The Impact of Senior Management Competencies on the Voluntary Adoption of an Innovative Technology
Bibliographic record
Abstract
ABSTRACT The adoption of innovative technologies holds both promise and risk. We focus on the voluntary adoption of innovative financial reporting and disclosure technologies (IFRDTs) using the voluntary adoption of XBRL as an exemplar for our study. In particular, since IFRDTs have both financial reporting (FR) and information technology aspects (IT), we examine the impact of IT and FR competencies possessed by members of the top management team (CEOs and CFOs) on the voluntary adoption of XBRL beyond the impact of environmental, organizational, technological, and financial characteristics of their firms. We find that the voluntary adoption of XBRL was positively associated with higher levels of IT competencies; but, surprisingly, voluntary adoption of this innovation was negatively associated with higher levels of FR competencies, regardless of the functional role played by the executive. These results extend the literature on the influence of management characteristics on corporate decisions and can be used as a guide for investigating top executives' roles in the voluntary adoption of other IFRDTs, such as the use of social media for financial reporting or voluntary standardized business reporting in jurisdictions where such reporting is not mandatory.
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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 | 0.007 | 0.039 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".