Integrating Service Quality with System and Information Quality: An Empirical Test in the E-Service Context1
Bibliographic record
Abstract
Wixom and Todd (2005) integrated the user satisfaction and the technology acceptance literatures to theorize about and account for the influence of the information technology artifact on usage. Based on Wixom and Todd’s integrated model of technology usage, we propose the 3Q model by investigating the role of service quality (SQ), in addition to system quality (SysQ) and information quality (IQ), in website adoption. Attention to SQ is critical, as consumer websites have increasingly become the target of SQ assessment made by consumers, not just traditional SysQ and IQ evaluations. As part of our study, we further theorize and empirically test the relationships among these three types of quality constructs and hypothesize that perceived SysQ influences perceived IQ and perceived SQ, and perceived IQ influences perceived SQ. Our study extends the Wixom and Todd model in the e-service context and is the first of its kind to empirically examine the combined impact of perceived SQ, perceived SysQ, and perceived IQ on usage intention. Our study advances the theoretical understanding of SQ and the relationships among perceptions of SysQ, IQ, and SQ in the e-service context. The results also inform practitioners that high IQ and SysQ can directly or indirectly improve SQ in the e-service context.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".