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Automated systems based on artificial intelligence (AI) increasingly support decisions with ethical implications where decision makers need to trust these systems. However, insights regarding trust in automated systems predominantly stem from contexts where the main driver of trust is that systems produce accurate outputs (e.g., alarm systems for monitoring tasks). It remains unclear whether what we know about trust in automated systems translates to application contexts where ethical considerations (e.g., fairness) are crucial in trust development. In personnel selection, as a sample context where ethical considerations are important, we investigate trust processes in light of a trust violation relating to unfair bias and a trust repair intervention. Specifically, participants evaluated preselection outcomes (i.e., sets of preselected applicants) by either a human or an automated system across twelve selection tasks. We additionally varied information regarding imperfection of the human and automated system. In task rounds five through eight, the preselected applicants were predominantly male, thus constituting a trust violation due to potential unfair bias. Before task round nine, participants received an excuse for the biased preselection (i.e., a trust repair intervention). The results of the online study showed that participants have initially less trust in automated systems. Furthermore, the trust violation and the trust repair intervention had weaker effects for the automated system. Those effects were partly stronger when highlighting system imperfection. We conclude that insights from classical areas of automation only partially translate to the many emerging application contexts of such systems where ethical considerations are central to trust processes. [ABSTRACT FROM AUTHOR]

Strikes are a recurrent phenomenon in many countries. However, research on strikes from a psychological perspective has been limited. By developing a sound measure to assess attitudes and behavioral reactions to strikes, we will be in a better position to evaluate these constructs in individuals and compare across studies. Therefore, we developed a scale to assess attitudes and behavioral reactions to strikes following classic scale development guidelines using four samples (total N = 1369; N1 = 304, N2 = 209, N3 = 443, N4 = 413). In Study 1, we used exploratory factor analysis to reduce the generated items to a scale and showed that the strike attitude and behavioral reactions scale consists of one affective factor (negative reactions towards strikes), one cognitive factor (legitimacy of strikes), and three behavioral factors (informing oneself about strikes, strike-related social network behavior, and support of strikers). Study 2 confirmed these five factors and showed acceptable psychometric properties. Study 3 supported the construct validity of the developed scale: the five factors were correlated with willingness to strike and attitudes towards unions, among other variables. Study 4 further showed that the scale can also capture attitudes and behavioral reactions towards specific strikes. Overall, these studies indicate that the strike attitude and behavioral reactions scale is a psychometrically sound measure consisting of five factors. [ABSTRACT FROM AUTHOR]

To enhance the quality and efficiency of information processing and decision-making, automation based on artificial intelligence and machine learning has increasingly been used to support managerial tasks and duties. In contrast to classical applications of automation (e.g., within production or aviation), little is known about how the implementation of automation for management changes managerial work. In a work design frame, this study investigates how different versions of automated decision support systems for personnel selection as a specific management task affect decision task performance, time to reach a decision, reactions to the task (e.g., enjoyment), and self-efficacy in personnel selection. In a laboratory experiment, participants (N = 122) were randomly assigned to three groups and performed five rounds of a personnel selection task. The first group received a ranking of the applicants by an automated support system before participants processed applicant information (support-before-processing group), the second group received a ranking after they processed applicant information (support-after-processing group), and the third group received no ranking (no-support group). Results showed that satisfaction with the decision was higher for the support-after-processing group. Furthermore, participants in this group showed a steeper increase in self-efficacy in personnel selection compared to the other groups. This study combines human factors, management, and industrial/organizational psychology literature and goes beyond discussions concerning effectiveness and efficiency in the emerging area of automation in management in an attempt to stimulate research on potential effects of automation on managers' job satisfaction and well-being at work. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to provide a deeper understanding of uncivil meeting behaviors (UMBs) by exploring their frequency, potential predictors, and perceived impact on meeting outcomes. Five forms of UMBs were identified and examined. Key situational variables (meeting characteristics) and individual differences (Big Five factors and the Dark Triad of personality) were explored as potential predictors of UMBs.Methodology: We collected data from two independent samples of meeting participants (Ns = 345, 170) via two online surveys. We used confirmatory factor analysis, correlations, hierarchical multiple regressions, and relative weight analyses to analyze the data.Findings: The findings demonstrated that attendees’ perceptions of UMBs were linked to lower ratings of meeting satisfaction and effectiveness. In particular, the ratings were most affected by the observation of attendees who did not participate actively and who showed inappropriate interpersonal behavior. Results further suggest situational variables (meeting purpose and meeting norms) and individual differences (narcissism, psychopathy, and agreeableness) as potential predictors of UMBs.Implications: By showing the consequences of UMBs on meeting outcomes and by providing insights into potential causes of engagement in UMBs, this study offers valuable input for running and leading work meetings.Originality/Value: No previous study has empirically examined how different forms of UMBs affect meeting outcomes. Additionally, the paper introduces situational and personality variables that may act as potential predictors of UMBs. [ABSTRACT FROM AUTHOR]

Research on faking behavior and underlying intentions has mostly employed an intraindividual perspective, stressing the role of individual-level predictors. Inspired by theoretical arguments (e.g., from socioecological psychology) and cross-cultural studies, we hypothesize and demonstrate that in addition to individual-level predictors, there are also regional differences in faking intentions (within a country) and systematic links to regional-level features. Specifically, we tested and compared individual- and region-level predictors of applicant faking intentions (N = 4860 MTurk workers) across the largest 50 Metropolitan Statistical Areas of the USA using multi-level techniques. We found individual-level effects of conscientiousness, competitive worldviews, and religiosity on individual-level faking intentions. On the regional level, macro-psychological conscientiousness was negatively associated with the average faking intentions in a region, while macro-psychological competitive worldviews (i.e., the prevailing competitive worldviews in a region) showed a positive relationship. Additionally, macro-psychological competitive worldviews predicted individual-level faking intentions even when controlling for individual-level competitive worldviews. No effects were found for regional parameters such as the economic situation of a region. We discuss implications for research and personnel selection. [ABSTRACT FROM AUTHOR]