Abstract
Diffusions of new products and technologies through social networks can be formalized as spreading of infectious diseases. However, while epidemiological models describe infection in terms of transmissibility, we propose a diffusion model that explicitly includes consumer decision-making affected by social influences and word-of-mouth processes. In our agent-based model consumers’ probability of adoption depends on the external marketing effort and on the internal influence that each consumer perceives in his/her personal networks. Maintaining a given marketing effort and assuming its effect on the probability of adoption as linear, we can study how social processes affect diffusion dynamics and how the speed of the diffusion depends on the network structure and on consumer heterogeneity. First, we show that the speed of diffusion changes with the degree of randomness in the network. In markets with high social influence and in which consumers have a sufficiently large local network, the speed is low in regular networks, it increases in small-world networks and, contrarily to what epidemic models suggest, it becomes very low again in random networks. Second, we show that heterogeneity helps the diffusion. Ceteris paribus and varying the degree of heterogeneity in the population of agents simulation results show that the more heterogeneous the population, the faster the speed of the diffusion. These results can contribute to the development of marketing strategies for the launch and the dissemination of new products and technologies, especially in turbulent and fashionable markets.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abrahamson E, Rosenkopf L (1997) Social Network Effects on the Extend of Innovation Diffusion. Organ Sci 8:289–309
Anderson RM, May RM (1992) Infectious Diseases in Humans. Oxford University Press, Oxford
Adner R, Levinthal D (2001) Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation. Manag Sci 47(5):611–628
Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Sci 286:509–512
Andrelini L, Ianni A (1996) Path Dependence and Learning from Neighbors. Games Econ Behav 13:144–77
Arenas A, Díaz-Guilera A, Perez CJ, Vega-Redondo F (2000) Self-Organized Evolution in a Socioeconomic Environment. Phys Rev E 61:3466–3469
Bemmaor AC (1994) Modelling the Diffusion of New Durable Goods: Word-of-Mouth Effect versus Consumer Heterogeneity. In: Gilles L, Lilien GL, Pras B (eds) Research Tradition in Marketing. Kluwer Academic Publishers, Boston, MA
Bemmaor AC, Lee Y (2002) The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates. Market Sci 21:209–220
Bass FM (1969) A New Product Growth for Model Consumer Durables. Manag Sci 15:215–227
Chatterjee R, Eliashberg J (1990) The Innovation Diffusion Process in the Heterogeneous Population: a Micromodelling Approach. Manag Sci 36:1057–1079
Coleman JS, Katz E, Menzel M (1966) Medical Innovation: a Diffusion Study. Bobbs Merrill, NY
Cowan R, Jonard N (2004) Network Structure and the Diffusion of Knowledge. J Econ Dynamics & Control 28:1557–1575
Delre SA, Jager W, Janssen MA (2004) Percolation and Innovation Diffusion Models Compared: do Network Structures and Social Preferences Matter? In: Proceedings of M2M2 Workshop and ESSA Conference, Valladolid, Spain
Dodds PS, Watts DJ (2005) A Generalized Model of Social and Biological Contagion. J Theor Biol 232:587–604
Ellison G (1993) Learning, Local Interaction and Coordination. Econometrica 61:1047–1072
Ennew C, Banerjee A, Li D (2000) Managing Word of Mouth Communication: Empirical Evidence from India. Int J Bank Mark 18(2):75–84
Epstein JM, Axtell R (1996) Growing Artificial Societies: Social Science from the Bottom Up. MIT Press, Cambridge, MA
Garcia-Diaz C, Witteloostuijn A (2005) Simulated Market Partitioning and Varying Degrees of Resource Space Heterogeneity. University of Groningen, forthcoming
Gladwell M (2000) The Tipping Point. How Little things Can Make a Big Difference. Little Brown and Company, London
Goldenberg J, Libai B, Solomon S, Jan N, Stauffer D (2000) Marketing percolation. Physica A 284:335–347
Goldenberg J, Libai B, Muller E (2001) Talk of the Network: a Complex System Look at the Underlying Process of Word-of-Mouth. Mark Lett 12(3):211–223
Granovetter MS (1978) Threshold Models of Collective Behavior. J Sociol 83:1420–1443
Granovetter M, Soong R (1986) Threshold Models of Interpersonal Effects in Consumer Demand. J Econ Behav Organ 7:83–99
Janssen MA, Jager W, (2003) Self-Organization of Market Dynamics: Consumer Psychology and Social Networks. Artif Life 9(4):343–356
Katz E, Lazarsfeld PF (1955) Personal influence. Free Press, Glencoe, IL
Katz E (1957) The Two-Step Flow of Communication: an Up-to-Date Report on a Hypothesis. Public Opin Q 21:61–78
Macy MW (1990) Learning Theory and the Logic of Critical Mass. Am Sociol Rev 55:809–826
Macy MW (1991) Chains of Cooperation: Threshold Effects in Collective Action. Am Sociol Rev 56:730–747
Mahajan V, Muller E (1979) Innovation Diffusion and New Product Growth Models in Marketing. J Mark 43:55–68
Mahajan V, Muller E, Wind J (eds) (2000) New Product Diffusion Models. Kluwer Academic Publishers, Boston MA
Moore C, Newman MEJ (2000) Epidemics and Percolation in Small-World Networks. Phys Rev E 61:5678–5682
Morris S (2000) Contagion. Rev Econ Studies 67:57–78
Oliver PE, Marwell G, Teixeira R (1985) A Theory of Critical Mass I. Interdependence, Group Heterogeneity, and the Production of Collective Action. Am J Sociol 94:502–534
Oliver PE, Marwell G (1988) The Paradox of the Group Size in Collective Action: A Theory of Critical Mass II. Am Sociol Rev 53:1–8
Newman MEJ (2002) Spread of Epidemic Disease on Networks. Phys Rev E 66:016128
Newman MEJ, Watts DJ (1999) Scaling and Percolation in the Small-World Network Model. Phys Rev E 60:7332–7342
Pastor-Satorras R, Vespignani A (2002) Epidemic Dynamics in Finite Size Scale-Free Networks. Phys Rev E 65:035108
Rogers EM (1995) Diffusion of innovation. 4th edn. The Free Press, NY
Rogers EM, Shoemaker FF (1971) Communication of Innovation: a Cross-Cultural Approach. The Free Press, NY
Rogers EM, Kincaid DL (1981) Communication Networks: Towards a New Paradigm for Research. Free Press, New York
Rosen E (2000) The Anatomy of Buzz. Doubleday, New York
Solomon S, Weisbuch G, de Arcangelis L, Jan N, Stauffer D (2000) Social Percolation Models. Physica A 277:239–247
Schelling T (1978) Micromotives and macrobehavior. Norton, New York
Stauffer D (1985) Introduction to Percolation Theory. Taylor and Francis Editions, London and Philadelphia
Valente T (1995) Network Models of the Diffusion of Innovations. Hampton Press, Cresskill, NJ
Valente TW (1996) Social Network Thresholds in the Diffusion of Innovation. Soc Netw 18:69–89
Wasserman S, Faust K (1994) Social Network Analysis: Methods and Applications. University of Cambridge Press, Cambridge, UK
Watts DJ, Strogatz SH (1998) Collective Dynamics of “Small-World” Networks. Nature 393:440–442
Watts DJ (2002) A Simple Model of Global Cascades on Random Networks. working paper, Santa Fe Institute
Weisbuch G, Stauffer D (2000) Hits and Flops Dynamics. Phys A 287:563–576
Young PH (1993) The Evolution of Conventions. Econometrica 61:57–94
Young PH (2002) The Diffusion of Innovation in Social Networks. Santa Fe Institute working paper 02-04–018
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper won the best student paper award at the North American Association for Computational Social and Organizational Science (NAACSOS) Conference 2005, University of Notre Dame, South Bend, Indiana, USA.
Preceding versions of this paper have been presented to the Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), 2005, University of Notre Dame, South Bend, USA and to the Conference of the European Social Simulation Association (ESSA), 2005, Koblenz, Germany.
Sebastiano Alessio Delre received his Master Degree in Communication Science at the University of Salerno. After one year collaboration at the Institute of Science and Technologies of Cognition (ISTC, Rome, Italy), now he is a PhD student at the faculty of economics, University of Groningen, the Netherlands. His work focus on how different network structures affect market dynamics. His current application domain concerns Agent-Based Simulation Models for social and economic phenomena like innovation diffusion, fashions and turbulent market.
Wander Jager is an associate professor of marketing at the University of Groningen. He studied social psychology and obtained his PhD in the behavioral and social sciences, based on a dissertation about the computer modeling of consumer behaviors in situations of common resource use. His present research is about consumer decision making, innovation diffusion, market dynamics, crowd behavior, stock-market dynamics and opinion dynamics. In his work he combines methods of computer simulation and empirical surveys. He is involved in the management committee of the European Social Simulation Association (ESSA).
Marco Janssen is an assistant professor in the School of Human Evolution and Social Change and in the Department of Computer Science and Engineering at Arizona State University. He got his degrees in Operations Research and Applied Mathematics. During the last 15 years, he uses computational tools to study social phenomena, especially human-environmental interactions. His present research focuses on diffusion dynamics, institutional innovation and robustness of social-ecological systems. He combined computational studies with laboratory and field experiments, case study analysis and archeological data. He is an associate editor-in-chief of the journal Ecology and Society.
Rights and permissions
About this article
Cite this article
Delre, S.A., Jager, W. & Janssen, M.A. Diffusion dynamics in small-world networks with heterogeneous consumers. Comput Math Organiz Theor 13, 185–202 (2007). https://doi.org/10.1007/s10588-006-9007-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10588-006-9007-2