Exploring the Determinants of Intention to Recommend GPT tools among HR Professionals

Abstract

This paper aims to examine the attitude towards adoption and intention to suggest web-based GPT tools among HR professionals in a nation that is still in the nascent stage of technology adoption. The study integrates the Task-Technology Fit (TTF), TAM (Technology Acceptance Model), and TRI (Technology Readiness Index) models to develop a comprehensive understanding of which antecedents contribute to explain users’ attitudes towards adopting GPT tools and their subsequent intention to recommend these technologies. To gather survey data of 307 HR professionals from 23 different organizations, a structured questionnaire was utilized. The respondents must have experience using GPT technologies. The survey questionnaire consisted of 32 items, and the collected data was analyzed using structural equation modeling (SEM) using Smart-PLS 4. The findings of the structural path analysis revealed that perceived usefulness, perceived ease of use, and technology readiness significantly influence HR professionals’ attitudes towards embracing GPT in their workplace. The study also found a significant positive relationship between attitude and intention to recommend. However, task characteristics did not significantly influence perceived usefulness, satisfaction, and continuous intention to use the technology have not been found to have a significant impact on attitude. This study has developed a new paradigm by combining these technology adoption models. As an early empirical study, this research contributes significantly to the existing research on the impact of disruptive technology on HRM and HR professionals. The findings provide valuable insights for the adoption of ChatGPT in different scenarios among HR professionals.