Financial advisors seek to accurately measure individuals' risk preferences and provide sound personalized investment advice. Both advice tasks are increasingly offered through automated online technologies. Little is known, however, about what drives individuals' acceptance of such automated financial advice and, from a consumer point of view, which firms may be best positioned to provide such advice.We generate novel insights on these questions by conducting a real-world empirical study using an interactive automated online tool that employs an innovative computer algorithm to build pension investment profiles, the "Pension Builder," and a large, representative sample.We focus on the role that two key firm characteristics have on consumer acceptance of pension investment advice generated by computer algorithms running on automated interactive online tools: profit orientation and role in the sales channel.We find that consumers' perceptions of trust and expertise of the firm providing the automated advice are important drivers of advice acceptance (besides a strong impact of the satisfaction with the consumer-online tool interaction), and that these constructs themselves are clearly influenced by the for-profit vs. not-for-profit orientation and the product provider vs. advisor only role in the sales channel of the firm providing the advice.We discuss the implications of our findings for marketers and policy makers and provide suggestions for future research.

The authors empirically explore how consumers update beliefs about a store's overall expensiveness. They estimate a learning model of store price image (SPI) formation with the impact of actual prices linked to category characteristics on a unique data set combining consumers’ store visit and purchase information with their price perceptions. The results identify characteristics that drive categories’ store price signaling power for different store formats. “Big ticket” categories with a narrow price range strongly shape consumers’ store price beliefs, whereas (volatile) prices of frequently or deeply promoted categories are less influential. At traditional supermarkets, consumers anchor and elaborate on prices of storable categories bought in large quantities and for which quality differentiation is high. For hard discounters, however, SPI is mostly shaped by frequently bought categories with narrow assortments. Notably, categories’ SPI signaling power is not proportional to their share of wallet at either type of chain. Managers can use these results to identify “Lighthouse” categories that signal low prices, yet make up a small portion of store spending, and in which price cuts do not overly hurt revenue.

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We propose that the positive effect of coming‐of‐age songs on ad effectiveness arises from a mediation process where the music‐evoked interpersonal memories of growing up stored in the brain and their accompanying emotions inevitably play a role, but not so straightforwardly as previously suggested. Rather, their effects work through the heightened familiarity of and peaked preferences for coming‐of‐age songs. We also propose that these sequentially mediated effects are moderated by viewers' developmental attachment styles. We test and find support for these propositions in three multimethod studies with more than 1200 participants born between the 40s and the mid‐70s and almost 60 popular songs released between the 60s and the 2010s. We discuss the implications of our findings, namely for age‐segmented video ads, and suggest future research directions.

In this paper we propose the use of preferred outcome distributions as a new method to elicit individuals' value and probability weighting functions in decisions under risk. Extant approaches for the elicitation of these two key ingredients of individuals' risk attitude typically rely on a long, chained sequence of lottery choices. In contrast, preferred outcome distributions can be elicited through an intuitive graphical interface, and, as we show, the information contained in two preferred outcome distributions is sufficient to identify non-parametrically both the value function and the probability weighting function in rank-dependent utility models. To illustrate our method and its advantages, we run an incentive-compatible lab study in which participants use a simple graphical interface -the Distribution Builder (Goldstein et al. 2008) -to construct their preferred outcome distributions, subject to a budget constraint. Results show that estimates of the value function are in line with previous research but that probability weighting biases are diminished, thus favoring our proposed approach based on preferred outcome distributions.