Demand forecasting for successive generations of mobile telecommunication service in Ethiopia

References

• Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–24. https://doi.org/10.1287/mnsc.15.5.215
   Web of Science ®Google Scholar
• Bass, F. M., Krishnan, T. V., & Jain, D. C. (1994). Why the bass model fits without decision variables. Marketing Science, 13(3), 203–223. https://doi.org/10.1287/mksc.13.3.203
   Web of Science ®Google Scholar
• Bass, P. I., & Bass, F. M. (2001), Diffusion of technology generations: A model of adoption and repeat sales, Working Paper, Bass Economics Inc., Frisco, TX.
   Google Scholar
• Basuroy, S., & Nguyen, D. (1998). Multinomial logit market share models: Equilibrium characteristics and strategic implications. Management Science, 44(10), 1396–1408. https://doi.org/10.1287/mnsc.44.10.1396
   Web of Science ®Google Scholar
• Brinkman, J., Coen-Pirani, D., & Sieg, H. (2015). Firm dynamics in an urban economy *. International Economic Review, 56(4), 1135–1164. https://doi.org/10.1111/iere.12133
   Web of Science ®Google Scholar
• Chanda, U., & Bardhan, A. K. (2008). Modelling innovation and imitation sales of products with multiple technological generations. The Journal of High Technology Management Research, 18(2), 173–190. https://doi.org/10.1016/j.hitech.2007.12.004
   Google Scholar
• Chien, C. F., Chen, Y. J., & Peng, J. T. (2010). Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. International Journal of Production Economics, 128(2), 496–509. https://doi.org/10.1016/j.ijpe.2010.07.022
   Web of Science ®Google Scholar
• Cooper, L. G., & Nakanishi, M. (1989). Market-share analysis: Evaluating competitive marketing effectiveness (Vol. 1). Springer Science & Business Media.
   Google Scholar
• Danaher, P. J., Hardie, B. G. S., & Putsis, W. R. (2001). Marketing-mix variables and the diffusion of successive generations of a technological innovation. Journal of Marketing Research, 38(4), 501–514. https://doi.org/10.1509/jmkr.38.4.501.18907
   Web of Science ®Google Scholar
• Dugo, H. (2016). Violence against free media and knowledge dissemination in Ethiopia: An analysis of the mechanisms of restrictions on information flow. Africology: The Journal of Pan African Studies, 9(10), 395–410.
   Google Scholar
• Günther, M. (2017), Diffusion of multiple technology generations: An agent-based simulation approach, 2016 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE: Honolulu, HI, USA.
   Google Scholar
• Guo, Z., & Chen, J. (2018). Multigeneration product diffusion in the presence of strategic consumers. Information Systems Research, 29(1), 206–224. https://doi.org/10.1287/isre.2017.0720
   Web of Science ®Google Scholar
• Hughes, N. (2010). Apple’s recurring revenue stream: 77% of iPhone 4 sales were upgrades. Apple Insider(June 25).
   Google Scholar
• Hung, H. C., Chiu, Y. C., & Wu, M. C. (2017). A modified lotka-volterra model for diffusion and substitution of multigeneration dram processing technologies. Mathematical Problems in Engineering, 2017. https://doi.org/10.1155/2017/3038203
   Google Scholar
• Islam, T., & Meade, N. (1997). The diffusion of successive generations of a technology: A more general model. Technological Forecasting and Social Change, 56(1), 49–60. https://doi.org/10.1016/S0040-1625(97)00030-9
   Web of Science ®Google Scholar
• Jiang, Z., & Jain, D. C. (2012). A generalized Norton–Bass model for multigeneration diffusion. Management Science, 58(10), 1887–1897. https://doi.org/10.1287/mnsc.1120.1529
   Web of Science ®Google Scholar
• Jun, D. B., Kim, S. K., Park, M. H., Bae, M. S., Park, Y. S., & Joo, Y. J. (2000). Forecasting demand for low earth orbit mobile satellite service in Korea. Telecommunication Systems, 14(1/4), 311–319. https://doi.org/10.1023/A:1019105920390
   Web of Science ®Google Scholar
• Jun, D. B., & Park, Y. S. (1999). A choice-based diffusion model for multiple generations of products. Technological Forecasting and Social Change, 61(1), 45–58. https://doi.org/10.1016/S0040-1625(98)00049-3
   Web of Science ®Google Scholar
• Kim, N., Chang, D. R., & Shocker, A. D. (2000). Modeling intercategory and generational dynamics for a growing information technology industry. Management Science, 46(4), 496–512. https://doi.org/10.1287/mnsc.46.4.496.12059
   Web of Science ®Google Scholar
• Lin, C., Kilicay-Ergin, Yu, G. E., Kilicay-Ergin, N. H., & Okudan, G. E. (2011). Agent-based modeling of dynamic pricing scenarios to optimize multiple-generation product lines with cannibalization. Procedia Computer Science, 6, 311–316. https://doi.org/10.1016/j.procs.2011.08.057
   Google Scholar
• Lu, H., Hess, S., Daly, A., & Rohr, C. (2017). Measuring the impact of alcohol multi-buy promotions on consumers‘ purchase behaviour. Journal Of Choice Modelling, 24, 75–95. https://doi.org/10.1016/j.jocm.2016.05.001
   Web of Science ®Google Scholar
• Mahajan, V., & Muller, E. (1996). Timing, diffusion, and substitution of successive generations of technological innovations: The IBM mainframe case. Technological Forecasting and Social Change, 51(2), 109–132. https://doi.org/10.1016/0040-1625(95)00225-1
   Web of Science ®Google Scholar
• Meade, N., & Islam, T. (2006). Modelling and forecasting the diffusion of innovation–A 25-year review. International Journal of forecasting, 22(3), 519–545.
   Web of Science ®Google Scholar
• National Bank of Ethiopia. (2015). Annual Report.
   Google Scholar
• Negi, R. (2009). User’s perceived service quality of mobile communications: Experience from Ethiopia. International Journal of Quality & Reliability Management, 26(7), 699–711. https://doi.org/10.1108/02656710910975769
   Google Scholar
• Norton, J. A., & Bass, F. M. (1987). A diffusion theory model of adoption and substitution for successive generations of high-technology products. Management Science, 33(9), 1069–1086. https://doi.org/10.1287/mnsc.33.9.1069
   Web of Science ®Google Scholar
• Radas, S. (2006). Diffusion models in marketing: How to incorporate the effect of external influence? Privredna Kretanja I Ekonomska Politika, 15(105), 30–51. https://hrcak.srce.hr/18398
   Google Scholar
• Rogers, E. M. (2003). Diffusion of innovations. Free Press. In New York (pp. 551).
   Google Scholar
• Speece, M. W., & Maclachlan, D. L. (1995). Application of a multi-generation diffusion model to milk container technology. Technological Forecasting and Social Change, 49(3), 281–295. https://doi.org/10.1016/0040-1625(95)00006-V
   Web of Science ®Google Scholar
• Temesgen, Z., Negi, R., & Ketema, E. (2009). An assessment of the marketing strategy for mobile services of Ethiopian telecommunication corporation. Services Marketing Quarterly, 31(1), 50–71. https://doi.org/10.1080/15332960903408443
   Google Scholar
• Tsai, B. H. (2013). Predicting the diffusion of LCD TVs by incorporating price in the extended Gompertz model. Technological Forecasting and Social Change, 80(1), 106–131.
   Web of Science ®Google Scholar
• Volland, B. (2012). The history of an inferior good: Beer consumption in Germany (No. 1219). Papers on Economics and Evolution. Max-Planck-Institut für Ökonomik.
   Google Scholar
• Zsifkovits, M., & Günther, M. (2015). Simulating resistances in innovation diffusion over multiple generations: An agent-based approach for fuel-cell vehicles. Central European Journal of Operations Research, 23(2), 501–522. https://doi.org/10.1007/s10100-015-0391-x
   Web of Science ®Google Scholar