IDEAS home Printed from https://ideas.repec.org/a/spr/opmare/v15y2022i3d10.1007_s12063-022-00260-0.html
   My bibliography  Save this article

RETRACTED ARTICLE: Modelling product lines diffusion: a framework incorporating competitive brands for sustainable innovations

Author

Listed:
  • Deepti Aggrawal

    (University School of Management and Entrepreneurship, Delhi Technological University)

  • Adarsh Anand

    (University of Delhi)

  • Gunjan Bansal

    (University of Delhi)

  • Gareth H. Davies

    (Swansea University)

  • Parisa Maroufkhani

    (The University of Waikato Joint Institute, Zhejiang University City College
    Zhejiang University City College)

  • Yogesh K. Dwivedi

    (Swansea University
    Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University))

Abstract

Understanding of consumer behavior, their changing demands due to increase in social interactions and communications, adoption of latest technologies over existing products have always been a set of fundamental activities for the firms. Keeping the objective of minimum process disruptions and discouraging product proliferation, firms always endeavor to match heterogeneous demands of consumers by emphasizing on the product line. Also, with globalization, rivalry amongst firms has reached a next level. Brands are trying to capture the market by coming up with various combinations of new product mix. Amongst various attributes of product mix, product line has helped firms to attract new potential buyers to a significantly good extent. Therefore, in today’s cutthroat competitive scenario, the concept of product line provides an opportunity for a firm to provide same kind of products with some variation at an altered pricing. The objective of this study is to understand how customers behave (with so many options) and deviate from one product to another product (within and outside the brand). All the possible customers’ shifting combinations that might impact the overall sales of product are captured through the proposed model. A mathematical innovation diffusion model is developed that is motivated by the concept of Bass model and multiple generational diffusion models. This modelling framework describes the scenario of competitive brands that offer multiple products in a marketplace and observing the shifting behavior of the customers and predict the sales when product lines are available. Validation of the model has been done on real-life sales data sets for automobiles industries of two different brands i.e., Hyundai and Maruti Suzuki. The importance of this study is to deliver a solution to the manufacturers that how consumer shifts from one brand to another brand. Therefore, it is imperative for the companies to develop such a product that would lead to customers’ loyalty towards the brand.

Suggested Citation

  • Deepti Aggrawal & Adarsh Anand & Gunjan Bansal & Gareth H. Davies & Parisa Maroufkhani & Yogesh K. Dwivedi, 2022. "RETRACTED ARTICLE: Modelling product lines diffusion: a framework incorporating competitive brands for sustainable innovations," Operations Management Research, Springer, vol. 15(3), pages 760-772, December.
    • Handle: RePEc:spr:opmare:v:15:y:2022:i:3:d:10.1007_s12063-022-00260-0
    DOI: 10.1007/s12063-022-00260-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12063-022-00260-0
    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/s12063-022-00260-0?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. Sinha, Avik & Sengupta, Tuhin & Kalugina, Olga & Gulzar, Muhammad Awais, 2020. "Does distribution of energy innovation impact distribution of income: A quantile-based SDG modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    2. 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.
    3. Cao, Guangming & Duan, Yanqing & Cadden, Trevor, 2019. "The link between information processing capability and competitive advantage mediated through decision-making effectiveness," International Journal of Information Management, Elsevier, vol. 44(C), pages 121-131.
    4. Michalek, Jeremy J. & Ebbes, Peter & Adigüzel, Feray & Feinberg, Fred M. & Papalambros, Panos Y., 2011. "Enhancing marketing with engineering: Optimal product line design for heterogeneous markets," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 1-12.
    5. Brander, James A & Eaton, Jonathan, 1984. "Product Line Rivalry," American Economic Review, American Economic Association, vol. 74(3), pages 323-334, June.
    6. Walsh, P.P. & Murphy, E. & Horan, D., 2020. "The role of science, technology and innovation in the UN 2030 agenda," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    7. Nina Edh Mirzaei & Per Hilletofth & Rudrajeet Pal, 2021. "Challenges to competitive manufacturing in high-cost environments: checklist and insights from Swedish manufacturing firms," Operations Management Research, Springer, vol. 14(3), pages 272-292, December.
    8. Kamolsook, Apinya & Badir, Yuosre F. & Frank, Björn, 2019. "Consumers' switching to disruptive technology products: The roles of comparative economic value and technology type," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 328-340.
    9. Qian, Lixian & Soopramanien, Didier, 2014. "Using diffusion models to forecast market size in emerging markets with applications to the Chinese car market," Journal of Business Research, Elsevier, vol. 67(6), pages 1226-1232.
    10. Hsiao, James Po-Hsun & Jaw, Chyi & Huan, Tzung-Cheng, 2009. "Information diffusion and new product consumption: A bass model application to tourism facility management," Journal of Business Research, Elsevier, vol. 62(7), pages 690-697, July.
    11. Malhotra, Abhishek & Schmidt, Tobias S. & Huenteler, Joern, 2019. "The role of inter-sectoral learning in knowledge development and diffusion: Case studies on three clean energy technologies," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 464-487.
    12. Adamuthe, Amol C. & Thampi, Gopakumaran T., 2019. "Technology forecasting: A case study of computational technologies," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 181-189.
    13. Oliveira, Gabriela D. & Roth, Richard & Dias, Luis C., 2019. "Diffusion of alternative fuel vehicles considering dynamic preferences," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 83-99.
    14. Sunder Kekre & Kannan Srinivasan, 1990. "Broader Product Line: A Necessity to Achieve Success?," Management Science, INFORMS, vol. 36(10), pages 1216-1232, October.
    15. 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).
    16. Zhang, Shuwei & Bauer, Nico & Yin, Guangzhi & Xie, Xi, 2020. "Technology learning and diffusion at the global and local scales: A modeling exercise in the REMIND model," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    17. Marvin B. Lieberman, 1987. "The learning curve, diffusion, and competitive strategy," Strategic Management Journal, Wiley Blackwell, vol. 8(5), pages 441-452, September.
    18. Ismagilova, Elvira & Slade, Emma & Rana, Nripendra P. & Dwivedi, Yogesh K., 2020. "The effect of characteristics of source credibility on consumer behaviour: A meta-analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    19. Lynn O. Wilson & John A. Norton, 1989. "Optimal Entry Timing for a Product Line Extension," Marketing Science, INFORMS, vol. 8(1), pages 1-17.
    20. Gunjan Bansal & Adarsh Anand & Deepti Aggrawal, 2021. "Modeling multi-generational diffusion for competitive brands: an analysis for telecommunication industries," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(4), pages 715-740, October.
    21. Gupta, Ranjit & Mejia, Cristian & Kajikawa, Yuya, 2019. "Business, innovation and digital ecosystems landscape survey and knowledge cross sharing," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 100-109.
    22. Dong-Young Kim & Pengcheng Zhu & Wenli Xiao & Yen-Ting Lin, 2020. "Customer degree centrality and supplier performance: the moderating role of resource dependence," Operations Management Research, Springer, vol. 13(1), pages 22-38, June.
    23. Elvira Ismagilova & Yogesh K. Dwivedi & Emma Slade & Michael D. Williams, 2017. "Electronic Word of Mouth (eWOM) in the Marketing Context," SpringerBriefs in Business, Springer, number 978-3-319-52459-7, January.
    24. Deepti Aggrawal & Adarsh Anand & Ompal Singh & P.K. Kapur, 2015. "Modelling successive generations for products-in-use and number of products sold in the market," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 24(2), pages 228-244.
    25. Wang, Yen-Yao & Wang, Tawei & Calantone, Roger, 2021. "The effect of competitive actions and social media perceptions on offline car sales after automobile recalls," International Journal of Information Management, Elsevier, vol. 56(C).
    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. Bo Tan & Zhiguo Zhu & Pan Jiang & Xiening Wang, 2023. "Modeling Multi-Generation Product Diffusion in the Context of Dual-Brand Competition and Sustainable Improvement," Sustainability, MDPI, vol. 15(17), pages 1-22, August.

    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. Claudio Giachetti & Giovanni Battista Dagnino, 2014. "Detecting the relationship between competitive intensity and firm product line length: Evidence from the worldwide mobile phone industry," Strategic Management Journal, Wiley Blackwell, vol. 35(9), pages 1398-1409, September.
    2. Kostas Axarloglou, 2008. "Product line extensions: causes and effects," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 9-21.
    3. 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.
    4. Hans H. Bauer & Marc Fischer, 2001. "Ein Ansatz zur simultanen Messung von Kannibalisierungs-, substitutiven Konkurrenz- und Neukäufereffekten am Beispiel von line extensions," Schmalenbach Journal of Business Research, Springer, vol. 53(5), pages 455-477, August.
    5. Wilhelm, Wilbert E. & Xu, Kaihong, 2002. "Prescribing product upgrades, prices and production levels over time in a stochastic environment," European Journal of Operational Research, Elsevier, vol. 138(3), pages 601-621, May.
    6. 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.
    7. Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
    8. Yogesh V. Joshi & David J. Reibstein & Z. John Zhang, 2009. "Optimal Entry Timing in Markets with Social Influence," Management Science, INFORMS, vol. 55(6), pages 926-939, June.
    9. Yogesh K. Dwivedi & Elvira Ismagilova & Nripendra P. Rana & Ramakrishnan Raman, 2023. "Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review," Information Systems Frontiers, Springer, vol. 25(3), pages 971-993, June.
    10. Palm, Alvar, 2022. "Innovation systems for technology diffusion: An analytical framework and two case studies," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    11. Krishen, Anjala S. & Dwivedi, Yogesh K. & Bindu, N. & Kumar, K. Satheesh, 2021. "A broad overview of interactive digital marketing: A bibliometric network analysis," Journal of Business Research, Elsevier, vol. 131(C), pages 183-195.
    12. Barry L. Bayus & William P. Putsis, Jr., 1999. "Product Proliferation: An Empirical Analysis of Product Line Determinants and Market Outcomes," Marketing Science, INFORMS, vol. 18(2), pages 137-153.
    13. Ilan Lobel & Jigar Patel & Gustavo Vulcano & Jiawei Zhang, 2016. "Optimizing Product Launches in the Presence of Strategic Consumers," Management Science, INFORMS, vol. 62(6), pages 1778-1799, June.
    14. Jackie Y. Luan & K. Sudhir, 2022. "Optimal Inter-Release Time between Sequentially Released Products," Cowles Foundation Discussion Papers 2325, Cowles Foundation for Research in Economics, Yale University.
    15. Sarangi, Subrat & Chakraborty, Abhishek & Triantis, Konstantinos P., 2021. "Multimarket competition effects on product line decisions – A multi-objective decision model in fast moving consumer goods industry," Journal of Business Research, Elsevier, vol. 133(C), pages 388-398.
    16. Hernández-Mireles, C. & Franses, Ph.H.B.F., 2010. "The Launch Timing of New and Dominant Multigeneration Technologies," ERIM Report Series Research in Management ERS-2010-022-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    17. Prasad Ashutosh & Bronnenberg Bart & Mahajan Vijay, 2004. "Product Entry Timing in Dual Distribution Channels: The Case of the Movie Industry," Review of Marketing Science, De Gruyter, vol. 2(1), pages 1-20, March.
    18. Bayrak, Busra & Guray, Busra & Uzunlar, Nilsu & Nadar, Emre, 2024. "Diffusion control in closed-loop supply chains: Successive product generations," International Journal of Production Economics, Elsevier, vol. 268(C).
    19. Ram Bala & Sumit Kunnumkal & Milind G. Sohoni, 2016. "Evergreening and operational risk under price competition," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(1), pages 71-89, February.
    20. Bersch, Christopher V. & Akkerman, Renzo & Kolisch, Rainer, 2021. "Strategic planning of new product introductions: Integrated planning of products and modules in the automotive industry," Omega, Elsevier, vol. 105(C).

    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:opmare:v:15:y:2022:i:3:d:10.1007_s12063-022-00260-0. 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.