AI Adoption in HRM: Exploring Trust Through Lens of Reliability, Credibility and Technical Competence

Remote collaboration of employees reframed with the lens of AI efficiency, trust and ethical visions

This study aims to examine the impact of artificial intelligence (AI)-driven workflows on efficiency and collaboration, shaping employees’ attitudes and intentions. In addition, it theoretically contributes by linking AI adoption to different levels of collaboration, showing how trust and risk influence engagement. This study conducted a survey with remote work employees in Indian information technology (IT) firms and received 386 respondents. The study further extended the unified theory of acceptance and use of technology (UTAUT2) model, and for a comprehensive analysis, partial least squares structural equation modeling using SmartPLS4 was used. The study findings underline the significant impact of AI adoption on employees’ attitudes and intentions. Results also demonstrate how trust and risk perceptions determine the depth of collaboration in remote AI-enabled work environments. It further provides insights into how traditional job practices adapt to an AI-integrated work environment. Finally, this study contributes to understanding organizational adaptation in an AI-enabled environment and gives practical and managerial insights for organizational leaders, practitioners and policymakers while ensuring a trust- and ethics-focused AI system in remote work. The findings contribute to collaboration theory by empirically showing how trust enables, and risk constrains, effective collaborative engagement in remote work. The rapid use of AI in remote work scenarios in Indian IT firms influences collaboration and work efficiency. However, this scenario is hindered by certain challenges related to stakeholder and employee trust and ethical concerns. This study provides a novel integration of UTAUT2 with collaboration frameworks, emphasizing the theoretical link between AI adoption, trust, risk and collaboration levels.

Artificial Intelligence Adoption in Human Resource Practices in Nepal: Trust, Performance, and Organizational Outcomes

Results have demonstrated that good practice in teaching and learning of AI requires responsible governance, and staff engagement, ethical practice, and access to continual AI-related information are needed to build confidence and attain institutional good practice.

Acceptance of and Trust in the Use of Artificial Intelligence in Organisational Management

It is indicated that effectively implementing AI in management requires parallel development of employee competencies, building trust in technology and implementing ethical and supervisory standards, in which technology plays a supporting role rather than replacing the human factor.

Human-Ai Interface: Assessing The Role Of Explainable Ai (Xai) In Mitigating Knowledge Worker Resistance And Fostering Trust During Ai-Driven Business Process Automation

Trust in AI Revisited – The Enduring Role of Propensity to Trust

The importance of trust in artificial intelligence (AI) continues to grow, as trust is widely regarded as a critical prerequisite for organizational AI adoption. In this context, intention to use AI can be understood as a consequence of the decision to trust AI and is therefore strongly influenced by trust. Moreover, trust is regarded as essential for understanding the impact of increasing interaction with AI systems on both individuals and society. Much of the discussion on trust in AI relies on frameworks derived from trust in automation, but these approaches remain largely theoretical and insufficiently validated. One important empirical contribution addressing this gap is the path model developed by Karg, Ritz and Asprion (2025), which examined trust in ChatGPT using a student sample. This model conceptualizes perceived trustworthiness through performance, process, and purpose. Together with a user’s propensity to trust, these factors are assumed to determine trust in AI. Karg, Ritz and Asprion (2025) demonstrated that perceived trustworthiness is significantly shaped by users’ inherent propensity to trust, in turn, influences the intention to use AI. The present study replicates this path model using a business sample to assess the robustness of the original findings and to advance theory building. An online survey was conducted among 97 employees of a major Swiss bank, employing identical items and methodologies as in the original study. The replication largely supports the original findings. However, in contrast to the original study, performance did not significantly predict trust in AI in the business sample. The findings further reinforce the argument that users’ dispositional characteristics may play a more decisive role in shaping perceived trustworthiness of and trust in AI systems.

AIS Electronic

This study contributes a unified research model for stakeholder-specific AI adoption, demonstrates a mixed quantitative-qualitative approach, and establishes a scalable foundation beginning with engineers and extending to other stakeholders through future empirical validation.