Strategic alignment of IT and human resources management in manufacturing SMEs
Bibliographic record
Abstract
Purpose Within the manufacturing sector, small- and medium-sized enterprises (SMEs) face specific challenges with regard to their strategic HRM capabilities. In this context, an emerging issue for both researchers and practitioners regards HR information systems (HRIS), i.e. the deployment of strategic IT capabilities to enable the firm’s high-performance work system (HPWS) capabilities and thus improve the performance of its HR function. The purpose of this paper is to address this issue by using a capability-based mediation perspective to study the strategic alignment of HR and IT. Design/methodology/approach A survey study of 206 manufacturing SMEs was realized and the data thus obtained was analyzed through structural equation modeling. Findings Results confirm that the HRIS capabilities of SMEs influence the performance of the HR function through their strategic alignment with the HPWS capabilities of these enterprises. Practical implications The results suggest that the manufacturing SMEs most active in developing their HRIS capabilities while developing their HPWS capabilities are most likely to develop a competitive advantage through the improved performance of their HR function. This is especially important in a time when firms of all sizes across the globe are waging a “war for talent,” and are enabled to do so by their strategic use of IT. Originality/value The results of the study constitute a valid basis for prediction and prescription with regards to the strategic alignment of human and IT resources.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".