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Studying innovation adoption using different distribution functions

Author

Listed:
  • Ankur Kumar

    (University of Delhi)

  • Ompal Singh

    (University of Delhi)

  • Adarsh Anand

    (University of Delhi)

  • P. K. Kapur

    (University of Delhi
    Amity University)

Abstract

In today's technologically advanced world, every market area is witnessing an increase in the demand for product innovation, so a company must keep innovating in order to get an advantage over rival businesses. Research in the field of innovation diffusion has been primarily based on modelling. The innovation diffusion model is a notion that describes how novel concepts, items, or technological advancements are embraced and disseminated within a community or social system. The Bass innovation diffusion model and many of its extended forms have been highly focused in marketing literature. In this paper, a unified approach is used to test whether the diffusion models are affected if we increase the number of parameters by taking different distribution functions for the adoption rate. Thus, a set of diffusion models that depict different market scenarios have been presented and compared with the standard model. The proposed model has been analyzed on Samsung mobile phone and I-Phone sales data sets and their inferences are quite promising.

Suggested Citation

  • Ankur Kumar & Ompal Singh & Adarsh Anand & P. K. Kapur, 2024. "Studying innovation adoption using different distribution functions," 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. 15(5), pages 1900-1907, May.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:5:d:10.1007_s13198-023-02204-8
    DOI: 10.1007/s13198-023-02204-8
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    References listed on IDEAS

    as
    1. P.K. Kapur & Anu G. Aggarwal & Amir Hossein Soleiman Garmabaki & Gurinder Singh, 2013. "Modelling diffusion of successive generations of technology: a general framework," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 16(4), pages 465-484.
    2. 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.
    3. 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.
    4. Vijay Mahajan & Robert A. Peterson, 1978. "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, INFORMS, vol. 24(15), pages 1589-1597, November.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2020. "Modeling technology diffusion: a study based on market coverage and advertising efforts," 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. 11(2), pages 154-162, July.
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