The rapid advancements in artificial intelligence (AI), particularly generative AI, have significantly transformed organizational practices. Generative AI tools like ChatGPT, Google Gemini, and Copilot are increasingly used across various sectors, including IT, education, and business. This study focuses on the impact of generative AI tools on human resource management (HRM) practices, particularly on employee engagement, organizational commitment, and performance. The integration of these technologies has shifted work styles from traditional methods to more efficient, smart work approaches. However, while the potential benefits of generative AI are acknowledged, understanding its effects on employee engagement, performance, and user satisfaction remains crucial. This study aims to bridge this gap by investigating the influence of generative AI on HRM outcomes and the mediating role of trust in this relationship. The widespread adoption of tools like ChatGPT in business schools globally highlights the need for research into their impact on various aspects of organizational performance and employee experience. This research is particularly relevant considering the potential long-lasting impacts of these tools on workforce dynamics.

Existing literature highlights the potential of generative AI to automate routine tasks, leading to increased productivity and performance. Studies have shown positive impacts of AI technologies on organizational performance, employee engagement, organizational commitment, and market valuations. The unique capabilities of chatbots enable timely work completion, while AI tools can minimize labor costs and improve human-computer interaction. However, concerns remain about potential job displacement and negative impacts on employee engagement and attitudes. The literature lacks a comprehensive model explaining the factors influencing generative AI adoption and its impact on HRM outcomes. This study draws upon the Technology Acceptance Model (TAM), the Technology Readiness Index (TRI), and the Stimulus-Organism-Response (SOR) framework to address this gap. These theories provide valuable perspectives on how user experience and trust influence employee engagement, organizational commitment, and performance within the context of generative AI. The review also identified a scarcity of studies examining generative AI's use in sectors such as healthcare, engineering, and management, emphasizing the need for further research in these areas.

This quantitative study employed a structured questionnaire to collect data from 500 information technology employees in the Hyderabad, India metro area. The questionnaire measured nine reflective constructs: optimism, innovativeness, ease of use, usefulness (modeled as a higher-order construct, User Perception), trust, organizational commitment, vigor, dedication (modeled as a higher-order construct, Employee Engagement), and employee performance. A seven-point Likert scale was used. The questionnaire underwent a pretest with 100 IT employees to ensure validity and reliability. Data collection occurred from January to March 2024 using Google Forms, with convenience sampling used to target employees using generative AI tools. 52 responses were excluded due to incomplete answers or inconsistencies. The study employed a sample size of 500, justified by power analysis (actual power 0.955, effect size 0.82), exceeding recommendations from Anderson and Gerbing (1984) and Gaskin (2023) for SEM analysis. Exploratory factor analysis (EFA) was conducted using IBM SPSS to verify the factor structure. The KMO measure of sampling adequacy (0.929) and Bartlett’s test of sphericity (p < 0.001) indicated the suitability of data for factor analysis. Structural equation modeling (SEM) using AMOS version 28 was used to test the hypotheses. The model fit indices for both the higher- and lower-order constructs indicated excellent model fit. Mediation analysis was performed to assess the mediating role of trust. Common method bias was evaluated using Harman's single-factor test and a latent common method factor approach.

The study supported all seven hypotheses. Optimism and innovativeness positively and significantly impacted user perception (H1 and H2). User perception positively and significantly influenced trust (H3), and trust positively and significantly influenced organizational commitment (H4). Trust partially mediated the relationship between user perception and organizational commitment (H7). Organizational commitment positively and significantly impacted employee engagement (H5), and employee engagement positively and significantly impacted employee performance (H6). The squared multiple correlations indicated substantial variance explained by the model: 63% for user perception, 38% for trust, 30% for organizational commitment, 50% for employee engagement, and 54% for employee performance. Harman's single factor test did not support common method bias, although the latent common method factor analysis suggested a low level of common method bias, deemed insignificant given the strength of the other findings. Path analysis clearly depicted the relationships between the constructs, revealing the significant influence of user perception on subsequent constructs and the crucial mediating role of trust.

The findings highlight the importance of user perception, trust, and organizational commitment in fostering employee engagement and performance in the context of generative AI tools. The partial mediation of trust indicates that while direct effects exist between user perception and organizational commitment, trust significantly enhances this relationship. The strong influence of optimism and user perception emphasizes the need for positive attitudes towards AI and a user-friendly experience. The results validate previous research indicating that AI tools can enhance organizational commitment, engagement, and performance, especially when user perception and trust are high. The model developed integrates several established theories, providing a valuable framework for understanding generative AI's impact on HRM practices. The findings emphasize the need for organizations to foster a positive work environment conducive to the successful implementation and adoption of generative AI, including addressing any concerns about job security or potential negative impacts.

This study contributes significantly to the understanding of generative AI's impact on HRM, extending existing theoretical frameworks (TAM, TRI, SOR) in the context of the IT industry. The findings reveal the critical role of user perception, trust, and organizational commitment in driving employee engagement and improved performance. Organizations should focus on enhancing user experience, building trust, and fostering a positive organizational culture to maximize the benefits of generative AI. Future research could explore these relationships in different cultural and organizational settings, using longitudinal studies to understand the evolution of these relationships over time. Further research could incorporate moderating variables such as gender and explore the impact of generative AI on psychological well-being and job satisfaction within the context of HRM practices.

The study's geographic limitation to Hyderabad, India, may limit the generalizability of findings to other regions with different cultural and educational backgrounds. The use of self-report questionnaires raises the possibility of response bias. The rapid evolution of AI technology necessitates ongoing research to keep findings current. The focus on the IT sector might not fully generalize to other sectors with different technological adoption levels.