Decision-making model for the effective e-services adoption in the Indian educational organizations
Bibliographic record
Abstract
Due to the advances in wireless network environments, consumers/end-user behaviors continue to expand in cyberspace. Similarly, university students (i.e. universities’ consumers) can easily shift from one university to another. In recent years, decision-makers in educational organizations have faced multi-criteria decision making (MCDM) problems in e-service adoption in order to improve quality standards and maintain students’ retention in highly competitive education environments. Generally, many required criteria in MCDM cannot be evaluated accurately since accurate data cannot be obtained from the decision makers’ assessments. Thus, this research aims to propose a decision-making model for identifying the factors that highly impact on e-service adoption in educational organizations. This new model combined the fuzzy Decision MAking Trial and Evaluation Laboratory (fuzzy DEMATEL) and fuzzy Techniques for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) to weight the interactions among the factors which were defined from a comprehensive review of literature and to determine the relative importance of these factors. The findings from our new proposed model: fuzzy DEMATEL-TOPSIS showed that environmental factors are the most important for effective e-service adoption among educational organizations in India. The proposed decision making model could guide educational organizations to improve their decisions related to technology adoption in their organizations. The conclusions and practical insights gleaned from this research could also hopefully be useful to school authorities in assisting with the adoption, acceptance, and usage of e-services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".