Meeting the Training Needs of SMEs: Is e-Learning a Solution?.
Bibliographic record
Abstract
Training is one of the basic means of human resources development in business organizations, aiming to motivate employees, to develop their potential and to help them perform better. The end of the 20 century has seen the advent of globalisation and the diffusion of new information and communication technologies. Businesses have to change and adapt to the requirements of the new knowledge-based and skill-based economy. Facing pressures from an increasingly competitive business environment, small and medium-sized enterprises (SMEs) are called upon to implement strategies that are enabled and supported by information technologies and e-business applications in order to compete with others’ organizations. One of these applications is e-Learning, whose aim is to enable the continuous assimilation of knowledge and skills by managers and employees, and thus support organisational training and development efforts through the use of the Internet and Web technologies. Little is known however as to the level of awareness of e-Learning in SMEs and as to the actual role played by e-Learning with regard to these firms’ training needs. A multiple case study of sixteen SMEs in the Atlantic region of Canada, including twelve that use e-Learning with varying degrees of intensity, was designed to explore this question. We observed the firms’ training process, identifying to what extent the SMEs know and use e-Learning, and to what extent e-Learning meets their training needs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".