Estimating decision rule differences between 'best' and 'worst' choices in a sequential best worst discrete choice experiment

Abstract

Since the introduction of Discrete Choice Analysis, countless efforts have been made to enhance the efficiency of data collection through choice experiments and to improve the behavioural realism of choice models. One example development in data collection are best-worst discrete choice experiments (BWDCE), which have the benefit of obtaining a larger number of observations per respondent, allowing for reliably estimating choice models even with smaller samples. In SWDCE, respondents are asked to alternatingly select the ‘best’/‘worst’ alternatives, until the choice set is exhausted. The use of BWDCE raises the question of decision-rule consistency through the stages of the experiment. We challenge the notion that the same fully compensatory decision rule is utilised throughout the experiment. We hypothesize that respondents may utilise one decision rule for selecting the ‘best’ and another for selecting the ‘worst’ alternatives. To test our hypothesis, we developed a model that combines the SBWMNL model for modelling best-worst data and the μRRM model that can account for variations in decision rules. Our results show that decision-rule heterogeneity does seem to be present in BWDCE: it is more likely that ‘best’ choices are made using a fully compensatory decision rule (maximising utility), whereas ‘worst’ choices are more likely made using a non-compensatory decision rule (minimising regret). Such behaviour is largely similar to how image theory describes the decision-making process in complex situations. Our findings give choice modellers new insight into the behaviour of respondents in best-worst experiments and allows them to represent their behaviour more accurately. Show more