ArticlePloS one2026
Exploring administrative staff's acceptance of generative AI in Chinese vocational colleges: A UTAUT-guided thematic study.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As Generative Artificial Intelligence (GenAI) technologies reshape institutional processes, higher vocational colleges are increasingly exploring their potential in administrative management. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this qualitative study examines how administrative personnel perceive and accept GenAI in vocational college administration. In-depth, semi-structured interviews were conducted with 16 administrative staff from three vocational colleges in Henan Province, China. Data were analyzed using ATLAS.ti-assisted thematic analysis through a hybrid deductive-inductive coding strategy. A deductive seed codebook was developed based on the four UTAUT constructs, while inductive coding captured emergent themes. To enhance analytical rigor, two coders independently analyzed five transcripts, achieving an inter-coder agreement of Cohen's κ = 0.84. Thematic saturation was reached after the 13th interview, with no new themes emerging from the subsequent sessions. The findings largely corroborate the relevance of the four core UTAUT constructs, performance expectancy, effort expectancy, social influence, and facilitating conditions, in explaining GenAI acceptance among administrative staff. Participants recognized GenAI's potential to improve operational efficiency, decision-making, service responsiveness, and workflow standardization. However, they also expressed concerns regarding technical complexity, resources limitations, policy ambiguity, data privacy, accountability, and job security. These findings suggest several GenAI-specific contextual factors, including governance readiness, perceived trustworthiness and controllability, institutional digital maturity, and ethical safeguards. The study provides an exploratory, context-specific application of UTAUT to vocational college administration and offers practical guidance for GenAI training, governance, and responsible implementation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.