Business Strategy, Work Processes and Human Resource Training : Are They Congruent?
Bibliographic record
Abstract
This study examines the extent to which human resource (HR) training (content and context) is contingent on business strategy (i.e. classified according to Miles and Snow (1984) typology—‘Designing strategic human resource systems’, Organizational Dynamics, 13, 36–52) as well as the characteristics of the work process. Sixty-five Spanish organizations (i.e. 65 senior executives and 65 senior HR officers) participated in the study. Using primarily factorial and cluster analyses, support was found to the assertion that companies adopting a particular type of training strategy/policy have a high degree of internal consistency amongst the training objectives sought. In terms of work processes and training, results indicate that under work processes where the content of work provides for enrichment and for long-term results, companies tend to adopt training strategies where emphasis is on enhancement of individual specialized skills aimed at improving direct productivity. By contrast, firms who use work processes that are characterized by repetitive and routine tasks, de-emphasize this type of training content. Results also indicae that limited level of contingencies exist between training policies and business strategy, especially when time dimension is also accounted for. While the theory suggests that organizations that have their HR training fit (contingent) on their business strategy are more effective, the empirical results portrayed in this study shows a more complex picture. Copyright © 2000 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".