Testing the Role of Contextual Deliberation in the Compromise Effect

Many phenomena of preference construction demonstrate a violation of the rationality premise in classical economic theories. One of the most well-known examples of preference construction is the compromise effect. This puzzling anomaly can be rationalized by contextual deliberation (i.e., endogenous information retrieval/acquisition that can partially resolve utility uncertainty before choice). In this research, we investigate the empirical validity of this explanation by performing falsification tests for its necessary predictions and identifying it from other potential accounts. We conduct five experiments with more than 1,000 participants and show that the compromise effect can be positively mediated by response time and cannot be eliminated by context information, but it can be moderated by manipulating the level of deliberation (i.e., time constraint, preference articulation, task order). These findings are consistent with the predictions of the theory of contextual deliberation. We also show that, on average, contextual deliberation (as proxied by response time) can uniquely account for about half of the total compromise effect. This paper was accepted by Yan Chen, behavioral economics and decision analysis

Towards psychology-aware preference construction in recommender systems: Overview and research issues

User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as content-based filtering and collaborative filtering, user preferences are represented, for example, as keywords, categories, and item ratings. In conversational recommendation approaches such as constraint-based and critiquing-based recommendation, user preferences are often represented on the semantic level in terms of item attribute values and critiques. In this article, we provide an overview of preference representations used in different types of recommender systems. In this context, we take into account the fact that preferences aren’t stable but are rather constructed within the scope of a recommendation process. In which way preferences are determined and adapted is influenced by various factors such as personality traits, emotional states, and cognitive biases. We summarize preference construction related research and also discuss aspects of counteracting cognitive biases.

On My Own: The Aversion to Being Observed during the Preference-Construction Stage

Previous research in consumer behavior and decision-making has explored many important aspects of social observation. However, the effect of social observation during the specific time wherein consumers construct their preferences remains relatively understudied. The present work seeks to fill this knowledge gap and adds to this literature by studying how consumers react to being observed during the preference-construction stage (i.e., prior to reaching their decision). While existing research on social observation focuses on accountability and self-presentation concerns, the present study uncovers an additional unique concern. Specifically, eight studies (three additional studies reported in the web appendix) find that being observed prior to reaching the decision threatens consumers’ sense of autonomy in making the decision, resulting in an aversion to being observed. Furthermore, we find that such threats lead consumers to terminate their decision by avoiding purchase or by choosing default options. Given the extent to which consumers are observed in the marketplace by other individuals and by online platforms, and given the rise in consumers’ privacy concerns associated with such practices, understanding consumer reactions to being observed in the pre-decisional stage is an important topic with practical implications.

A tourism preference construction model based on complex network analysis

The travel notes contain a wealth of tourists' behavior information, which provides a new way to study tourists' preferences. How to mine the text of online travel notes accurately and efficiently has become the key to research tourists' preferences. In this paper, the theory and technology of text mining were introduced into the research of tourists' preference through a large number of online travel notes accumulated on the Internet. The main research work of this paper was as follows: (1) The tourists' preference model was constructed by complex network method; (2) The travel notes data of Sanya tourists as an example was crawled and analyzed. In this paper, the theory of network travel data and text mining is introduced into the study of tourists' preferences, which not only improves the data quality of traditional preference research field, but also provides a new method for mastering tourists' preferences more accurately.