Aligning the e-HRM and Strategic HRM Capabilities of Manufacturing SMEs: A “Gestalts” Perspective
Bibliographic record
Abstract
Abstract Purpose This study aims to explore the relationship between IT and HRM in the context of manufacturing SMEs, more specifically the relationship between strategic HRM and e-HRM as well as the performance effects of this relationship. The conceptual framework is founded upon the resource-based view (RBV), specifically upon the strategic HRM and e-HRM capabilities of SMEs and upon the strategic alignment of these capabilities in the form of capability configurations or “gestalts.” Methodology/approach To answer the research questions, a questionnaire was constructed and mailed to 1854 manufacturing SMEs in the province of Quebec, Canada, producing 216 valid responses that were used for statistical analysis purposes. Capability configurations were identified through a cluster analysis of the e-HRM and strategic HRM capabilities developed by these firms. Findings Using structural equation modeling to validate the research model, a causal analysis confirmed a positive influence of the sampled SMEs’ strategic orientation upon their development of strategic HRM capabilities. More importantly, a higher level of alignment between the SMEs’ strategic HRM and e-HRM capabilities was associated to a higher level of strategic HRM performance. Originality/value To our knowledge, ours is the first study to show interest in the effect of the strategic alignment of HRM and IT capabilities upon HRM performance, by adopting a configurational perspective and considering organizational IT from a functional point of view. Given the specific context of SMEs, the focus was on e-HRM capabilities related to the IT infrastructure of these organizations and the IT competencies of individuals related to HRM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| 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".