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

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.

[1]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[2]  F.-M. Tseng,et al.  Quadratic interval innovation diffusion models for new product sales forecasting , 2008, J. Oper. Res. Soc..

[3]  Paul Windrum,et al.  A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems , 2007 .

[4]  Bruce R. Robinson,et al.  Dynamic Price Models for New-Product Planning , 1975 .

[5]  Marco Janssen,et al.  Diffusion dynamics in small-world networks with heterogeneous consumers , 2007, Comput. Math. Organ. Theory.

[6]  Stefan Fürnsinn Outwitting the Dilemma of Scale , 2008 .

[7]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .

[8]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[9]  S. Strogatz Exploring complex networks , 2001, Nature.

[10]  H. Leibenstein Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand , 1950 .

[11]  Estelle Brodman,et al.  Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information Within the R&D Organization (Book Review) , 1978 .

[12]  Osman Balci,et al.  Verification, Validation, and Testing , 2007 .

[13]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[14]  Jack Homer,et al.  A diffusion model with application to evolving medical technologies , 1987 .

[15]  Greg A. Stevens,et al.  3,000 Raw Ideas = 1 Commercial Success! , 1997 .

[16]  Yoshiteru Nakamori,et al.  Agent-based modeling on technological innovation as an evolutionary process , 2005, Eur. J. Oper. Res..

[17]  Michael R. Solomon,et al.  Marketing: Real People, Real Choices (Маркетинг: реальные люди, реальный выбор) , 1976 .

[18]  A Kudrolli,et al.  Velocity correlations in dense granular gases. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Greg A. Stevens,et al.  3,000 Raw Ideas Equals 1 Commercial Success! , 1997 .

[20]  Tammo H. A. Bijmolt,et al.  Targeting and timing promotional activities : An agent-based model for the takeoff of new products , 2007 .

[21]  Kathleen M. Eisenhardt,et al.  Developing Theory Through Simulation Methods , 2006 .

[22]  Sven F. Crone,et al.  Forecasting and operational research: a review , 2008, J. Oper. Res. Soc..

[23]  B. Bollobás The evolution of random graphs , 1984 .

[24]  Vijay Mahajan,et al.  New Product Diffusion Models in Marketing: A Review and Directions for Research: , 1990 .

[25]  H. Sexton Advertising , 1898, The American Journal of Dental Science.

[26]  Iqbal Adjali,et al.  Agent-Based Modelling — Intelligent Customer Relationship Management , 2003 .

[27]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[28]  M. Laroche,et al.  Antecedents of consumer relative preference for interpersonal information sources in pre-purchase search , 2005 .

[29]  Wander Jager,et al.  Stimulating diffusion of green products , 2002 .

[30]  Frank H. Maier New product diffusion models in innovation management—a system dynamics perspective , 1998 .

[31]  Philip M. Parker,et al.  Aggregate diffusion forecasting models in marketing: A critical review , 1994 .

[32]  M. Markus,et al.  Oscillations and turbulence induced by an activating agent in an active medium. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Levent Yilmaz,et al.  Validation and verification of social processes within agent-based computational organization models , 2006, Comput. Math. Organ. Theory.

[34]  Susan Howick,et al.  Understanding the drivers of broadband adoption: the case of rural and remote Scotland , 2008, J. Oper. Res. Soc..

[35]  Bart Nooteboom,et al.  Innovation, Learning and Industrial Organisation , 1999 .

[36]  M. Newman,et al.  Random graphs with arbitrary degree distributions and their applications. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  G. Deffuant,et al.  An Individual‐Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit1 , 2005, American Journal of Sociology.

[38]  Eric von Hippel,et al.  'Pyramiding': Efficient Identification of Rare Subjects , 2008 .

[39]  Leilani Arthurs,et al.  Verification and Validation of Agent-based and Equation-based Simulations: A Comparison * , 2005 .

[40]  Andrew N. K. Chen,et al.  Assessing Value in Organizational Knowledge Creation: Considerations for Knowledge Workers , 2005, MIS Q..

[41]  W. Jager The four P's in social simulation, a perspective on how marketing could benefit from the use of social simulation , 2007 .

[42]  A. Borshchev,et al.  From System Dynamics and Discrete Event to Practical Agent Based Modeling : Reasons , Techniques , Tools , 2004 .

[43]  Sean Kilcarr A hard look at biodiesel , 2006 .

[44]  C. Castaldi,et al.  Strategies for the Diffusion of Innovations on Social Networks , 2005 .

[45]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[46]  E. Rogers Diffusion of Innovations , 1962 .

[47]  M. Macy,et al.  FROM FACTORS TO ACTORS: Computational Sociology and Agent-Based Modeling , 2002 .