Marketing Letters

, Volume 10, Issue 3, pp 205–217

Combining Sources of Preference Data for Modeling Complex Decision Processes


  • Jordan J. Louviere
    • Faculty of EconomicsUniversity of Sydney
  • Robert J. Meyer
    • Wharton School of BusinessUniversity of Pennsylvania
  • David S. Bunch
    • Graduate School of ManagementUniversity of California
  • Richard Carson
    • Department of EconomicsUniversity of California
  • Benedict Dellaert
    • Center for Economic ResearchTilburg University
  • W. Michael Hanemann
    • Department of Agricultural and Resource EconomicsUniversity of California
  • David Hensher
    • Faculty of EconomicsUniversity of Sydney
  • Julie Irwin
    • Wharton School of BusinessUniversity of Pennsylvania

DOI: 10.1023/A:1008050215270

Cite this article as:
Louviere, J.J., Meyer, R.J., Bunch, D.S. et al. Marketing Letters (1999) 10: 205. doi:10.1023/A:1008050215270


We review current state-of-the-art practices for combining preference data from multiple sources and discuss future research possibilities. A central theme is that any one data source (e.g., a scanner panel source) is often insufficient to support tests of complex theories of choice and decision making. Hence, analysts may need to embrace a wider variety of data types and measurement tools than traditionally have been considered in applied decision making and choice research. We discuss the viability of preference-stationarity assumptions usually made when pooling data, as well as random-utility theory-based approaches for combining data sources. We also discuss types of models and data sources likely to be required to make inferences about and estimate models that describe choice dynamics. The latter discussion is speculative insofar as the body of literature on this topic is small.

Choice modelingstarted preference datadata poolingcontext effectschoice dynamics

Copyright information

© Kluwer Academic Publishers 1999