Voluntary Turnover: Knowledge Management Friend or Foe
Bibliographic record
Abstract
The onset of the knowledge era has affected all industries. Without exception, the Canadian financial services industry has transformed itself due to the knowledge-intensive structure it possesses. However, high competition and career-minded professionals have created a situation in which leading financial services firms are losing key human capital each day -- capital that can and will be used against them in the modern fast-paced labour market. In the fight for the brightest senior executives, portfolio managers and fund administrators, human resource professionals must pay attention to the investments they are making in their employees through training and development, while monitoring reward and recognition programs so that loss of intellectual capital is kept to a minimum. This study examines 19 Canadian financial service firms and their current human capital practices. Results show that while human resource managers are effectively managing the people in their organizations through: 1) training and development, 2) performance reviews and 3) the effective management of fluctuating workforce demands, this study highlights the need for greater attention to be paid to the leveraging of human capital that exists within their knowledge-intensive workforce. Furthermore, research findings strongly suggest the need to increase knowledge management behaviours such as the valuation and codification of organizational knowledge assets.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.058 | 0.024 |
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; both teacher heads agree on what is shown here.
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".