google Scholar 

  • Smith M, Bailey J and Brynjolfsson E (2001). Understanding digital markets: Review and assessment. In: Brynjolfsson E and Kahin B (eds). Understanding the Digital Economy. MIT Press: Cambridge, MA.

    Google Scholar 

  • Sznajd-Weron K and Weron R. (2003). How effective is advertising in duopoly markets? Physica A: Statistical Mechanics and its Applications 324 (1–2): 437–444.

    Article  Google Scholar 

  • Terna P (1998). Simulation tools for social scientists: Building agent based models with SWARM. Journal of Artificial Societies and Social Simulation 1 (2): 2.

    Google Scholar 

  • Terzi S and Cavalieri S (2004). Simulation in the supply chain context: A survey. Computers in Industry 53 (1): 3–16.

    Article  Google Scholar 

  • Tesfatsion L (2002). Agent-based computational economics: Growing economies from the bottom up. Artificial Life 8 (1): 55–82.

    Article  Google Scholar 

  • Tesfatsion L and Judd KL (2006). Handbook of Computational Economics: Agent-Based Computational Economic. 2nd edn, North-Holland Publishing: Amsterdam, The Netherlands.

    Google Scholar 

  • van Eck PS, Jager W and Leeflang PSH (2011). Opinion leaders’ role in innovation diffusion: A simulation study. Journal of Product Innovation Management 28 (2): 187–203.

    Article  Google Scholar 

  • Watts DJ and Dodds PS (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research 34 (4): 441–458.

    Article  Google Scholar 

  • Watts DJ and Strogatz SH (1998). Collective dynamics of ‘small-world’networks. Nature 393 (6684): 440–442.

    Article  Google Scholar 

  • Zenobia B, Weber C and Daim T (2009). Artificial markets: A review and assessment of a new venue for innovation research. Technovation 29 (5): 338–350.

    Article  Google Scholar 

  • Zhang T and Zhang D (2007). Agent-based simulation of consumer purchase decision-making and the decoy effect. Journal of Business Research 60 (8): 912–922.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the anonymous referees for their helpful comments and suggestions from which the paper benefited substantially.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L Yilmaz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Negahban, A., Yilmaz, L. Agent-based simulation applications in marketing research: an integrated review. J Simulation 8, 129–142 (2014). https://doi.org/10.1057/jos.2013.21

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jos.2013.21

Keywords

Navigation