The findings of this study could help bank executives, human resource managers, and policymakers understand the importance of human-focused implementation of AI, ongoing AI skill-building, and favorable organizational practices that boost employee satisfaction and innovation.
Abstract
This study aims to explore the impact of Human–AI Collaboration on the Innovation Behavior of employees in the banking sector, focusing on the mediation effect of Job Satisfaction and the moderation effect of AI Self-Efficacy. The study draws its concept of Human–AI Collaboration from the Job Demands–Resources (JD–R) Theory, which considers HACC as a strategic organizational resource that can boost employees' motivation and innovative performance. Quantitative, Cross Sectional. A quantitative, cross-sectional research design was used. Structured questionnaires were used to gather data on 384 employees of commercial banks who have experienced the use of AI in their work. To ensure the respondents had the relevant work experience with AI, purposive sampling was used and explore the direct, mediating, and moderating relationships, the proposed conceptual model was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique by SmartPLS 4. The results show that Human–AI Collaboration has a significant positive impact on Job Satisfaction and Innovation Behavior. Job Satisfaction positively impacts Innovation Behavior and partially mediates the link between Human–AI Collaboration and Innovation Behavior, suggesting that a collaborative environment with AI can foster innovation by enhancing employee work satisfaction. The findings of this study could help bank executives, human resource managers, and policymakers understand the importance of human-focused implementation of AI, ongoing AI skill-building, and favorable organizational practices that boost employee satisfaction and innovation. To truly unlock the strategic benefits of Human–AI Collaboration, organizations must include investment in employee capability development within their AI investments. This study adds to the growing increasingly relevant literature on Human–AI Collaboration by combining technological and psychological aspects in one framework. It expands on the JD–R Theory by clarifying the strategic role of Human–AI Collaboration as a job resource that fosters innovation by Job Satisfaction, and shows the contingent role of AI Self-Efficacy in AI-enabled workplaces.
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 development of Artificial Intelligence (AI) has transformed the nature of work by creating new forms of interaction between humans and intelligent systems. In the technology sector, human–AI collaboration and digital competence have become important capabilities that influence employees’ ability to innovate and achieve superior work outcomes. This study aims to examine the role of human–AI collaboration and digital competence in shaping employee creativity and work performance in the Indonesian technology sector. This research employed a quantitative approach using a survey method involving 130 employees from technology-related organizations in Indonesia. Data were collected through a structured questionnaire using a five-point Likert scale and analyzed using Structural Equation Modeling–Partial Least Square (SEM-PLS) with SmartPLS 3. The results indicate that human–AI collaboration has a positive and significant effect on employee creativity and work performance. Digital competence also significantly influences employee creativity and work performance. Furthermore, employee creativity demonstrates the strongest influence on work performance. These findings highlight that AI adoption alone is insufficient to improve organizational outcomes; instead, the effectiveness of AI-driven transformation depends on employees’ digital capabilities and their ability to collaborate creatively with intelligent technologies. This study contributes to the literature on digital transformation by providing empirical evidence regarding the interaction between technological capabilities and human resources in improving workplace performance.
C. Hakim, Syaefullah Syaefullah· West Science Social and Huma...· 0 citations
The rapid integration of artificial intelligence (AI) into the education sector has raised a question that goes beyond technology itself: When is AI's use beneficial to employees instead of a burden? The study builds on the theories of Self-Determination Theory (SDT), Social Exchange Theory (SET), and the Technology Acceptance Model (TAM) and proposes the following mediation model: AI Adoption Leadership Style influences Task Performance and Job Stress, with Perceived Job Security and Psychological Need satisfaction serving as the mediating psychological mechanisms.Based on the theories of SDT, SET, and TAM, this study proposes the following mediation model: AI Adoption Leadership Style affects Task Performance and Job Stress, while Perceived Job Security and Psychological Need satisfaction act as the psychological mediation pathway. A total of 359 respondents from the education sector of Punjab, Pakistan, were selected to collect the data and analyzed using partial least squares structural equation modeling (PLS-SEM) software, namely SmartPLS 4. Strong reliability and validity were obtained in the measurement model and good fit was achieved for the structural model (R² = 0.44-0.49, SRMR = 0.044). AI Adoption Leadership Style significantly and positively affected Psychological Need (β = 0.668) and Perceived Job Security (β = 0.663), and had significant direct effect on Task Performance (β = 0.433) and Job Stress (β = -0.503). The results showed that the mediation model for the relationship between Perceived Job Security and Task Performance was complementary partial mediation (β = 0.336) and the mediation model for the relationship between Psychological Need and Job Stress was complementary partial mediation (β = -0.246). All eight hypotheses were supported with both stress related pathways appearing in the hypothesized negative direction. The findings contribute to the existing human-resource and technology-adoption literature and have important implications for educational leaders who are tasked with leading change through the use of AI in an under-researched context of education in South Asia.
Muhammad Habib, Shahzaib Imran, Sheraz Anjum et al.· Kashmir Journal of Academic...· 0 citations
This study aimed to analyze the effects of work engagement, person–job fit, and knowledge-sharing behavior on employee performance through innovative work behavior as a mediating variable. This research was conducted at the Office of Cooperatives and SMEs of Central Java Province. The research problem highlighted the importance of improving employee performance to address increasingly dynamic and innovation-driven organizational demands. This study employed a quantitative approach using a census method, in which the population consisted of 118 employees and the sample included 117 respondents. Data were collected through questionnaires distributed to employees at the Office of Cooperatives and SMEs of Central Java Province. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to examine the direct and indirect effects among variables. The results showed that work engagement, person–job fit, and knowledge-sharing behavior had positive and significant effects on innovative work behavior and employee performance. Innovative work behavior also had a positive and significant effect on employee performance and was found to mediate the effects of work engagement, person–job fit, and knowledge-sharing behavior on employee performance. These findings indicated that improved employee performance was influenced not only by individual factors and the work environment but also by employees’ ability to generate and implement innovations in their work.
Unknown authors· Journal Research of Social S...· 0 citations
Drawing upon the Job Demands-Resources (JD-R) model, this study explores the mechanism through which Dependence on AI affects employees’ innovative behavior, focusing on the mediating role of cognitive flexibility and the moderating role of job complexity. Using questionnaire data from full-time employees collected between December 2024 and February 2025, this study conducts confirmatory factor analysis, correlation analysis, hierarchical regression and the Bootstrap test with AMOS, SPSS and the PROCESS macro. The results show that Dependence on AI significantly and positively predicts employees’ innovative behavior, and cognitive flexibility plays a partial mediating role between the two variables. Job Complexity serves as a negative moderator for the association between Cognitive Flexibility and Innovative Behaviors, and the positive linkage of Cognitive Flexibility to Innovative Behaviors becomes relatively weaker when Job Complexity is high. This study expands the cognitive mediating path through which Dependence on AI influences innovative behavior, refines the theoretical boundary of demand–resource interaction in the JD-R model, and provides practical implications for organizations to rationally guide human–machine collaboration and enhance employees’ innovative ability in intelligent scenarios.
With generative artificial intelligence (GenAI) increasingly embedded in hotel service processes, human–AI collaboration is shifting from simple automation to cognitive collaboration. Yet it remains unclear whether generative AI empowers employees or creates psychological challenges. Drawing on self-determination theory, social cognitive theory and regulatory focus theory, this study aims to develop a theoretical model examining the effects of GenAI–employee collaboration (employee-led vs GenAI-led) on hotel employees’ job satisfaction.
This study conducted two scenario-based experiments and one field experiment. Study 1 tested the main effect of GenAI-employee collaboration on job satisfaction and the mediating role of AI self-efficacy. Study 2 used a field experiment across three hotels with different star ratings to test the robustness and external validity of the findings. Study 3 adopted a 2 × 2 between-subjects design to examine the moderating role of work regulatory focus and the moderated mediation mechanism.
This study demonstrates that employee-led collaboration leads to higher job satisfaction than GenAI-led collaboration. AI self-efficacy mediates the relationship between collaboration type and job satisfaction. Work regulatory focus further moderates these effects. Promotion-focused employees report higher AI self-efficacy and job satisfaction under employee-led collaboration, whereas prevention-focused employees report higher AI self-efficacy and job satisfaction under GenAI-led collaboration.
Hotels should align generative AI workflows with task characteristics and employees’ motivational orientations. Employee-led collaboration is better suited to complex service tasks, while GenAI-led collaboration is better suited to standardized tasks. Managers should also enhance employees’ AI self-efficacy through scenario-based training and feedback.
This study shifts attention from whether generative AI is used to who leads GenAI-employee collaboration. It identifies AI self-efficacy as a key mechanism and work regulatory focus as a boundary condition. It also deepens understanding of human-technology fit in GenAI-employee collaboration.
Pengyi Shen, Xianye Liu, Jinan Xu· International Journal of Con...· 0 citations
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