IDEAS home Printed from https://ideas.repec.org/a/pch/abante/v3y2000i2p143-163.html
   My bibliography  Save this article

Difusion De Internet En Chile

Author

Listed:
  • PABLO MARSHALL

    (Escuela de Administración, Pontificia Universidad Católica de Chile)

Abstract

The Internet has changed the way companies and persons communicate. This paper considers the diffusion of Internet in Chile. Using traditional methods in marketing, particularly the Bass model, the potential number of Internet users and the characteristics of the diffusion process are estimated, in terms of innovation and imitation factors, are estimated. A new methodology, applied to the Bass model, where the coeficients of the model change over time according to a stochastic process, is also presented. The estimation process is carried out with a filter based on simulations. The results show that the diffusion process of Internet in Chile is determined, almost completely, by imitation factors and that this characteristic has changed very little over the last years. On the other hand, the potential number of users has changed during the diffusion process. With data up to the end of 1999, the potential number of users is estimated to be 8 million.

Suggested Citation

  • Pablo Marshall, 2000. "Difusion De Internet En Chile," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 3(2), pages 143-163.
    • Handle: RePEc:pch:abante:v:3:y:2000:i:2:p:143-163
    as

    Download full text from publisher

    File URL: http://www.abante.cl/files/ABT/Contenidos/Vol-3-N2/1%20Marshall.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hernes, Gudmund, 1976. " Diffusion and Growth-The Non-homogeneous Case," Scandinavian Journal of Economics, Wiley Blackwell, vol. 78(3), pages 427-436.
    3. Vijay Mahajan & Robert A. Peterson, 1978. "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, INFORMS, vol. 24(15), pages 1589-1597, November.
    4. Tanizaki, Hisashi & Mariano, Roberto S., 1998. "Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 263-290.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, 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. 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.
    2. 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.
    3. Lionel Richefort & Jean-Louis Fusillier, 2010. "Imitation, rationalité et adoption de technologies d'irrigation améliorées à l'île de la Réunion," Economie & Prévision, La Documentation Française, vol. 0(2), pages 59-73.
    4. 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.
    5. Wagner A. Kamakura & Siva K. Balasubramanian, 1987. "Long‐term forecasting with innovation diffusion models: The impact of replacement purchases," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 6(1), pages 1-19.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Benson Tsz Kin Leung, 2022. "Innovation Diffusion among Case-based Decision-makers," Papers 2203.05785, arXiv.org, revised Jan 2023.
    11. 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.
    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. 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.
    14. Sumiko Asai, 2009. "Sales Patterns of Hit Music in Japan," Journal of Media Economics, Taylor & Francis Journals, vol. 22(2), pages 81-101.
    15. de Bondt, Gabe & Marqués-Ibáñez, David, 2004. "The high-yield segment of the corporate bond market: a diffusion modelling approach for the United States, the United Kingdom and the euro area," Working Paper Series 313, European Central Bank.
    16. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    17. Mitrović, Slobodan & Radojičić, Valentina & Stojanović, Mirjana & Marković, Goran, 2018. "The capacity expansion approach in optical transport networks with fixed and flexible grids," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 310-316.
    18. 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).
    19. Marianela Fornerino, 2002. "Les modèles de diffusion d'innovations en marketing et l'adoption d'Internet en France," Post-Print hal-00455217, HAL.
    20. 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.

    More about this item

    Keywords

    Bass Model; Montecarlo Simulation; Diffusion Models; Internet; Chile;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    Statistics

    Access and download statistics

    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:pch:abante:v:3:y:2000:i:2:p:143-163. 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: Eduardo Walker (email available below). General contact details of provider: https://edirc.repec.org/data/eapuccl.html .

    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.