The results indicate that AI self-efficacy and digital HRM practices function as significant positive mediators, facilitating the conversion of AI adoption into enhanced work performance and innovation outcomes.
Abstract
In the context of accelerating artificial intelligence (AI) development, this study explores how AI Usage contributes to employee job performance and innovation performance by activating cognitive and HR system–level mechanisms. Adopting an integrative individual–organizational perspective, this study examines the mediating roles of AI self-efficacy and digital human resource management (HRM) practices in translating AI adoption into employee performance outcomes. Survey data were collected from firms located in major Chinese cities (Beijing, Shenzhen, Xi’an, and Zhengzhou), resulting in 750 valid responses for analysis. The results indicate that AI self-efficacy and digital HRM practices function as significant positive mediators, facilitating the conversion of AI adoption into enhanced work performance and innovation outcomes. Theoretically, this study advances knowledge management studies by highlighting the complementary roles of individual cognitive beliefs and HR systems in enabling AI-driven learning and capability development. Practically, the findings suggest that organizations should embed AI technologies in HR systems that foster learning, knowledge utilization, and continuous innovation.
The results show that AI use for work exerts the strongest positive impact on employees' behavioral engagement, followed by emotional and cognitive engagement, and both human-like and functionality trust in AI positively moderate the relationship between work-related AI and behavioral engagement.
Liang Ma, Zhihao Qi, Xin Zhang et al.· Internet Research· 0 citations
The rapid expansion of artificial intelligence (AI) in human resource management has substantially reshaped how organizations attract, recruit, develop, evaluate, and retain their workforce. Research examining the combined employee-level effects of AI-enabled digital HRM (AI-DHRM)—through psychological mediating processes and under technology-related boundary conditions—remains sparse, particularly in emerging economies. Drawing on Social Exchange Theory, the Job Demands-Resources model, and UTAUT2, this study conceptualizes AI-DHRM as a reflective higher-order construct comprising four functionally distinct sub-dimensions, specifies a dual mediation model integrating an affective-relational pathway (person-organization [P-O] fit perception) and a motivational-agentic pathway (psychological empowerment), and identifies technology trust and privacy concern as critical boundary conditions.
A time-lagged, two-wave survey was administered to full-time employees of 61 AI-HRM-adopting organizations in Bangladesh, yielding 487 matched responses. AI-DHRM practices, mediators, moderators, and controls were measured at Time 1; all five outcomes were measured five weeks later at Time 2. Data were analyzed in IBM AMOS 26.0 using a two-stage structural equation modeling approach, with bias-corrected bootstrapped mediation (
n
= 5,000 resamples), the index of moderated mediation, and Johnson-Neyman analysis.
AI-DHRM practices exerted significant positive effects on job satisfaction (β = 0.41, 95% CI [0.29, 0.53]), work engagement (β = 0.38, 95% CI [0.26, 0.50]), job performance (β = 0.35, 95% CI [0.25, 0.45]), and employee wellbeing (β = 0.33, 95% CI [0.21, 0.45]), and significantly reduced turnover intention (β = −0.29, 95% CI [−0.41, −0.17]). Both P-O fit perception and psychological empowerment partially mediated these relationships across all five outcomes. Technology trust strengthened, and privacy concern attenuated—but did not reverse—the AI-DHRM-mediator pathways. Moderated mediation was confirmed across all ten conditional indirect effects.
The findings establish AI-DHRM as an integrated system that employees experience as a coherent organizational investment, transmitted through two complementary psychological channels and conditional on trust and privacy perceptions. Organizations should design AI-HRM as a coherent bundle and treat trust-building and privacy-by-design as prerequisites to deployment. Findings rest on self-report data from a single country and warrant replication.
Hriday Chandra Shil, S. K. M. A. H. Rabby, Md. Mostafizur Rahman et al.· Frontiers in Artificial Inte...· 0 citations
The integration of artificial intelligence (AI) into human resource management (HRM) represents a major transformation in how organizations manage their workforce, while intrapreneurial behavior has become a key source of innovation and competitive advantage. This study develops a conceptual framework on the relationship between AI-enabled HRM practices and intrapreneurial behavior informed by a selective review of existing empirical literature across diverse industries and geographical contexts, drawing on quantitative, qualitative, and mixed-method studies. Grounded in Self-Determination Theory, the proposed framework suggests that AI-enabled HRM practices foster intrapreneurial behavior through four key psychological mechanisms: employee autonomy, psychological safety, self-efficacy, and employee exploration. These mechanisms enable employees to generate innovative ideas, recognize opportunities, take initiative, engage in calculated risk-taking, and develop networks that support intrapreneurial activities. The framework further proposes that organizational culture and firm size shape the strength of these relationships by influencing the extent to which AI-enabled HRM practices can be effectively translated into innovative employee behaviors. The framework also recognizes the dual nature of AI implementation, which can enhance employee capabilities while also raising concerns such as job insecurity. Overall, this study integrates fragmented literature into a unified, literature grounded framework and provides directions for future research and managerial practice in AI-enabled HRM and intrapreneurship
Prof. V. K. Gupta, Pragya Singh· Asian Journal of Multidimens...· 0 citations
This study focuses on human resource management issues in the digital age from a legal perspective, based on dual-core theory, and empirically examines the impact of AI-enabled HR practices on organizational performance. The core dimensions of AI-enabled HR in this study include AI-powered recruitment, training, and performance appraisal, with employee adaptability introduced as a mediating variable. The study employed a quantitative research design, collecting 247 validated questionnaires from employees in digital organizations using partial least squares structural equation modeling (PLS-SEM) for empirical testing. The reliability and validity of the measurement model exceeded 0.7, the average extracted variance value was greater than 0.5, and the model passed the discriminant validity test according to the Fornell-Larcker criterion. The path test results demonstrate that all types of AI-enabled HR practices that comply with laws and regulations, along with employee adaptability, have a significant positive impact on organizational performance. Furthermore, employee adaptability is a positive mediating factor in the relationship between AI-enabled recruitment and organizational performance. This study complements the empirical evidence in this field regarding the interactive impact of AI-assisted human resources systems and the adaptability of employees.
M. al-Jarrah, Ahmad Tayseer Mahmud Masadeh, Mohammad Ahmad Nayef Alakash et al.· Decision Science Letters· 0 citations
The findings reveal that AI adoption in HRM is positively associated with both organizational performance and organizational efficiency, and HR process efficiency was found to play a mediating role in these relationships, indicating that improvements in HR processes are a key pathway through which AI generates organizational benefits.
O. Akintola, S. O. Chukwuedo, Imad Yasir Nawaz et al.· Journal of Business and Digi...· 0 citations
The findings reveal that AI self-efficacy has a positive and significant effect on perceived ease of use and perceived usefulness, and perceived usefulness significantly influence behavioral intention, which subsequently has a positive effect on the actual use of AI tools.
Alfionita Mariska Saputri, R. Kurniasih, Siti Zulaikha Wulandari et al.· The International Conference...· 0 citations
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