Application of the SERVQUAL Model for the Evaluation of the Service Quality in Moroccan Higher Education: Public Engineering School as a Case Study
Bibliographic record
Abstract
The quality of higher education systems currently represents a major challenge for the development of societies. In Morocco, engineering education is at the heart of this development, it is a major and necessary lever which, due to an increasingly demanding job market, faces several challenges. According to Moroccan Directorate for Strategies and Information Systems (2018) these challenges are classified into two categories: quantitative (low rate of Moroccan engineers compared to global figures; 1.57 graduates in engineering per 10,000 inhabitants in 2016) and qualitative (adaptation of the academic curriculum to the needs of the job market). However, little work has been done on the introduction of service assessment tools in higher education in Morocco (Akrim, Figari, Mottier-Lopez, & Talbi, 2010).In our article, we are interested in the SERVQUAL method (SERVice QUALity-Quality of Service). This approach, initially designed to measure customer satisfaction in a company, allows, when applied to higher education, to measure student satisfaction at the university. Based on a bibliographic research, we have identified the five dimensions of the model that impact the quality of service.Through the application of this model to a sample of students from a public engineering school, we have been able to determine that tangible elements and physical installations have the biggest impact on service quality with a negative quality gap (-2.0275). As a result, more efforts are needed in these dimensions to improve service quality.In conclusion, the SERVQUAL model, applied to the educational system and more precisely higher education, allows to quantify the non-quality by measuring the gap between the perception of the students and their expectations for a good service. It has the advantage of helping decision-makers take corrective actions needed to improve the service quality provided by universities as a part of a process of continuous improvement to achieve higher degree of excellence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| 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 | 0.001 |
| 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".