A Commentary on Post‐Pandemic Challenges and Opportunities for the Accounting Profession: Insights from a Systematic Literature Review*
Bibliographic record
Abstract
ABSTRACT This empirically grounded commentary explores the impacts of the COVID‐19 pandemic on the strategic direction of Canada's accounting profession and highlights opportunities and challenges that lie ahead in the post‐pandemic era. We undertake a systematic literature review using deductive and inductive approaches within both the academic accounting literature and a selection of publications targeting accounting practitioners. Our deductive framework uses Chartered Professional Accountants of Canada's (CPA Canada) Foresight initiative, while our inductive approach identifies themes that do not fit within the Foresight initiative. We conclude that the accounting profession will be challenged to balance the pursuit of new opportunities arising from disruptive technologies, real‐time data, and new organizational value drivers while simultaneously reflecting its roots in financial reporting, auditing, and taxation. Our findings also suggest that the profession should pay more attention to the human aspects of the profound changes that are underway. Specifically, the profession focuses heavily on how accountants' work will change due to disruptive forces but not enough on how these changes impact accountants from a broader human resource management perspective (e.g., mental health challenges, alternative work arrangements, retraining, and upskilling). Our work differs from prior reviews as we incorporate both academic accounting and accounting practitioner‐focused publications to propose a research agenda intended to encourage more practically relevant accounting research.
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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 | 0.062 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.006 |
| 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".