Abstract

Sustainability has become a critical concern of many societies worldwide. The need for a more sustainable mode of producing and consuming goods and services while balancing related environmental, social, and economic consequences (i.e., the triple bottom line) is evident. Although research offers insights into many aspects of this necessary transformation, little is known about the extent to which firms and consumers stress environmental, social, and economic sustainability in their communication. This research addresses these questions by conceptualizing the interplay between sustainability-related firm-generated and user-generated content as a signaling phenomenon. In addition, the authors develop a custom dictionary that enables researchers and practitioners to identify and analyze sustainability-related textual data. An illustrative application based on major data sources (corporate websites, Amazon, and YouTube) indicates significant divergence in how firms and consumers communicate about sustainability. Building on this first conceptual and empirical foray into sustainability-related firm-generated and user-generated content, this research outlines open research questions and potential use cases for the provided analytical tool.

Abstract

Psychology and economics (together known as behavioral economics) are two prominent disciplines underlying many theories in marketing. The extensive marketing literature documents consumers’ nonrational behavior even though behavioral biases might not always be consistently termed or formally described. In this review, we identify and synthesize empirical research on behavioral biases in marketing. We document the key findings according to three classes of deviations (i.e., nonstandard preferences, nonstandard beliefs, and nonstandard decision making) and the four phases of consumer purchase decision making (i.e., need recognition, pre-purchase, purchase, and post-purchase). Our organizing framework allows us to (1) synthesize instructive marketing papers in a concise and meaningful manner and (2) identify connections and differences within and across categories in both dimensions. In our review, we discuss specific implications for management and avenues for future research.

Abstract

Traditionally, choice-based conjoint analysis relies on the assumption of rational decision makers that use all available information. However, several studies suggest that people ignore some information when making choices. In this paper, we build upon recent developments in the choice literature and employ a latent class model that simultaneously allows for attribute non-attendance (ANA) and preference heterogeneity. In addition, we relate visual attention derived from eye tracking to the probability of ANA to test, understand, and validate ANA in a marketing context. In two empirical applications, we find that a) our proposed model fits the data best, b) the majority of respondents indeed ignore some attributes, which has implications for willingness-to-pay estimates, segmentation, and targeting, and c) even though the latent class model identifies ANA well without eye tracking information, our model with visual attention helps to better understand ANA and individual-level behavior.

Abstract

Identifying consumer heterogeneity is a central topic in marketing. While the main focus has been on developing models and estimation procedures that allow uncovering consumer heterogeneity in preferences, a new stream of literature has focused on models that account for consumers’ heterogeneous attribute information usage. These models acknowledge that consumers may ignore subsets of attributes when making decisions, also commonly termed “attribute nonattendance" (ANA). In this paper, we explore the performance of choice models that explicitly account for ANA across ten different applications, which vary in terms of the choice context, the associated financial risk, and the complexity of the purchase decision. We systematically compare five different models that either neglect ANA and preference heterogeneity, account only for one at a time, or account for both across these applications. First, we showcase that ANA occurs across all ten applications. It prevails even in simple settings and high-stakes decisions.

Second, we contribute by examining the direction and the magnitude of biases in parameters. We find that depending on the true preference distribution, often related to whether the attribute enables horizontal or vertical differentiation of products, neglecting ANA may lead to under or overestimating preference heterogeneity respectively. Lastly, we present how the empirical results translate into managerial implications and provide guidance to practitioners on when these models are beneficial.

Description

This paper examines how choice design complexity and product category involvement influence attribute non-attendance—consumers' tendency to ignore certain product information when making decisions. Findings from three experiments show that increasing choice complexity—both in the number of attributes and alternatives—reduces attribute attendance. Notably, the relationship between the number of alternatives and attendance follows an inverted U-shape, where moderate complexity enhances cognitive effort. Additionally, greater involvement leads to higher attribute attendance, as engaged consumers are less likely to oversimplify choices. These insights shed light on how choice design and consumer engagement affect decision-making across various contexts.

Description

Digital platforms increasingly rely on advance reservations to manage scarce capacity, yet many customers fail to honor their bookings. No-shows and late cancellations reduce realized utilization, create negative externalities for venues, and weaken platform reliability. Many platforms have introduced monetary penalties to discourage speculative booking and improve attendance, although such charges may be perceived as punitive and could reduce engagement or accelerate churn.

We study these trade-offs using data from a European multi-sport subscription platform that introduced no-show and late-cancellation fees during our observation period. 

Description

The use of incentive alignment mechanisms in choice-based conjoint (CBC) studies has become state-of-the-art. Incentive alignment aims to reduce hypothetical bias, leading to higher external validity and more realistic WTP measures. Lottery rewards are often employed to mitigate high implementation costs, i.e., respondents’ choices are consequential at the realization probability. While in theory, any positive realization probability should induce truth-telling, recent studies find that higher ones lead to more attention to the choice-relevant information and better demand forecasting. This study investigates whether similar patterns hold when the realization probabilities are very low to begin with (e.g., 1% or lower) – a typical case for many studies (e.g., in the case of durable goods). The experimental results suggest that, indeed, the marginal increases in the realization probability matter and result in an improvement of external predictive validity. Furthermore, the role of ambiguity in the way the realization probabilities are communicated to the respondents – as an objective probability, an interval, or as an ambiguous prospect (e.g., one lucky winner) is explored. The findings suggest that respondents seem to penalize for ambiguity in communication. The follow-up study aims to further investigate the role of risk and ambiguity preferences on the effectivness of incentive alignment mechanims and ways of improving it. 

Description

Increasing transparency about how information firms have about consumers is collected, processed and used is often viewed by practitioners, regulators and researchers as an important measure to help customers make more informed decisions regarding the disclosure of personal information. However, evidence for the effect of transparency on disclosure behavior is mixed, both in terms of the magnitude and direction of the effect. To integrate prior findings, we conduct a comprehensive meta-analytic review on the effect of transparency on disclosure behavior. We contribute to our understanding of transparency by distinguishing between two interventions: transparency as an increase in the quantity of information and transparency as an increase in the quality of information. Moreover, we identify several moderators of the effect. Our findings carry implications for practitioners as well as regulators on design aspects of transparency-measures across domains.