Marketing

In most marketing experiments, managerial decisions are not based directly on the estimates of the parameters, but rather on functions of these estimates. For example, many managerial decisions are driven by whether or not a feature is valued more than the price the consumer will be asked to pay. In other cases, some managerial decisions are weighed more heavily than others. The standard measures used to evaluate experimental designs (e.g., A-efficiency or D-efficiency) do not accommodate these phenomena.

Polyhedral methods for choice-based conjoint analysis provide a means to adapt choice-based questions at the individual-respondent level and provide an alternative means to estimate partworths when there are relatively few questions per respondent as in a web-based questionnaire. However, these methods are deterministic and are susceptible to the propagation of response errors. They also assume, implicitly, a uniform prior on the partworths.

The relation between emotion and rationality is assessed by reviewing empirical findings from multiple disciplines. Two types of emotional phenomena are examined—incidental emotional states and integral emotional responses—and three conceptions of rationality are considered—logical, material, and ecological. Emotional states influence reasoning processes, are often misattributed to focal objects, distort beliefs in an assimilative fashion, disrupt self-control when intensely negative but do not necessarily increase risk-taking.

We propose and test a new approach for modeling consumer heterogeneity in conjoint estimation based on convex optimization and statistical machine learning. We develop methods both for metric and choice data. Like hierarchical Bayes (HB), our methods shrink individual-level partworth estimates towards a population mean. However, while HB samples from a posterior distribution that is influenced by exogenous parameters (the parameters of the second-stage priors), we minimize a convex loss function that depends only on endogenous parameters.

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This paper examines the preferences of advice seekers for human information sources. We focus on the case in which advice providers can be people with high or low technical expertise (high in technical knowledge) and/or socially connected (connected to many others). Somewhat contrary to intuition, information sources who are high on social connectivity are shown to be relatively more attractive for more innovative products. Consistent with this, a meta-analysis indicates that the correlation between knowledge and opinion leadership is indeed lower for more innovative products.

Purpose — The purpose of this paper is to develop a comprehensive model that combines brand knowledge and brand relationship perspectives on brands and shows how knowledge and relationships affect current and future purchases.

Design/methodology/approach — The paper uses structural equation modeling to test the significance of the overall model and the specified paths.

Theoretical work on the pricing of information reveals that competition between independent information sellers can result in prices that are negatively related to the quality or reliability of the information. The theory argues that when information products are unreliable (low quality), independent products become complements, and competition can increase prices. The goal of this study is to test empirically the theory's counterintuitive predictions with the help of an experimental market based on a business simulation.

Bias in the market for news is well-documented. Recent research in economics explains the phenomenon by assuming that consumers want to read (watch) news that is consistent with their tastes or prior beliefs rather than the truth. The present paper builds on this idea but recognizes that (i) besides "biased" consumers, there are also "conscientious" consumers whose sole interest is in discovering the truth, and (ii) consistent with reality, media bias is constrained by the truth. These two factors were expected to limit media bias in a competitive setting. Our results reveal the opposite.