Perceptions and Expectations of Staffs from Training Services Provided by NISOC's Training centre Based on SERVQUAL Model
Bibliographic record
Abstract
Introduction: The evaluation of training services quality in training centers is one of the major steps in improving quality. Deciding the amount of difference between the present condition and expected condition can facilitate the promotion of training services quality. Methods: This study was a descriptive–survey. The population size was the 1217 learners attending the on-the-job training courses in training centers (computer, Technical-specific and management) in the Training and Development Department of National Iranian South Oil Company (NISOC) in the second half of 2014. The sample volume was decided 292 people using the Cochran Formula (1977).The sample size of each training center was chosen based on the percentage of the learners in the population size using the randomstratified method. The data collection was done via SERVQUAL standard questionnaire. The questionnaire measured the gap in the five dimensions of service quality. The data were analyzed using SPSS software as well as conducting descriptive statistics, Wilcoxon and Kruskal-Wallis Test. Results and Analysis: The findings showed no gaps for the two dimensions of empathy and reliability and a negative one for the tangible (-0.66), responsiveness (-0.41) and assurance (-1.44). No difference was found between the quality of training services in training centers. Conclusion: Although the mean of percent condition is higher than the average(4.01) but the observed gap between the expectations and perceptions proves that the learners expectation for the provided services were not fulfilled. As a result it is suggested that for establishing the customer-care culture, the dimensions which had the biggest gap be prioritized when allocating the budget.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".