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An agent-based simulation approach for the new product diffusion of a novel biomass fuel

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Journal of the Operational Research Society

Abstract

Marketing activities support the market introduction of innovative goods or services by furthering their diffusion and, thus, their success. However, such activities are rather expensive. Managers must therefore decide which specific marketing activities to apply to which extent and/or to which target group at which point in time. In this paper, we introduce an agent-based simulation approach that supports decision-makers in these concerns. The practical applicability of our tool is illustrated by means of a case study of a novel, biomass-based fuel that will likely be introduced on the Austrian market within the next 5 years.

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References

  • Aaker DA, Batra R and Myers JG (1992). Advertising Management, 4th edn. Prentice Hall: Englewood Cliffs.

    Google Scholar 

  • Alkemade F and Castaldi C (2005). Strategies for the diffusion of innovations on social networks . Comput Econ 25: 3–23.

    Article  Google Scholar 

  • Allen TJ (1978). Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information within the R&D Organization . MIT Press: Cambridge.

    Google Scholar 

  • Balci O (1998). Verification, validation, and testing . In: Banks J (ed). Handbook of Simulation. Wiley: New York, pp. 335–393.

    Chapter  Google Scholar 

  • Bass F (1969). A new product growth for model consumer durables . Mngt Sci 15: 215–227.

    Article  Google Scholar 

  • Baxter N, Collings D and Adjali I (2003). Agent-based modelling: Intelligent customer relationship management . BT Tech J 21: 126–132.

    Article  Google Scholar 

  • Bonabeau E (2002). Agent-based modeling: Methods and techniques for simulating human systems . P Nat A Sci 99: 7280–7287.

    Article  Google Scholar 

  • Borshchev A and Filippov A (2004). From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools . In: Kennedy M, Winch GW, Langer RS, Rowe JI and Yanni JM (eds). Proceedings of the 22nd International Conference of the Systems Dynamics Society. Wiley: Chichester, pp. 1–22.

    Google Scholar 

  • Brown JJ and Reingen PH (1987). Social ties and word-of-mouth referral behavior . J Cons Res 14: 350–362.

    Article  Google Scholar 

  • Chen ANK and Edgington TM (2005). Assessing value in organizational knowledge creation: Considerations for knowledge workers . MIS Q 29: 279–309.

    Google Scholar 

  • Davis JP, Eisenhardt KM and Bingham CB (2007). Developing theory through simulation methods . Acad Mngt Rev 32: 480–499.

    Article  Google Scholar 

  • Deffuant G, Huet S and Amblard F (2005). An individual-based model of innovation diffusion mixing social value and individual benefit . Am J Soc 110: 1041–1069.

    Article  Google Scholar 

  • Delre SA, Jager W, Bijmolt THA and Janssen MA (2007a). Targeting and timing promotional activities: An agent-based model for the takeoff of new products . J Bus Res 60: 826–835.

    Article  Google Scholar 

  • Delre SA, Jager W and Janssen MA (2007b). Diffusion dynamics in small-world networks with heterogeneous consumers . Comput Math Org Th 13: 185–202.

    Article  Google Scholar 

  • Erdös P and Rényi A (1960). On the evolution of random graphs . Publ Math Inst Hung Acad Sci 5: 17–61.

    Google Scholar 

  • Fagiolo G, Moneta A and Windrum P (2007). A critical guide to empirical validation of agent-based models in economics: Methodologies, procedures, and open problems . Comput Econ 30: 195–226.

    Article  Google Scholar 

  • Fildes R, Nikolopoulos K, Crone SF and Syntetos AA (2008). Forecasting and operational research: A review . J Opl Res Soc 59: 1150–1172.

    Article  Google Scholar 

  • Fürnsinn S (2007). Outwitting the dilemma of scale: Cost and energy efficient scale-down of the Fischer-Tropsch fuel production from biomass. PhD thesis, Vienna University of Technology.

  • Homer JB (1987). A diffusion model with application to evolving medical technologies . Technol Forecast Soc 31: 197–218.

    Article  Google Scholar 

  • Howick S and Whalley J (2008). Understanding the drivers of broadband adoption: The case of rural and remote Scotland . J Opl Res Soc 59: 1299–1311.

    Article  Google Scholar 

  • Jager W (2007). The four P's in social simulation, a perspective on how marketing could benefit from the use of social simulation . J Bus Res 60: 868–875.

    Article  Google Scholar 

  • Janssen MA and Jager W (2002). Stimulating diffusion of green products . J Evol Econ 12: 283–306.

    Article  Google Scholar 

  • Kennedy RC, Xiang X, Cosimano TF, Arthurs LA, Maurice PA, Madey GR and Cabaniss SE (2006). Verification and validation of agent-based and equation-based simulations: A comparison. In: Proceedings of the Spring Simulation Multiconference 2006. Huntsville, Society for Modelling and Simulation International: San Diego, CA, pp 95–102.

  • Kilcarr S (2006). A hard look at biodiesel . Fleet Owner 101: 48–52.

    Google Scholar 

  • Leibenstein H (1950). Bandwagon, snob, and Veblen effects in the theory of consumers' demand . Q J Econ 64: 183–207.

    Article  Google Scholar 

  • Ma T and Nakamori Y (2005). Agent-based modeling on technological innovation as an evolutionary process . Eur J Opl Res 166: 741–755.

    Article  Google Scholar 

  • Macy MW and Willer R (2002). From factors to actors: Computational sociology and agent-based modeling . Ann Rev Soc 28: 143–166.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Maier FH (1998). New product diffusion models in innovation management: A system dynamics perspective . Syst Dynam Rev 14: 285–308.

    Article  Google Scholar 

  • McFadden D (1974). Conditional logit analysis of qualitative choice behaviour . In: Zaremba P (ed). Frontiers in Economics. Academic Press: New York, pp. 105–142.

    Google Scholar 

  • Mourali M, Laroche M and Pons F (2005). Antecedents of consumer relative preference for interpersonal information sources in pre-purchase search . J Cons Behav 4: 307–318.

    Article  Google Scholar 

  • Newman MEJ, Strogatz SH and Watts DJ (2001). Random graphs with arbitrary degree distributions and their applications . Phys Rev E 64: 1–17.

    Google Scholar 

  • Nooteboom B (1999). Innovation, learning and industrial organisation . Cambridge J Econ 23: 127–150.

    Article  Google Scholar 

  • Parker PM (1994). Aggregate diffusion forecasting models in marketing: A critical review . Int J Forecasting 10: 353–380.

    Article  Google Scholar 

  • Robinson B and Lakhani C (1975). Dynamic price models for new-product planning . Mngt Sci 21: 1113–1122.

    Article  Google Scholar 

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

    Google Scholar 

  • Solomon MR and Stuart EW (2003). Marketing: Real People, Real Choices, 3rd edn. Prentice Hall: Upper Saddle River.

    Google Scholar 

  • Stevens GA and Burley J (1997). 3,000 raw ideas equals one commercial success . Res Tech Mngt 40: 16–27.

    Google Scholar 

  • Strogatz SH (2001). Exploring complex networks . Nature 410: 268–276.

    Article  Google Scholar 

  • Tesfatsion L (2009). Empirical validation and verification of agent-based computational models. http://www.econ.iastate.edu/tesfatsi/empvalid.htm, accessed 20 October 2009.

  • Tseng FM (2008). Quadratic interval innovation diffusion models for new product sales forecasting . J Opl Res Soc 59: 1120–1127.

    Article  Google Scholar 

  • von Hippel EA, Franke N and Prügl R (2008). Pyramiding: Efficient identification of rare subjects. Working Paper 4720-08, Sloan School of Management, Massachusetts Institute of Technology.

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

    Article  Google Scholar 

  • Wright R (2000). Advertising . Prentice Hall: Harlow.

    Google Scholar 

  • Yilmaz L (2006). Validation and verification of social processes within agent-based computational organization models . Comput Math Org Th 12: 283–312.

    Article  Google Scholar 

Download references

Acknowledgements

Financial support from the Austrian Science Fund (FWF) by grant No. P20136-G14 is gratefully acknowledged. Furthermore, we are indebted to Stefan Fürnsinn for supporting this work with his expertise on BioFiT.

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Correspondence to C Stummer.

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Günther, M., Stummer, C., Wakolbinger, L. et al. An agent-based simulation approach for the new product diffusion of a novel biomass fuel. J Oper Res Soc 62, 12–20 (2011). https://doi.org/10.1057/jors.2009.170

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  • DOI: https://doi.org/10.1057/jors.2009.170

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