Empowering School of Accounting Websites through Quality Assurance: Development and Implementation of a User-perceived Instrument
Bibliographic record
Abstract
Today’s institutions of higher education are facing an increased number of significant challenges taking place in the political, economic, social and technological environment. Accordingly, the issues of performance, accountability, and marketing strategies have become ever more important. It has been suggested that universities that are more market or customer orientated can perform better. In dealing with customers, most universities have utilised Web technologies for both informational and promotional purposes. The question is whether they have designed their Web sites well enough in order to gain the benefits from Web utilisation. Previous studies on quality of Web sites are not lacking, but most of them have focussed mainly on business Web sites. Empirical research that focuses on the Web site quality of institutions of higher education has been scarce. This study focuses on the websites of Schools of Accounting, and selects the websites of Schools of Accounting in New Zealand’s universities as the research objects. The main objectives of this study are to develop and validate an instrument for measuring School of Accounting website quality from the perspectives of the users, and to implement the proposed instrument to measure and rank the quality of websites of Schools of Accounting in New Zealand. The results from this initial application substantiated the validity and reliability of the instrument.
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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 | 0.045 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".