Does greater access to employees with information technology capability improve financial reporting quality?
Bibliographic record
Abstract
Abstract Although information technology (IT) plays an essential role in financial reporting, many companies today lack sufficient human capabilities to utilize IT competently. We examine the association between a firm's access to IT‐capable labor and financial reporting quality (FRQ). We proxy for access to IT‐capable labor using workforce measures in the metropolitan statistical area (MSA) where the firm operates, including (1) the number of IT‐related college degrees relative to the total active workforce, (2) the level of education of IT graduates, (3) the income level of IT graduates, and (4) a composite measure. We find that firms in MSAs with a higher IT‐competent labor force are associated with fewer financial reporting misstatements and internal control issues. This study contributes to the emerging literature stream examining the influence of geographic labor characteristics on firm‐level outcomes and the research on the impact of IT capability on financial reporting processes. We also inform the current movement of integrating IT knowledge into the education curriculum.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".