Towards a learning centred view of intellectual capital : the value of learning and knowledge in the human resource strategies of innovative small and medium-sized computer service firms in British Columbia
Bibliographic record
Abstract
This study examines the value placed on learning and knowledge by innovative small and medium-sized computer service firms, using a framework for analysis adapted from intellectual capital (IC). Knowledge is conceived as a specialized and appreciable asset capable of being acquired, nurtured, developed, managed and commercially exploited. Emphasis is placed on evaluating the strategies and practices that drive company learning and knowledge transfer in smaller computer service companies with respect to developing and making use of the knowledge owned by employees, customers and the corporation. The study evaluates the extent to which short-term commercial objectives impose conceptual boundaries on particular forms of intellectual capital - skill development, experience and recurrent learning. Data is drawn from case studies and a broader, provincial sample of companies based in British Columbia with ten to one hundred employees. The study concludes that small and medium-sized computer service firms narrowly conceptualize and undervalue knowledge in the area of human resource development, where supporting learning processes and structures are often poorly developed. Companies place a premium value on new forms of knowledge with an obvious potential to improve the skills and experience necessary to support commercial products and services. In contrast, learning, knowledge and expertise related to professional development and more long-term organizational and/or career benefits are consistently and systematically marginalized. The study outlines limitations of the IC framework in terms of the intrinsic values attributed to sources and types of knowledge. Of particular concern is the importance attached to formalized, external relationships with customers and the undervaluing of knowledge with less obvious or potentially more long-term commercial value. Suggestions for using the framework in small and medium-sized computer firms include broadening the existing narrow focus of external knowledge sources to encompass the extensive network of business relationships companies exploit, while the undervaluing of knowledge found in professional development is countered by linking benefits to returns from specific, rather than generic commercial goals. Emphasis is placed on potential gains in efficiency and productivity offered by improvements to learning and organizational processes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".