Linkage mechanism of antecedents for employees' continuous adoption of artificial intelligence virtual assistants

Xi-xi Zhang and Xing-lin Hao

Technological Forecasting and Social Change, 2025, vol. 220, issue C

Abstract: Artificial intelligence (AI) virtual assistants play a pivotal role in facilitating new labor patterns and serve as a significant driving force for enterprises to achieve intelligent development. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this paper explores the necessary conditions and antecedent configurations for employees' continuous adoption of AI virtual assistants by integrating necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA). Additionally, it employs network analysis model to examine the structural relationships among antecedents. The findings reveal that: (1) None of the antecedents constitute necessary conditions for employees' continuous adoption of AI virtual assistants; (2) There are five antecedent configurations, such as “leading adoption - autonomous driven,” which enable a high level of continuous adoption among employees; (3) Perceived behavioral control, satisfaction with virtual assistants, and virtual assistant compatibility serve as core nodes within the antecedent relationship network regarding employees' continuous adoption of AI virtual assistants. Through a comprehensive analysis of necessary conditions, sufficiency configurations, and complex relational networks of antecedents, this research enhances our understanding of factors influencing the continuous adoption of AI virtual assistants and offers new insights into facilitating organizational intelligent transformation from the micro-level perspective of employees.

Keywords: AI virtual assistants; Organizational context; Employee adoption of technology; Continuance intention; UTAUT; Integrated approach (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:220:y:2025:i:c:s0040162525003488

DOI: 10.1016/j.techfore.2025.124317

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