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A Panel-Data Based Method for Merging Joint Space and Market Response Function Estimation

Author

Listed:
  • William L. Moore

    (University of Utah)

  • Russell S. Winer

    (Vanderbilt University)

Abstract

Multidimensional Scaling (MDS) techniques have proven to be useful in understanding market structures at a single point in time. In addition to examining snapshots of markets, it has been suggested that the construction of longitudinal joint spaces in conjunction with econometric modeling techniques would provide useful insights into the dynamic structure of a market. However, several pragmatic factors related to data collection have limited the amount of research in this area. In this paper, a method is proposed that enables researchers to use commonly available consumer diary panel data to construct a time series of joint space maps and then to integrate these maps into market response models. Two illustrations using longitudinal maps demonstrate successful applications of this methodology.

Suggested Citation

  • William L. Moore & Russell S. Winer, 1987. "A Panel-Data Based Method for Merging Joint Space and Market Response Function Estimation," Marketing Science, INFORMS, vol. 6(1), pages 25-42.
    • Handle: RePEc:inm:ormksc:v:6:y:1987:i:1:p:25-42
    DOI: 10.1287/mksc.6.1.25
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    Cited by:

    1. Moon, Sangkil & Jalali, Nima & Song, Reo, 2022. "Green-lighting scripts in the movie pre-production stage: An application of consumption experience carryover theory," Journal of Business Research, Elsevier, vol. 140(C), pages 332-345.
    2. Maximilian Matthe & Daniel M. Ringel & Bernd Skiera, 2023. "Mapping Market Structure Evolution," Marketing Science, INFORMS, vol. 42(3), pages 589-613, May.
    3. Albritton, M. David & McMullen, Patrick R., 2007. "Optimal product design using a colony of virtual ants," European Journal of Operational Research, Elsevier, vol. 176(1), pages 498-520, January.
    4. Sangkil Moon & Gary J. Russell, 2008. "Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach," Management Science, INFORMS, vol. 54(1), pages 71-82, January.
    5. Gruca, Thomas S. & Klemz, Bruce R., 2003. "Optimal new product positioning: A genetic algorithm approach," European Journal of Operational Research, Elsevier, vol. 146(3), pages 621-633, May.
    6. Sudharshan, D. & Ravi Kumar, K. & Gruca, Thomas S., 1995. "NICHER: An approach to identifying defensible product positions," European Journal of Operational Research, Elsevier, vol. 84(2), pages 292-309, July.

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