Bibliographic record
Abstract
Information technology departments are increasingly viewed as service providers to business users (Kettinger and Lee 1994), and service quality has been proposed as a measure of IT effectiveness (Pitt et al. 1995). Previous research (Kettinger and Lee 1994; Pitt et al. 1995) has adapted the SERVQUAL scale (Parasuraman et al. 1988) from service marketing literature, using it to gauge business users’ expected and perceived levels of IT service quality. In this research, we investigate the other half of the IT-user relationship and look inside the IT function to identify variables that affect IT service quality. By focusing on IT professionals and the IT department, we hope to gain a deeper understanding of service quality and to assist managers in pinpointing the causes of service shortfalls. This study offers a theory-based extension to IT service quality research. We look to the organizational psychology literature, with its stream of theory-based research on organizational climate to connect management practices with organizational outcomes through employees’ shared cognition of their work environment (Campbell et al. 1970; Kopelman et al. 1990). Much of that literature has focused on one type of climate, that is, service climate (or climate for service), which has been established as a predictor of the quality of service provided to customers (Schneider, Ashworth et al. 1996; Schneider and Bowen 1985; Schneider, Parkington, and Buxton 1980; Schneider, White, and Paul 1998). Building on these studies, we apply service climate theories to the IT context and introduce a new construct, IT service climate. We propose a conceptual model that links IT professionals’ climate perceptions with antecedent and outcome variables, including IT service quality. This study represents an effort to comprehensively introduce organizational climate as a useful theoretical lens for researchers interested in many IT-related phenomena.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".