Author
Listed:
- Zhang, Xi-xi
- Hao, Xing-lin
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.
Suggested Citation
Zhang, Xi-xi & Hao, Xing-lin, 2025.
"Linkage mechanism of antecedents for employees' continuous adoption of artificial intelligence virtual assistants,"
Technological Forecasting and Social Change, Elsevier, vol. 220(C).
Handle:
RePEc:eee:tefoso:v:220:y:2025:i:c:s0040162525003488
DOI: 10.1016/j.techfore.2025.124317
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:220:y:2025:i:c:s0040162525003488. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.