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Aggregate diffusion forecasting models in marketing: A critical review

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  1. Kristof Decock & Koenraad Debackere & Anne- Mieke Vandamme & Bart Looy, 2020. "Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2703-2715, September.
  2. C. H. Skiadas & A. N. Giovanis, 1997. "A stochastic Bass innovation diffusion model for studying the growth of electricity consumption in Greece," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 13(2), pages 85-101, June.
  3. Gabe Bondt & David Ibáñez, 2005. "High-Yield Bond Diffusion in the United States, the United Kingdom, and the Euro Area," Journal of Financial Services Research, Springer;Western Finance Association, vol. 27(2), pages 163-181, April.
  4. Bähr-Seppelfricke, Ulrike, 2000. "Die Wirkung von Produkteigenschaften auf die Diffusion von Produktgruppen: Empirische Überprüfung in einem aggregierten Diffusionsmodell," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 525, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
  5. Silvio Di Fabio, 2017. "Diffusione tecnologica e ICT: modelli ed applicazioni," PRISMA Economia - Societ? - Lavoro, FrancoAngeli Editore, vol. 2017(3), pages 92-106.
  6. Fildes, Robert & Kumar, V., 2002. "Telecommunications demand forecasting--a review," International Journal of Forecasting, Elsevier, vol. 18(4), pages 489-522.
  7. Mesak, Hani I. & Bari, Abdullahel & Babin, Barry J. & Birou, Laura M. & Jurkus, Anthony, 2011. "Optimum advertising policy over time for subscriber service innovations in the presence of service cost learning and customers' disadoption," European Journal of Operational Research, Elsevier, vol. 211(3), pages 642-649, June.
  8. Goldenberg, J & Libai, B & Solomon, S & Jan, N & Stauffer, D, 2000. "Marketing percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 284(1), pages 335-347.
  9. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
  10. Madden, Gary & Coble-Neal, Grant & Dalzell, Brian, 2004. "A dynamic model of mobile telephony subscription incorporating a network effect," Telecommunications Policy, Elsevier, vol. 28(2), pages 133-144, March.
  11. Qin, Ruwen & Nembhard, David A., 2012. "Demand modeling of stochastic product diffusion over the life cycle," International Journal of Production Economics, Elsevier, vol. 137(2), pages 201-210.
  12. Jacob Goldenberg & Oded Lowengart & Daniel Shapira, 2009. "Zooming In: Self-Emergence of Movements in New Product Growth," Marketing Science, INFORMS, vol. 28(2), pages 274-292, 03-04.
  13. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
  14. Philip Hans Franses, 2003. "The diffusion of scientific publications: The case of Econometrica, 1987," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 29-42, January.
  15. Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
  16. Ben Maalla, El Mehdi & Kunsch, Pierre L., 2008. "Simulation of micro-CHP diffusion by means of System Dynamics," Energy Policy, Elsevier, vol. 36(7), pages 2308-2319, July.
  17. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
  18. Shun-Chen Niu, 2006. "A Piecewise-Diffusion Model of New-Product Demands," Operations Research, INFORMS, vol. 54(4), pages 678-695, August.
  19. Yang, Hai & Meng, Qiang, 2001. "Modeling user adoption of advanced traveler information systems: dynamic evolution and stationary equilibrium," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(10), pages 895-912, December.
  20. John D. Sterman & Rebecca Henderson & Eric D. Beinhocker & Lee I. Newman, 2007. "Getting Big Too Fast: Strategic Dynamics with Increasing Returns and Bounded Rationality," Management Science, INFORMS, vol. 53(4), pages 683-696, April.
  21. Fernández-Durán, J.J., 2014. "Modeling seasonal effects in the Bass Forecasting Diffusion Model," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 251-264.
  22. Soloviev, Vladimir, 2009. "Экономико-Математическое Моделирование Рынка Программного Обеспечения: Монография. — М.: Вега-Инфо, 2009. — 176 С [Economic and mathematical modelling of software market]," MPRA Paper 28974, University Library of Munich, Germany.
  23. Riikonen, Antti & Smura, Timo & Töyli, Juuso, 2016. "The effects of price, popularity, and technological sophistication on mobile handset replacement and unit lifetime," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 313-323.
  24. Shidong Wang & Renaud Foucart & Cheng Wan, 2014. "Comeback kids: an evolutionary approach of the long-run innovation process," Papers 1411.2167, arXiv.org, revised Jul 2016.
  25. van den Bulte, C. & Stremersch, S., 2003. "Contagion and heterogeneity in new product diffusion: An emperical test," ERIM Report Series Research in Management ERS-2003-077-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  26. Mingxing Wu & Liya Wang & Ming Li, 2015. "An approach based on the Bass model for analyzing the effects of feature fatigue on customer equity," Computational and Mathematical Organization Theory, Springer, vol. 21(1), pages 69-89, March.
  27. Ramírez-Hassan, Andrés & Montoya-Blandón, Santiago, 2020. "Forecasting from others’ experience: Bayesian estimation of the generalized Bass model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 442-465.
  28. Takanori Ida & Shin Kinoshita & Masayuki Sato, 2008. "Conjoint analysis of demand for IP telephony: the case of Japan," Applied Economics, Taylor & Francis Journals, vol. 40(10), pages 1279-1287.
  29. Rui Leite & Aurora Teixeira, 2012. "Innovation diffusion with heterogeneous networked agents: a computational model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 125-144, October.
  30. Rajkumar Venkatesan & Trichy V. Krishnan & V. Kumar, 2004. "Evolutionary Estimation of Macro-Level Diffusion Models Using Genetic Algorithms: An Alternative to Nonlinear Least Squares," Marketing Science, INFORMS, vol. 23(3), pages 451-464, August.
  31. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
  32. M Günther & C Stummer & L M Wakolbinger & M Wildpaner, 2011. "An agent-based simulation approach for the new product diffusion of a novel biomass fuel," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 12-20, January.
  33. Kurdgelashvili, Lado & Shih, Cheng-Hao & Yang, Fan & Garg, Mehul, 2019. "An empirical analysis of county-level residential PV adoption in California," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 321-333.
  34. Venkatesan, Rajkumar & Kumar, V., 2002. "A genetic algorithms approach to growth phase forecasting of wireless subscribers," International Journal of Forecasting, Elsevier, vol. 18(4), pages 625-646.
  35. de Bondt, Gabe & Marqués-Ibáñez, David, 2004. "The high-yield segment of the corporate bond market: a diffusion modelling approach for the United States, the United Kingdom and the euro area," Working Paper Series 313, European Central Bank.
  36. S Howick & J Whalley, 2008. "Understanding the drivers of broadband adoption: the case of rural and remote Scotland," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(10), pages 1299-1311, October.
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