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.

Previous economics literature has explored the role of visual attention on choice in isolation without accounting for other influences such as habits and goals or learning effects, nor their interrelationship. In this paper, we: (i) develop a novel joint framework to explore the relationship between visual attention, observed heterogeneity from stated habits and goals, and choice outcomes while accounting for shorter- and longer-term learning effects; and (ii) investigate whether accounting for these relationships improves model predictive power and behavioral insights. The empirical analysis used an eye-tracked discrete choice experiment on sugar-sweetened beverage purchasing (n = 152 adults with 20 choice tasks). Results suggest that habits, goals, and shorter-term learning are key drivers of information acquisition whereas cumulative choices (longer-term learning) affect subsequent choice outcome. Importantly, ignoring the joint relationship between habits, visual attention and choice may exaggerate the role of visual attention, leading to incorrect behavioral insights and reduced prediction accuracy.