Impacto de la regla de decisión en el modelado de la difusión de innovaciones
Impact of the decision rule in innovation diffusion modeling
Cadavid, Lorena; Cardona, Carlos Jaime Franco
http://dx.doi.org/10.1590/0103-6513.053212
Production, vol.25, n4, p.750-763, 2015
Resumo
Este artículo analiza el impacto de la regla de decisión que representa el comportamiento de los individuos en la curva de difusión pronosticada por los modelos de difusión de innovaciones a nivel individual. Para ello, se hace uso de un modelo basado en agentes, en el cual la difusión ocurre dentro de una red tipo mundo pequeño, y se analiza el fenómeno usando 4 reglas de decisión diferentes: (1) una regla de umbrales con externalidades positivas, (2) una regla de umbrales con externalidades positivas y negativas, (3) una regla basada en el modelo de Bass y (4) una regla basada en la Teoría del Comportamiento Planeado. Los resultados obtenidos rechazan la hipótesis de igualdad entre las diferentes curvas de difusión. Se concluye que la regla de decisión tiene un impacto significativo en la curva de difusión pronosticada por los modelos de difusión a nivel individual.
Palavras-chave
Simulación. Difusión de innovaciones. Adopción de innovaciones. Modelado basado en agentes. Regla de decisión.
Abstract
In this, paper we analyze the impact of the decision rule to represent the behavior of individuals in the diffusion curve predicted by models of innovation diffusion at the individual level. We use an agent-based model, in which diffusion takes place in a small-world network, and analyze the phenomenon using 4 different decision rules: (1) threshold decision rule with positive externalities, (2) threshold decision rule with positive and negative externalities, (3) decision rule based on the Bass model and (4) decision rule based on the Theory of Planned Behavior. The results reject the equality hypothesis among different diffusion curves, so we conclude the decision rule has a significant impact on the diffusion curve predicted by diffusion models at the individual level.
Keywords
Simulation. Innovation diffusion. Adoption of innovations. Agent-based modeling. Decision rule.
References
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Manson, S. M. (2006). Bounded rationality in agent- based models: experiments with evolutionary programs. International Journal of Geographical Information Science, 20(9), 991-1012. http://dx.doi.org/10.1080/13658810600830566
Montalvo, C., & Kemp, R. (2008). Cleaner technology diffusion: case studies, modeling and policy. Journal of Cleaner Production, 16(1), S1-S6. http://dx.doi.org/10.1016/j.jclepro.2007.10.014
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Plackett, R. (1983). Karl Pearson and the chi-squared test. International Statistical Review, 51(1), 59-72. http://dx.doi.org/10.2307/1402731
Rahmandad, H., & Sterman, J. (2008). Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Management Science, 54(5), 998-1014. http://dx.doi.org/10.1287/mnsc.1070.0787
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Rogers, E. M. (1983). Diffusion of innovations (Vol. 11, 3rd ed.). London: Collier Macmillan Publishers.
Rohlfs, J. (2001). Bandwagon effects in high-technology industries (Vol. 27). Cambridge: MIT Press.
Schelling, T. C. (1978). Micromotives and macrobehavior. New York: W. W. Norton & Company.
Varian, H. R. (1985). Price discrimination and social welfare. The American Economic Review, 75(4), 870-875.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. http://dx.doi.org/10.1111/j.1540-5915.2008.00192.x
Watts, D. J. (2000). Small worlds: the dynamics of networks between order and randomn. Mathematical Association of America, 107(7), 664-668.
Watts, D. J., & Strogatz, S. H. (1998). Models of the small world. Nature, 393, 440-442. PMid:9623998. http://dx.doi.org/10.1038/30918
Zhang, T., Gensler, S., & Garcia, R. (2011). A study of the diffusion of alternative fuel vehicles: an agent-based modeling approach. Journal of Product Innovation Management, 28(2), 152-168. http://dx.doi.org/10.1111/j.1540-5885.2011.00789.x
Alkemade, F., & Castaldi, C. (2005). Strategies for the diffusion of innovations on social networks. Computational Economics, 25(1-2), 3-23. http://dx.doi.org/10.1007/s10614-005-6245-1
Allen, B. (1982). Some stochastic processes of interdependent demand and technological diffusion of an innovation exhibiting externalities among adopters. International Economic Review, 23(3), 595-608. http://dx.doi.org/10.2307/2526377
Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244-254.
Barabási, A.-L. (1999a). Mean-field theory for scale-free random netowork. Physica A: Statistical Mechanics and its Applications, 272(1-2), 173-187. http://dx.doi.org/10.1016/S0378-4371(99)00291-5
Barabási, A.-L. (1999b). Emergence of scaling in random networks. Science, 286(5439), 509-512. PMid:10521342. http://dx.doi.org/10.1126/science.286.5439.509
Bass, F. M. (1969). A new product gowth for model consumer durables. Management Science, 15(5), 215-227. http://dx.doi.org/10.1287/mnsc.15.5.215
Berger, T. (2001). Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics, 25(2-3), 245-260. http://dx.doi.org/10.1111/j.1574-0862.2001.tb00205.x
Buchanan, J. M. (1969). External diseconomies, corrective taxes, and market structure. The American Economic Review, 59(1), 174-177.
Chuttur, M. (2009). Overview of the technology acceptance model: origins, developments and future directions. Sprouts: Working Papers on Information Systems, 9(37), 1-23.
Gardner, M. (1970). Mathematical games: the fantastic combinations of John Conway’s new solitaire game “Life”. Scientific American, 223(4), 120-123. http://dx.doi.org/10.1038/scientificamerican1070-120
Georgescu, S., & Okuda, H. (2008). A distributed multi-agent framework for simulating the diffusion of innovations. Journal of Power and Energy Systems, 2(6), 1320-1332. http://dx.doi.org/10.1299/jpes.2.1320
Gilbert, N. (1997). A simulation of the structure of academic science. Sociological Research Online, 2(2), 1-17. http://dx.doi.org/10.5153/sro.85
Goldenberg, J., Libai, B., Solomonc, S., Jand, N., & Stauffere, D. (2000). Marketing percolation. Physica A: Statistical Mechanics and its Applications, 284(1-4), 335-347. http://dx.doi.org/10.1016/S0378-4371(00)00260-0
Goldenberg, J., & Efroni, S. (2001). Using cellular automata modeling of the emergence of innovations. Technological Forecasting and Social Change, 68(3), 293-308. http://dx.doi.org/10.1016/S0040-1625(00)00095-0
Hale, J., Householder, B., & Green, K. (2002). The theory of reasoned action. In J. Dillard & M. Pfau (Eds.), The persuasion handbook: developments in theory and practice (pp. 865). Thousand Oaks: Sage Publications.
Hsu, C. L., & Lin, J. C. (2008). Acceptance of blog usage: the roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65-74. http://dx.doi.org/10.1016/j.im.2007.11.001
Katz, M. L., & Shapiro, C. (1986). Technology adoption in the presence of network externalities. The Journal of Political Economy, 94(4), 822-841. http://dx.doi.org/10.1086/261409
Katz, M. L., & Shapiro, C. (1992). Product introduction with network externalities. The Journal of Industrial Economics, 40(1), 55-83. http://dx.doi.org/10.2307/2950627
Kemp, R., & Volpi, M. (2008). The diffusion of clean technologies: a review with suggestions for future diffusion analysis. Journal of Cleaner Production, 16(1), S14-S21. http://dx.doi.org/10.1016/j.jclepro.2007.10.019
Kiesling, E., Günther, M., Stummer, C., & Wakolbinger, L. M. (2012). Agent-based simulation of innovation diffusion: a review. Central European Journal of Operations Research, 20(2), 183-230. http://dx.doi.org/10.1007/s10100-011-0210-y
Macvaugh, J., & Schiavone, F. (2010). Limits to the diffusion of innovation: a literature review and integrative model. European Journal of Innovation Management, 13(2), 197-221. http://dx.doi.org/10.1108/14601061011040258
Mahajan, V., Muller, E., & Bass, F. (1990). New product diffusion models in marketing: a review and directions for research. The Journal of Marketing, 54(1), 1-26. http://dx.doi.org/10.2307/1252170
Manson, S. M. (2006). Bounded rationality in agent- based models: experiments with evolutionary programs. International Journal of Geographical Information Science, 20(9), 991-1012. http://dx.doi.org/10.1080/13658810600830566
Montalvo, C., & Kemp, R. (2008). Cleaner technology diffusion: case studies, modeling and policy. Journal of Cleaner Production, 16(1), S1-S6. http://dx.doi.org/10.1016/j.jclepro.2007.10.014
Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: a critical review and research directions. International Journal of Research in Marketing, 27(2), 91-106. http://dx.doi.org/10.1016/j.ijresmar.2009.12.012
Plackett, R. (1983). Karl Pearson and the chi-squared test. International Statistical Review, 51(1), 59-72. http://dx.doi.org/10.2307/1402731
Rahmandad, H., & Sterman, J. (2008). Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Management Science, 54(5), 998-1014. http://dx.doi.org/10.1287/mnsc.1070.0787
Rao, K. U., & Kishore, V. V. N. (2010). A review of technology diffusion models with special reference to renewable energy technologies. Renewable and Sustainable Energy Reviews, 14(3), 1070-1078. http://dx.doi.org/10.1016/j.rser.2009.11.007
Rogers, E. M. (1983). Diffusion of innovations (Vol. 11, 3rd ed.). London: Collier Macmillan Publishers.
Rohlfs, J. (2001). Bandwagon effects in high-technology industries (Vol. 27). Cambridge: MIT Press.
Schelling, T. C. (1978). Micromotives and macrobehavior. New York: W. W. Norton & Company.
Varian, H. R. (1985). Price discrimination and social welfare. The American Economic Review, 75(4), 870-875.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. http://dx.doi.org/10.1111/j.1540-5915.2008.00192.x
Watts, D. J. (2000). Small worlds: the dynamics of networks between order and randomn. Mathematical Association of America, 107(7), 664-668.
Watts, D. J., & Strogatz, S. H. (1998). Models of the small world. Nature, 393, 440-442. PMid:9623998. http://dx.doi.org/10.1038/30918
Zhang, T., Gensler, S., & Garcia, R. (2011). A study of the diffusion of alternative fuel vehicles: an agent-based modeling approach. Journal of Product Innovation Management, 28(2), 152-168. http://dx.doi.org/10.1111/j.1540-5885.2011.00789.x