Catalyzing AI-driven change: how perceived leaders' communication framing motivates team AI adoption
Aiwen Xie et al.
Abstract
Purpose This research investigates how leaders can effectively drive the integration and application of artificial intelligence (AI) in the workplace, a pressing managerial challenge. Grounded in social information processing theory, we propose and test a mediated model in which employees' perceptions of their leaders' AI-related communication framing (opportunity vs. threat) influence team AI adoption through team psychological capital. We further examine the moderating effect of Chaxu climate, a significant cultural factor, on this relationship. Design/methodology/approach We test our hypotheses through a two-study design. Study 1 employs a multi-wave questionnaire survey of employee-leader dyads in high-tech enterprises, and Study 2 utilizes a scenario-based experiment, collectively providing robust evidence of causality. Findings Our results consistently demonstrate that leaders' opportunity-framing communication enhances team psychological capital, which in turn promotes AI adoption. Conversely, leaders' threat-framing communication diminishes team psychological capital, thereby impeding AI adoption. Furthermore, our findings reveal that Chaxu climate moderates these effects; a stronger Chaxu climate weakens the positive impact of opportunity-framing communication and amplifies the negative impact of threat-framing communication. Originality/value This research integrates insights from leadership communication, team psychological processes, and cultural studies within the context of AI adoption. It offers practical managerial implications for enhancing communication efficacy, facilitating efficient AI integration and advancing organizational AI transformation, particularly within team-based work models.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.