Standard Deviation vs. Gini Coefficient: Effects of Different Indicators of Classroom Status Hierarchy on Bullying Behavior.

Classroom status hierarchy (the degree to which popularity is unequally distributed in a classroom) has often been examined as a predictor of bullying. Although most research has relied on an operationalization of status hierarchy as the classroom standard deviation (SD) of popularity, other fields (e.g., sociology, economics) have typically measured resource inequality using the Gini coefficient. This multilevel study examines the concurrent and prospective associations of both status hierarchy indicators (referred to as SD-hierarchy and Gini-hierarchy) with peer-reported bullying, controlling for key variables (i.e., the structure of the classroom status hierarchy, average classroom level of popularity). The final sample included 3017 students (45.3% self-identified as a boy; T1 Mage = 13.04, SD = 1.73, approximately 93% born in Finland) from 209 classrooms. Concurrently, classroom SD-hierarchy was positively, linearly associated with bullying, whereas there was a curvilinear (inverted U) association between Gini-hierarchy and bullying. No significant longitudinal associations were found. The findings suggest that Gini-hierarchy provides unique information beyond the SD-hierarchy.

Nomen est omen? How and when company name fluency affects return expectations.

Investors perceive stocks of companies with fluent names as more profitable. This perception may result from two different channels: a direct, non-deliberate affect toward fluent names or a deliberate interpretation of fluent names as a signal for company quality. We use preregistered experiments to disentangle these channels and test their limitations. Our results indicate the existence of a significant non-deliberate fluency effect, while the deliberate fluency effect can be activated and deactivated in boundary cases. Both effects are consistent across different groups of participants. However, whereas the fluency effect is strong in isolation, it has limitations when investors are confronted with additional information about the stock.

Psychological price perception may exert a weaker effect on purchasing decisions than previously suggested: Results from a large online experiment fail to reproduce either a left-digit or perceptual-fluency effect.

Retail store prices are frequently set to either a just-below (e.g., $1.99) or rounded to (e.g., $2.00) integer levels. Previous studies proposed two price-perception effects that may underly such psychological pricing strategies. First, the left-digit effect (LDE) assumes consumers read prices left-to-right. Cognitive limitations let consumers overweight the impact of digits on the left side of the price while underweighting digits on the right side of the price. This effect appears to conflict with the contradictory perceptual fluency effect (PFE), which proposes that a rounded price is more perceptual fluent and, thus, more attractive to consumers. To address these paradoxical effects, we conducted an online experiment with 266 participants making a total of 4788 purchasing decisions where we systematically varied the purchasing prices of otherwise identical lottery tickets across two price levels. Against expectations, we found no support for either of the two price-perception effects. We propose three possible explanations of these null results.

Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds.

Algorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectively avoid algorithmic systems. The mechanisms that give rise to these individual differences are currently poorly understood. Previous studies have suggested two possible effects that may underlie this variability: users may differ in their predictions of the efficacy of algorithmic systems, and/or in the relative thresholds they hold to place trust in these systems. Based on a novel judgment task with a large number of within-subject repetitions, here we report evidence that both mechanisms exert an effect on experimental participant's degree of algorithm adherence, but, importantly, that these two mechanisms are independent from each-other. Furthermore, participants are more likely to place their trust in an algorithmically managed fund if their first exposure to the task was with an algorithmic manager. These findings open the door for future research into the mechanisms driving individual differences in algorithm adherence, and allow for novel interventions to increase adherence to algorithms.