Artificial Intelligence Adoption in Human Resource Practices in Nepal: Trust, Performance, and Organizational Outcomes
TL;DR
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.
Abstract
Artificial Intelligence (AI) is affecting human resource (HR) practices internationally, but little is known about AI adoption in Global South countries, such as Nepal. This research analyzes employees' attitudes towards AI implementation in HR processes and its consequences for trust, job performance, and organizational outcomes. For this research, the constructs were defined as: trust was operationalized as employees' beliefs about the reliability and integrity of AI-based HR decisions; fairness was conceptualized as justice and bias in AI-based processes; and transparency was conceptualized as the degree to which a user's perceptions of disclosure and openness of the mechanisms behind AI algorithms and decisions. These constructs were measured using a number of Likert-scale items adapted from validated scales. This enabled the evaluation of each construct and was included in the overall mean scores for trust, fairness, and transparency (m = 3.51). A Systematic Review and a causal Configurational explanation were used to conduct the research, which involved 41 employees from the service, banking, IT, and telecommunications industries in Nepal. The evidence shows a moderate level of AI-HR adoption (mean=3.69) and a moderate level of trust, fairness, and transparency (mean=3.51) within participants. The results also indicate that employees held favorable views regarding the impact of AI on their individual performance (M=3.98) and on organizational outcomes (M=3.96). Despite a high level of digital readiness (mean = 3.98), a number of concerns, including decline of human decision making (56.1%), job loss (51.2%), and privacy (46.3%), were expressed. Results have demonstrated that good practice in teaching and learning of AI requires responsible governance. Employee achievement is also positively associated with the company's success (r = 0.73, p < 0.01). Employee job (β=0.42, p<0.001) and AI utilization (β=0.34, p=0.014) were significant predictors of organizational performance in the regression analysis. Staff engagement, ethical practice, and access to continual AI-related information are needed to build confidence and attain institutional good practice.