The effects of culture and HRM practices on firm performance
Bibliographic record
Abstract
Purpose The purpose of this study was to examine the effects of organizational culture and human resource management (HRM) effectiveness on financial performance of a sample of Singapore‐based companies involved in mergers and acquisition activities. Design/methodology/approach The study used the method of content analysis to collect information on cultural values and HRM effectiveness, using Kabanoff's content analysis dictionary. Culture profiles were then assigned to organizations in the sample following the results from cluster analysis. Various financial ratios were used to measure organizational performance. Finally, regression analysis was performed to test various hypotheses. Findings The key finding of the study is that organizations with either elite or leader value profile, when complemented by human resource effectiveness, had a better financial performance as compared to organizations with meritocratic or collegial value profiles. It thus follows that, to achieve better financial results by undertaking merger and acquisition activities organizations need to have elite or leadership value profile. Originality/value This study makes a contribution to the literature by producing new empirical evidence to bear on the effect of organizational culture and human resource effectiveness on financial performance of merging acquiring organizations from a newly industrialized Asian country.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".