How the communication style of AI collaborators influences gig workers’ acceptance: from the mind-role fit perspective

Purpose The adoption of artificial intelligence (AI) collaborators in the gig economy provides the opportunity to build gig workers’ work identity. However, gig workers may reject AI collaborators. This study aims to explore the adaptive conditions under which different communication styles of AI collaborators influence gig workers’ acceptance. Design/methodology/approach Using a mind-role fit perspective, we develop a research model to investigate the interaction effect of communication style (social-oriented and task-oriented) and task type (high- and low-creative tasks) on gig workers’ acceptance. And the mediating role of the perceived fit, which includes perceived uniqueness and perceived task efficiency, is also studied. Two online experiments are performed to test the research model. Findings When performing high-creative gig tasks, the social-oriented AI induces higher perceived uniqueness and perceived task efficiency, further increasing acceptance. In contrast, when performing low-creative gig tasks, the task-oriented AI induces higher perceived task efficiency, thus leading to higher acceptance. Our findings show the adaptability of gig task types to different communication styles. Originality/value Our findings offer a valid explanation for resolving the contradiction between social- and task-oriented communication styles in previous studies. In addition, these findings provide insightful suggestions for gig platform designers regarding implementing effective AI strategies to enhance gig workers’ engagement.