Aug 2026· Frontiers in Psychology· Vol 17· 0 citations· 82 references
Medicine
TL;DR
It is proposed that, from employees’ perspective, AI reliance is associated with higher levels of involution through elevated performance expectations and anxiety, and the importance of protecting employee wellbeing, communicating realistic performance expectations, and maintaining workplace social capital when implementing AI in digitally enabled organizations.
Abstract
The rapid integration of artificial intelligence (AI) into organizational contexts is reshaping how employees work, interact, and respond to changing workplace dynamics. Although AI is often expected to improve efficiency and reduce workload, emerging evidence points to a potential dark side, as AI adoption may intensify competitive pressure within organizations. Motivated by this paradox, this study examines how AI reliance may erode workplace social capital by fostering involution—an excessive and inefficient form of competition characterized by escalating effort and diminishing returns. This research focuses on digitally enabled and highly competitive workplace settings, where employee performance and outputs are more visible, transparent, and comparable. We propose that, from employees’ perspective, AI reliance is associated with higher levels of involution through elevated performance expectations and anxiety. Using survey data from employees in China, the study hypotheses were tested using structural equation modeling. The results support the proposed model and indicate a significant sequential mediation pattern linking AI reliance, performance expectations, employee anxiety, and workplace involution. Notably, the direct relationship between AI reliance and involution is not significant, suggesting that AI reliance is associated with employees’ defensive competitive behavior primarily through employees’ perception of external evaluative pressures and internal psychological responses. These findings highlight the unintended social consequences of AI adoption in organizations, and underscore the importance of protecting employee wellbeing, communicating realistic performance expectations, and maintaining workplace social capital when implementing AI in digitally enabled organizations.
This study investigates the efficiency paradox of AI, emphasizing the impact of technical optimization on human values, social cohesiveness, and workplace culture and indicates that the ethical and human-centric integration of AI is crucial to ensure that efficiency gains align with inclusivity, justice, and the maintenance of social well-being in contemporary workplaces.
Nandini Aquila Lukito, Nazwa Sabila· International Journal Admini...· 0 citations
Artificial Intelligence (AI) is rapidly transforming the nature of work and organizational management across the globe. Human Resource Management (HRM), traditionally reliant on human judgment and administrative processes, is increasingly being reshaped by intelligent technologies capable of automating recruitment, employee assessment, workforce planning, performance management, and learning and development activities. This article examined the implications of Artificial Intelligence for the future of work and explores how AI is redefining contemporary Human Resource Management practices and employment relations. Drawing on existing literature, the paper discusses the opportunities presented by AI, including enhanced efficiency, improved decision-making, predictive analytics, and personalized employee experiences. It also highlights emerging challenges such as algorithmic bias, employee privacy concerns, job displacement, ethical dilemmas, and the need for workforce reskilling. The paper is anchored on the Technology Acceptance Model and Socio-Technical Systems Theory, which provide useful frameworks for understanding the interaction between technology and organizational actors. The article concluded that while AI offers significant opportunities for improving HR effectiveness, organizational performance, and employment relations, successful implementation requires a balanced approach that integrates technological innovation with human-centered management practices and responsible workplace governance. Organizations are encouraged to develop ethical AI governance frameworks, invest in digital competencies, and redesign HR systems to support sustainable workforce transformation.
Habiganuchi Godfrey Bekwe, Nathaniel A. Ngerebara· International journal of res...· 0 citations
Introduction Artificial intelligence is increasingly reshaping accounting and finance work, yet its psychological implications for employees remain insufficiently understood. While existing studies have mainly emphasized the productivity and efficiency outcomes of AI, less is known about how AI usage relates to employee resilience in accounting and finance roles. To address this gap, this study examines the relationship between AI usage and employee resilience and explores the psychological mechanism and boundary condition underlying this relationship. Methods Based on the Job Demands-Resources (JD-R) theory, this study develops a moderated mediation model in which perceived work meaningfulness acts as the mediator and job complexity serves as the moderator. Data were obtained from employees in accounting and finance-related positions through a three-wave time-lagged survey. After matching responses across the three waves and screening invalid cases, 332 valid questionnaires were used for analysis. Results The results showed that AI usage positively predicted employee resilience, and perceived work meaningfulness mediated this relationship. Job complexity further moderated the relationship between AI usage and perceived work meaningfulness, such that the positive relationship was stronger under conditions of low job complexity. Discussion This study extends the JD-R framework to AI-enabled work contexts by conceptualizing AI usage as a work-related resource associated with employee resilience. The findings identify perceived work meaningfulness as a key psychological mechanism and job complexity as an important boundary condition, providing theoretical and practical implications for AI implementation in accounting and finance settings.
Yuanyuan Ji, Huaming Wu· Frontiers in Psychology· 0 citations
Hybrid work models developed largely in response to workplace disruptions caused by the COVID-19 pandemic, treating location flexibility as the central organizing question. This paper argues that artificial intelligence has moved the debate past questions of where work happens toward questions of how work is structured, decided, and governed. Drawing on Dynamic Capabilities Theory, the Resource-Based View, Knowledge-Based View, Sociotechnical Systems Theory, Organizational Learning Theory, Contingency Theory, and Human Capital Theory, the paper develops a conceptual framework linking AI capability, organizational agility, human-AI collaboration, leadership capability, employee empowerment, organizational learning, innovation performance, and organizational performance. The paper examines how AI reshapes managerial decision-making by distinguishing decision augmentation from decision automation, and considers the governance tensions between centralized and decentralized approaches to AI deployment. It also considers how algorithmic management affects trust, autonomy, and engagement among employees, and identifies the leadership competencies that gain value once routine managerial tasks are delegated to algorithmic systems. Because the paper is conceptual, it proposes a research design using PLS-SEM to test the relationships identified, along with measurement constructs, sampling considerations, and reliability and validity procedures for a future empirical study. The paper closes with implications for managers, policy makers, and organizational designers, and identifies open questions that current literature has not resolved, including the long-term effects of algorithmic management on organizational trust and the conditions under which decentralized AI governance outperforms centralized models. The paper does not report new empirical data. Its contribution lies in integrating separate streams of research on AI adoption, organizational design, and leadership into a single framework intended to guide subsequent empirical testing.
R. Narenderajan· Scholedge International Jour...· 0 citations
This paper aims to examine the relationship between employees' use of artificial intelligence (AI) for work and digital-enabled innovative performance (DEIP) from the perspective of employee engagement and trust in AI. Based on these perspectives, this study identifies specific solutions for achieving high levels of DEIP.
Drawing on job demands-resources (JD-R) theory and analyzing data from 431 employees, this paper proposes a research model to investigate how employee AI use affects employees' DEIP through partial least squares structural equation modeling and highlights the configurations of causal conditions associated with DEIP through fuzzy-set qualitative comparative analysis (fsQCA).
The results show that AI use for work exerts the strongest positive impact on employees' behavioral engagement, followed by emotional and cognitive engagement. Employee engagement (three types mention before) play a partial mediating role between work-related AI and DEIP. Furthermore, both human-like and functionality trust in AI positively moderate the relationship between work-related AI and behavioral engagement. Finally, a total of four solutions leads to a high level of DEIP.
Organizations should enhance employees' engagement and trust in AI through training and supportive implementation strategies. Managers should adopt context-sensitive approaches that align AI use, engagement and trust to improve DEIP.
Firstly, this study enriches the literature on DEIP and JD-R theory by exploring the AI-performance link via employee engagement. Secondly, this paper supplements work-related AI literature by clarifying AI trust's boundary conditions. Thirdly, this paper contributes to the performance literature by identifying key solutions for DEIP from a configuration perspective.
Liang Ma, Zhihao Qi, Xin Zhang et al.· Internet Research· 0 citations
This study explains how employees translate the day-to-day value they derive from generative artificial intelligence (GAI) into employees' perceived insider status and knowledge sharing behavior.
Integrating the job demands–resources theory, we theorize four value perceptions of GAI, functional, epistemic, conditional and emotional, as distinct socio-cognitive cues from which employees infer perceived insider status. A cross-sectional survey design was employed using a structured questionnaire administered to employees across multiple industries where GAI is embedded in daily work practices. The final sample consisted of 298 valid responses, and the proposed relationships were tested using covariance-based structural equation modeling, including mediation and moderation analyses.
We found that emotional value is positively associated with perceived insider status, conditional value is negatively associated with perceived insider status, and functional and epistemic value show no direct associations. Perceived insider status predicts greater knowledge sharing, which in turn is linked to stronger commitment, more citizenship behavior, deeper identification and lower alienation. Knowledge of artificial intelligence strengthens the associations of functional and emotional value with perceived insider status, and weakens the association of conditional value with perceived insider status.
The findings re-orient technology value theorizing toward socioemotional inclusion, identify perceived insider status as a proximal mechanism connecting everyday GAI experiences to cooperative behavior, and specify knowledge of artificial intelligence as a capability amplifier that shapes how value cues are read as signals of membership. Practical guidance follows for embedding GAI in routine workflows, investing in literacy and designing for emotionally positive user experiences.
Mai Nguyen, Tuan Phong Nham, Danish Mehraj et al.· Journal of Enterprise Inform...· 0 citations