Bibliographic record
Abstract
This study established and empirically validated a model for predicting factors influencing users' behavioural intentions for using XBRL tools. This study explored the behavioural intention of using XBRL tools from the point of view of users by applying the UTAUT model with the addition of trust and satisfaction. An online survey was conducted by using the modified study model to comply with the research objectives. An online survey of 267 respondents obtained and analysed using structural equation modelling (SEM) and IBM SPSS AMOS. The findings show that trust and satisfaction influenced behavioural intent significantly and positively. In turn, the effort expectancy and performance expectancy had a significant impact on satisfaction. The results showed that in the presence of satisfaction there was no direct effect of effort expectancy and performance expectancy on the behavioural intention to use XBRL tools and the emergence of a direct effect of confidence on the behavioural intention to use XBRL tools. The findings correspond with the previous studies and provide a practical reference for XBRL tool developers and decision-makers involved in developing and using XBRL tools for tagging and analysing financial reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".