Behavioral Biases in Marketing 

(with Katharina Dowling, Daniel Guhl, Daniel Klapper, Martin Spann, and Lucas Stich)

Journal of the Academy of Marketing Science, 48(3), 449-477.


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.

Inferring Attribute Non-attendance Using Eye Tracking in Choice-based Conjoint Analysis 

(with Daniel Guhl and Daniel Klapper)

Journal of Business Research, 111, 290-304.


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.

Working Papers

When Zeros Count: Confounding in Preference Heterogeneity and Attribute Non-Attendance 

(with Daniel Guhl and Friederike Paetz)


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.

Work in Progress

Lottery Rewards in Incentive-aligned Choice-based Conjoint Studies


The use of incentive alignment mechanisms in choice-based conjoint (CBC) studies or discrete choice experiments 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, I investigate the role of ambiguity in the way the realization probabilities are communicated to the respondents – as an objective probability (e.g., 1:200 chances of winning), an interval (1:200 to 1:400 chances of winning), or as an ambiguous prospect (one lucky winner from the pool of participants). I find that respondents seem to penalize for ambiguity in communication, and even more so in the case of using an interval of chances of winning. I further investigate the role of risk and ambiguity preferences on the external predictive validity of incentive-aligned CBC. 

The Effect of Information Transparency on Consumers' Data Disclosure Behavior: A Meta-Analysis

(with Maja Adena, Camila Back, Alaa Elgayar, Daniel Guhl, Daniel Klapper, Martin Spann, and Lucas Stich)


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.   

Information Processing Patterns during Choice. The Effect of Complexity and Product Category Involvement on Attribute Non-Attendance

(with Alaa Elgayar)


From a marketer’s standpoint, it is crucial to understand how potential customers process product information offered to them in a purchase scenario. When modeling choice behavior under standard random utility theory, we typically assume that decision-makers behave in a “rational” manner, i.e., exhibit stable preferences, process all available information, and apply compensatory decision rules. However, following the bounded rationality framework, the validity of these assumptions has been widely challenged. Experimental studies demonstrate that decision-makers may ignore some product attributes when making choices, commonly termed attribute non-attendance (ANA). Such behavior may be due to limited cognitive capacity and information overload or consumers may find some attributes not relevant. However, few studies have ventured into explicitly examining context- and person-related drivers of ANA as a decision heuristic. The contribution of our paper is two-fold. First, this paper investigates the joint effects of choice design complexity and product category involvement on ANA using three between-subject discrete choice experiments, where two aspects of complexity (number of attributes and alternatives) and product category involvement are experimentally manipulated. We hypothesize that an increase in complexity and decrease in involvement adversely affect attendance. Second, we explicitly test whether the proposed associations may vary according to the type of attribute information. Inspecting stated and inferred ANA, our results demonstrate complexity-induced ANA for one design dimension, a positive effect of involvement on attendance and the moderating effect of extrinsic attributes.