Adoption of voice assistants for airport procedures and services: an empirical study using the UTAUT2 model and SEM analysis
Bibliographic record
Abstract
Purpose The study aims to examine the adoption of voice assistants (VAts) in developing countries for airport procedures and services, focusing on user behavior and technology readiness, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework to identify key predictors of intention and actual use. Design/methodology/approach A mixed-methods study was conducted in Vietnam, with a total of 330 responses collected. After screening for validity, 314 responses were retained for the quantitative survey and the remaining 14 participants were engaged in qualitative interviews. Quantitative data were analyzed using structural equation modeling to validate the conceptual framework, and qualitative insights were used to complement and contextualize the statistical findings. Findings The findings indicate that seven UTAUT2 factors significantly shaped users’ intention to use voice assistants, which in turn strongly predicted actual usage, highlighting the key mediating role of behavioral intention. Research limitations/implications The study explores the adoption of VAts in airport services in developing countries, extending the UTAUT2 framework. It provides insights for airport managers and developers to design and implement VAts that meet user expectations, improve service efficiency and enhance customer experience, ultimately promoting sustained adoption of technology in public service settings. Originality/value This is among the first studies applying UTAUT2 to VAts adoption in airports within a developing country. It contributes to theoretical expansion and offers practical insights for airport managers and developers to tailor VAts to enhance user experience and encourage sustained usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".