Bibliographic record
Abstract
Purpose The purpose of this paper is to look at the issues concerning barriers that managers face in relation to participation in training and transfer of training, which have become increasingly important to HRD scholars and practitioners. To date, these areas have largely been examined independently. This paper aims to argue that there is an increasing need to understand and explore these two areas in unison. Design/methodology/approach Although this paper is primarily conceptual in nature, in order to investigate a model derived from relevant literature, survey data from 137 Canadian employees, mostly from the broader public sector, was examined. These respondents completed a short transfer of training questionnaire three months after a one‐day managerial training programme. In this study, open‐ended questions investigating training barriers are analysed. Findings The exploratory examination of information from participants of a managerial training programme suggests that the model which links literature on participation in training and transfer of training warrants additional examination. Most significantly, there was substantial overlap between the participation and transfer barriers with the most common barriers being linked to “lack of time” and “unsupportive culture”. Research limitations/implications The main limitation of this paper is the relatively small sample size with regard to data concerning barriers to transfer. However, the authors feel that a key implication is that a “bridge of understanding” is created concerning the numerous factors that impact participation in training, transfer of training and the relationships between them. Hence, HRD practitioners and scholars can now use this model to begin to understand how they might improve the overall quality of training programmes and to further explore the relationship between transfer and participation. Originality/value The conceptual model developed further integrates the respective literatures pertaining to management training participation and transfer of learning in the workplace. The proposed model shows how barriers to participation can become barriers to transfer and how barriers to transfer from one programme may become barriers to participation to subsequent learning activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.013 | 0.081 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".