Strategic alignment of HRM practices in manufacturing SMEs: a<i>Gestalts</i>perspective
Bibliographic record
Abstract
Purpose This paper seeks to take aGestaltsperspective to analyze the alignment between the HRM practices and strategic capabilities of SMEs. Design/methodology/approach Based on Miles and Snow's adaptive cycle, this study examines the coherence of HRM practices with the strategic capabilities of manufacturing SMEs (n=176) in terms of products, markets, and networks. A principal component factor analysis was first made to reduce the HRM practices into a lesser number of factors. A clustering algorithm was then used to determine three groups of SMEs orGestalts. Finally, an a posteriori examination of the performance of each group was made. Findings SMEs align their HRM practices with their realized strategy within three configurations, namely local, international, and world‐class SMEs. Regardless of their strategic choices, these SMEs achieve comparable levels of performance. Research limitations/implications TheGestaltsperspective seems effective in its capacity to describe the role of the HRM function. While the firms surveyed are fairly representative of Canadian manufacturing SMEs, there might yet exist a bias in that these are firms that have chosen to undertake a benchmarking exercise. Originality/value The study is one of the first to use Miles and Snow's adaptive cycle as a foundation to specify the type of activities that researchers should consider in assessing the SME's overall degree of strategic alignment. A practical implication for owner‐managers is that their strategic choices in terms of product innovation, market expansion or network extension must be inter‐linked with the development of their HRM practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".