The impact of training on firm outcomes: longitudinal evidence from Canada
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of three training indicators, namely offer, participation and cost, on three firm outcomes, namely voluntary turnover, firm performance and profit. Design/methodology/approach The empirical analysis is carried out using firm-level data sourced from a Canadian national data set. In total, data from 5,237 for-profits firms with ten employees or more were analyzed longitudinally over eight years. Results were generated by XTREG fixed effect longitudinal analyses between the three variables of training, voluntary turnover, firm performance and profit. Findings Training offer, operationalized as the number of different formal training programs offered annually by an employer, significantly decreases voluntary turnover while it significantly increases performance and profit. Training participation, operationalized as the percentage of employees receiving training per year, has a significant positive impact on voluntary turnover. Training cost, operationalized as the annual cost of training per employee, has no impact on the three firm outcomes. Practical implications Among the various human resource practices a firm can use to strengthen its human capital, training can have a significant impact of its own. Investing in a diversified training offer brings value to a firm by decreasing employee voluntary turnover while increasing firm performance and profit. Originality/value This research contributes to the strategic impact of organizational training, demonstrating the impact of training on key organizational outcomes over time. Further, this paper contributes to the empirical literature by making a distinction between voluntary and involuntary turnover. Last, even though this study does not entirely addresses the problem of possible reverse causality, using longitudinal objective data, this study addresses several limits of past research at the macro-level of analysis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".