Incorporating preference uncertainty in best worst scaling.

In this paper, we enhance the Best-Worst Scaling (BWS) method by incorporating participants' preference uncertainty into the conventional BWS, known as case 1. In this context, respondents are tasked with making trade-offs among a set of items of interest. Applying this novel extended BWS method to a sample of Argentinian wine consumers (n = 342), we aim to a) provide a more informative elicitation of consumers' relative preferences for 16 wine attributes; b) identify the level of uncertainty with each of the attributes, exploring differences between the most and least important wine attributes influencing purchasing wine; and c) compare the results of the extended BWS with the standard BWS. Our findings indicate variability in uncertainty levels on the importance of wine attributes when purchasing wine within and across attributes. Moreover, accounting for participants' preference uncertainty can alter the ranking of preferences obtained through the standard approach. This alteration is due to both accounting for preference uncertainty itself as well as the uncertainty indicator used. Although this approach is a way to mitigate biases associated with respondents' preference certainty, it is recommended that preference uncertainty heterogeneity is investigated using different indicators.