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

  • M. Günther
  • C. Stummer
  • L. M. Wakolbinger
  • M. Wildpaner
Part of the The OR Essentials series book series (ORESS)


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.


Opinion Leader Reference Price Information Level Marketing Activity Biomass Fuel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Aaker DA, Batra R and Myers JG (1992). Advertising Management, 4th edn. Prentice Hall: Englewood Cliffs.Google Scholar
  2. Alkemade F and Castaldi C (2005). Strategies for the diffusion of innovations on social networks. Comput Econ 25: 3–23.CrossRefGoogle Scholar
  3. 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
  4. Balci O (1998). Verification, validation, and testing. In: Banks J (ed). Handbook of Simulation. Wiley: New York, pp 335–393.CrossRefGoogle Scholar
  5. Bass F (1969). A new product growth for model consumer durables. Mngt Sci 15: 215–227.CrossRefGoogle Scholar
  6. Baxter N, Collings D and Adjali I (2003). Agent-based modelling: Intelligent customer relationship management. BT Tech J 21:126–132.CrossRefGoogle Scholar
  7. Bonabeau E (2002). Agent-based modeling: Methods and techniques for simulating human systems. P Nat A Sci 99: 7280–7287.CrossRefGoogle Scholar
  8. 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
  9. Brown JJ and Reingen PH (1987). Social ties and word-of-mouth referral behavior. J Cons Res 14: 350–362.CrossRefGoogle Scholar
  10. Chen ANK and Edgington TM (2005). Assessing value in organizational knowledge creation: Considerations for knowledge workers. MIS Q 29: 279–309.Google Scholar
  11. Davis JP, Eisenhardt KM and Bingham CB (2007). Developing theory through simulation methods. Acad Mngt Rev 32: 480–499.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. Delre SA, Jager W and Janssen MA (2007b). Diffusion dynamics in small-world networks with heterogeneous consumers. Comput Math Org Th 13: 185–202.CrossRefGoogle Scholar
  15. Erdös P and Rényi A (1960). On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5: 17–61.Google Scholar
  16. 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.CrossRefGoogle Scholar
  17. Fildes R, Nikolopoulos K, Crone SF and Syntetos AA (2008). Forecasting and operational research: A review. J Opl Res Soc 59: 1150–1172.CrossRefGoogle Scholar
  18. 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.Google Scholar
  19. Homer JB (1987). A diffusion model with application to evolving medical technologies. Technol Forecast Soc 31: 197–218.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. Janssen MA and Jager W (2002). Stimulating diffusion of green products. J Evol Econ 12: 283–306.CrossRefGoogle Scholar
  23. 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.Google Scholar
  24. Kilcarr S (2006). A hard look at biodiesel. Fleet Owner 101: 48–52.Google Scholar
  25. Leibenstein H (1950). Bandwagon, snob, and Veblen effects in the theory of consumers’ demand. Q J Econ 64: 183–207.CrossRefGoogle Scholar
  26. Ma T and Nakamori Y (2005). Agent-based modeling on technological innovation as an evolutionary process. Eur J Opl Res 166: 741–755.CrossRefGoogle Scholar
  27. Macy MW and Willer R (2002). From factors to actors: Computational sociology and agent-based modeling. Ann Rev Soc 28: 143–166.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. Maier FH (1998). New product diffusion models in innovation management: A system dynamics perspective. Syst Dynam Rev 14: 285–308.CrossRefGoogle Scholar
  30. 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
  31. Mourali M, Laroche M and Pons F (2005). Antecedents of consumer relative preference for interpersonal information sources in prepurchase search. J Cons Behav 4: 307–318.CrossRefGoogle Scholar
  32. 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
  33. Nooteboom B (1999). Innovation, learning and industrial organisation. Cambridge J Econ 23: 127–150.CrossRefGoogle Scholar
  34. Parker PM (1994). Aggregate diffusion forecasting models in marketing: A critical review. Int J Forecasting 10: 353–380.CrossRefGoogle Scholar
  35. Robinson B and Lakhani C (1975). Dynamic price models for new-product planning. Mngt Sci 21: 1113–1122.CrossRefGoogle Scholar
  36. Rogers EM (2003). Diffusion of Innovations, 5th edn. Free Press: New York.Google Scholar
  37. Solomon MR and Stuart EW (2003). Marketing: Real People, Real Choices, 3rd edn. Prentice Hall: Upper Saddle River.Google Scholar
  38. Stevens GA and Burley J (1997). 3,000 raw ideas equals one commercial success. Res Tech Mngt 40: 16–27.Google Scholar
  39. Strogatz SH (2001). Exploring complex networks. Nature 410: 268–276.CrossRefGoogle Scholar
  40. Tesfatsion L (2009). Empirical validation and verification of agent-based computational models, http:/, accessed 20 October 2009.Google Scholar
  41. Tseng FM (2008). Quadratic interval innovation diffusion models for new product sales forecasting. J Opl Res Soc 59: 1120–1127.CrossRefGoogle Scholar
  42. 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.Google Scholar
  43. Watts DJ and Strogatz SH (1998). Collective dynamics of ‘small-world’ networks. Nature 393: 440–442.CrossRefGoogle Scholar
  44. Wright R (2000). Advertising. Prentice Hall: Harlow.Google Scholar
  45. Yilmaz L (2006). Validation and verification of social processes within agent-based computational organization models. Comput Math Org Th 12: 283–312.CrossRefGoogle Scholar

Copyright information

© Operational Research Society 2014

Authors and Affiliations

  • M. Günther
    • 1
  • C. Stummer
    • 1
  • L. M. Wakolbinger
    • 1
  • M. Wildpaner
    • 2
  1. 1.University of ViennaViennaAustria
  2. 2.Research Institute of Molecular PathologyViennaAustria

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