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Modeling cross-price effects on inter-category dynamics: The case of three computing platforms

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  • Kim, Namwoon
  • Srivastava, Rajendra K.

Abstract

Existing research on the market evolution model focused on new product sales growth for a single product category. Accordingly, this approach did not imply any interactions among separate but related product categories that could affect each other's market growth. However, the importance of analyzing these inter-category relationships is emphasized because such an analysis helps managers to better understand the underlying market dynamics and develop more profitable product-line strategies for a multi-product market. The current study suggests a sales growth model that can be used to analyze the inter-category product relationships and forecast sales of these related products. The authors develop a simultaneous equation model that incorporates the cross-price effects on inter-category dynamics for technological product markets. It deals with the price effect of one product category on the market size of other categories that serve similar customer utilities. The model is empirically tested based on the sales and price data of three computing platforms--mainframe, mini, and micro computers, which dynamically interact within the broader "computing" market. The results show that the sales of a given category of computing platform is significantly affected by the price of the category itself and by that of the related categories as well. The model is validated by comparing it with its alternative model specifications of similar purpose based on several established model comparison criteria. Managerial implications, contributions, and limitations of the model are also discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jomega:v:35:y:2007:i:3:p:290-301
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    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. repec:ucp:bknber:9780226304557 is not listed on IDEAS
    3. David C. Schmittlein & Vijay Mahajan, 1982. "Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 1(1), pages 57-78.
    4. Roger M. Heeler & Thomas P. Hustad, 1980. "Problems in Predicting New Product Growth for Consumer Durables," Management Science, INFORMS, vol. 26(10), pages 1007-1020, October.
    5. Vijay Mahajan & Robert A. Peterson, 1978. "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, INFORMS, vol. 24(15), pages 1589-1597, November.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Shlomo Kalish, 1983. "Monopolist Pricing with Dynamic Demand and Production Cost," Marketing Science, INFORMS, vol. 2(2), pages 135-159.
    8. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    9. Paul Chwelos, 2003. "Approaches to performance measurement in hedonic analysis: Price indexes for laptop computers in the 1990's," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 12(3), pages 199-224.
    10. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    11. Roland T. Rust & David C. Schmittlein, 1985. "A Bayesian Cross-Validated Likelihood Method for Comparing Alternative Specifications of Quantitative Models," Marketing Science, INFORMS, vol. 4(1), pages 20-40.
    12. Robert J. Gordon, 1990. "The Measurement of Durable Goods Prices," NBER Books, National Bureau of Economic Research, Inc, number gord90-1.
    13. Nelson, Randy A & Tanguay, Tim L & Patterson, Christopher D, 1994. "A Quality-Adjusted Price Index for Personal Computers," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(1), pages 23-31, January.
    14. Jain, Dipak C & Rao, Ram C, 1990. "Effect of Price on the Demand for Durables: Modeling, Estimation, and Findings," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 163-170, April.
    15. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    16. Roland T. Rust & Duncan Simester & Roderick J. Brodie & V. Nilikant, 1995. "Model Selection Criteria: An Investigation of Relative Accuracy, Posterior Probabilities, and Combinations of Criteria," Management Science, INFORMS, vol. 41(2), pages 322-333, February.
    17. 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.
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