IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v15y2024i5d10.1007_s13198-023-02109-6.html
   My bibliography  Save this article

Innovation diffusion model based on advertising expenditure with change-point

Author

Listed:
  • Shiva

    (J. C. Bose University of Science and Technology, YMCA)

  • Neetu Gupta

    (J. C. Bose University of Science and Technology, YMCA)

  • Anu G. Aggarwal

    (University of Delhi)

Abstract

This research focuses on the role of advertising expenditure in determining the success of a product and its impact on the rate of product sales. We introduce the concept of change-point, which considers fluctuations in the adoption rate due to shifts in advertising strategies and competitor dynamics. The product diffusion models incorporating advertising expenditure and change-points are proposed and validated using two real-world product advertising and sales datasets. The models’s accuracy and applicability has been assessed through $$R^2$$ R 2 (coefficient of determination) and mean square error (MSE). By investigating the relationship between advertising expenditure, change-point, and product sales, this study provides valuable insights for marketing and advertising strategies, aiding in better decision-making processes.

Suggested Citation

  • Shiva & Neetu Gupta & Anu G. Aggarwal, 2024. "Innovation diffusion model based on advertising expenditure with change-point," 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 1794-1801, May.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:5:d:10.1007_s13198-023-02109-6
    DOI: 10.1007/s13198-023-02109-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-023-02109-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-023-02109-6?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. 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. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, vol. 34(2), pages 167-185, February.
    4. 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.
    5. Trichy V. Krishnan & Dipak C. Jain, 2006. "Optimal Dynamic Advertising Policy for New Products," Management Science, INFORMS, vol. 52(12), pages 1957-1969, December.
    6. 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.
    7. 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.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. 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.
    10. Joe A. Dodson, Jr. & Eitan Muller, 1978. "Models of New Product Diffusion Through Advertising and Word-of-Mouth," Management Science, INFORMS, vol. 24(15), pages 1568-1578, November.
    11. Dan Horsky & Leonard S. Simon, 1983. "Advertising and the Diffusion of New Products," Marketing Science, INFORMS, vol. 2(1), pages 1-17.
    12. 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.
    13. Arda Yenipazarli, 2015. "A road map to new product success: warranty, advertisement and price," Annals of Operations Research, Springer, vol. 226(1), pages 669-694, March.
    14. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    15. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    16. Albert C. Bemmaor & Janghyuk Lee, 2002. "The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates," Marketing Science, INFORMS, vol. 21(2), pages 209-220, November.
    Full references (including those not matched with items on IDEAS)

    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. Shakshi Singhal & Yasmeen Bano & Ompal Singh, 2024. "Demand forecasting of technological products considering re-purchasing and dynamic pricing: a modified diffusion model," 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 1981-1998, May.
    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.
    3. Ferreira, Kevin D. & Lee, Chi-Guhn, 2014. "An integrated two-stage diffusion of innovation model with market segmented learning," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 189-201.
    4. 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.
    5. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," 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. 20(2), pages 183-230, June.
    6. Arda Yenipazarli, 2015. "A road map to new product success: warranty, advertisement and price," Annals of Operations Research, Springer, vol. 226(1), pages 669-694, March.
    7. Ruiz-Conde, Enar & Wieringa, Jaap E. & Leeflang, Peter S.H., 2014. "Competitive diffusion of new prescription drugs: The role of pharmaceutical marketing investment," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 49-63.
    8. Hariharan, Vijay Ganesh & Talukdar, Debabrata & Kwon, Changhyun, 2015. "Optimal targeting of advertisement for new products with multiple consumer segments," International Journal of Research in Marketing, Elsevier, vol. 32(3), pages 263-271.
    9. Benson Tsz Kin Leung, 2022. "Innovation Diffusion among Case-based Decision-makers," Papers 2203.05785, arXiv.org, revised Jan 2023.
    10. 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.
    11. Chang, Byeong-Yun & Li, Xu & Kim, Yun Bae, 2014. "Performance comparison of two diffusion models in a saturated mobile phone market," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 41-48.
    12. Olivier Toubia & Jacob Goldenberg & Rosanna Garcia, 2014. "Improving Penetration Forecasts Using Social Interactions Data," Management Science, INFORMS, vol. 60(12), pages 3049-3066, December.
    13. Donald Lehmann & Mercedes Esteban-Bravo, 2006. "When giving some away makes sense to jump-start the diffusion process," Marketing Letters, Springer, vol. 17(4), pages 243-254, December.
    14. 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.
    15. Yan, Xiaoming & Liu, Ke, 2009. "Optimal control problems for a new product with word-of-mouth," International Journal of Production Economics, Elsevier, vol. 119(2), pages 402-414, June.
    16. Nelly S. Kolyan & Alexander E. Plesovskikh & Roman V. Gordeev, 2023. "Predictive Assessment of the Potential Electric Vehicle Market and the Effects of Reducing Greenhouse Gas Emissions in Russia," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(3), pages 497-521.
    17. Dong, Changgui & Sigrin, Benjamin & Brinkman, Gregory, 2017. "Forecasting residential solar photovoltaic deployment in California," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 251-265.
    18. 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.
    19. Avagyan, Vardan & Esteban-Bravo, Mercedes & Vidal-Sanz, Jose M., 2014. "Licensing radical product innovations to speed up the diffusion," European Journal of Operational Research, Elsevier, vol. 239(2), pages 542-555.
    20. Fruchter, Gila E. & Van den Bulte, Christophe, 2011. "Why the Generalized Bass Model leads to odd optimal advertising policies," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 218-230.

    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:spr:ijsaem:v:15:y:2024:i:5:d:10.1007_s13198-023-02109-6. See general information about how to correct material in RePEc.

    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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.