With the rapidly emerging trend of employing Artificial Intelligence technologies within modern economics. This study is an attempt to fill the research gap associated with the factors that have influence with the adoption of artificial intelligence in human resources information systems on HR-leaders intention to use it. It empirically investigates the influences that trust, technological readiness, facilitating condition and performance expectancy on HR-professional’s behavioral intention to use AI in HRM. Besides, examine the moderating effect of age and experience on the proposed associations. Data were collected from by online questionnaire from 185 HR managers. A structural framework was introduced to test the relationship between study latent variables. Result exhibited that trust and performance expectancy has a significant influence on HR-professionals behavioral intention to use AI-HRIS. Trust and technological readiness showed a significant influence on HR-professionals performance expectancy of using AI-HRIS. While facilitating condition, organizational size and technological readiness did not show a significant influence on HR-professionals behavioral intention toward using AI-HRIS. Lastly, Age and Experience did not have a moderating effect on trust and performance expectancy association with the behavioral intention toward using AI-HRIS. The findings of this study contribute to the theory development of information technology diffusion in HRM.

The primary goal of this article is to contribute to the field of technology adoption research by providing researchers, organizations, HR leaders, service providers, and decision-makers with advanced understanding and valid inputs on the development of AI-based HR solutions and the determinants of adoption. The overall objective of this research is to determine the general attitude of HR managers toward the adoption of AI in HRM and to assess the factors that determine the adoption of AI from the perspective of HR managers. The proposed adoption factors were grouped into four constructs, innovation characteristics, trust, technology-organizational-environment (TOE) factors, and emphasized HR roles within the organization. The research was conducted among HR managers in Middle Eastern countries, specifically Jordan, Kuwait, Saudi Arabia, and Qatar. An online questionnaire was used to collect data from a total of 389 respondents. The results showed that respondents were largely positive toward AI applications in HRM. This positive attitude can be inferred from the mean values of two variables, relative advantage and attitude toward the application of AI in HRM. The research results showed that HR managers have a positive attitude and confidence that emerging AI applications can contribute to supporting the efficiency, effectiveness, and quality of HRM. In addition, the results showed a constructive perception of the relative benefits of AI. Researchers, policymakers, and service providers are also recommended to investigate the phenomenon from two perspectives, first, the impact of attitudes on actual adoption decisions and second, the factors that influence this impact. Keywords: artificial intelligence, HRM, technology adoption, HR leaders, technologicalorganizational- environmental

This study aims to investigate HR leaders’ trust in AI application in talent acquisition and the role of technology trust as a predictor of HR leaders’ attitude toward its adoption. The sample was drawn from the HR professionals’ network in the Middle East using an online survey with 389 responses. The study results concluded that HR leaders have a positive attitude toward the adoption of AI applications in the talent acquisition function. Additionally, HR leaders perceived it as highly advantageous and this perception positively influenced their attitude. Further, it is concluded that. HR leaders possess high trust in AI-based talent acquisition solutions and that their perception about its reliability, credibility and technical competence are significant predictors of this trusting belief.