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Technology diffusion model with change in adoption rate and repeat purchases: a case of consumer balking

Author

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  • Saurabh Panwar

    (University of Delhi)

  • P. K. Kapur

    (Amity University)

  • Ompal Singh

    (University of Delhi)

Abstract

Understanding the diffusion paradigm of emerging technological products is crucial for continuous development. In this study, a value-based diffusion model is proposed to forecast the market performance of new products. The model analyzes the interaction between the consumer’s psychological perspective on dynamic price and goodwill of the new product using a two-dimensional framework. A Cobb–Douglas production function is applied to model the different dimensions of product adoption. The model overcomes the limitation of previous studies by incorporating the variation in the adoption rate. In addition, this study also introduces a concept of change-point in the percentage of repeat purchases. A time instance at which the rate of occurrence of a phenomenon alters is called change-point. Moreover, the rapid introduction of new and improved version of products makes a potential buyer balk (i.e. hesitate or wait). Therefore, the actual potential market is always lower than the estimated market size. Thus, the concept of consumer balking is incorporated in the present study in a quantitative manner. Furthermore, the LCD monitor actual sales data is used to validate the model. Findings recommend that the proposed model have superior fitting and prediction ability as compared with conventional diffusion models. The novelty of the study is that it is flexible and has the ability to describe the real market scenario. Moreover, it includes multiple factors that drive the diffusion of new technology in data analysis.

Suggested Citation

  • Saurabh Panwar & P. K. Kapur & Ompal Singh, 2021. "Technology diffusion model with change in adoption rate and repeat purchases: a case of consumer balking," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 29-36, February.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:1:d:10.1007_s13198-020-01028-0
    DOI: 10.1007/s13198-020-01028-0
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    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    3. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2019. "Modeling Technological Substitution by Incorporating Dynamic Adoption Rate," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-24, February.
    4. P.K. Kapur & Anu G. Aggarwal & Amir H.S. Garmabaki & Abhishek Tandon, 2015. "Multi-generational innovation diffusion modelling: a two dimensional approach," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 7(1), pages 1-18.
    5. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    6. Chul-Yong Lee & Sung-Yoon Huh, 2017. "Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
    7. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    8. P.K. Kapur & Kuldeep Chaudhary & Anu G. Aggarwal & P.C. Jha, 2012. "On the development of innovation diffusion model using stochastic differential equation incorporating change in the adoption rate," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 14(4), pages 472-484.
    9. Tsai, Bi-Huei, 2013. "Predicting the diffusion of LCD TVs by incorporating price in the extended Gompertz model," Technological Forecasting and Social Change, Elsevier, vol. 80(1), pages 106-131.
    10. Dan Horsky, 1990. "A Diffusion Model Incorporating Product Benefits, Price, Income and Information," Marketing Science, INFORMS, vol. 9(4), pages 342-365.
    11. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    12. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
    13. Stummer, Christian & Kiesling, Elmar & Günther, Markus & Vetschera, Rudolf, 2015. "Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach," European Journal of Operational Research, Elsevier, vol. 245(1), pages 157-167.
    14. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    15. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    16. 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.
    17. Morris A. Cohen & Seungjin Whang, 1997. "Competing in Product and Service: A Product Life-Cycle Model," Management Science, INFORMS, vol. 43(4), pages 535-545, April.
    18. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    19. van Oorschot, Johannes A.W.H. & Hofman, Erwin & Halman, Johannes I.M., 2018. "A bibliometric review of the innovation adoption literature," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 1-21.
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    Cited by:

    1. Yongho Lee & Taesu Cheong, 2022. "Service Level Constrained Distribution-Free Newsvendor Problem with Balking Penalty," Mathematics, MDPI, vol. 10(14), pages 1-14, July.

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