Effects of Advice on Effectiveness and Efficiency of Tax Planning Tasks
Bibliographic record
Abstract
Abstract Supervisory advice is generally given to tax professionals in public accounting firms before they commence tax planning tasks. The objectives of giving advice are to achieve effectiveness and efficiency in tax planning, as well as for training tax professionals. An experiment with 54 tax professionals, from accounting firms across Canada, was conducted to determine the effects of supervisory advice on effectiveness and efficiency in performing tax planning tasks of different complexity. Advice results in lower effectiveness in lower‐complexity tasks, as evidenced by more technically inadequate tax plans, signs of confusion and overdetermined solutions (i.e., unnecessary information in the tax plans). In higher‐complexity cases, the results suggest a limited improvement in effectiveness, as evidenced by more technically adequate plans, but at a cost of limiting insightful judgment. On the other hand, advice results in limited gains in efficiency for both the lower and higher‐complexity tasks. This study extends the advice and tax literatures by investigating the role of advice in the performance of tax planning tasks of different complexity, which has not been examined in other research. This study also contributes to tax practice, as public accounting firms should consider the limited gains in efficiency with the decrease in effectiveness for lower‐complexity tasks and the potential to limit insightful judgment for higher complexity tasks. The results of this study suggest that firms face trade‐offs in achieving efficiency, effectiveness and the training objective.
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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 | 0.005 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".