Using artificial intelligence for hiring talents in a moderated mechanism

Globally, artificial intelligence (AI) occupies a burgeoning space among recruiters as it replaces many of the recruitment and selection tasks while hiring the talents. Despite the existence and acceptance of AI being unprecedented among savvy recruiters, the study of it in developing countries' contexts is still at a fancy stage. Particularly, the extant literature documented that very little is known about the intention and actual use (AU) of AI to hire talents with the intervening effects of voluntariness of usage (VU), tenure, and education of the recruiters elsewhere. Hence, using the doctrine of the extended unified theory of acceptance and use of technology (UTAUT), the present study aims to unpack the intention and AU of AI among hiring professionals in the context of Bangladesh, a developing country in the South Asian region. A multi-item questionnaire survey was employed to collect the data of recruiters from talent acquisition departments in both manufacturing and service organizations with a convenience sampling technique. We used partial least square-based structural equation modeling (PLS-SEM) version 4.0.8.9 to analyze the data. Results showed that performance expectancy (PE), facilitating conditions (FC), and hedonic motivation (HM) have a significant influence on the intention to use (IU) AI (p < 0.05), and IU also predicts AU of AI significantly (p < 0.05). The moderating influence of VU has an insignificant effect on the positive influence of IU on AU. Moreover, the multi-group analysis showed that there is no significant difference between young adults and old adults and highly educated and lowly educated on the association between IU and AU. The findings in this study showed important notations that contributed to advancing the knowledge and filling the gap in the extant literature. Additionally, it also provides fresh insights for developing policy interventions to hire professionals for thriving AI adoption in the context of developing countries effectively.