Challenges in enhancing enterprise resource planning systems for compliance with Sarbanes‐Oxley Act and analogous Canadian legislation
Bibliographic record
Abstract
Purpose This paper aims to examine major challenges faced by companies in enhancing their enterprise resource planning (ERP) systems for compliance with regulatory internal control requirements, specifically those imposed by the Sarbanes–Oxley Act (SOX) of 2002 and analogous Canadian legislation. Design/methodology/approach Data were collected through case studies of four medium‐sized and large companies that use ERP systems and that have operations in the USA and Canada, thus being subject to SOX and/or similar Canadian regulations. Findings The companies faced some technical, process and cultural challenges in implementing regulatory control compliance. In all companies, existing ERP systems were not able to meet all control requirements without some modifications or add‐on applications. Control implementations have been long, complicated and costly processes, which are not fully completed. Detailed analyses and documentation of existing systems, controls and processes were required in all companies. The protection of systems security and the segregation of duties were perceived to be major technical obstacles. Cultural factors resulted in additional challenges, notably resistance to change. Research limitations/implications The findings of this study enhance the understanding of ERP systems design features, processes and challenges in implementing regulatory controls. As such, they provide a foundation for further empirical studies and for building models of ERP systems effectiveness in implementing effective controls. Practical implications The study provides managers insight into challenges in enhancing ERP systems for regulatory control compliance. Lessons learned can contribute to the development and sharing of best practices and to overall organizational effectiveness. Originality/value Using an interdisciplinary approach, the study provides new evidence on the extent to which ERP systems meet regulatory internal control requirements.
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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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".