Automatic price appraisals: why they matter and how to measure them.

Accurately estimating consumers' willingness-to-pay (WTP) is crucial to product design, pricing decisions, and the design of competitive marketing strategies. However, traditional self-report measures of WTP are susceptible to many reporting biases, including tactical responding or an inability to make accurate estimates. Importantly, appraisals also occur automatically (i.e., in the absence of substantial time, intention, awareness, and/or substantial cognitive resources) and implicit measures used to capture automatic appraisals are less susceptible to the sort of reporting biases that self-report measures can be affected by. However, the only existing implicit measure for assessing automatic price appraisals (the Task Rule Congruency paradigm, 'TRC') is impractical because of the large number of trials and time it requires. Accordingly, here we introduce the Implicit Attribute Classification Task (IMPACT), test its effectiveness for the measurement of automatic price appraisals (Study 1), and directly compare its effectiveness and utility with that of the TRC (Study 2). We find that the IMPACT is an efficient measure of automatic price appraisals, that it produces considerably larger effects compared to the TRC, and that it does so while substantially shortening the procedure. We also discuss how the IMPACT scores can be used to derive an implicit measure of willingness to pay. Our findings make a substantial contribution to both research and practice by providing an effective tool that facilitates, for the first time, an efficient exploration of implicit WTP.

The role of valence, dominance, and pitch in perceptions of artificial intelligence (AI) conversational agents' voices.

There is growing concern that artificial intelligence conversational agents (e.g., Siri, Alexa) reinforce voice-based social stereotypes. Because little is known about social perceptions of conversational agents' voices, we investigated (1) the dimensions that underpin perceptions of these synthetic voices and (2) the role that acoustic parameters play in these perceptions. Study 1 (N = 504) found that perceptions of synthetic voices are underpinned by Valence and Dominance components similar to those previously reported for natural human stimuli and that the Dominance component was strongly and negatively related to voice pitch. Study 2 (N = 160) found that experimentally manipulating pitch in synthetic voices directly influenced dominance-related, but not valence-related, perceptions. Collectively, these results suggest that greater consideration of the role that voice pitch plays in dominance-related perceptions when designing conversational agents may be an effective method for controlling stereotypic perceptions of their voices and the downstream consequences of those perceptions.

Predicting meat consumption from concurrent, automatic appraisals: Introducing nuance to product appraisals.

Research into the relationship between automatic product appraisals and consumer behaviour has largely been limited to measuring generic product evaluations (i.e., positive vs. negative). Especially in the context of meat consumption, this approach seems inadequate, as conflicting evaluative product dimensions may play a role in the preference for a plant-based vs. a meat-based diet (e.g., sustainability vs. taste vs. healthiness). We discuss the limitations of this approach and provide a novel tool that can measure automatic appraisals of several stimulus dimensions simultaneously. Using this tool, we register automatic appraisals (health, taste, price, sustainability, ethicality) of meat and vegetarian stimuli, and compare automatic and explicit appraisals in relation to a range of outcome measures, including self-reported likelihood of purchase and reducing meat consumption, willingness to pay, self-reported frequency of meat consumption, and Body Mass Index. Our findings suggest that the measured automatic appraisals represent unique constructs and vary in the degree to which they inform behaviour. Further, variation in the prediction of the outcome variables suggests that the appraisals captured by the explicit and automatic measures differed. Demonstrating unique contributions of the individual automatic appraisals has crucial implications for future research to understand behaviour and improve existing models.