Tangible Rewards for More Than Just Productivity: Examining Canadian Public Accounting Firms' Rewards Programs*
Bibliographic record
Abstract
ABSTRACT Companies spend significant amounts of money on tangible rewards programs, even during the economic turmoil of the COVID‐19 pandemic. The prevalence, growth, and significance of these expenditures highlight the importance of understanding the purpose and use of these programs by organizations. Research on public accounting (PA) firms' compensation plans has focused on the balance between professional and commercial incentives in partner profit‐sharing schemes but has failed to examine the incentives for nonpartner audit professionals. However, it is exactly these professionals who do a substantial amount of work on audit engagements. This paper has three main purposes. First, we investigate the nature and composition of PA firms' tangible rewards programs and provide a detailed description. Second, we examine the use of firms' tangible rewards programs to provide evidence of what actions are being rewarded. We use Almer et al.'s (2005, Behavioral Research in Accounting 17: 1–22) framework, which presents dimensions of the auditors' professional contribution, and explores whether firms recognize these dimensions using tangible rewards. Third, we develop future research questions to help explore the use of tangible rewards in firms without structured output. We collect archival data on the use of tangible rewards from each of the Big 4 PA firms and three of the next four largest international accounting firms in Canada. We find that firms use their tangible rewards programs for “building a culture of recognition,” for performance incentives, and for employee and firm development, thus rewarding a broad set of measures beyond the incentive measures for hours worked.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".