Generative AI as a catalyst for HRM practices: mediating effects of trust

Abstract

Abstract This study investigated the impact of generative AI tools on human resource management practices, organizational commitment, employee engagement, and employee performance. The authors also investigated the mediating role of trust in the relationship between user perception and organizational commitment. A structured questionnaire was used to collect the data from the information technology industry employees to measure the 9 reflective constructs optimism, innovativeness, ease of use, usefulness, trust, organizational commitment, vigor, dedication, and employee performance. Two constructs, ease of use and usefulness, are modeled as a higher-order construct – User Perception, whereas vigor and Dedication are modeled as another higher-order construct, Employee Engagement. The mode-fit indices were assessed for both the higher- and lower-order constructs, and the model-fit indices for both the models, higher- and lower-order constructs reveal an excellent model fit. Positive and statistically significant impacts were observed between the study constructs. The impact of organizational commitment on employee engagement was positive and statistically significant, and in turn, the impact of employee engagement on employee performance was also positive and statistically significant. Trust partially mediated the relationship between user perception and organizational commitment, fostering enhanced employee engagement and performance. The theories of technology readiness, stimulus-organization-response, and technology acceptance model were utilized in the development of the study’s theoretical framework, which provided fresh perspectives on how employee engagement, organizational commitment, and performance are influenced by user experience and trust in the context of generative AI. Theoretically, it opens up new applications such as personalized education and services, digital art, and realistic virtual assistants that were previously unfeasible or impractical for automation. The practical implications are that the field of interdisciplinary research as well as the information technology industry will be greatly impacted by generative AI. To facilitate swift adoption, the principles of generative AI are conceptualized from model-, system-, and application-level perspectives in addition to a social-technical perspective, where they are explicated and defined. In the end, our research has given future scholars a significant research agenda that will enable them to study generative AI from different theoretical perspectives while incorporating the concepts from these theories.