Human resource policies, management accounting and organisational performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationships between human resource (HR) policies, management accounting and organisational performance in Canada, Japan and the UK. Design/methodology/approach A cross case analysis of the observations emerging from each of six case studies (two in Canada, two in Japan and two in the UK) result in a set of 13 findings. Findings The seven main HR policies emerging from this study are the “job for life” (in one British and two Japanese cases), recruitment, training, performance‐related bonus scheme, teamwork, organisational culture and pensions. Important communication links between HR managers and management accountants are budgets, strategic plans, performance‐related bonus scheme and decision making. The “job for life” policy, employee recruitment decisions, viewing employees as assets (rather than costs), training, performance‐related bonus scheme, teamwork, organisational culture and a good pension scheme all had an impact on organisational performance. Research limitations/implications It is very difficult to link specific HR policies with changes in organisational performance because of the number of other variables affecting organisational performance and the time lags involved. Originality/value Several of the case studies are making real progress in establishing links between specific HR policies and changes in organisational performance by using benchmarking or employee opinion surveys or a combination of the results of both external benchmarking and employee opinion surveys over a number of years.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".