Performance management strategies: a competitive advantage for high technology firms: a study in the Okanagan Valley Region of British Columbia, Canada
Bibliographic record
Abstract
High technology firms are important to economic growth. A key factor in survival\nand growth of these firms is the attraction and retention of qualified workers. This\nexploratory research compares how high technology firms use performance\nmanagement strategies to gain a competitive advantage and, at the same time,\ninvestigates the role of human capital. The eight high technology firms selected for\nstudy are located in the Okanagan Valley region of British Columbia Canada and\neach was pre-qualified as a small or medium-sized enterprise - two with 10 to 19\nemployees, four with 20 to 49, and two with 50 to 200. For this research, eight high\ntechnology case studies were constructed from interviews with the firms' managers.\nSingle and multiple case analyses examined performance management processes\nfrom the perspectives of: integration with business strategy, application to business\nperformance, use by managers and supervisors, and linkage with human resource and\nreward practices. The findings indicated that these firms have a well-developed\nunderstanding of performance management but opportunities for executing strategies\nwith this process are weaker. As well, those firms with human resource managers\nhave a distinct employee focus, whereas those without emphasise firm performance.\nThe results also indicate that all firms view superior technology and customer service\nas their common differentiating qualities; nevertheless, human capital was endorsed\nas either a competitive advantage or an integral component. Regardless,\nperformance management has potential for greater role in the crafting and executing\nof strategy than these firms employ. Further, although culture and change are high\npriorities to these firms, none are capitalising fully on the cultural and change\nelements inherent in performance management processes. The conclusions for\nmanagers are threefold; a) to develop and implement formal strategic plans, b) to\nintegrate performance management and human resource practices, and c) to build a\nhuman capital pool that sustains their firm's competitive advantage. From a\ntheoretical perspective, the implications are twofold: a) performance management is\na bridge for executing strategic planning and realising a competitive advantage and b)\nprincipal value of human capital is in its capability to sustain a firm's competitive\nadvantage. The findings and their implications offer high technology managers and\nfuture researchers a useful framework for growth and research respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".