Volumetric choice experiments (VCEs)
Richard Carson,
Thomas C. Eagle,
Towhidul Islam and
Jordan J. Louviere
Journal of choice modelling, 2022, vol. 42, issue C
Abstract:
Volumetric Choice Experiments (VCEs) are designed to capture purchase quantities rather than a single, discrete choice. They can be seen as an extension of Discrete Choice Experiments (DCEs) where individuals decide how many units of a specific good or service to buy/use rather than deciding whether to buy/use it or not. There is different information in such integer count data than is contained in traditional binary or multinomial discrete choices, which presents new opportunities and interesting challenges. Like DCEs, VCEs have different components ranging from experimental design to modelling and our focus is on the overall process of implementation rather than detailed analysis of components. Our empirical examples come from large-scale VCEs embedded in surveys administered to samples drawn from Information Resources, Inc. (IRI) consumer panel for two product categories: single serve-coffee K-pods and canned tuna. The response for each alternative is a planned purchase count, possibly zero. These counts are fit using a negative binomial regression with a multilevel mixed-effects specification. Our VCE design allows for statistical identification of own- (brand by size) and cross-price elasticities, plus the effects of other attributes and demographics and their interactions with prices. The external validity of our approach is compared to results on actual canned tuna data purchases from the same IRI panelists. Advantages and limitations of VCEs as well as many unresolved research issues are discussed.
Keywords: Count data; Discrete choice experiment (DCE); Experimental design; Negative binomial models; Volumetric choice experiments (VCE) (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:42:y:2022:i:c:s175553452200001x
DOI: 10.1016/j.jocm.2022.100343
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