Using Polynomial Modeling to Understand Service Quality in E–Government Websites1
Bibliographic record
Abstract
As e–government websites grow in functionalities and capabilities, there is a need to better understand the nuanced role of service quality to enable governments to better address citizens’ online service needs. Such an understanding should help improve overall e–government use by citizens. Thus motivated, our paper investigates how users respond to the service quality perception–expectation gap in e–government websites. We draw on rational choice theory (RCT) to develop a theoretical model linking expected and perceived information systems (IS) service quality to continued e–government website use intentions. The proposed model is empirically tested using polynomial modeling and response surface analysis. The results indicate that, in contrast to the organizational context, for e–government websites, both agreement and disagreement between expected and perceived IS service quality are positively associated with continued use intention. In our sample, as high as 77 percent of respondents appear to be in the zone of tolerance, suggesting that users can tolerate wide variations in service quality before they consider seeking alternatives to e–government websites.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".