It-Mediated Customer Service Content and Delivery in Electronic Governments: An Empirical Investigation of the Antecedents of Service Quality1
Bibliographic record
Abstract
Despite extensive deliberations in contemporary literature, the design of citizen-centric e-government websites remains an unresolved theoretical and pragmatic conundrum. Operationalizing e-government service quality to investigate and improve the design of e-government websites has been a much sought-after objective. Yet, there is a lack of actionable guidance on how to develop e-government websites that exhibit high levels of service quality. Drawing from marketing literature, we undertake a goal approach to this problem by delineating e-government service quality into aspects of IT-mediated service content and service delivery. Whereas service content describes the functions available on an e-government website that assist citizens in completing their transactional goals, service delivery defines the manner by which these functions are made accessible via the web interface as a delivery channel. We construct and empirically test a research model that depicts a comprehensive collection of web-enabled service content functions and delivery dimensions desirable by citizens. Empirical findings from an online survey of 647 respondents attest to the value of distinguishing between service content functions and delivery dimensions in designing e-government websites. Both service content and delivery are found to be significant contributors to achieving e-government service quality. These IT-mediated service content functions and delivery dimensions represent core areas of e-government website design where the application of technology makes a difference, especially when considered in tandem with the type of transactional activity. A split sample analysis of the data further demonstrates our model’s robustness when applied to e-government transactions of varying frequency.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".