Analysing the Factors Contributing to the Decline of Auditors Globally and Avenue for Future Research: A Scoping Review
Bibliographic record
Abstract
Aim: This article explores the contributing factors to the decline in the number of auditors globally and aims to provide the consequences and possible recommendations. Auditors play a critical role in ensuring transparency, trust, and credibility of financial statements. However, the profession is experiencing a decline across the globe. The decrease in the number of registered auditors has become a pressing issue, raising concerns about the future of the assurance industry’s ability to maintain the number of registered auditors and continue providing assurance services to public and private entities or companies. Methodology: A scoping-review methodology was adopted to analyse the existing literature on the global decline in the number of auditors. This approach utilises research evidence to identify trends, challenges, and opportunities within the audit profession. Relevant studies were sourced from databases such as ScienceDirect, Google Scholar, and ResearchGate, as well as the grey literature. Main findings: This study identifies a combination of factors driving the decline of auditors globally. Economic pressures, such as cost reduction initiatives and outsourcing, have impacted the demand for traditional auditing services. Complex regulatory requirements have increased barriers to entry, while technological advancements, such as artificial intelligence, are disrupting traditional auditing roles. Additionally, the profession suffers from negative perceptions regarding workload, remuneration, and work–life balance, discouraging new entrants. Practical implications: The findings emphasise the urgent need for the auditing profession to adapt to evolving challenges. Stakeholders, including regulatory bodies and professional organisations, must address issues such as technological integration, career development pathways, and regulatory simplification. Enhanced public awareness campaigns and training initiatives are critical to attracting and retaining professional talent. Contribution: This study contributes to the limited body of knowledge on the global decline of auditors by creating a broad spectrum of evidence. It highlights actionable strategies to address the profession’s challenges and provides a foundation for future research on sustaining the relevance of auditors in a dynamic global economy.
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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 | 0.036 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".