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A High-Tech Product Market Share Model with Customer Expectations

Author

Listed:
  • Eileen Bridges

    (Kent State University)

  • Chi Kin (Bennett) Yim

    (Rice University)

  • Richard A. Briesch

    (New York University)

Abstract

In a product category based on dynamic technology, new products enter the market in rapid succession, and the competitive situation changes almost daily. Because technological features of available products tend to improve while prices tend to decline, customers develop expectations that may influence their purchase decisions. We model the impact of customer expectations regarding and on product market share in the personal computer industry, finding significant nonlinear effects of both. These effects are observed when actual product price and/or technology differ from expectations by a threshold amount. Our results suggest implementable implications for high-tech product managers: in particular, price and technology should meet, but not exceed, customer expectations. This does not mean that managers should strive for mediocrity; rather, continuous improvement should be implemented so that product development efforts lead customer expectations.

Suggested Citation

  • Eileen Bridges & Chi Kin (Bennett) Yim & Richard A. Briesch, 1995. "A High-Tech Product Market Share Model with Customer Expectations," Marketing Science, INFORMS, vol. 14(1), pages 61-81.
    • Handle: RePEc:inm:ormksc:v:14:y:1995:i:1:p:61-81
    DOI: 10.1287/mksc.14.1.61
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    Citations

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    Cited by:

    1. Alfred Taudes & Christian Rudloff, 2012. "Integrating inventory control and a price change in the presence of reference price effects: a two-period model," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 75(1), pages 29-65, February.
    2. Fowler, Kendra & Bridges, Eileen, 2010. "Consumer innovativeness: Impact on expectations, perceptions, and choice among retail formats," Journal of Retailing and Consumer Services, Elsevier, vol. 17(6), pages 492-500.
    3. Kim, Namwoon & Srivastava, Rajendra K. & Han, Jin K., 2001. "Consumer decision-making in a multi-generational choice set context," Journal of Business Research, Elsevier, vol. 53(3), pages 123-136, September.
    4. Sang-June Park & Youjae Yi, 2017. "A composite measure of performance–expectation and performance-only measures," The Service Industries Journal, Taylor & Francis Journals, vol. 37(15-16), pages 936-947, December.
    5. Namwoon Kim & Jin K. Han & Rajendra K. Srivastava, 2002. "A Dynamic IT Adoption Model for the SOHO Market: PC Generational Decisions with Technological Expectations," Management Science, INFORMS, vol. 48(2), pages 222-240, February.
    6. Sumitro Banerjee & Miklos Sarvary, 2009. "How incumbent firms foster consumer expectations, delay launch but still win the markets for next generation products," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 445-481, December.
    7. Tülin Erdem & Michael Keane & T. Öncü & Judi Strebel, 2005. "Learning About Computers: An Analysis of Information Search and Technology Choice," Quantitative Marketing and Economics (QME), Springer, vol. 3(3), pages 207-247, September.
    8. Zhang, Qin & Seetharaman, P.B. & Narasimhan, Chakravarthi, 2012. "The Indirect Impact of Price Deals on Households’ Purchase Decisions Through the Formation of Expected Future Prices," Journal of Retailing, Elsevier, vol. 88(1), pages 88-101.
    9. Bowon Kim & Fouad El Ouardighi & Sangsun Park, 2012. "Optimal dynamics of technology and price in a duopoly market," Applied Economics Letters, Taylor & Francis Journals, vol. 19(11), pages 1017-1022, July.
    10. Baojun Jiang & K. Sudhir & Tianxin Zou, 2021. "Effects of Cost‐Information Transparency on Intertemporal Price Discrimination," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 390-401, February.
    11. Lee G. Cooper & Penny Baron & Wayne Levy & Michael Swisher & Paris Gogos, 1999. "PromoCast™: A New Forecasting Method for Promotion Planning," Marketing Science, INFORMS, vol. 18(3), pages 301-316.
    12. Sandeep D. Pillai & Brent Goldfarb & David A. Kirsch, 2020. "The origins of firm strategy: Learning by economic experimentation and strategic pivots in the early automobile industry," Strategic Management Journal, Wiley Blackwell, vol. 41(3), pages 369-399, March.
    13. Subramanian Balachander & Kannan Srinivasan, 1998. "Modifying Customer Expectations of Price Decreases for a Durable Product," Management Science, INFORMS, vol. 44(6), pages 776-786, June.
    14. Davis Brennan & Currim Imran S. & Sarin Rakesh K., 2012. "Reference Dependence and Conjoint Analysis," Review of Marketing Science, De Gruyter, vol. 10(1), pages 1-29, September.
    15. Drechsler, Wenzel & Natter, Martin, 2011. "Do Price Charts Provided by Online Shopbots Influence Price Expectations and Purchase Timing Decisions?," Journal of Interactive Marketing, Elsevier, vol. 25(2), pages 95-109.
    16. Hyun S. Shin & Dominique M. Hanssens & Kyoo il Kim, 2016. "The role of online buzz for leader versus challenger brands: the case of the MP3 player market," Electronic Commerce Research, Springer, vol. 16(4), pages 503-528, December.

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