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Modeling the Evolution of Markets with Indirect Network Externalities: An Application to Digital Television

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  • Sachin Gupta

    (Kellogg Graduate School of Management, Northwestern University, 2001 Sheridan Road, Evanston Illinois, 60208)

  • Dipak C. Jain

    (Kellogg Graduate School of Management, Northwestern University, 2001 Sheridan Road, Evanston Illinois, 60208)

  • Mohanbir S. Sawhney

    (Kellogg Graduate School of Management, Northwestern University, 2001 Sheridan Road, Evanston Illinois, 60208)

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    Abstract

    The usefulness of a technology product for an end-user often depends on the availability of complementary software products and services. Computers require software, cameras require film, and DVD players require movie programming in order for customers to value the whole product. This phenomenon, where the demand for hardware products is mediated by the supply of complementary software products, is called an network externality. Indirect network externalities create a two-way contingency between the demand for the hardware product and the supply of software products, and result in a strategic interdependence between the actions of hardware manufacturers and the actions of software providers. Indirect network externalities are gaining economic significance in technology markets, because hardware and software are typically provided by independent firms, and both sets of firms have an incentive to free-ride on each others' demand creation efforts. Despite the ubiquity of this phenomenon, it has largely been ignored in the marketing science literature. We present a conceptual and operational model for the evolution of markets with indirect network externalities. The key feature of our framework is to model the between the actions of hardware manufacturers and software complementors, created by the of consumer demand for the whole product on the actions of manufacturers as well as complementors. In addition, we incorporate marketing-mix effects on consumer response, as well as heterogeneity in consumer preferences for hardware and software attributes. We model consumer response using a latent-class choice model. To estimate the complementor response functions, we use a modified Delphi technique that allows us to convert qualitative response data into quantitative response functions. We integrate the consumer and complementor response models to create a simulation model that generates forecasts of market shares and sales volumes for competing technologies, as a function of marketing-mix effects and exogenously specified regulatory scenarios. The modeling framework is of interest to new product modelers interested in creating empirical models and decision-support systems for forecasting demand in technology markets characterized by indirect network externalities. The decision-support aspects of the modeling framework should appeal to managers interested in understanding and quantifying the complex interplay between hardware manufacturers and software complementors in the evolution of markets with indirect network externalities. We present an application of the modeling framework to the U.S. digital television industry, and use the framework to characterize the competition among analog and digital TV technologies. Our results suggest that complementor actions play an important role in the acceptance of digital TV technologies in general, and high definition television (HDTV) in particular. We find that forecasts that ignore the influence of indirect network externalities would be seriously biased in favor of HDTV. We illustrate how the modeling framework can be used to identify and profile customer segments in the digital TV market based on their utility for hardware-related features as well as programming-related features. We also illustrate the decision-support capabilities of the modeling framework by evaluating the sensitivity of the forecasts to varying marketing, regulatory, and complementor response scenarios. We derive implications for marketing and public affairs policies of the hardware manufacturers. The developments in the digital TV industry generally support our finding that HDTV will be a niche product, and will diffuse slower than originally expected due in part to the lack of programming. The delays in the introduction of digital TV to the marketplace also suggest that most forecasts for infrastructure-intensive technologies like digital TV may be too optimistic simply because they underestimate the delays in agreeing upon technology standards and resolving regulatory debates.

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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 18 (1999)
    Issue (Month): 3 ()
    Pages: 396-416

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    Handle: RePEc:inm:ormksc:v:18:y:1999:i:3:p:396-416

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    Related research

    Keywords: Indirect Network Externalities; Demand Forecasting; New Products; Chicken-and-Egg; HDTV; Endogeneity; Heterogeneity; Conjoint Analysis; Technology;

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    Cited by:
    1. McIntyre, David P. & Chintakananda, Asda, 2014. "Competing in network markets: Can the winner take all?," Business Horizons, Elsevier, vol. 57(1), pages 117-125.
    2. Sillanpää, Antti & Laamanen, Tomi, 2009. "Positive and negative feedback effects in competition for dominance of network business systems," Research Policy, Elsevier, vol. 38(5), pages 871-884, June.
    3. Heinz, B. & Graeber, M. & Praktiknjo, A.J., 2013. "The diffusion process of stationary fuel cells in a two-sided market economy," Energy Policy, Elsevier, vol. 61(C), pages 1556-1567.
    4. Kim, Heejung, 2012. "Standardization in technology adoption: A comparison of broadcast TV cases," 19th ITS Biennial Conference, Bangkok 2012: Moving Forward with Future Technologies - Opening a Platform for All 72483, International Telecommunications Society (ITS).
    5. den Hartigh, E. & Langerak, F. & Commandeur, H.R., 2002. "The Effects of Self-Reinforcing Mechanisms on Firm Performance," ERIM Report Series Research in Management, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasm ERS-2002-46-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.
    6. Nair, Harikesh S. & Chintagunta, Pradeep & Dube, Jean-Pierre, 2003. "Empirical Analysis of Indirect Network Effects in the Market for Personal Digital Assistants," Research Papers 1948, Stanford University, Graduate School of Business.
    7. Claussen, Jörg & Kretschmer, Tobias & Spengler, Thomas, 2010. "Market leadership through technology – Backward compatibility in the U.S. Handheld Video Game Industry," Discussion Papers in Business Administration, University of Munich, Munich School of Management 12716, University of Munich, Munich School of Management.
    8. den Hartigh, E. & Langerak, F. & Commandeur, H.R., 2000. "A Managerial Perspective on the Logic of Increasing Returns," ERIM Report Series Research in Management, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasm ERS-2000-48-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.
    9. Karaca-Mandic, Pinar, 2003. "Network Effects in Technology Adoption: The Case of DVD Players," Department of Economics, Working Paper Series, Department of Economics, Institute for Business and Economic Research, UC Berkeley qt3zj05321, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    10. Amit Mehra & Gireesh Shrimali, 2008. "Introduction of Software Products and Services Through "Public" Beta Launches," Working Papers 08-11, NET Institute.
    11. Stremersch, S. & Tellis, G.J. & Franses, Ph.H.B.F. & Binken, J.L.G., 2007. "Indirect Network Effects in New Product Growth," ERIM Report Series Research in Management, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasm ERS-2007-019-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.
    12. 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.
    13. Guoyin Jiang & Feicheng Ma & Youtian Wang, 2012. "A review on the evolution of user acceptance behaviour in collaborative e-commerce," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 6(1), pages 62-78.
    14. Binken, J.L.G. & Stremersch, S., 2008. "The Effect of Superstar Software on Hardware Sales in System Markets," ERIM Report Series Research in Management, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasm ERS-2008-025-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.

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