Bibliographic record
Abstract
Purpose This paper aims to examine the impact of the Sarbanes–Oxley Act of 2002 (SOX) on the academic business ethics literature with the intent of making this research more accessible to those researchers and practitioners working in business ethics and other related fields. Specifically, the authors outline the types and scope of SOX-related research, examine the extent of reliance on SOX, identify which theoretical frameworks and research approaches are used and point out under-researched areas. Design/methodology/approach Using a descriptive approach, the authors examine the theoretical perspectives, classifying these perspectives into four groupings (economics, ethics/moral, psychological and sociological). Using counts, categorization and content analyses, the authors provide an overview of 115 articles with further analysis provided for articles relying heavily (n = 14) or moderately (n = 42) on SOX. Findings Whistleblowing and codes of ethics are well-researched topics. However, employment of some theories (e.g. signaling theory and stakeholder theory) and qualitative approaches are used less often. Other under-researched issues in the sample include CEO/CFO certifications, cost of compliance, auditor disclosures and empirical investigation of SOX and auditor independence (or corporate culture). Research limitations/implications The authors’ decision to use certain databases, search terms and research methods, and to focus on business ethics journals and English language articles are possible limitations. Originality/value The authors’ contributions comprise an examination of the scope of SOX topics and detailing how reliant the research is on SOX. The authors identify trends in this literature and provide evidence of the broad theoretical frameworks to better understand the breadth and depth of theories used.
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 | 0.016 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".