Implementation of E-Training in Developing Country: Empirical Evidence from Jordan
Bibliographic record
Abstract
This paper is to emphasize the factors that aid e-training adoption in the developing country like Jordan. This paper is based on past review literature databases. The literature recognized the role of computer self-efficacy, availability of resources and perceived support in e-training adoption. This paper using the technology acceptance model (TAM) for modelling framework and explained the importance of these variables in e-training adoption in developing country context. The author found that the combined role of computer self-efficacy, technological infrastructure, Internet facilities and technical support is critical for e-training adoption in developing countries, particularly in Jordan.Thus, the authors proposed the combination of these variables which would encourage future research on the use of TAM in technology adoption. Research limitations/implications – This paper gives an elaboration of the role of computer self-efficacy, perceived cost, availability of resources and perceived support with TAM as base of the framework. This provides researchers the opportunity to test the proposed framework empirically and further suggest other variables that can aid e-training adoption in the context of developing country.Practical implications – The result of this paper can serve as a guide to managers and policymakers to have a better understanding of the requirements for e-training adoption, especially in developing countries. This will go a long way towards designing good policies that could maximise e-training results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".