Organizational factors’ effects on the success of e-learning systems and organizational benefits: An empirical study in Taiwan
Bibliographic record
Abstract
E-learning development for enterprises is still in its infancy in that scholars are still working on identifying the critical success factors for e-learning in organizational contexts. This study presents a framework considering how organizational factors affect the quality and service of e-learning systems and how these factors influence organizational benefits in the view of IS success model and resource-based theory. A questionnaire survey of 120 Taiwanese companies was performed to validate the framework. The results show that top management support, information security policy, and institutional policy are positively related to system quality, while top management support, organizational learning culture, and institutional policy are positively related to system service. Additionally, system service is significantly related to organizational benefits. Our model provides two novel aspects of e-learning study. Firstly, we extend IS success model by incorporating four organizational factors as antecedences influencing system quality and system service. Secondly, the model is framed and examined on an organizational level, which provides a top-down view for managers when designing and implementing e-learning systems in the organizational context.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".