EFFECTIVENESS OF COMPENSATION STRATEGIES IN CANADIAN TECHNOLOGY-INTENSIVE FIRMS
Bibliographic record
Abstract
The purpose of this study was to examine the role technological intensity in the choice of compensation policies, and the influence of such policies on market performance and turnover in high and low technological intensity firms. Using a survey of 252 Canadian firms, we show technology intensity has significant influence on compensation policies. A second survey of 128 Canadian organizations shows that several compensation strategies are better adapted to firms in high technology environments. More specifically, we found that greater pay bonuses and emphasis on group performance incentive plans are positively associated with organizational market performance in high tech firms. Results show that extensive use of individual performance pay plans in high tech firms increases the rate of turnover, whereas the use of group incentive plans decreases the rate of turnover. A growing body of literature has examined the relationship between human resource policies and practices, and organizational performance (Huselid, 1995; Delany & Huselid, 1996; Arthur, 1994; MacDuffie, 1995). The universalistic perspective states that some human resource practices are always better than others and all organizations should adopt such practices (Pfeffer, 1994). Universalistic predictions were found for HR practices such as staffing (Tersptra & Rozell,
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".