Talking Papers

Modeling Simultaneous Multiple Goal Pursuit and Adaptation in Consumer Choice

Joffre Swait, Jennifer Argo, Lianhua Li

Forthcoming, Journal of Marketing Research 


Goals are constructs that direct choice behavior by guiding a decision maker towards desirable (or away from undesirable) end-states. Oftentimes, consumers are motivated to satisfy multiple goals within a single choice. While recognizing this possibility, the literature has not directly formulated models of choice as a multi-goal problem. We develop such a model, referred to as the Multiple-Goal-Based-Choice-Model, that incorporates 1) simultaneous multiple goal pursuit and 2) context-driven goal adaptation, but 3) does not require a priori identification of the number or nature of the goals. Goal adaptation within a single choice instance, allied to repeated choices, is the key to empirical identification of multiple latent goals. The proposed model is tested and supported using discrete choice experimental data on digital cameras via multiple validation exercises. The model can lead to significantly different policy implications with regards to consumers’ valuation for new product designs, compared to extant utility-based choice models.

The full publication can be found here.

Values for the ICECAP - Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life 

Elisabeth Huynh, Joanna Coast, John Rose, Philip Kinghorn, Terry Flynn 


End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure, with a large number of members of the general public, could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life.

The full publication can be found here.

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