1,721,046 research outputs found
The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers
No full textResume = Resume? The effects of blockchain, social media, and classical resumes on resume fraud and applicant reactions to resumes
No full text10 Jahre Deutschlandstipendium – 10 Jahre Hochschulfundraising und Stipendienkultur in Deutschland
No full textVor zehn Jahren war Hochschulfundraising in Deutschland noch ein Thema für wenige Hochschulen. Philanthropie als Motiv der privaten Förderung insbesondere staatlicher Hochschulen stellte zu diesem Zeitpunkt eher noch die Ausnahme dar. Mit dem Start des Deutschlandstipendienprogramms änderte sich dies. Seit 2011 werden durch dieses Programm bundesweit Studierende durch eine Matching-Funds-Konstruktion gefördert. Dabei werden die durch die privaten Förderinnen und Förderer zur Verfügung gestellten Mittel durch Staatsgelder verdoppelt. Das Interesse an Hochschulfundraising, das in Deutschland im internationalen Vergleich damals noch schwach ausgeprägt war, wuchs über die Jahre zügig.
Die Herausgeber haben das Jubiläum des Deutschlandstipendiums im Jahr 2021 zum Anlass genommen, einen Sammelband zu initiieren, der zehn Jahre Deutschlandstipendium wissenschaftlich fundiert und mit praktischem Bezug zusammenfasst
Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management
No full textGamification in the classroom: Examining the impact of gamified quizzes on student learning
No full textIs anybody listening? The impact of automatically evaluated job interviews on impression management and applicant reactions
No full textChanging the means of managerial work: effects of automated decision support systems on personnel selection tasks
Full text linkTo 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-beforeprocessing 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
Introducing and Testing the Creepiness of Situation Scale (CRoSS)
Full text linkWhen people interact with novel technologies (e.g., robots, novel technological tools), the word “creepy” regularly pops up. We define creepy situations as eliciting uneasy feelings and involving ambiguity (e.g., on how the behave or how to judge the situation). A common metric for creepiness would help evaluating creepiness of situations and developing adequate interventions against creepiness. Following psychometrical guidelines, we developed the Creepiness of Situation Scale (CRoSS) across four studies with a total of N = 882 American and German participants. In Studies 1–3, participants watched a video of a creepy situation involving technology. Study 1 used exploratory factor analysis in an American sample and showed that creepiness consists of emotional creepiness and creepy ambiguity. In a German sample, Study 2 confirmed these subdimensions. Study 3 supported validity of the CRoSS as creepiness correlated positively with privacy concerns and computer anxiety, but negatively with controllability and transparency. Study 4 used the scale in a 2 (male vs. female experimenter) × 2 (male vs. female participant) × 2 (day vs. night) field study to demonstrate its usefulness for non-technological settings and its sensitivity to theory-based predictions. Results indicate that participants contacted by an experimenter at night-time reported higher feelings of creepiness. Overall, these studies suggest that the CRoSS is a psychometrically sound measure for research and practice
Justice, trust, and moral judgements when personnel selection is supported by algorithms
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