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Success in High-Technology Markets: Is Marketing Capability Critical?

Author

Listed:
  • Shantanu Dutta

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Om Narasimhan

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Surendra Rajiv

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

We propose a conceptual framework—with the resource-based view (RBV) of the firm as its theoretical underpinning—to explain interfirm differences in firms' profitability in high-technology markets in terms of differences in their functional capabilities. Specifically, we suggest that marketing, R&D, and operations capabilities, along with interactions among these capabilities, are important determinants of relative financial performance within the industry. This paper contributes to the RBV literature by proposing the input-output perspective to conceptualize the notion of capabilities. Specifically, this approach entails modeling a firm's functional activities—viz., marketing, R&D and operations—as transformation functions that relate the productive factors/resources to its functional objectives, if the firm were to deploy these resources most efficiently. Any underattainment of the functional objective, then, is attributable to functional inefficiency, or equivalently, to a lower functional capability of the firm. The input-output conceptualization of a firm's capabilities is then estimated using the stochastic frontier estimation (SFE) methodology. SFE provides the appropriate econometric technique to empirically estimate the efficient frontier and hence the level of efficiency achieved by the various firms. Our study contributes to a number of literatures, both methodologically and substantively. First, it contributes both conceptually and methodologically to the RBV literature. Conceptually, our study suggests that firm capabilities can be viewed in an input–output framework. Methodologically, the study suggests the use of stochastic frontier estimation to operationalize and estimate firm capabilities. This methodology is, to the best of our knowledge, the first to allow the researcher/manager to capabilities from archival data. Substantively, our study contributes to the literature on market orientation by suggesting that a stronger market orientation of a firm should be reflected in a higher marketing capability. It also adds to the literature on “design for manufacturability” by explicating the complementarity among the various functional capabilities and offering empirical evidence on their relative importance in influencing a firm's performance. Finally, our study builds on prior literature that has highlighted the importance of marketing–R&D coordination as important determinants of new product development and success. We highlight below some of our main findings. • A strong base of innovative technologies enhances a firm's sales by favorably influencing consumers' expectations about the externality benefits associated with its product. This suggests that a past track record of consistent innovation is a credible signal to current and potential customers of the firm's continued excellence in a technologically evolving market. Given the importance of influencing customers, managers need to tailor their marketing activities around the need to inform customers of the technological excellence of their firm. Thus, customers need to be informed of the innovative technologies that the firm possesses and of the future R&D initiatives undertaken by it. Similarly, any potential applications of innovative technology developed by the firm, and of technologies under development, should be emphasized. • Marketing capability has its greatest impact on the (quality-adjusted) innovative output for firms that have a strong technological base. In other words, firms with a strong R&D base are the ones with the most to gain from a strong marketing capability. • Marketing capability strongly influences the width of applicability of innovations, i.e., a firm's marketing capability enhances its ability to generate innovative technologies that have applications across a range of industries. This result carries a strong message for managers: A strong market orientation is one of the most fertile sources of ideas for innovation. Thus, marketing needs to be involved from the beginning of the innovation process—namely, right at the stage when technological ideas are being generated. • The most important determinant of a firm's performance is the interaction of marketing and R&D capabilities. This supports the assertion that firms in high-technology markets need to excel at two things: the ability to come up with innovations constantly, and the ability to commercialize these innovations into the kinds of products that capture consumer needs and preferences. This finding offers further evidence on the importance of coordination between R&D and marketing, as suggested in the extant marketing literature. Finally, using archival data, our methodology can be used to benchmark a firm's capabilities, with other firms in the industry, along various functional dimensions. This would be an important step in making more informed resource-allocation decisions. Thus, the firm can spend more money on those capabilities where it most lags the competition, or on those capabilities that are shown to have the maximum impact on firm performance.

Suggested Citation

  • Shantanu Dutta & Om Narasimhan & Surendra Rajiv, 1999. "Success in High-Technology Markets: Is Marketing Capability Critical?," Marketing Science, INFORMS, vol. 18(4), pages 547-568.
  • Handle: RePEc:inm:ormksc:v:18:y:1999:i:4:p:547-568
    DOI: 10.1287/mksc.18.4.547
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    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    3. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    4. Albert, M. B. & Avery, D. & Narin, F. & McAllister, P., 1991. "Direct validation of citation counts as indicators of industrially important patents," Research Policy, Elsevier, vol. 20(3), pages 251-259, June.
    5. Hoffman, Dennis L & Pagan, Adrian R, 1989. "Post-Sample Prediction Tests for Generalized Method of Moments Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 51(3), pages 333-343, August.
    6. Manuel Trajtenberg, 1990. "A Penny for Your Quotes: Patent Citations and the Value of Innovations," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 172-187, Spring.
    7. Füsun Gönül & Kannan Srinivasan, 1993. "Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Methodological and Managerial Issues," Marketing Science, INFORMS, vol. 12(3), pages 213-229.
    8. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    9. Zvi Griliches, 1984. "R&D, Patents, and Productivity," NBER Books, National Bureau of Economic Research, Inc, number gril84-1, May.
    10. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    11. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1993. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(3), pages 577-598.
    12. Abbie Griffin & John R. Hauser, 1993. "The Voice of the Customer," Marketing Science, INFORMS, vol. 12(1), pages 1-27.
    13. Kumbhakar, Subal C., 1987. "The specification of technical and allocative inefficiency in stochastic production and profit frontiers," Journal of Econometrics, Elsevier, vol. 34(3), pages 335-348, March.
    14. Ferrier, Gary D. & Lovell, C. A. Knox, 1990. "Measuring cost efficiency in banking : Econometric and linear programming evidence," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 229-245.
    15. Subal Kumbhakar, 1997. "Efficiency estimation with heteroscedasticity in a panel data model," Applied Economics, Taylor & Francis Journals, vol. 29(3), pages 379-386.
    16. Martin S. Eichenbaum & Lars Peter Hansen & Kenneth J. Singleton, 1988. "A Time Series Analysis of Representative Agent Models of Consumption and Leisure Choice Under Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 103(1), pages 51-78.
    17. S.A. Lippman & R.P. Rumelt, 1982. "Uncertain Imitability: An Analysis of Interfirm Differences in Efficiency under Competition," Bell Journal of Economics, The RAND Corporation, vol. 13(2), pages 418-438, Autumn.
    18. Irwin, Douglas A & Klenow, Peter J, 1994. "Learning-by-Doing Spillovers in the Semiconductor Industry," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1200-1227, December.
    19. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    20. Zvi Griliches, 1984. "Introduction to "R & D, Patents, and Productivity"," NBER Chapters, in: R&D, Patents, and Productivity, pages 1-20, National Bureau of Economic Research, Inc.
    21. Bauer, Paul W., 1990. "Recent developments in the econometric estimation of frontiers," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 39-56.
    22. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    23. Richard P. Rumelt, 1991. "How much does industry matter?," Strategic Management Journal, Wiley Blackwell, vol. 12(3), pages 167-185, March.
    24. Baltagi, Badi H & Griffin, James M, 1988. "A Generalized Error Component Model with Heteroscedastic Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 745-753, November.
    25. William Boulding & Richard Staelin, 1995. "Identifying Generalizable Effects of Strategic Actions on Firm Performance: The Case of Demand-Side Returns to R&D Spending," Marketing Science, INFORMS, vol. 14(3_supplem), pages 222-236.
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