The St. Petersburg Paradox Despite Risk-seeking Preferences: An Experimental Study

The St. Petersburg Paradox is one of the oldest challenges of expected value theory. Thus far, explanations of the paradox aim at small probabilities being perceived as zero and the boundedness of utility of the outcome. This paper provides experimental results showing that neither diminishing marginal utility of the outcome nor perception of small probabilities can explain the paradox. We find that even in situations where subjects are risk-seeking, and zeroingout small probabilities supports risk-taking, the St. Petersburg Paradox exists. This indicates that the paradox cannot be resolved by the arguments advanced to date

The Influence of Serotonin Deficiency on Choice Deferral and the Compromise Effect

Psychological and physiological states such as mood, hunger, stress, and sleep deprivation are known to affect decision-making processes and therefore crucially influence consumer behavior. A possible biological mechanism underlying the observed variability of consumer behavior is the context-sensitive variation in the levels of neuromodulators in the brain. In a series of four experimental studies, the authors pharmaceutically reduce the levels of the neurotransmitter serotonin in the brain to diminish the availability of subjects’ cognitive resources. In doing so, they study how serotonin brain levels influence (1) subjects’ tendency to avoid buying and (2) consumers' preference for product options positioned as a compromise in a given choice set rather than for more extreme alternatives (i.e., the compromise effect). Using realistic product choice scenarios in a binding decision framework, they find that a reduction of brain serotonin levels leads to choice deferral and decreases the compromise effect, both as a within-subjects and as a between-subjects choice phenomenon. As such, this study provides neurobiological evidence for the assumption that the compromise effect is the result of deliberate and demanding thought processes rather than intuitive decision making

Partial Least Squares Structural Equation Modeling-Based Discrete Choice Modeling: An Illustration in Modeling Hospital Choice with Latent Class Segmentation

The aim of this chapter is to showcase the effectiveness of partial least squares structural equation modeling (PLS-SEM) in estimating choices based on data derived from discrete choice experiments. To achieve this aim, we employ a PLS-SEM-based discrete choice modelling approach to analyze data from a large study in the German healthcare sector. Our primary focus is to reveal distinct customer segments by exploring variations in their preferences. Our results demonstrate similarities to other segmentation techniques, such as latent class analysis in the context of multinomial logit analysis. Consequently, employing PLS-SEM to examine data from discrete choice experiments holds great promise in deepening our understanding of consumer choices

On the applicability of the BDM mechanism in product evaluation.

This study implements a setting in which subjects evaluate relatively highly priced consumer goods according to the BDM mechanism with an a priori known buyback option at the actual market price. In this context, subjects stated a price that was significantly lower than the buyback price, which contradicts the idea of utility maximizing individuals in incentive-compatible BDM tasks

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Forty years of context effect research in marketing: a bibliometric analysis

Research on context effects shows that the composition of choice sets and choice framing strongly influences consumer decision-making. Researchers have identified various context effect types and provide insight into their antecedents, consequences, and mechanisms of action. However, the research on context effects is spread across several fields, making it difficult to grasp the entire scope. Reviews focusing on specific effect types can facilitate rigorous research and publication practices, but they focus primarily on prominent context effects, neglecting others. Furthermore, those reviews do not provide insight into the structure of scholarly networks that result from research collaborations and shape, generate, distribute, and preserve the intellectual knowledge of the context effect domain. Addressing these issues, we present a large-scale bibliometric analysis of the field, that helps navigate the context effect landscape, highlights its themes, and identifies knowledge gaps. An interactive web application also allows for our analyses to be customized and extended

Crossing incentive alignment and adaptive designs in choice-based conjoint: A fruitful endeavor

Choice-based conjoint (CBC) analysis is one of the most widely applied market research techniques. This study focuses on two principles that seek to enhance CBC: incentive alignment and adaptive choice-based conjoint (ACBC) analysis. Individually, these principles have proven to increase the forecasting accuracy of CBC, however there is a lack of research considering both principles simultaneously. Drawing on two lab and two online experiments, the present study fills this gap, and tests whether combining both principles yields superior results. On the one hand, the findings reveal that incentive-aligned CBC and hypothetical ACBC predict comparatively well. On the other hand, ACBC proposes a more efficient cost-per-information ratio in studies with a high sample size. Moreover, the newly introduced incentive-aligned ACBC achieves the best predictions. Based on our studies, we help market researchers to decide whether to apply incentive alignment, ACBC, or both, depending on the context of a specific application

Neurological Outpatients Prefer EEG Home-Monitoring over Inpatient Monitoring-An Analysis Based on the UTAUT Model

Home monitoring examinations offer diagnostic and economic advantages compared to inpatient monitoring. In addition, these technical solutions support the preservation of health care in rural areas in the absence of local care providers. The acceptance of patients is crucial for the implementation of home monitoring concepts. The present research assesses the preference for a health service that is to be introduced, namely an EEG home-monitoring of neurological outpatients-using a mobile, dry-electrode EEG (electroencephalography) system-in comparison to the traditional long-time EEG examination in a hospital. Results of a representative study for Germany (n = 421) reveal a preference for home monitoring. Importantly, this preference is partially driven by a video explaining the home monitoring system. We subsequently analyzed factors that influence the behavioral intention (BI) to use the new EEG system, drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The strongest positive predictor of BI is the belief that EEG home-monitoring will improve health quality, while computer anxiety and effort expectancy represent the strongest barriers. Furthermore, we find the UTAUT model's behavioral intention construct to predict the patients' decision for or against home monitoring more strongly than any other patient's characteristic such as gender, health condition, or age, underlying the model's usefulness