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.
Alkemade F and Castaldi C (2005). Strategies for the diffusion of innovations on social networks . Comput Econ 25: 3–23.
Allen TJ (1978). Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information within the R&D Organization . MIT Press: Cambridge.
Balci O (1998). Verification, validation, and testing . In: Banks J (ed). Handbook of Simulation. Wiley: New York, pp. 335–393.
Bass F (1969). A new product growth for model consumer durables . Mngt Sci 15: 215–227.
Baxter N, Collings D and Adjali I (2003). Agent-based modelling: Intelligent customer relationship management . BT Tech J 21: 126–132.
Bonabeau E (2002). Agent-based modeling: Methods and techniques for simulating human systems . P Nat A Sci 99: 7280–7287.
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.
Brown JJ and Reingen PH (1987). Social ties and word-of-mouth referral behavior . J Cons Res 14: 350–362.
Chen ANK and Edgington TM (2005). Assessing value in organizational knowledge creation: Considerations for knowledge workers . MIS Q 29: 279–309.
Davis JP, Eisenhardt KM and Bingham CB (2007). Developing theory through simulation methods . Acad Mngt Rev 32: 480–499.
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.
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.
Delre SA, Jager W and Janssen MA (2007b). Diffusion dynamics in small-world networks with heterogeneous consumers . Comput Math Org Th 13: 185–202.
Erdös P and Rényi A (1960). On the evolution of random graphs . Publ Math Inst Hung Acad Sci 5: 17–61.
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.
Fildes R, Nikolopoulos K, Crone SF and Syntetos AA (2008). Forecasting and operational research: A review . J Opl Res Soc 59: 1150–1172.
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.
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.
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.
Janssen MA and Jager W (2002). Stimulating diffusion of green products . J Evol Econ 12: 283–306.
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.
Leibenstein H (1950). Bandwagon, snob, and Veblen effects in the theory of consumers' demand . Q J Econ 64: 183–207.
Ma T and Nakamori Y (2005). Agent-based modeling on technological innovation as an evolutionary process . Eur J Opl Res 166: 741–755.
Macy MW and Willer R (2002). From factors to actors: Computational sociology and agent-based modeling . Ann Rev Soc 28: 143–166.
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.
Maier FH (1998). New product diffusion models in innovation management: A system dynamics perspective . Syst Dynam Rev 14: 285–308.
McFadden D (1974). Conditional logit analysis of qualitative choice behaviour . In: Zaremba P (ed). Frontiers in Economics. Academic Press: New York, pp. 105–142.
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.
Newman MEJ, Strogatz SH and Watts DJ (2001). Random graphs with arbitrary degree distributions and their applications . Phys Rev E 64: 1–17.
Nooteboom B (1999). Innovation, learning and industrial organisation . Cambridge J Econ 23: 127–150.
Parker PM (1994). Aggregate diffusion forecasting models in marketing: A critical review . Int J Forecasting 10: 353–380.
Robinson B and Lakhani C (1975). Dynamic price models for new-product planning . Mngt Sci 21: 1113–1122.
Rogers EM (2003). Diffusion of Innovations, 5th edn. Free Press: New York.
Solomon MR and Stuart EW (2003). Marketing: Real People, Real Choices, 3rd edn. Prentice Hall: Upper Saddle River.
Stevens GA and Burley J (1997). 3,000 raw ideas equals one commercial success . Res Tech Mngt 40: 16–27.
Strogatz SH (2001). Exploring complex networks . Nature 410: 268–276.
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.
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.
Wright R (2000). Advertising . Prentice Hall: Harlow.
Yilmaz L (2006). Validation and verification of social processes within agent-based computational organization models . Comput Math Org Th 12: 283–312.
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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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