Analyzing Universities Service Quality to Student Satisfaction; Academic and Non-Academic Analyses
Notice bibliographique
Résumé
Indonesia has many higher learning institutions both public and private sectors such as colleges, polytechnics, institutes and universities and they are competing among them to get students to enroll in their institutions. It has become competitive among them to get students than before. The growing competition among higher learning institutions had forced them to strive to improve their service quality provided to students. The student satisfaction of service quality can be divided into two parts, namely satisfaction in academic and non-academic. The purpose of this research is to determine whether the academic and non-academic service quality affect student satisfaction of Economics Faculty Universitas Negeri Semarang. This research used an exploratory method that explaining the relationship between hypothesis testing, making prediction and getting the implicit meaning of problems that want to be solved. This study was conducted at the Economics Faculty of Universitas Negeri Semarang. The data analysis used SEM PLS. The population in this study were students from the Economics Faculty of Universitas Negeri Semarang who registered in 2015 and graduated in 2018. The total number of population in this study were 3,596 students majoring in Economics Education, Accounting, Management, and Economic Development. This study used a stratified sampling technique where students from all disciplines and levels were determined using the Slovin formula. Questionnaires were distributed to a sample of 360 students and were administered by trained enumerators. Data were collected using self-administered assessment questionnaires of a five Likert scale and analyzed using SEM PLS 6.0 Warp PLS. The results of this research were, first, academic service quality did not influence student satisfaction. Second, the non-academic service quality has a positive and significant influence on student satisfaction. This is because the supported learning infrastructure was found to be a factor that satisfied the students compared to teaching methods that was carried out by faculty members. It was also found that attitude and behavior in academic aspect were not significant in improving the students’ satisfaction. Therefore, it is suggested that Faculty of Economics of UNNES should focus on maintaining and improving the service quality of non-academic aspects in order to compete with other higher learning institutions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».