Establishing IT Service Climate as an Antecedent of IT Service Quality
Bibliographic record
Abstract
The SERVQUAL scale has been used to measure the quality of IT service experienced by business customers. Recent research has extended the IT service quality research by studying the other half of the server-customer relationship, i.e., the IT department (Jia and Reich, ICIS 2005). Building from organizational climate theories, a new construct, IT Service Climate, has been proposed as an antecedent of IT service quality. This paper reports on an in-progress empirical research project that aims to 1) develop a valid measurement instrument for the IT service climate construct, and 2) test the hypothesis that IT service climate is an antecedent of IT service quality. To date, a multidimensional measurement instrument has been developed and pilot tested. Final data collection is underway to further validate the instrument and establish it as an antecedent of IT service quality. Potential contributions to both IT research and practice are discussed.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.011 |
| 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".