HIGH PERFORMANCE WORK SYSTEMS: A CAUSAL FRAMEWORK OF TRAINING, INNOVATION, AND ORGANIZATIONAL PERFORMANCE IN CANADA
Bibliographic record
Abstract
The processes that link High Performance Work System (HPWS) practices and organizational performance are not fully understood. Using resource-based theory, this research focuses on training, by separating it from other HPWS practices, and human capital development as a source of sustained competitive advantage. The first purpose of my research is to examine the relationships between the HPWS practice of training, innovation, and organizational performance, and look at the mediating effect of innovation over time at the workplace level. The results indicate that the temporal pathway from training to innovation to organizational performance is positive and significant even after controlling for reverse-causality. Strategic activity is also explored and is found to be a significant moderator. This study contributes to knowledge by identifying the importance of aligning business strategy with training, as well as other HPWS practices and innovation to achieve improved organizational performance outcomes. The second purpose of this research is to explore the factors that act to expand or limit the HPWS practice of training, with a focus on the outcomes of employers' decisions to offer training, employees' decisions to accept or decline training, and the job-related training received by employees. The results indicate that the employee-level factors: participating in HPWS practices, use of technology, and using new technology are significant contributors to employers' decisions to offer and employees' receipt of training. Further, employees' perception of the existence of a gap between the skills required for the job and their current skills contributes to employees accepting employer offers of training.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".