Evaluating User Satisfaction of IT Services Through Service Quality Approach
Bibliographic record
Abstract
Unika Atma Jaya, recognized as one of Indonesia's leading private universities, utilizes information system technology to support both operational and academic activities.However, there is noticeable inconsistency in the implementation of this technology, especially in effectively integrating it with relevant work units.Several existing information systems fall short of meeting user satisfaction.This research investigates user satisfaction with Information Technology (IT) services at Unika Atma Jaya using the Service Quality (Servqual) model, which includes Tangibles (X1), Reliability (X2), Responsiveness (X3), Assurance (X4), and Empathy (X5) as variable.The analysis reveals that the Tangibles and Reliability dimensions significantly contribute to positive user satisfaction, with respective significant values of 0.000 and 0.001, along with corresponding T-table values of 4.197 and 3.323.This underscores the crucial role of tangible aspects, like facilities, and the reliability of service delivery in enhancing overall user satisfaction.Conversely, the Responsiveness dimension, with a significant value of 0.251 and a T-table value of 1.150, does not show a statistically significant impact on user satisfaction, indicating that users' perceptions of prompt service delivery may not be a decisive factor.Furthermore, the Assurance dimension exhibits a significant negative impact, with a value of 0.000 and a T-table value of -3.542, emphasizing the need for careful management in this area to prevent adverse effects on overall user satisfaction.In conclusion, a focus on improving Tangibles and Reliability dimensions, while addressing Assurance-related challenges, is vital for optimizing the user satisfaction landscape at Unika Atma Jaya.Recommendations include targeted enhancements in IT services in Tangibles and Reliability, specifically in applications and on-campus facilities.These numerical findings serve as a strategic basis for developing more effective, consistent, and responsive IT services to meet user expectations in the future, particularly within the relevant unit (BSTI).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".