UNDERSTANDING THE ANTECEDENTS AND CONSEQUENCES OF E-GOVERNMENT SERVICE QUALITY: AN EMPIRICAL INVESTIGATION
Bibliographic record
Abstract
Difficulties in defining and understanding the antecedents and consequences of e-government service quality have stymied the design of efficacious e-government websites. This study thus presents a working definition of e-government service quality that bridges the gap between MIS and marketing literatures. We then explore the delineation between service content and delivery quality as potential antecedents of e-government service quality. Together with cognitive and system-salient consequences derived from prior research, we construct and empirically test an egovernment service quality model on a sample of 647 existing e-government service participants. 15 out of 17 hypotheses were supported, thereby attesting to the saliency of the constructs and relationships embodied in our model. Further, the structural properties of our model were validated using both LISREL and PLS analytical techniques. This lends credibility to our explanations and predictions by affirming the stability of our theoretical base upon which the hypothesized relationships were generated.
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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 | 0.017 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".