IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitmx/v16y2019i01ns021987701950010x.html
   My bibliography  Save this article

Modeling Technological Substitution by Incorporating Dynamic Adoption Rate

Author

Listed:
  • Saurabh Panwar

    (Department of Operational Research, University of Delhi, Delhi, India)

  • P. K. Kapur

    (#x2020;Amity Center for Interdisciplinary Research, Amity University, Noida, Uttar Pradesh, India)

  • Ompal Singh

    (Department of Operational Research, University of Delhi, Delhi, India)

Abstract

Modeling diffusion dynamics of multi-generation innovation requires a critical examination of external factors that may affect its diffusion process. It has been observed that due to companies continuously varying marketing strategies, the adoption rate of an innovation alters with time. However, there are other factors such as the launch of a new competitive product or improved product generation, which may affect the growth of an innovation. The time-instance at which these changes are observed is called change-point. Motivated by this phenomenon, the present research identifies the launch of a new generation as a change-point where adoption function of the previous generation experiences a structural change. The objective of the current research is to improve the forecasting accuracy of a diffusion model for technological innovations by integrating essential factors that affect the diffusion process. From the findings of empirical analysis, it can be inferred that the proposed two-generational diffusion model illustrates the diffusion pattern of Dynamic Random Access Memory (DRAM) semiconductors remarkably well. In fact, the computed results show that the suggested model has better forecasting ability than previously established multi-generation models.

Suggested Citation

  • 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.
  • Handle: RePEc:wsi:ijitmx:v:16:y:2019:i:01:n:s021987701950010x
    DOI: 10.1142/S021987701950010X
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S021987701950010X
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S021987701950010X?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Vijay Mahajan & Robert A. Peterson, 1978. "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, INFORMS, vol. 24(15), pages 1589-1597, November.
    3. Shwu-Ing Wu & Li-Pang Ho, 2014. "The Influence of Perceived Innovation and Brand Awareness on Purchase Intention of Innovation Product — An Example of iPhone," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-22.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
    6. 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.
    7. Nnaemeka Vincent Emodi & Girish Panchakshara Murthy & Chinenye Comfort Emodi & Adaeze Saratu Augusta Emodi, 2017. "Factors Influencing Innovation and Industrial Performance in Chinese Manufacturing Industry," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1-32, December.
    8. 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.
    9. 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.
    10. Tomasz Kijek & Arkadiusz Kijek, 2010. "Modelling of innovation diffusion," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 3, pages 53-68.
    11. K. K. Aggarwal & Alok Kumar, 2014. "Economic Order Quantity Model with Innovation Diffusion Criterion under Influence of Price-Dependent Potential Market Size," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 1-25.
    12. Leon Pretorius & Siebert Benade & Sunita Kruger, 2011. "Technology Diffusion And Forecasting: The Case Of Computational Fluid Dynamics (Cfd) For Simulation Of Greenhouse Internal Environments," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 27-39.
    13. Zhengrui Jiang & Dipak C. Jain, 2012. "A Generalized Norton-Bass Model for Multigeneration Diffusion," Management Science, INFORMS, vol. 58(10), pages 1887-1897, October.
    14. Robertson, Alastair & Soopramanien, Didier & Fildes, Robert, 2007. "Segmental new-product diffusion of residential broadband services," Telecommunications Policy, Elsevier, vol. 31(5), pages 265-275, June.
    15. Namwoon Kim & Dae Ryun Chang & Allan D. Shocker, 2000. "Modeling Intercategory and Generational Dynamics for A Growing Information Technology Industry," Management Science, INFORMS, vol. 46(4), pages 496-512, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2021. "Predicting diffusion dynamics and launch time strategy for mobile telecommunication services: an empirical analysis," Information Technology and Management, Springer, vol. 22(1), pages 33-51, March.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Singhal, Shakshi & Anand, Adarsh & Singh, Ompal, 2020. "Studying dynamic market size-based adoption modeling & product diffusion under stochastic environment," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    2. 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.
    3. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    4. Guseo, Renato & Guidolin, Mariangela, 2015. "Heterogeneity in diffusion of innovations modelling: A few fundamental types," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 514-524.
    5. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2021. "Predicting diffusion dynamics and launch time strategy for mobile telecommunication services: an empirical analysis," Information Technology and Management, Springer, vol. 22(1), pages 33-51, March.
    6. 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.
    7. Shi, Xiaohui & Chumnumpan, Pattarin, 2019. "Modelling market dynamics of multi-brand and multi-generational products," European Journal of Operational Research, Elsevier, vol. 279(1), pages 199-210.
    8. Shi, Xiaohui & Li, Feng & Bigdeli, Ali Ziaee, 2016. "An examination of NPD models in the context of business models," Journal of Business Research, Elsevier, vol. 69(7), pages 2541-2550.
    9. Guseo, Renato & Mortarino, Cinzia & Darda, Md Abud, 2015. "Homogeneous and heterogeneous diffusion models: Algerian natural gas production," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 366-378.
    10. Negahban, Ashkan & Smith, Jeffrey S., 2018. "Optimal production-sales policies and entry time for successive generations of new products," International Journal of Production Economics, Elsevier, vol. 199(C), pages 220-232.
    11. Kim, Namwoon & Srivastava, Rajendra K., 2007. "Modeling cross-price effects on inter-category dynamics: The case of three computing platforms," Omega, Elsevier, vol. 35(3), pages 290-301, June.
    12. Herbert Dawid & Reinhold Decker & Thomas Hermann & Hermann Jahnke & Wilhelm Klat & Rolf König & Christian Stummer, 2017. "Management science in the era of smart consumer products: challenges and research perspectives," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 203-230, March.
    13. Ashkan Negahban & Jeffrey S. Smith, 2018. "A joint analysis of production and seeding strategies for new products: an agent-based simulation approach," Annals of Operations Research, Springer, vol. 268(1), pages 41-62, September.
    14. Jha, Ashutosh & Saha, Debashis, 2020. "“Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models”," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    15. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    16. A. Negahban & J.S. Smith, 2016. "The effect of supply and demand uncertainties on the optimal production and sales plans for new products," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3852-3869, July.
    17. Zhiling Guo & Jianqing Chen, 2018. "Multigeneration Product Diffusion in the Presence of Strategic Consumers," Information Systems Research, INFORMS, vol. 29(1), pages 206-224, March.
    18. Namwoon Kim & Dae Ryun Chang & Allan D. Shocker, 2000. "Modeling Intercategory and Generational Dynamics for A Growing Information Technology Industry," Management Science, INFORMS, vol. 46(4), pages 496-512, April.
    19. Gupta, Ruchita & Jain, Karuna, 2016. "Competition effect of a new mobile technology on an incumbent technology: An Indian case study," Telecommunications Policy, Elsevier, vol. 40(4), pages 332-342.
    20. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijitmx:v:16:y:2019:i:01:n:s021987701950010x. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.worldscinet.com/ijitm/ijitm.shtml .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitm/ijitm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.