Modeling Store Choices with Cross-Sectional and Pooled Cross-Sectional Data

A Comparison

Bibliographic Data

ID4886477
AuthorsJ-C Thill (University of Georgia, corresponding author)
Year1995
Volume27
Issue8
Pages1303-1315
Publication date1995-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning A Economy and Space (JOURNAL)
Journal identifiersISSN: 0308-518X • E-ISSN: 1472-3409
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1068/a271303
OpenAlexW1992443556
LanguageEN
Citations received3
References cited27

Contrary to many other types of spatial decisions, shopping destination choice behavior is highly repetitive. For the practitioner looking for good predictors of store patronage, for reliable marginal utility estimates and reliable market share predictions, a central concern is with the type of data best suited to the research question, given the existing logistic and financial constraints. Different approaches can be recognized in the literature in which conventional discrete choice models are applied to shopping destination choice problems. In this paper, two of the most common practices are assessed and compared. First, the choice model is estimated with all choices of a relevant destination observed during a certain period of time (pooled cross-sectional data). The alternative approach consists in an estimation with the choice of the destination where the majority of purchases takes place (cross-sectional data). In the particular data set employed here, no evidence is found to support the idea that a multinomial logit model estimated with cross-sectional data does not perform as well as a model estimated with pooled cross-sectional data. Both models are found to be similar in their ability to identity the main predictors of store choice. Models developed on either data sets have marginal utility estimates that exhibit no statistically significant differences. Finally, market share predictions derived from both models are not statistically different. It appears, therefore, that there is no need to collect repeated patronage data over an extended period of time. The practitioner who wishes to use a conventional discrete choice model may avoid spending much time and money by gathering limited data on regular patronage patterns. In addition to this practical implication, the conclusions suggest that regular shopping destinations are chosen in accordance with the same behavioral motives as ancillary destinations are

Choice set · Cross-sectional data · Cross-sectional study · Data set · Discrete choice · Econometrics · Economics · Estimation · Logistic regression · Logit · Mixed logit · Multinomial distribution · Multinomial logistic regression · Nested logit · Statistics · Survey data collection · Computer Science · Economic and Environmental Valuation · Housing Market and Economics · Mathematics · Regional Economics and Spatial Analysis

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