The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.

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Understanding the factors that influence trust in public health information is critical for designing successful public health campaigns during pandemics such as COVID ‐19. We present findings from a cross‐sectional survey of 454 US adults—243 older (65+) and 211 younger (18–64) adults—who responded to questionnaires on human values, trust in COVID ‐19 information sources, attention to information quality, self‐efficacy, and factual knowledge about COVID ‐19. Path analysis showed that trust in direct personal contacts ( B = 0.071, p = .04) and attention to information quality ( B = 0.251, p < .001) were positively related to self‐efficacy for coping with COVID ‐19. The human value of self‐transcendence, which emphasizes valuing others as equals and being concerned with their welfare, had significant positive indirect effects on self‐efficacy in coping with COVID ‐19 (mediated by attention to information quality; effect = 0.049, 95% CI 0.001–0.104) and factual knowledge about COVID ‐19 (also mediated by attention to information quality; effect = 0.037, 95% CI 0.003–0.089). Our path model offers guidance for fine‐tuning strategies for effective public health messaging and serves as a basis for further research to better understand the societal impact of COVID ‐19 and other public health crises.

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