Combining Sources of Preference Data for Modeling Complex Decision Processes

Jordan J. Louviere, Robert J. Meyer, David S. Bunch, Richard Carson, Benedict Dellaert, William Hanemann, David Hensher, Julie Irwin

Research output: Contribution to journalArticle

54 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)205-217
Number of pages13
JournalMarketing Letters
Volume10
Issue number3
StatePublished - 1999
Externally publishedYes

Fingerprint

Decision process
Modeling
Data sources
Decision making
Viability
Dynamic choice
Analysts
Pooling
Inference
Stationarity
Utility theory
Random utility

Keywords

  • Choice dynamics
  • Choice modeling
  • Context effects
  • Data pooling
  • Started preference data

ASJC Scopus subject areas

  • Business and International Management
  • Economics and Econometrics
  • Marketing

Cite this

Louviere, J. J., Meyer, R. J., Bunch, D. S., Carson, R., Dellaert, B., Hanemann, W., ... Irwin, J. (1999). Combining Sources of Preference Data for Modeling Complex Decision Processes. Marketing Letters, 10(3), 205-217.

Combining Sources of Preference Data for Modeling Complex Decision Processes. / Louviere, Jordan J.; Meyer, Robert J.; Bunch, David S.; Carson, Richard; Dellaert, Benedict; Hanemann, William; Hensher, David; Irwin, Julie.

In: Marketing Letters, Vol. 10, No. 3, 1999, p. 205-217.

Research output: Contribution to journalArticle

Louviere, JJ, Meyer, RJ, Bunch, DS, Carson, R, Dellaert, B, Hanemann, W, Hensher, D & Irwin, J 1999, 'Combining Sources of Preference Data for Modeling Complex Decision Processes', Marketing Letters, vol. 10, no. 3, pp. 205-217.
Louviere JJ, Meyer RJ, Bunch DS, Carson R, Dellaert B, Hanemann W et al. Combining Sources of Preference Data for Modeling Complex Decision Processes. Marketing Letters. 1999;10(3):205-217.
Louviere, Jordan J. ; Meyer, Robert J. ; Bunch, David S. ; Carson, Richard ; Dellaert, Benedict ; Hanemann, William ; Hensher, David ; Irwin, Julie. / Combining Sources of Preference Data for Modeling Complex Decision Processes. In: Marketing Letters. 1999 ; Vol. 10, No. 3. pp. 205-217.
@article{34574a9c59ea47faa0ff27399b0802b6,
title = "Combining Sources of Preference Data for Modeling Complex Decision Processes",
abstract = "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.",
keywords = "Choice dynamics, Choice modeling, Context effects, Data pooling, Started preference data",
author = "Louviere, {Jordan J.} and Meyer, {Robert J.} and Bunch, {David S.} and Richard Carson and Benedict Dellaert and William Hanemann and David Hensher and Julie Irwin",
year = "1999",
language = "English (US)",
volume = "10",
pages = "205--217",
journal = "Marketing Letters",
issn = "0923-0645",
publisher = "Springer New York",
number = "3",

}

TY - JOUR

T1 - Combining Sources of Preference Data for Modeling Complex Decision Processes

AU - Louviere, Jordan J.

AU - Meyer, Robert J.

AU - Bunch, David S.

AU - Carson, Richard

AU - Dellaert, Benedict

AU - Hanemann, William

AU - Hensher, David

AU - Irwin, Julie

PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

KW - Choice dynamics

KW - Choice modeling

KW - Context effects

KW - Data pooling

KW - Started preference data

UR - http://www.scopus.com/inward/record.url?scp=0001087909&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0001087909&partnerID=8YFLogxK

M3 - Article

VL - 10

SP - 205

EP - 217

JO - Marketing Letters

JF - Marketing Letters

SN - 0923-0645

IS - 3

ER -