The Impact of Executive Team Competencies on XBRL Aoption
Bibliographic record
Abstract
The issue of determinants of a search-facilitating technology such as “Extended Business Reporting Language (XBRL)” has drawn considerable attention from the global academic community. This research focuses on executive team characteristics to investigate their association with the voluntary adoption of XBRL technology beyond the effect of firm characteristics. We investigated whether these characteristics (information system- and/or business/financial related- competencies) within the executive team affected the quality of the XBRL-tagged filings. Our findings demonstrate higher levels of information systems competencies were positively associated with early adoption of XBRL; whereas, higher levels of other business related-competencies (e.g. financial expertise) were negatively associated with it. Furthermore, IS competency was negatively associated with the technical aspects of XBRL. These results extend the literature on the influence of management on corporate decisions and can be used as a guide for investigating voluntary adoption of other reporting technologies, and further inform regulators and users of XBRL.
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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 | 0.010 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".