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Algorithms are increasingly used by human resource departments to evaluate employee performance. While the algorithms are perceived to be objective and neutral by removing human biases, they are often perceived to be less fair than human managers. This research proposes dignity as an important construct in explaining the discrepancy in perceived fairness and investigates remedial steps for improving dignity and fairness for algorithm-based employee evaluations. Three experiments' results show that those evaluated by algorithms perceive lower levels of dignity, leading them to believe the process is less fair. In addition, we find that providing justifications for algorithm usage in employee evaluations improves perceived dignity. However, human-algorithm collaboration does not enhance perceived dignity. [ABSTRACT FROM AUTHOR]

• Compared to human-generated online reviews, ChatGPT-generated reviews were considered less useful, less trustworthy and less authentic. • Perceived authenticity mediates the relationships between online review source (human vs ChatGPT) and online review trust and online review usefulness. • The review source effects on usefulness, trust and authenticity are robust regardless of review valence.. While artificial intelligence's (AI) promise for fake online review detection has been investigated, other questions have emerged with ChatGPT's introduction, a generative AI platform. This research provides a much-needed investigation into consumer reactions to reviews perceived to be generated by ChatGPT vs. a human. Results from three studies in TripAdvisor hotel review and Yelp restaurant review contexts revealed that consumers rate reviews as less useful, trustworthy, and authentic if they perceived ChatGPT generated them. Further, the findings were robust regardless of review valence and even when participants were informed the reviewer possessed a TripAdvisor Expert badge. These findings' theoretical and practical implications are discussed, along with limitations. [ABSTRACT FROM AUTHOR]

Companies are increasingly turning to AI software to select candidates, despite concerns that hiring algorithms may produce biased evaluations. This study explores the public perceptions of algorithms used in resume and video interview screening. In addition, the effects of individual characteristics on these perceptions are examined. Using a nationally representative sample, we find that the public generally has a negative attitude towards the use of algorithms in hiring, and the majority do not consider them fair and effective. We also find clear individual differences regarding the perceptions towards algorithms. Specifically, males, people with higher education level and people with higher income have more positive perceptions towards hiring algorithms than their counterparts. The findings contribute to the emerging body of research on hiring algorithms and suggest strategies to increase public acceptance of hiring algorithms. • Majority of the respondents do not consider hiring algorithms fair and effective. • Compared to video screening algorithms, resume screening algorithms are considered fairer and more effective. • Women are less positive towards the hiring algorithms than men. • Respondents with high income and more education are more positive towards hiring algorithms than others. [ABSTRACT FROM AUTHOR]