Modelling and forecasting the diffusion of innovation - A 25-year review

International Journal of Forecasting

Volumn 22, Issue 3, 2006, Pages 519-545

  Meade, Nigel ^a   Islam, Towhidul ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF GUELPH   (Canada)

Author keywords

Diffusion models; Marketing; Technology forecasting; Telecommunications forecasting

Indexed keywords

EID: 33745933045     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2006.01.005     Document Type: Article

References (157)

1. Armstrong J.S., Brodie R.J., and McIntyre S.H. Forecasting methods for marketing - Review of empirical research. International Journal of Forecasting 3 (1987) 355-376
2. Bain A.D. Demand for new commodities. The Journal of the Royal Statistical Society, Series A 16 (1963) 285-299
3. Baptista R. Do innovations diffuse faster within geographical clusters?. International Journal of the Economics of Business 6 (1999) 107-129
4. Baptista R. The diffusion of process innovations: A selective review. International Journal of Industrial Organisation 18 (2000) 515-535
5. Bass F.M. A new product growth model for consumer durables. Management Science 15 (1969) 215-227
6. Bass F.M. The relationship between diffusion rates, experience curves, and demand elasticities for consumer durable technological innovations. Journal of Business 53 (1980) 51-67
7. Bass F.M., and Bultez A.V. A note on optimal strategic pricing of technological innovations. Marketing Science 1 (1982) 371-378
8. Bass F.M., Gordon K., Ferguson T.L., and Githens M.L. DIRECTV: Forecasting diffusion of a new technology prior to product launch. Interfaces 31 (2001, May/June) S82-S93
9. Bass F., Jain D., and Krishnan T. Modelling the marketing influence in new product diffusion. In: Mahajan V., Muller E., and Wind Y. (Eds). New product diffusion models (2000), Kluwer Academic Publishers, London 99-122
10. Bass F.M., Krishnan T., and Jain D. Why the Bass model fits without decision variables. Marketing Science 13 3 (1994) 203-223
11. Baumgartner H., and Steenkamp J.B.E.M. An investigation into the construct-validity of the arousal seeking tendency scale, version 2. Educational and Psychological Measurement 54 (1994) 993-1001
12. Bayus B. Forecasting sales of new contingent products: An application to the compact disc markets. Journal of Product Innovation Management 4 (1987, December) 243-255
13. Bayus B.L., Hong S., and Labe Jr. R.P. Developing and using forecasting models of consumer durables. Journal of Production and Innovation Management 6 (1989) 5-19
14. Bemmaor A.C. Modelling the diffusion of new durable goods: Word-of-mouth effect versus consumer heterogeneity. In: Laurent G., Lilien G.L., and Pras B. (Eds). Research traditions in marketing (1994), Kluwer, Boston, MA 201-213
15. Bemmaor A.C., and Lee J. The impact of heterogeneity and ill-conditioning on diffusion model parameter estimates. Marketing Science 21 (2002) 209-220
16. Bewley R., and Fiebig D. Flexible logistic growth model with applications in telecommunications. International Journal of Forecasting 4 (1988) 177-192
17. Bewley R., and Griffiths W. The penetration of CDs in the sound recording market: Issues in specification, model selection and forecasting. International Journal of Forecasting 19 1 (2003) 111-121
18. Blackman W.A. A mathematical model for trend forecasts. Technological Forecasting and Social Change 3 (1972) 441-452
19. Bonus H. Quasi-Engel curves, diffusion and the ownership of major consumer durables. Journal of Political Economy 81 (1973) 655-677
20. Boswijk H.P., and Franses P.H. On the econometrics of the Bass diffusion model. Journal of Business and Economic Statistics 23 3 (2005) 255-268
21. Bottomley P.A., and Fildes R. The role of prices in models of innovation diffusion. Journal of Forecasting 17 (1998) 539-555
22. Chatfield C. Calculating interval forecasts. Journal of Business and Economic Statistics 11 (1993) 121-135
23. Chatterjee R., and Eliashberg J. The innovation diffusion process in a heterogeneous population: A micromodelling approach. Management Science 36 (1990) 1057-1079
24. Chow G.C. Technological change and demand for consumers. American Economic Review 57 (1967) 1117-1130
25. Danaher P.J., Hardie B.G.S., and Putsis W.P. Marketing-mix variables and the diffusion of successive generations of a technological innovation. Journal of Marketing Research 38 (2001) 501-514
26. Desiraju R., Nair H., and Chintagunta P. Diffusion of new pharmaceutical drugs in developing and developed nations. International Journal of Research in Marketing 21 (2004) 341-357
27. Dolan R.J., and Jeuland A.P. Experience curves and dynamic demand models: Implications for optimal pricing strategies. Journal of Marketing 45 1 (1981) 52-62
28. Duesenberry J.S. Income, saving and the theory of consumer behavior (1949), Harvard University Press, Cambridge, MA
29. Easingwood C., Mahajan V., and Muller E. A non-symmetric responding logistic model for forecasting technological substitution. Technological Forecasting and Social Change 20 (1981) 199-213
30. Easingwood C., Mahajan V., and Muller E. A non-uniform influence innovation diffusion model of new product acceptance. Marketing Science 2 (1983, Summer) 273-296
31. Fernandez V. Decisions to replace consumer durable goods: An econometric application of Weiner and renewal process. The Review of Economics and Statistics 82 3 (2000) 452-461
32. Fisher J.C., and Pry R.H. Simple substitution model of technological change. Technological Forecasting and Social Change 3 (1971) 75-88
33. Floyd A. Trend forecasting: A methodology for figure of merit. In: Bright J. (Ed). Technological forecasting for industry and government (1962), Prentice Hall, New Jersey 95-105
34. Fourt L.A., and Woodlock J.W. Early prediction of early success of new grocery products. Journal of Marketing 25 (1960, October) 31-38
35. Frank L.D. An analysis of the effect of the economic situation on modelling and forecasting the diffusion of wireless communications in Finland. Technological Forecasting and Social Change 71 (2004) 391-403
36. Furman J.L., Porter M.E., and Stern S. The determinants of national innovative capacity. Research Policy 31 (2002) 899-933
37. Ganesh J., and Kumar V. Capturing the cross-national learning effects: An analysis of an industrial technology diffusion. Journal of Academy of Marketing Science 24 (1996, Fall) 328-337
38. Ganesh J., Kumar V., and Subramanian V. Learning effects in multinational diffusion of consumer durables: An exploratory investigation. Journal of Academy of Marketing Science 25 3 (1997) 214-228
39. Gatignon H.A., Eliashaberg J., and Robertson T.S. Modelling multinational diffusion patterns: An efficient methodology. Marketing Science 8 (1989) 231-247
40. Geroski P.A. Models of technology diffusion. Research Policy 29 (2000) 603-625
41. Givon M., Mahajan V., and Muller E. Software piracy: Estimation of lost sales and the impact on software diffusion. Journal of Marketing 59 (1995) 29-37
42. Givon M., Mahajan V., and Muller E. Assessing the relationship between user-based market share and unit sales-based market share for pirated software brands in competitive markets. Technological Forecasting and Social Change 55 (1997) 131-144
43. Goldenberg J., Libai B., Solomon S., Jan N., and Stauffer D. Marketing percolation. Physica A 284 (2000) 335-347
44. Goswami D., and Karmeshu. Study of population heterogeneity in innovation diffusion model: Estimation based on simulated annealing. Technological Forecasting and Social Change 71 (2004) 705-722
45. Gottardi G., and Scarso E. Diffusion models in forecasting: A comparison with the Box-Jenkins approach. European Journal of Operational Research 75 (1994) 600-616
46. Gregg J.V., Hassel C.H., and Richardson J.T. Mathematical trend curves: An aid to forecasting (1964), Oliver and Boyd, Edinburgh
47. Grewal R., Mehta R., and Kardes F.R. The timing of repeat purchases of consumer durable goods: The role of functional bases of consumer attitudes. Journal of Marketing Research 41 1 (2004) 101-115
48. Gruber H., and Verbovan F. The diffusion of mobile telecommunication services in the European Union. European Economic Review 45 (2001) 577-588
49. Guttiérrez R., Nafidi A., and Guttiérrez Sánchez R. Forecasting total natural gas consumption in Spain by using the stochastic Gompertz innovation diffusion model. Applied Energy 80 (2005) 115-124
50. Hardie B.G.S., Fader P.S., and Wisniewski M. An empirical comparison of new product trial forecasting models. Journal of Forecasting 17 (1998) 209-229
51. Harvey A.C. Time series forecasting based on the logistic curve. Journal of the Operational Research Society 35 (1984) 641-646
52. Hausman J. Mobile telephone. In: Cave M.E. (Ed). Handbook of telecommunications economics vol. 1 (2002), Elsevier Science B.V. 564-604 Chapter 13
53. Helsen K., Jedidi K., and DeSarbo W.S. A new approach to country segmentation utilizing multinational diffusion patterns. Journal of Marketing 57 (1993) 60-71
54. Ho T.-H., Savin S., and Terwiesch C. Managing demand and sales dynamics in new product diffusion under supply constraint. Management Science 48 (2002) 187-206
55. Hofstede G. The cultural relativity of organizational practices and theories. Journal of International Business Studies 14 (1983) 75-89
56. Hofstede G. Culture's consequences: International differences in work related values (1984), Sage, London
57. Horsky D. A diffusion model incorporating product benefits, price, income and information. Marketing Science 9 (1990) 342-365
58. Horsky D., and Simon L.S. Advertising and the diffusion of new products. Marketing Science 2 1 (1983) 1-17
59. Islam T., and Fiebig D.B. Modelling the development of supply-restricted telecommunications markets. Journal of Forecasting 20 (2001) 249-264
60. Islam T., Fiebig D.B., and Meade N. Modelling multinational telecommunications demand with limited data. International Journal of Forecasting 18 (2002) 605-624
61. Islam T., and Meade N. Forecasting the development of the market for business telephones in the UK. Journal of the Operational Research Society 47 (1996) 906-918
62. Islam T., and Meade N. The diffusion of successive generations of a technology: A more general model. Technological Forecasting and Social Change 56 (1997) 49-60
63. Islam T., and Meade N. Modelling diffusion and replacement. European Journal of Operations Research 125 (2000) 551-570
64. Jain, D.C. (1992). Marketing Mix Effects on the Diffusion of Innovations, Working Paper, Kellogg Graduate School of Management, Northwestern University.
65. Jain D.C., Mahajan V., and Muller E. Innovation diffusion in presence of supply restrictions. Marketing Science 10 (1991) 83-90
66. Jain D.C., and Rao R.C. Effect of price on the demand for durables: Modeling, estimation, and findings. Journal of Business and Economic Statistics 8 2 (1990) 163-170
67. Jun D.B., and Park Y.S. A choice based diffusion model for multiple generations of products. Technological Forecasting and Social Change 61 (1999) 45-58
68. Jun D.B., Kim S.K., Park Y.S., Park M.H., and Wilson A.R. Forecasting telecommunication service subscribers in substitutive and competitive environments. International Journal of Forecasting 18 (2002) 561-581
69. Kalish S. Monopolistic pricing with dynamic demand and production cost. Marketing Science 2 (1983) 135-160
70. Kalish S. A new product adoption model with price, advertising and uncertainty. Management Science 31 (1985) 1569-1585
71. Kalish S., and Lilien G.L. Optimal price subsidy policy for accelerating the diffusion of innovation. Marketing Science 2 (1983) 407-420
72. Kalish S., Mahajan V., and Muller E. Waterfall and sprinkler new product strategies in competitive global markets. International Journal of Research in Marketing 12 (1995) 105-119
73. Kamakura W.A., and Balasubramanian S.K. Long-term forecasting with innovation diffusion models: The impact of replacement purchase. Journal of Forecasting 6 (1987) 1-19
74. Kamakura W.A., and Balasubramanian S.K. Long-term view of the diffusion of durables. International Journal of Research in Marketing 5 (1988) 1-13
75. Karshenas M., and Stoneman P. A flexible model of technological diffusion incorporating economic factors with an application to the spread of colour television ownership in the UK. Journal of Forecasting 11 (1992) 577-601
76. Kiiski S., and Pohjola M. Cross-country diffusion of the internet. International Economics and Policy 14 (2002) 297-310
77. Kim N., Chang D.R., and Shocker A.D. Modelling intercategory and generational dynamics for a growing information technology industry. Management Science 46 (2000) 496-512
78. Kohli R., Lehmann D.R., and Pae J. Extent and impact of incubation time in new product diffusion. Journal of Production Innovation Management 16 (1999) 134-144
79. Krishnan T.V., Bass F.M., and Jain D.C. Optimal pricing strategy for new products. Management Science 45 (1999) 1650-1663
80. Krishnan T.V., Bass F.M., and Kumar V. Impact of a late entrant on the diffusion of a new product/service. Journal of Marketing Research 37 (2000) 269-278
81. Kumar S., and Swaminathan J.M. Diffusion of innovations under supply constraints. Operations Research 51 (2003) 866-879
82. Kumar U., and Kumar V. Technological innovation diffusion: The proliferation of substitution model and easing the user dilemma. IEEE Transactions on Engineering Management 39 (1992) 158-168
83. Kumar V., Ganesh R., and Echambadi R. Cross-national diffusion research: What do we know and how certain are we?. Journal of Product Innovation Management 15 (1998) 255-268
84. Kumar V., and Krishnan T.V. Multinational diffusion models: An alternative framework. Marketing Science 21 (2002) 318-330
85. Lee C. Determinants of national innovativeness and international market segmentation. International Marketing Review 7 5 (1990) 39-49
86. Lee J., Boatwright P., and Kamakura W. A Bayesian model for pre-launch sales forecasting of recorded music. Management Science 49 (2003) 179-196
87. Lee J.C., and Lu K.W. On a family of data-based transformed models useful in forecasting technological substitution. Technological Forecasting and Social Change 31 (1987) 61-78
88. Lenk P.J., and Rao A.G. New products from old: Forecasting product adoption by hierarchical Bayes procedures. Marketing Science 9 (1990) 42-53
89. Libertore M.J., and Bream D. Adoption and implementation of digital-imaging technology in the banking and insurance industries. IEEE Transactions on Engineering Management 44 (1997) 367-377
90. Liebermann E., and Paroush J. Economic aspects of diffusion models. Journal of Economics and Business 34 (1982) 95-100
91. Lilien G.R., Rangaswamy A., and van den Bulte C. Diffusion models: Managerial applications and software. In: Mahajan V., Muller E., and Wind Y. (Eds). New product diffusion models (2000), Kluwer Academic Publishers, London
92. Lynn M., and Gelb B.D. Identifying innovative national markets for technical consumer goods. International Marketing Review 13 (1996) 43-57
93. Mahajan V. 'Commentary on Bemmaor, A.C., Modelling the diffusion of new durable goods: Word-of mouth effect versus consumer heterogeneity'. In: Laurent G., Lilien G.L., and Pras B. (Eds). Research traditions in marketing (1994), Kluwer, Boston, MA 201-213
94. Mahajan V., and Muller E. Timing, diffusion, and substitution of successive generations of technological innovations: The IBM mainframe case. Technological Forecasting and Social Change 51 (1996, February) 109-132
95. Mahajan V., Muller E., and Bass F.M. New-product diffusion models: A review and directions for research. Journal of Marketing 54 (1990) 1-26
96. Mahajan V., Muller E., and Bass F.M. New-product diffusion models. Handbook in operations research and management science: Chapter 8. Marketing vol. 5 (1993), North Holland, Amsterdam, Netherlands
97. In: Mahajan V., Muller E., and Wind Y. (Eds). New product diffusion models (2000), Kluwer Academic Publishers, London
98. Mahajan V., Muller E., and Wind Y. New product diffusion models: From theory to practice. In: Mahajan V., Muller E., and Wind Y. (Eds). New product diffusion models (2000), Kluwer Academic Publishers, London 3-24
99. Mahajan V., and Peterson R.A. Innovation diffusion in a dynamic potential adapter population. Management Science 24 (1978) 1589-1597
100. Mahajan V., and Peterson R.A. Models for innovation diffusion (1985), Sage, California, USA
101. Mahajan V., Sharma S., and Buzzell R.B. Assessing the impact of competitive entry on market expansion and incumbent sales. Journal of Marketing 567 (1993) 39-52
102. Mahler A., and Rogers E.M. The diffusion of interactive communication innovations and the critical mass: The adoption of telecommunications services by German banks. Telecommunications Policy 23 (1999) 719-740
103. Mansfield E. Technical change and the rate of imitation. Econometrica 29 (1961) 741-766
104. Marchetti C. Primary energy substitution models: On the interaction between energy and society. Technological Forecasting and Social Change 10 (1977) 345-356
105. McCarthy C., and Ryan J. An econometric model of television ownership. Economic and Social Review 7 (1976) 256-277 (Dublin)
106. Meade N. The use of growth curves in forecasting market development - A review and appraisal. Journal of Forecasting 3 (1984) 429-451
107. Meade N. Forecasting using growth curves - An adaptive approach. Journal of the Operational Research Society 36 (1985) 1103-1115
108. Meade N. Technological substitution: A framework of stochastic models. Technological Forecasting and Social Change, 36 389 (1989) 400
109. Meade N., and Islam T. Growth curve forecasting: An empirical comparison. International Journal of Forecasting 11 (1995) 199-215
110. Meade N., and Islam T. Prediction intervals for growth curve forecasts. Journal of Forecasting 14 (1995) 413-430
111. Meade N., and Islam T. Technological forecasting-model selection, model stability and combining models. Management Science 44 (1998) 1115-1130
112. Meade N., and Islam T. Forecasting the diffusion of innovations. In: Armstrong J.S. (Ed). Principles of forecasting (2001), Kluwer Academic Publishers, Boston
113. Meade N., and Islam T. Modelling the dependence between the times to international adoption of two related technologies. Technological Forecasting and Social Change 70 (2003) 759-778
114. Midgley D.F., and Dowling G.R. Innovativeness: The concept and its measurement. Journal of Consumer Research 4 (1978) 229-242
115. Migon H.S., and Gamerman D. Generalized exponential growth models - A Bayesian approach. Journal of Forecasting 12 (1993) 573-584
116. Norton J.A., and Bass F.M. A diffusion theory model of adoption and substitution for successive generations of high-technology products. Management Science 33 (1987) 1069-1086
117. Norton J.A., and Bass F.M. Evolution of technological generations: The law of capture. Sloan Management Review (1992) 66-77
118. Olson J., and Choi S. A product diffusion model incorporating repeat purchases. Technological Forecasting and Social Change 27 (1985) 385-397
119. Padmanabhan V., and Bass F.M. Optimal pricing of successive generations of product advances. International Journal of Research in Marketing 10 (1993) 185-207
120. Parker P.M. Price elasticity dynamics over the product life cycles. Journal of Marketing Research 29 3 (1992) 358-367
121. Putsis W.P. Temporal aggregation in diffusion models of first-time purchase: Does choice of frequency matter?. Technological Forecasting and Social Change 51 (1996) 265-279
122. Putsis W.P. Parameter variation and new product diffusion. Journal of Forecasting 17 3-4 (1998) 231-257
123. Robinson B., and Lakhani C. Dynamic pricing models for new product planning. Management Science 10 (1975) 1113-1122
124. Rogers E.M. Diffusion of innovations (1962), The Free Press, New York
125. Rogers E.M. Diffusion of innovations. Fourth Edition (1995), The Free Press, New York
126. Russell T. Comments on 'The relationship between diffusion rates, experience curves and demand elasticities for consumer durable technological innovations'. Journal of Business 53 3 (1980) 69-73
127. Schmittlein D.C., and Mahajan V. Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Science 1 1 (1982, Winter) 57-78
128. Sharif M.N., and Islam M.N. The Weibull distribution as a general model for forecasting technological change. Technological Forecasting and Social Change 18 (1980) 247-256
129. Sharif M.N., and Kabir C. System dynamics modelling for forecasting multilevel technological substitution. Technological Forecasting and Social Change 9 (1976) 89-112
130. Sharma L.A., Basu S.C., and Bhargava S.C. A new model of innovation diffusion. Journal of Scientific and Industrial Research 52 (1993) 151-158
131. Shocker A.D., Bayus B.L., and Kim N. Product complements and substitutes in the real world: The relevance of "other products". Journal of Marketing 68 (2004) 28-40
132. Simon H., and Sebastian K.-H. Diffusion and advertising: German telephone campaign. Management Science 33 (1987, April) 451-466
133. Smith F.E. Population dynamics in Daphnia magna and a new model for population growth. Ecology 44 (1963) 651-663
134. Snellman J.S., Vesala J.M., and Humphrey D.B. Substitution of non-cash payment instruments for cash in Europe. Journal of Financial Services Research 19 2/3 (2001) 131-145
135. Sohn S.Y., and Ahn B.J. Multi-generation diffusion model for economic assessment of new technology. Technological Forecasting and Social Change 70 (2003) 251-264
136. Speece M.W., and MacLachlan D.L. Forecasting fluid milk package type with a multi-generation new product diffusion model. IEEE Transactions on Engineering Management 39 (1992, May) 169-175
137. Srinivasan V., and Mason C.H. Non-linear least squares estimation of new product diffusion models. Marketing Science 5 2 (1986, Spring) 169-178
138. Steenkamp J.-B.E.M., Hofstede F., and Wedel M. A cross-national investigation into the individual and national cultural antecedents of consumer innovativeness. Journal of Marketing 63 (1999) 55-69
139. Steffens P.R. An aggregate sales model for consumer durables incorporating a time-varying mean replacement age. Journal of Forecasting 20 (2001) 63-77
140. Steffens P.R. A model of multiple unit ownership as a diffusion process. Technological Forecasting and Social Change 70 (2003) 901-917
141. Sultan F., Farley J.U., and Lehman D.R. A meta-analysis of applications of diffusion models. Journal of Marketing Research 27 (1990) 70-77
142. Suslick S.B., Harris D.P., and Allan L.H.E. SERFIT: An algorithm to forecast mineral trends. Computers and Geoscience 21 (1995) 703-713
143. Takada H., and Jain D. Cross-national analysis of diffusion of consumer durable goods in Pacific rim countries. Journal of Marketing 55 (1991, April) 48-54
144. Talukdar D., Sudhir K., and Ainslie A. Investigating new product diffusion across products and countries. Marketing Science 21 1 (2002) 97-114
145. Tanner J.C. Forecasts of vehicles and traffic in Great Britain. TRRL laboratory report vol. 650 (1974), Transport and Road Research Laboratory, Department of Transport, UK
146. Tanner J.C. Long term forecasting of vehicle ownership and road traffic. The Journal of the Royal Statistical Society, Series A 141 (1978) 14-63
147. Teng T.C., Grover V., and Guttler W. Information technology innovations: General diffusion patterns and its relationships to innovation characteristics. IEEE Transactions on Engineering Management 49 (2002) 13-27
148. Thomson G.L., and Teng J.-T. Optimal pricing and advertising policies for new product oligopoly models. Marketing Science 3 (1984) 148-168
149. Van den Bulte C. New product diffusion acceleration: Measurement and analysis. Marketing Science 19 (2000) 366-380
150. Van den Bulte C., and Lilien G.L. Bias and systematic change in the parameter estimates of macro-level diffusion models. Marketing Science 16 (1997) 338-353
151. Van den Bulte C., and Stremersch S. Social contagion and income heterogeneity in new product diffusion: A meta-analytic test. Marketing Science 23 (2004) 530-544
152. Venkatesan R., Krishnan T.V., and Kumar V. Evolutionary estimation of macro-level diffusion models using genetic algorithms: An alternative to nonlinear least squares. Marketing Science 3 (2004) 451-464
153. Venkatesan R., and Kumar V. A genetic algorithms approach to forecasting of wireless subscribers. International Journal of Forecasting 18 4 (2002) 625-646
154. Versluis C. DRAMs, fibre and energy compared with three models of penetration. Technological Forecasting and Social Change 69 (2002) 263-286
155. Wareham J., Levy A., and Shi W. Wireless diffusion and mobile computing: Implications for the digital divide. Telecommunications policy 28 (2004) 439-457
156. Xie J., Song M., Sirbu M., and Wang Q. Kalman filter estimation of new product diffusion models. Journal of Marketing Research 34 (1997) 378-393
157. Young P. Technological growth curves, a competition of forecasting models. Technological Forecasting and Social Change 44 (1993) 375-389