IDEAS home Printed from https://ideas.repec.org/a/kap/mktlet/v32y2021i4d10.1007_s11002-021-09578-4.html
   My bibliography  Save this article

Adoption patterns over time: a replication

Author

Listed:
  • Gil Appel

    (George Washington University)

  • Eitan Muller

    (New York University and The Interdisciplinary Center (IDC))

Abstract

Based on new data, we replicate Mahajan et al.’s (1990) paper on adopter categories and Goldenberg et al.’s (2002) paper on saddles and offer explanations and extensions. We use a new dataset to replicate the results, namely, the U.S. Consumer Technology Association’s Sales & Forecasts, which provides longitudinal data on numerous consumer electronic products. Goldenberg, Libai, and Muller utilized the same source for 1999, while we use the updated 2021 report for the adopter category as well as the saddle replication, thus employing the same data source for both studies. We find that in the adoption of consumer electronics, there are fewer saddles, and these saddles are shorter and shallower in 2021 than they were in 1999. Regarding adopter categories, we break the data down by decades and show that, while the early adopter categories just barely decelerated over the six decades of our analysis, the average growth of the new dataset is much faster, with the peak occurring considerably sooner than that of the earlier data.

Suggested Citation

  • Gil Appel & Eitan Muller, 2021. "Adoption patterns over time: a replication," Marketing Letters, Springer, vol. 32(4), pages 499-511, December.
    • Handle: RePEc:kap:mktlet:v:32:y:2021:i:4:d:10.1007_s11002-021-09578-4
    DOI: 10.1007/s11002-021-09578-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11002-021-09578-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11002-021-09578-4?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. Chu, Kar-Hai & Pitts, Stephanie R. & Wipfli, Heather & Valente, Thomas W., 2017. "Coding communications across time: Documenting changes in interaction patterns across adopter categories," Network Science, Cambridge University Press, vol. 5(4), pages 441-460, December.
    2. 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.
    3. 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.
    4. 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.
    5. Muller, Eitan, 2020. "Delimiting disruption: Why Uber is disruptive, but Airbnb is not," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 43-55.
    6. Jacob Goldenberg & Barak Libai & Eitan Muller & Renana Peres, 2006. "Blazing Saddles: the early and mainstream markets in the High-Tech product life cycle," Israel Economic Review, Bank of Israel, vol. 4(2), pages 85-108.
    7. 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.
    8. Peter N. Golder & Gerard J. Tellis, 2004. "Growing, Growing, Gone: Cascades, Diffusion, and Turning Points in the Product Life Cycle," Marketing Science, INFORMS, vol. 23(2), pages 207-218, December.
    9. Gil Appel & Lauren Grewal & Rhonda Hadi & Andrew T. Stephen, 2020. "The future of social media in marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 79-95, January.
    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. 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.
    2. Kocaman, Barış & Gelper, Sarah & Langerak, Fred, 2023. "Till the cloud do us part: Technological disruption and brand retention in the enterprise software industry," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 316-341.
    3. Olivier Toubia & Jacob Goldenberg & Rosanna Garcia, 2014. "Improving Penetration Forecasts Using Social Interactions Data," Management Science, INFORMS, vol. 60(12), pages 3049-3066, December.
    4. Nejad, Mohammad G. & Amini, Mehdi & Babakus, Emin, 2015. "Success Factors in Product Seeding: The Role of Homophily," Journal of Retailing, Elsevier, vol. 91(1), pages 68-88.
    5. Jacob Goldenberg & Oded Lowengart & Daniel Shapira, 2009. "Zooming In: Self-Emergence of Movements in New Product Growth," Marketing Science, INFORMS, vol. 28(2), pages 274-292, 03-04.
    6. Hong, Jungsik & Koo, Hoonyoung & Kim, Taegu, 2016. "Easy, reliable method for mid-term demand forecasting based on the Bass model: A hybrid approach of NLS and OLS," European Journal of Operational Research, Elsevier, vol. 248(2), pages 681-690.
    7. Deepa Chandrasekaran & Gerard J. Tellis, 2008. "Global Takeoff of New Products: Culture, Wealth, or Vanishing Differences?," Marketing Science, INFORMS, vol. 27(5), pages 844-860, 09-10.
    8. Vakratsas, Demetrios & Kolsarici, Ceren, 2008. "A dual-market diffusion model for a new prescription pharmaceutical," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 282-293.
    9. Abedi, Vahideh Sadat, 2019. "Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    10. Teck-Hua Ho & Shan Li & So-Eun Park & Zuo-Jun Max Shen, 2012. "Customer Influence Value and Purchase Acceleration in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 236-256, March.
    11. Sang-Gun Lee & Eui-bang Lee & Chang-Gyu Yang, 2014. "Strategies for ICT product diffusion: the case of the Korean mobile communications market," Service Business, Springer;Pan-Pacific Business Association, vol. 8(1), pages 65-81, March.
    12. 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.
    13. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    14. 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).
    15. Ryo Iwata & Kaoru Kuramoto & Satoshi Kumagai, 2022. "Detecting Chasms and Cracks Using Innovator Scores and Agent Interactions," International Review of Management and Marketing, Econjournals, vol. 12(6), pages 1-15, November.
    16. Franses, Philip Hans, 2021. "Modeling box office revenues of motion pictures✰," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    17. Katharina Elisabeth Fischer & Tom Stargardt, 2016. "The diffusion of generics after patent expiry in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 1027-1040, November.
    18. M. Berk Ataman & Carl F. Mela & Harald J. van Heerde, 2008. "Building Brands," Marketing Science, INFORMS, vol. 27(6), pages 1036-1054, 11-12.
    19. Sebastian Schneider & Frank Huber, 2022. "You paid what!? Understanding price-related word-of-mouth and price perception among opinion leaders and innovators," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(1), pages 64-80, February.
    20. Ashish Sood & Gareth M. James & Gerard J. Tellis, 2009. "Functional Regression: A New Model for Predicting Market Penetration of New Products," Marketing Science, INFORMS, vol. 28(1), pages 36-51, 01-02.

    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:kap:mktlet:v:32:y:2021:i:4:d:10.1007_s11002-021-09578-4. 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.