Does strategic human resource management matter in high‐tech sector? Some learning points for SME managers
Bibliographic record
Abstract
Purpose The main purpose of this paper is to examine the nature and impact of human resource capabilities and involvement on the firm's performance in the SME sector. Design/methodology/approach This research is based on an empirical survey of Chief Executive Officers (CEOs) and their perception of the HR involvement in strategy development in high tech SMEs operating in the electronics industry in the UK. Postal questionnaire is the main data collection instrument for this research. A combination of qualitative and quantitative approaches has been employed for data analysis. Findings The important conclusion reached is that increasing the core competencies of the firm, in particular in HR, is the key element to the success of the firm. Moreover, it is posed that the growing involvement of the HR in the development and implementation of business strategy will lead to the increased effectiveness of the organisation and the industry as a whole. Finally, the competitive advantage a firm enjoys can come from the distinctive capabilities which provide it with a core competence in HR. Research limitations/implications The present study is concerned solely with small and medium‐sized enterprises in the electrical and electronic manufacturing sector in the UK. A further comparative international‐wide study is recommended. Practical implications In order to increase firm performance and to benefit from HR capabilities, it is recommended that practitioners and SME CEOs increase the involvement of their HR specialists in the processes of strategic management in their firms. Originality/value The principal contribution of this first‐time study has been the attempt to explore the CEO's perceptions of HR, its capabilities and its degree of strategic involvement as significant determining factors to ensure competitive advantages for the firm in a highly changing market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".