A meta‐analysis of the UTAUT model: Eleven years later
Bibliographic record
Abstract
Abstract The unified theory of acceptance and use of technology (UTAUT) has been widely used to investigate factors influencing the adoption and use of information systems and technologies (IS/IT). However, studies using UTAUT are not conclusive in terms of statistical significance, direction, and magnitude. Through a meta‐analysis of empirical studies on UTAUT from 2003 to 2013, we determine how parsimonious, accurate, and robust UTAUT is at predicting acceptance and use of technology. A meta‐analysis of 74 publications reveals that performance expectancy, effort expectancy, and social influence explain IS/IT adoption, while behavioural intention is the most often measured dependent variable operationalized as a proxy for system use, supporting the strength of UTAUT as an explanatory model of IS/IT acceptance and use. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.
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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 | 0.063 | 0.156 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.040 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".