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  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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  • P-ISSN2287-1608
  • E-ISSN2287-1616
  • KCI

Forecasting the Diffusion of Innovative Products Using the Bass Model at the Takeoff Stage: A Review of Literature from Subsistence Markets

Asian Journal of Innovation and Policy / Asian Journal of Innovation and Policy, (P)2287-1608; (E)2287-1616
2019, v.8 no.1, pp.141-161
Suddhachit Mitra (Institute of Rural Management Anand (IRMA))

Abstract

A considerable amount of research has been directed at subsistence markets in the recent past with the belief that these markets can be tapped profitably by marketers. Consequently, such markets have seen the launch of a number of innovative products. However, marketers of such forecasts need timely and accurate forecasts regarding the diffusion of their products. The Bass model has been widely used in marketing management to forecast diffusion of innovative products. Given the idiosyncrasies of subsistence markets, such forecasting requires an understanding of effective estimation techniques of the Bass model and their use in subsistence markets. This article reviews the literature to achieve this objective and find out gaps in research. A finding is that there is a lack of timely estimates of Bass model parameters for marketers to act on. Consequently, this article sets a research agenda that calls for timely forecasts at the takeoff stage using appropriate estimation techniques for the Bass model in the context of subsistence markets.

keywords
Bass model, takeoff stage, subsistence markets, critical review

참고문헌

1.

Akinola, A.A. (1986) An application of Bass's model in the analysis of diffusion of cocoa‐spraying chemicals among Nigerian cocoa farmers, Journal of Agricultural Economics, 37(3), 395-404.

2.

Atteridge, A., Weitz, N. and Nilsson, M. (2013) Technology innovation in the Indian clean cooking sector: identifying critical gaps in enabling conditions, Working Paper, Stockholm Environment Institute.

3.

Avila, L.A.P., Lee, D.J. and Kim, T. (2018) Diffusion and competitive relationship of mobile telephone service in Guatemala: an empirical analysis, Telecommunications Policy, 42(2), 116-126.

4.

Bass, F.M. (1969) A new product growth for model consumer durables, Management Science, 15(5), 215-227.

5.

Boateng, R. (2016) Research Made Easy, CreateSpace Independent Publishing Plat form.

6.

Bretschneider, S.I. and Mahajan, V. (1980) Adaptive technological substitution models, Technological Forecasting and Social Change, 18(2), 129-139.

7.

Chandrasekaran, D. and Tellis, G.J. (2007) A critical review of marketing research on diffusion of new products, Review of Marketing Research, 3(1), 39-80.

8.

DeSilva, H., Ratnadiwakara, D. and Zainudeen, A. (2009) Social influence in mobile phone adoption: evidence from the bottom of pyramid in emerging Asia, Available at SSRN 1564091.

9.

Du, K.L. and Swamy, M. (2016) Simulated Annealing Search and Optimization by Metaheuristics, 29-36, Springer.

10.

Fisher, J.C. and Pry, R.H. (1971) A simple substitution model of technological change, Technological Forecasting and Social Change, 3, 75-88.

11.

Fourt, L.A. and Woodlock, J.W. (1960) Early prediction of market success for new grocery products, Journal of Marketing, 25(2), 31-38.

12.

Gore, A. and Lavaraj, U. (1987) Innovation diffusion in a heterogeneous population, Technological Forecasting and Social Change, 32(2), 163-167.

13.

Griliches, Z. (1957) Hybrid corn: An exploration in the economics of technological change, Econometrica, Journal of the Econometric Society, 501-522.

14.

Guo, Y. and Liu, W. (2014) Empirical research on the diffusion of home appliances to the rural areas in China based on Bayesian estimated bass model, Modelling and Computer Technologies, 18(10), 35-39.

15.

Heeks, R. (2012) IT innovation for the bottom of the pyramid, Communications of the ACM, 55(12), 24-27.

16.

Heeler, R.M. and Hustad, T.P. (1980) Problems in predicting new product growth for consumer durables, Management Science, 26(10), 1007-1020.

17.

Hwang, J., Cho, Y. and Long, N.V. (2009) Investigation of factors affecting the diffusion of mobile telephone services: an empirical analysis for Vietnam, Telecommunications Policy, 33(9), 534-543.

18.

Jeon, J. and Suh, Y. (2017) Analyzing the major issues of the 4th industrial revolution, Asian Journal of Innovation and Policy, 6(3), 262-273.

19.

Lamberson, P. (2008) The diffusion of hybrid electric vehicles, Future Research Directions in Sustainable Mobility and Accessibility, Available at http://www. umsmart.org/project_research/Future_directions.pdf.

20.

Lenk, P.J. and Rao, A.G. (1990) New models from old: forecasting product adoption by hierarchical Bayes procedures, Marketing Science, 9(1), 42-53.

21.

Mahajan, V., Muller, E. and Bass, F.M. (1990) New product diffusion models in marketing: a review and directions for research, The Journal of Marketing, 1-26.

22.

Massiani, J. (2013) The use of stated preferences to forecast alternative fuel vehicles market diffusion: comparisons with other methods and proposal for a synthetic utility function, Available at SSRN 2275756.

23.

McRoberts, N. (2008) A diffusion model for the adoption of agricultural innovations in structured adopting populations, working paper, Edinburgh: Land Economy Research Group.

24.

Meade, N. and Islam, T. (2015) Forecasting in telecommunications and ICT - a review, International Journal of Forecasting, 31(4), 1105-1126.

25.

Mitra, S. (2018) A methodology to forecast diffusion of innovative products with scarce data: two cases from subsistence markets, Paper presented at the Eighteenth Consortium of Students in Management Research (COSMAR), An Annual Research Consortium, Bangalore, November 29-30, 2018.

26.

Moon, Y.E. and Christina L.D. (2002) Microsoft: positioning the tablet PC, Harvard Business School Case 502-051, Revised January 2003.

27.

Muller, E., Peres, R. and Mahajan, V. (2009) Innovation diffusion and new product growth, Cambridge, MA: Marketing Science Institute.

28.

Ofek, E. (2005a) Forecasting the adoption of a new product, Cambridge: Mass.: Harvard Business School Case 0-505-062.

29.

Ofek, E. (2005b) Forecasting the adoption e-books, Cambridge: Mass.: Harvard Business School Case 0-505-063.

30.

Peres, R., Muller, E. and 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.

31.

Purohit, P. and Kandpal, T.C. (2005) Renewable energy technologies for irrigation water pumping in India: projected levels of dissemination, energy delivery and investment requirements using available diffusion models, Renewable and Sustainable Energy Reviews, 9(6), 592-607.

32.

Putsis, W.P. (1998) Parameter variation and new product diffusion, Journal of Forecasting, 17(3‐4), 231-257.

33.

Ratcliff, R. and Doshi, K. (2016) Using the Bass model to analyze the diffusion of innovations at the Base of the Pyramid, Business & Society, 55(2), 271-298.

34.

Roe-Dale, R., Brown, C. and Staton, M. (2015) Modelling the diffusion of manual irrigation pumps in lower income countries with the Bass model, International Journal of Social Entrepreneurship and Innovation, 3(4), 245-258.

35.

Rogers, E.M. (1962) Diffusion of Innovations, New York, Free Press.

36.

Rogers, E.M. (1976) New product adoption and diffusion, Journal of Consumer Research, 290-301.

37.

Rogers, E.M. (1983) Diffusion of Innovations, New York: The Free Press.

38.

Rutenbar, R.A. (1989) Simulated annealing algorithms: an overview, IEEE Circuits an Devices Magazine, 5(1), 19-26.

39.

Ryan, B. and Gross, N. (1950) Acceptance and diffusion of hybrid corn seed in two Iowa communities, Research Bulletin - Iowa Agriculture and Home Economics Experiment Station, 29(372), 1.

40.

Schmittlein, D.C. and Mahajan, V. (1982) Maximum likelihood estimation for an innovation diffusion model of new product acceptance, Marketing Science, 1(1), 57-78.

41.

Shrimali, G., Slaski, X., Thurber, M.C. and Zerriffi, H. (2011) Improved stoves in India: a study of sustainable business models, Energy Policy, 39(12), 7543-7556.

42.

Slaski, X. and Thurber, M. (2009) Research note: cookstoves and obstacles to technology adoption by the poor, working paper, Program on Energy and Sustainable Development, 89.

43.

Srinivasan, V. and Mason, C.H. (1986) Technical note-nonlinear least squares estimation of new product diffusion models, Marketing Science, 5(2), 169-178.

44.

Talukdar, D., Sudhir, K. and Ainslie, A. (2002) Investigating new product diffusion across products and countries, Marketing Science, 21(1), 97-114.

45.

Venkatesan, R., Krishnan, T.V. and Kumar, V. (2004) Evolutionary estimation of macro-level diffusion models using genetic algorithms: an alternative to nonlinear least squares, Marketing Science, 23(3), 451-464.

46.

Venkatesan, R. and Kumar, V. (2002) A genetic algorithms approach to growth phase forecasting of wireless subscribers, International Journal of Forecasting, 18(4), 625-646.

47.

Viswanathan, M., Sridharan, S. and Ritchie, R. (2010) Understanding consumption and entrepreneurship in subsistence marketplaces, Journal of Business Research, 63(6), 570-581.

48.

Webster’s New World College Dictionary (2004) 4th Edition, Cleveland: Wiley.

49.

Wenrong, W., Xie, M. and Tsui, K. (2006) Forecasting of mobile subscriptions in Asia pacific using Bass diffusion model, June, 2006 IEEE International Conference, Management of Innovation and Technology, 1, 300-303.

50.

White, M.R.M. (1988) Small firms' innovation: why regions differ, Policy Studies Institute, 690.

51.

Xie, J., Song, X.M., Sirbu, M. and Wang, Q. (1997) Kalman filter estimation of new product diffusion models, Journal of Marketing Research, 378-393.

Asian Journal of Innovation and Policy