IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-00455217.html
   My bibliography  Save this paper

Les modèles de diffusion d'innovations en marketing et l'adoption d'Internet en France

Author

Listed:
  • Marianela Fornerino

    (MKT - Marketing - EESC-GEM Grenoble Ecole de Management)

Abstract

The innovation diffusion models developed in marketing are applied to the adoption of Internet in France. The most classical of them, the Bass model is adjusted to the estimations of Médiamétrie. The NUI model (Easingwood, Mahajan et Muller, 1983) has been utilized to introduce the network externalities by incorporating the increasing influence of interpersonal communication on the penetration as a function of previous adopters. Some predictions of the penetration of Internet in France are proposed.

Suggested Citation

  • Marianela Fornerino, 2002. "Les modèles de diffusion d'innovations en marketing et l'adoption d'Internet en France," Post-Print hal-00455217, HAL.
    • Handle: RePEc:hal:journl:hal-00455217
    Note: View the original document on HAL open archive server: http://hal.grenoble-em.com/hal-00455217
    as

    Download full text from publisher

    File URL: http://hal.grenoble-em.com/hal-00455217/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    2. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
    3. Faïz Gallouj, 1994. "Economie de l'innovation dans les services," Post-Print hal-01111989, HAL.
    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. Marianela Fornerino, 2002. "Les modèles de diffusion d'innovations en marketing et l'adoption d'Internet en France," Grenoble Ecole de Management (Post-Print) hal-00455217, HAL.
    2. Chen, Chaojung & Watanabe, Chihiro & Griffy-Brown, Charla, 2007. "The co-evolution process of technological innovation—An empirical study of mobile phone vendors and telecommunication service operators in Japan," Technology in Society, Elsevier, vol. 29(1), pages 1-22.
    3. Haris Krijestorac & Rajiv Garg & Vijay Mahajan, 2020. "Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls," Information Systems Research, INFORMS, vol. 31(2), pages 449-472, June.
    4. Velickovic, Stevan & Radojicic, Valentina & Bakmaz, Bojan, 2016. "The effect of service rollout on demand forecasting: The application of modified Bass model to the step growing markets," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 130-140.
    5. Caulkins, Jonathan P. & Feichtinger, Gustav & Tragler, Gernot & Wallner, Dagmar, 2010. "When in a drug epidemic should the policy objective switch from use reduction to harm reduction?," European Journal of Operational Research, Elsevier, vol. 201(1), pages 308-318, February.
    6. Benson Tsz Kin Leung, 2022. "Innovation Diffusion among Case-based Decision-makers," Papers 2203.05785, arXiv.org, revised Jan 2023.
    7. 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.
    8. Olivier Toubia & Jacob Goldenberg & Rosanna Garcia, 2014. "Improving Penetration Forecasts Using Social Interactions Data," Management Science, INFORMS, vol. 60(12), pages 3049-3066, December.
    9. Gadi Fibich & Ro'i Gibori, 2010. "Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure," Operations Research, INFORMS, vol. 58(5), pages 1450-1468, October.
    10. Sumiko Asai, 2009. "Sales Patterns of Hit Music in Japan," Journal of Media Economics, Taylor & Francis Journals, vol. 22(2), pages 81-101.
    11. Chorowski, Michał & Nowak, Andrzej & Andersen, Jørgen Vitting, 2023. "What makes products trendy: Introducing an innovation adoption model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    12. S. Beifuss & W. Proskurowski & F. E. Udwadia, 1997. "Incorporating Managerial Thinking in Prediction and Control: Case Study of Market Penetration," Journal of Optimization Theory and Applications, Springer, vol. 92(2), pages 225-248, February.
    13. Yoon Seong Kim & Eun Jin Han & So Young Sohn, 2017. "Demand Forecasting for Heavy-Duty Diesel Engines Considering Emission Regulations," Sustainability, MDPI, vol. 9(2), pages 1-16, January.
    14. Adarsh Anand & Richie Aggarwal & Ompal Singh, 2019. "Using Weibull Distribution for Modeling Bimodal Diffusion Curves: A Naive Framework to Study Product Life Cycle," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(07), pages 1-17, November.
    15. Bing Jing, 2011. "Social Learning and Dynamic Pricing of Durable Goods," Marketing Science, INFORMS, vol. 30(5), pages 851-865, September.
    16. Jun, Duk B. & Kim, Seon K. & Park, Yoon S. & Park, Myoung H. & Wilson, Amy R., 2002. "Forecasting telecommunication service subscribers in substitutive and competitive environments," International Journal of Forecasting, Elsevier, vol. 18(4), pages 561-581.
    17. Park, Sang-June & Choi, Sungchul, 2016. "Valuation of adopters based on the Bass model for a new product," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 63-69.
    18. 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.
    19. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    20. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

    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:hal:journl:hal-00455217. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.