Factors affecting the role of HR managers in international mergers and acquisitions
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the reasons behind human resource (HR) managers' participation in the international mergers and acquisitions (IM&A) process building on the general discussion of the factors explaining the roles of HR in organisations. Design/methodology/approach Six sets of factors can be found to affect the roles of HR managers in general: the orientation of top management to people management; the skills, abilities and competencies of HR managers themselves; the HR function and its characteristics; the expectations that line managers have of HR; external factors; and internal factors. This review forms the basis for subsequent data analysis in the context of IM&As. The factors that contribute to HR managers' participation are studied from HR and other management's perspectives. Based on interviews with 12 corporate level managers in three Finnish international industrial companies. Findings The results show that top management sees the participation of HR managers as being very important and agree that it should be a common policy. The factors explaining the roles in the case organisations focused on certain factor groups and were similar across the cases. Based on empirical analysis, this study finds that the most important contributing factors to HR managers' participation are HR managers' own capability and activity throughout the IM&A process. Originality/value This study has analysed the reasons related to the roles of HR managers in an IM&A context in general, not just the strategic role within. Based on the case studies it seems, however, that a seat on the management team and HR managers' business competencies as well as personal skills contribute to the strategic role.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".