THE EFFECT OF ACADEMIC SERVICE QUALITY ON STUDENT SATISFACTION IN GRADUATE PROGRAM AT THE FACULTY OF ECONOMICS AND BUSINESS UPN VETERAN EAST JAVA - Teguh Imanto; Rifki Suwaji (SEAN Institute, 2023)
This research reveals that the current demands on tertiary institutions and faculties are not only limited to academic achievement, but also include other aspects such as accountability, proof of achievement, assessment, quality certification, and success of alumni in getting relevant jobs. Therefore, managers of educational institutions need to become more professional in providing educational services to their students. This study uses a quantitative descriptive method using a survey through questionnaires as a data collection tool. Correlational statistical analysis was used to examine the relationship between the variables studied. Based on the results of the Hypothesis test on the Determinant Coefficient the resulting R number is 0.539. This shows that the correlation or relationship between student attitudes towards student satisfaction (Dependent Variable) and the independent variables (Physical Evidence Variables, reliability and responsiveness) is 53.9%. The coefficient of determination or adjusted R squre that is produced is 0.268 or 26.8 %. But for the number of independent variables more than 2 (in this case 3) it is better to use the R value of 53.9%. This means that the magnitude of the influence of physical evidence, reliability and responsiveness on student satisfaction in the faculties of economics and business is 53.9% while the remaining is 46.1%. Test results = F get a calculated F value of 12,854 which fulfills the requirements greater than the f table value of 2.70, and a sig value of 0,000 is obtained. sig. value which is obtained < ɑ = 0.05 which means that physical evidence, reliability and responsiveness simultaneously have a significant effect on student satisfaction in the undergraduate program at the Faculty of Economics. In the T test Significant Test Responsiveness to Student Satisfaction. In the significant test between responsiveness and student satisfaction, a sig value was obtained. 0.047 > 0.05. Thus Responsiveness to student satisfaction can be said to have an effect. Significant test of physical evidence with student satisfaction. Significant test between Physical Evidence Variable and student satisfaction, then obtained sig.0.015. >0.05. This proves the effect of the variable Physical Evidence on student satisfaction. The test is significant between reliability and student satisfaction. In a significant test between the reliability variable and student satisfaction, the sig value is obtained. 0.022 <0, 05 means a significant effect between reliability and student satisfaction. based on the significance value (Sig.) obtained of 0.000, which is greater than the significance level value α = 0.05. In addition, it was also found that the calculated F value was 12,854, which was greater than the table F value of 2.70. This shows that physical evidence, reliability, and responsiveness simultaneously have a significant effect on student satisfaction. The R value obtained is 0.539 indicating that the effect of physical evidence, reliability, and responsiveness on student satisfaction in the economics and business faculties is 53.9%. The remaining 46.1% can be explained by other factors outside the model which are not included in the multiple linear regression analysis. ) is obtained by 0.000, which is greater than the significance level α = 0.05. In addition, it was also found that the calculated F value was 12,854, which was greater than the table F value of 2.70. This shows that physical evidence, reliability, and responsiveness simultaneously have a significant effect on student satisfaction. The R value obtained is 0.539 indicating that the effect of physical evidence, reliability, and responsiveness on student satisfaction in the economics and business faculties is 53.9%. The remaining 46.1% can be explained by other factors outside the model which are not included in the multiple linear regression analysis. ) is obtained by 0.000, which is greater than the significance level α = 0.05. In addition, it was also found that the calculated F value was 12,854, which was greater than the table F value of 2.70. This shows that physical evidence, reliability, and responsiveness simultaneously have a significant effect on student satisfaction. The R value obtained is 0.539 indicating that the effect of physical evidence, reliability, and responsiveness on student satisfaction in the economics and business faculties is 53.9%. The remaining 46.1% can be explained by other factors outside the model which are not included in the multiple linear regression analysis. which is greater than the value of F table, namely 2.70. This shows that physical evidence, reliability, and responsiveness simultaneously have a significant effect on student satisfaction. The R value obtained is 0.539 indicating that the effect of physical evidence, reliability, and responsiveness on student satisfaction in the economics and business faculties is 53.9%. The remaining 46.1% can be explained by other factors outside the model which are not included in the multiple linear regression analysis. which is greater than the value of F table, namely 2.70. This shows that physical evidence, reliability, and responsiveness simultaneously have a significant effect on student satisfaction. The R value obtained is 0.539 indicating that the effect of physical evidence, reliability, and responsiveness on student satisfaction in the economics and business faculties is 53.9%. The remaining 46.1% can be explained by other factors outside the model which are not included in the multiple linear regression analysis. and responsiveness to student satisfaction in the economics and business faculties is 53.9%. The remaining 46.1% can be explained by other factors outside the model which are not included in the multiple linear regression analysis. and responsiveness to student satisfaction in the economics and business faculties is 53.9%. The remaining 46.1% can be explained by other factors outside the model which are not included in the multiple linear regression analysis.