Amini, M., Wakolbinger, T., Racer, M. and Nejad, M.G., 2012. Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach. European journal of operational research, 216(2), pp.301-311. https://doi.org/10.1016/j.ejor.2011.07.040.
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), pp.245-260. https://doi.org/10.1111/j.1574-0862.2001.tb00205.x.
Bohlmann, J.D., Calantone, R.J. and Zhao, M., 2010. The effects of market network heterogeneity on innovation diffusion: An agent‐based modeling approach. Journal of Product Innovation Management, 27(5), pp.741-760. https://doi.org/10.1111/j.1540-5885.2010.00748.x.
Delre, S.A., Jager, W. and Janssen, M.A., 2007. Diffusion dynamics in small-world networks with heterogeneous consumers. Computational and Mathematical Organization Theory, 13(2), pp.185-202. https://doi.org/10.1007/s10588-006-9007-2.
Delre, S.A., Jager, W., Bijmolt, T.H. and Janssen, M.A., 2010. Will it spread or not? The effects of social influences and network topology on innovation diffusion. Journal of Product Innovation Management, 27(2), pp.267-282. https://doi.org/10.1111/j.1540-5885.2010.00714.x.
Fazeli, A. and Jadbabaie, A., 2012, December. Game theoretic analysis of a strategic model of competitive contagion and product adoption in social networks. In 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) (pp. 74-79). IEEE.
Goldenberg, J., Libai, B., Solomon, S., Jan, N. and Stauffer, D., 2000. Marketing percolation. Physica A: statistical mechanics and its applications, 284(1-4), pp.335-347. https://doi.org/10.1016/S0378-4371(00)00260-0.
Günther, M. and Stummer, C., 2018. Simulating the diffusion of competing multi-generation technologies: An agent-based model and its application to the consumer computer market in Germany. In Operations research proceedings, 2016, Springer, Cham. pp. 569-574. https://doi.org/10.1007/978-3-319-55702-1_75.
Günther, M., Stummer, C., Wakolbinger, L. M., and Wildpaner, M., 2011. An agent-based simulation approach for the new product diffusion of a novel biomass fuel. Journal of the Operational Research Society, 62(1), pp. 12-20. https://doi.org/10.1057/jors.2009.170.
Heppenstall, A., Evans, A. and Birkin, M., 2006. Using hybrid agent-based systems to model spatially-influenced retail markets. Journal of Artificial Societies and Social Simulation, 9(3). Available at: https://www.jasss.org/9/3/2.html.
Kiesling, E., Günther, M., Stummer, C. and Wakolbinger, L.M., 2012. Agent-based simulation of innovation diffusion: a review. Central European Journal of Operations Research, 20(2), pp.183-230. https://doi.org/10.1007/s10100-011-0210-y.
Kim, S., Lee, K., Cho, J.K. and Kim, C.O., 2011. Agent-based diffusion model for an automobile market with fuzzy TOPSIS-based product adoption process. Expert Systems with Applications, 38(6), pp.7270-7276. https://doi.org/10.1016/j.eswa.2010.12.024.
Miremadi, A. and Faghanie, E., 2012. An empirical study of consumer buying behavior and its influence on consumer preference in Iranian FMCG market: A case study. International Business and Management, 5(1), pp.146-152. https://doi.org/10.3968/J.IBM.1923842820120501.1115.
Moldovan, S. and Goldenberg, J., 2004. Cellular automata modeling of resistance to innovations: Effects and solutions. Technological Forecasting and Social Change, 71(5), pp.425-442. https://doi.org/10.1016/S0040-1625(03)00026-X.
Rogers, E. M. 1962, Diffusion of Innovations, Free Press, New York.
Rosales, C.R., Whipple, J.M. and Blackhurst, J., 2018. The impact of distribution channel decisions and repeated stockouts on manufacturer and retailer performance. IEEE transactions on engineering management, 66(3), pp.312-324. https://doi.org/10.1109/TEM.2018.2835653.
Ryan, B. and Gross, N.C., 1943. The diffusion of hybrid seed corn in two Iowa communities. Rural sociology, 8(1), pp.15-24.
Sonderegger-Wakolbinger, L.M. and Stummer, C., 2015. An agent-based simulation of customer multi-channel choice behavior. Central European Journal of Operations Research, 23(2), pp.459-477. https://doi.org/10.1007/s10100-015-0388-5.
Stummer, C., Kiesling, E., Günther, M. and Vetschera, R., 2015. Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach. European Journal of Operational Research, 245(1), pp.157-167. https://doi.org/10.1016/j.ejor.2015.03.008.
Stummer, C., Lüpke, L. and Günther, M., 2021. Beaming market simulation to the future by combining agent-based modeling with scenario analysis. Journal of Business Economics, 91(9), pp.1469-1497. https://doi.org/10.1007/s11573-021-01046-9.
Sturley, C., Newing, A. and Heppenstall, A., 2018. Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours. The International Review of Retail, Distribution and Consumer Research, 28(1), pp.27-46. https://doi.org/10.1080/09593969.2017.1397046.
Valente, T.W. and Rogers, E.M., 1995. The origins and development of the diffusion of innovations paradigm as an example of scientific growth. Science communication, 16(3), pp.242-273. https://doi.org/10.1177/1075547095016003002.
Zhang, T., Gensler, S. and Garcia, R., 2011. A study of the diffusion of alternative fuel vehicles: An agent‐based modeling approach. Journal of Product Innovation Management, 28(2), pp.152-168. https://doi.org/10.1111/j.1540-5885.2011.00789.x.
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