E-government Adoption in Developing Countries: Need of Customer-centric Approach: A Case of Pakistan
Bibliographic record
Abstract
The e-government implementation in developing countries is always less successful and objectively hard to achieve and the reason behind is a less citizen-centric approach. Therefore, the effect of trust and social influence will be studied while understanding the adoption behavior of citizens in developing countries. Specifically, a case of selected e-service (e-filling of taxation by ‘Federal Board of Revenue' (FBR)) will be studied in Pakistan. The sole purpose of the study is to pull the external factors like trust and social influence to increase e-government adoption in the massively populated region of the world. The quantitative approach will be followed where the current users of selected e-service will be inquired under the modified version of a generic framework of ‘Technology Adoption Model' (TAM). The sample size of 153 is filtered and analyzed by using Structural Equation Modeling (SPSS AMOS) to study the intentions of the citizens. In methodological terms, deductive, quantitative method is adopted in interpretive philosophical manner. Collectively, trust and social influence are studied in order to find the impact on the intentions of citizens in the developing countries. However, the trust is the strongest predictor after social influence is recorded. Similarly, the usefulness observed to be a strong predictor of intentions in comparison of ease of use in the current scenario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".