International Journal of Management Science and Business Administration
Volume 9, Issue 5, July 2023, Pages 7-23
The Effects of Good University Governance on the Public Services Quality of State Islamic University
DOI: 10.18775/ijmsba.1849-5664-5419.2014.95.1001
URL: https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.95.1001
Abdul Mubarok1, Tarmizi Achmad2, Teguh Yuwono3, Sudjono4, Pardomuan Robinson Sihombing5.1Doctoral Student of Public Administration, Faculty of Social and Political Sciences, Diponegoro University, Semarang, Indonesia
2Department of Accounting, Faculty of Economics and Business, Diponegoro University, Semarang, Indonesia
3,4Department of Public Administration, Faculty of Social and Political Sciences, Diponegoro University, Semarang, Indonesia
5The Central Bureau of Statistics, Jakarta, Indonesia
Abstract: This article aims to determine whether good university governance affects the public service quality of State Islamic Institutes. Public services quality indicators used in this research adopt the criterion proposed by Parasuraman. The study occurred in one of the State Islamic Institutes (IAIN), Syekh Nurjati Cirebon, West Java, Indonesia. The population and samples were taken from Tarbiyah and Teacher Training Faculty of IAIN Syekh Nurjati Cirebon students. The research employed a quantitative method by developing a valid and reliable questionnaire survey to collect empirical data. A 1 to 5 Likert scale measured the constructs, and the scale represents the rank of strongly disagree to agree strongly.
Furthermore, the data were analyzed using the Structural Equation Modelling (SEM) tool based on the variant and Partial Least Square (PLS). The results show that implementing sound university governance principles significantly and directly affected public service quality. As the implementation of the research results, it is highly recommended to improve the application of the principles of Good University Governance: transparency, accountability (especially to stakeholders), responsiveness (participation), independence (in opinion and decision-making), fairness, assurance of quality and relevance, effectiveness and efficiency, and non-profit; to improve public service quality based on the principles of tangibles, and process-oriented: reliability, responsiveness, assurance, and empathy.
Keywords: Good University Governance, Public Services Quality, State Islamic Institutes (IAIN), Partial Least Square (PLS), Cirebon
1. Introduction
Higher education plays an important role in responding to the challenges of the demographic bonus and global competition in 2030-2040, bonuses for workers of productive age (15-64 years). In Indonesia, universities can be academies, institutes, polytechnics, colleges, and universities. Universities can provide academic, professional, and vocational education with Diplomas (D1, D2, D3, D4), Bachelor’s Degrees (S1), Master’s Programs (S2), Doctoral Programs (S3), and Specialist Education Programs. State Universities are managed by the Indonesian Ministry of Education, Culture, Research, and Technology, while State Religious Universities in Indonesia are responsible for the Ministry of Religion. State Religious Universities consist of Islamic, Christian, Catholic, Hindu, and Buddhist universities.
Furthermore, State Islamic Universities are universities in Indonesia that management is under the Ministry of Religious Affairs of the Republic of Indonesia. Technical academic development of State Islamic Universities is carried out by the Indonesian Ministry of Education, Culture, Research, and Technology, while functionally, it is carried out by the Ministry of Religious Affairs. Three types of universities are classified into this category: State Islamic Universities, State Institute of Islamic Studies, and State College of Islamic Studies.
Furthermore, a study (Handayani, 2015; McKinsey Global Institute, 2020) showed that in the global job market in 2030, Indonesia is expected to experience a shortage of educated and skilled workers and an excess of unskilled workers. Higher education plays a role in preparing, developing, and improving the quality of human resources in terms of knowledge, skills, and experience. In accordance with the 2012 Higher Education Law mandate, higher education, as part of the national education system, has a strategic role in educating the nation’s life and advancing science and technology by paying attention to and applying humanities and cultural values and Indonesian nation empowerment. For this reason, World Class University (WCU) is believed to be a necessity and a solution to survive and compete in the global market, so universities are increasingly required to respond to society’s needs because work is more dynamic and complex.
Good University Governance is a derivative concept of governance. The author further identifies the relationship between governance and the quality of public services (Anam et al., 2022) and mentions that the reputation of WCU State Islamic Universities could determine strategic steps that have to be carried out, such as 1) Increasing the quantity and quality of published State Islamic Religious Universities research results, 2) Providing quality teaching services and in accordance with stakeholder needs, 3) Developing Website at State Islamic Universities, 4) Leadership at State Islamic Religious Universities, 5) Implementing of good university governance at State Islamic Religious Universities, 6) Alliancing and Networking of State Islamic Universities along with other institutions, 7) Internationalization of State Islamic Universities, and 8) Application of college culture at State Islamic Universities.
By these strategic steps, we can define World Class Universities, namely campuses that rank high in the rankings carried out by institutions with an international reputation. Several ranking institutions are known to universities in Indonesia, such as Times Higher Education Supplement (THES), Webometrics, and Shanghai Jiao Tong University (SJTU). Furthermore, the success of an organization, in this case, including universities, cannot be separated from efforts to continuously improve the quality of services because the quality of public services is an indicator of competitiveness (Szabó, 2019). The forms of higher education services include as follows: a) learning services: materials, preparation, process teaching qualities as well as quality of evaluation; b) academic guidance services and thesis or other final project guidance services: lecturers’ mastery of research methodologies, commitment to carry out their duties as a counselor to the fullest, aspects of equity; c) library and internet services: not only qualified from the physical side of the building but also other physical equipment, the book collection continues to increase both of title and copies, therefore its existence able to create a maximum contribution to increasing students’ knowledge of a subject; and d) practicum services: practicum services centered in laboratories with quality human resources (lecturers and staff), facilities, and practicum work systems. So that with these services, it can produce quality graduates, meaning that they graduate on time and get a decent job.
There are many indicators that can be used as a tool to measure service quality. These indicators vary widely according to the focus and context of their use in research. (Rokicki and Nogalski, 2021) Suggested that to measure service quality to be valid and reliable, two types of measures must be used, namely process-oriented measures and results-oriented measures, presented in Table 1 below:
Table 1: Comparison of Public Service Indicators
| No. | Scholars | Indicators | |
| Results Oriented | Process Oriented | ||
| 1 | (Longenecker and Fink, 2001) | Efficiency | |
| Effectiveness | |||
| 2 | (Lajo, 2023) | Economy | |
| Efficiency | |||
| Effectiveness | |||
| Equity | |||
| 3 | (Rabin et al., 2018) | Responsivities | |
| Responsibilities | |||
| Accountabilities | |||
| 4 | (Parasuraman et al., 1991) | Tangibles | Reliability |
| Responsiveness | |||
| Assurance | |||
| Empathy | |||
| 5 | (Teas, 1994) | Punctuality | Public interest |
| Speed, convenience, and affordability | Legal certainty | ||
| Equal rights | |||
| Balance of rights and obligations | |||
| Professionalism | |||
| Participatory | |||
| Equality of treatment/ non-discrimination | |||
| Openness | |||
| Accountability | |||
| Special facilities and treatment for groups
susceptible |
|||
Source: (Septi et al., 2013)
Considering the table above, the indicators of the quality of public services from (Parasuraman et al., 1991), which are then used in this study, already include results and processes. Result-oriented indicators are tangibles and process-oriented: reliability, responsiveness, assurance, and empathy. Furthermore, the comparison of the principles of Good University Governance according to several experts is presented in Table 2 below:
Table 2: Principles Comparison of Good University Governance Bases on Scholars
| No. | The Principles of Good University Governance | Scholars |
| 1 | Information disclosure (transparency), accountability, responsibility/responsiveness, independence, and fairness | (Nurlatifa et al., 2021) |
| 2 | Participation, rule of law, transparency, responsiveness, consensus-oriented, equality and inclusivity, effectiveness and efficiency, and accountability | (Yudianto et al., 2021) |
| 3 | Governance structure, autonomy, accountability, leadership, and transparency | (Martini et al., 2020) |
| 4 | Transparency, accountability (especially to stakeholders), responsiveness (participation), independence (in opinion and decision making), fairness, assurance of quality and relevance, effectiveness and efficiency, and non-profit. | (Hanum et al., 2021) |
Source: (Muhsin et al., 2020)
To realize World Class University at State Islamic Religious Universities, it is necessary to research the Effect of Good University Governance on the Quality of Public Service of State Islamic Religious Universities. By knowing this influence, University at State Religious Universities can focus more on realizing a World Class University with recommendations or improvements that must be made.
The research location is State Islamic Institutes (IAIN) Syekh Nurjati Cirebon, Indonesia, chosen with the following considerations:
- The only State Islamic Universities in Region III Cirebon, including Cirebon Regency, Cirebon City, Indramayu Regency, Majalengka Regency, and Kuningan Regency, and its location is very strategic because it is a border area of West Java and Central Java with the number of students in 2021 reached 8,000 spreads across the Faculty of Tarbiyah and Teacher Training, the Faculty of Usuluddin Adab and Da’wah, and the Faculty of Sharia and Islamic Economics;
- Bumi Cakrabuana on the Pasundan land level in the XIV century became the largest center for the development of Islamic da’wah and education in the archipelago; two significant figures, Sheikh Quro, and Sheikh Nurjati, carried out Islamic da’wah and education activities through Pesambangan Jati person. Peguron in Cirebon Nagari developed into a mosque led by Sunan Gunung Djati. Furthermore, the mosque became the center of da’wah, educational, social, cultural, and political activities.
2. Research Methods
The number of students at IAIN Syekh Nurjati Cirebon in 2022 is 11,399, but to determine the number of research samples, researchers focus on the student population of the Faculty of Tarbiyah and Teacher Training (FITK) IAIN Sheikh Nurjati Cirebon with the most majors and students, namely 5,188. The number of research samples was determined by the formula Slovin in Wahyudi (2017), at the error level (e) 10%; the formula is as follows:
n = 5188/ (1+5188. (0,1)2)
n = 5188/ 52,88
n = 98,11 (rounded to 99)
then the number of samples in this study was 100 students (exaggerated), with the determination of the research sample being done randomly. This research is a quantitative study and was conducted based on primary data. The list of answers to alternative statements in this study uses a Likert scale of 1 to 5 with a series of sequences: Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree. With the scoring criteria in Table 3
Table 3: List of Alternative Statements
| Statements | Statement Score | |
| Positive | Negative | |
| Strongly Agree | 5 | 1 |
| Agree | 4 | 2 |
| Neutral | 3 | 3 |
| Disagree | 2 | 4 |
| Strongly Disagree | 1 | 5 |
Source: (Sugiyono, 2016)
The questions in the questionnaire were direct questions and closed questions. The research instrument was divided into three parts: 1) General Information, 2) Good University Governance, and 3) Public Service Quality. The questionnaire was carefully designed by combining the literature and adopting several well-established questions from previous research, especially in the sections on Good University Governance (Directorate General of Higher Education, 2019) and Quality of Public Services (Parasuraman et al., 1991).
The data analysis had two parts: 1) general information analysis of a representative sample through frequency and percentage distributions, and 2) analysis of eight variables of good university governance and five variables of public service quality from Parasuraman. The variable of good university governance was a reflective latent variable, while the variable of public service quality was a formative latent variable.
3. Results and Discussion
Table 4: Respondent Profile
| No. | Descriptions | Frequencies | Percentages |
| A | Gender | ||
| 1 | Male | 36 | 36 % |
| 2 | Female | 64 | 64 % |
| B | Age | ||
| 1 | 18 years old | 12 | 12 % |
| 2 | 19 years old | 22 | 22 % |
| 3 | 20 years old | 46 | 46 % |
| 4 | 21 years old | 9 | 9 % |
| 5 | 22 years old | 6 | 6 % |
| 6 | 23 years old | 4 | 4 % |
| 7 | 25 years old | 1 | 1 % |
| C | Semester | ||
| 1 | Semester 2 | 19 | 19 % |
| 2 | Semester 4 | 62 | 62 % |
| 3 | Semester 6 | 11 | 11 % |
| 4 | Semester 8 | 8 | 8 % |
3.1 Descriptive Analysis
Table 5: Descriptive Analysis
| Variable | X | Y |
| mean | 3.68 | 3.69 |
| std | 1.03 | 1.10 |
| mode | 4 | 4 |
| min | 1 | 1 |
| max | 5 | 5 |
| Indicator | Mean | Standard Deviation | Median | Min | Max |
| X01 | 3.99 | 0.78 | 4 | 2 | 5 |
| X02 | 3.89 | 0.85 | 4 | 2 | 5 |
| X03 | 3.95 | 0.85 | 4 | 1 | 5 |
| X04 | 3.91 | 0.81 | 4 | 1 | 5 |
| X05 | 3.88 | 0.84 | 4 | 1 | 5 |
| X06 | 4.07 | 0.78 | 4 | 2 | 5 |
| X07 | 4.07 | 0.90 | 4 | 1 | 5 |
| X08 | 3.89 | 0.92 | 4 | 1 | 5 |
| X09 | 4.18 | 0.88 | 4 | 1 | 5 |
| X10 | 3.95 | 0.87 | 4 | 1 | 5 |
| X11 | 3.89 | 0.89 | 4 | 1 | 5 |
| X12 | 4.00 | 0.88 | 4 | 1 | 5 |
| X13 | 3.92 | 0.92 | 4 | 1 | 5 |
| X14 | 3.83 | 0.91 | 4 | 1 | 5 |
| X15 | 2.30 | 1.05 | 2 | 1 | 5 |
| X16 | 3.89 | 0.96 | 4 | 2 | 5 |
| X17 | 4.04 | 0.72 | 4 | 2 | 5 |
| X18 | 4.06 | 0.81 | 4 | 2 | 5 |
| X19 | 3.97 | 0.79 | 4 | 2 | 5 |
| X20 | 2.24 | 0.93 | 2 | 1 | 5 |
| X21 | 2.30 | 0.92 | 2 | 1 | 5 |
| X22 | 3.89 | 0.84 | 4 | 2 | 5 |
| X23 | 3.95 | 0.82 | 4 | 2 | 5 |
| X24 | 2.58 | 1.13 | 3 | 1 | 5 |
| X25 | 3.82 | 0.82 | 4 | 2 | 5 |
| X26 | 3.77 | 0.76 | 4 | 2 | 5 |
| X27 | 2.32 | 0.85 | 2 | 1 | 4 |
| X28 | 3.66 | 0.99 | 4 | 1 | 5 |
| X29 | 3.60 | 1.05 | 4 | 1 | 5 |
| X30 | 2.38 | 0.95 | 2 | 1 | 5 |
| X31 | 3.79 | 0.89 | 4 | 2 | 5 |
| X32 | 3.89 | 0.90 | 4 | 1 | 5 |
| X33 | 3.86 | 0.92 | 4 | 2 | 5 |
| X34 | 3.96 | 0.88 | 4 | 1 | 5 |
| X35 | 3.96 | 0.84 | 4 | 1 | 5 |
| X36 | 2.60 | 1.19 | 3 | 1 | 5 |
| X37 | 3.89 | 0.80 | 4 | 2 | 5 |
| X38 | 3.94 | 0.80 | 4 | 2 | 5 |
| X39 | 3.80 | 0.80 | 4 | 1 | 5 |
| X40 | 3.92 | 0.83 | 4 | 2 | 5 |
| X41 | 3.93 | 0.86 | 4 | 1 | 5 |
| X42 | 2.66 | 1.26 | 2 | 1 | 5 |
| X43 | 3.94 | 0.77 | 4 | 2 | 5 |
| X44 | 3.88 | 0.79 | 4 | 2 | 5 |
| X45 | 2.66 | 1.14 | 3 | 1 | 5 |
| X46 | 3.73 | 0.89 | 4 | 2 | 5 |
| X47 | 3.72 | 0.83 | 4 | 1 | 5 |
| X48 | 3.88 | 0.78 | 4 | 2 | 5 |
| X49 | 3.87 | 0.84 | 4 | 1 | 5 |
| X50 | 3.92 | 0.80 | 4 | 2 | 5 |
| X51 | 2.60 | 1.18 | 2 | 1 | 5 |
| X52 | 3.89 | 0.80 | 4 | 2 | 5 |
| X53 | 3.80 | 0.96 | 4 | 1 | 5 |
| X54 | 2.10 | 0.77 | 2 | 1 | 4 |
| X55 | 3.83 | 0.91 | 4 | 1 | 5 |
| X56 | 3.98 | 0.77 | 4 | 2 | 5 |
| X57 | 3.78 | 0.76 | 4 | 2 | 5 |
| X58 | 3.95 | 0.78 | 4 | 2 | 5 |
| X59 | 3.98 | 0.81 | 4 | 1 | 5 |
| X60 | 3.95 | 0.83 | 4 | 1 | 5 |
| X61 | 3.84 | 0.87 | 4 | 2 | 5 |
| X62 | 3.95 | 0.83 | 4 | 2 | 5 |
| X63 | 3.90 | 0.81 | 4 | 2 | 5 |
| X64 | 3.99 | 0.77 | 4 | 2 | 5 |
| X65 | 3.96 | 0.88 | 4 | 1 | 5 |
| X66 | 3.97 | 0.82 | 4 | 2 | 5 |
| X67 | 3.91 | 0.78 | 4 | 2 | 5 |
| X68 | 3.94 | 0.71 | 4 | 3 | 5 |
| X69 | 3.88 | 0.79 | 4 | 2 | 5 |
| X70 | 3.93 | 0.89 | 4 | 2 | 5 |
| X71 | 3.89 | 0.81 | 4 | 2 | 5 |
| X72 | 3.94 | 0.77 | 4 | 2 | 5 |
| Y01 | 4.03 | 0.83 | 4 | 2 | 5 |
| Y02 | 4.08 | 0.78 | 4 | 2 | 5 |
| Y03 | 4.14 | 0.85 | 4 | 2 | 5 |
| Y04 | 4.02 | 0.82 | 4 | 2 | 5 |
| Y05 | 3.81 | 0.94 | 4 | 1 | 5 |
| Y06 | 4.12 | 0.79 | 4 | 2 | 5 |
| Y07 | 4.05 | 0.85 | 4 | 2 | 5 |
| Y08 | 4.29 | 0.77 | 4 | 2 | 5 |
| Y09 | 3.95 | 0.89 | 4 | 1 | 5 |
| Y10 | 4.22 | 0.81 | 4 | 2 | 5 |
| Y11 | 4.13 | 0.81 | 4 | 2 | 5 |
| Y12 | 4.12 | 0.79 | 4 | 2 | 5 |
| Y13 | 4.13 | 0.83 | 4 | 2 | 5 |
| Y14 | 4.22 | 0.77 | 4 | 2 | 5 |
| Y15 | 4.06 | 0.87 | 4 | 1 | 5 |
| Y16 | 4.12 | 0.85 | 4 | 2 | 5 |
| Y17 | 4.10 | 0.79 | 4 | 2 | 5 |
| Y18 | 4.07 | 0.79 | 4 | 2 | 5 |
| Y19 | 4.02 | 0.80 | 4 | 2 | 5 |
| Y20 | 4.02 | 0.86 | 4 | 1 | 5 |
| Y21 | 4.07 | 0.82 | 4 | 2 | 5 |
| Y22 | 2.57 | 1.20 | 2 | 1 | 5 |
| Y23 | 3.99 | 0.77 | 4 | 2 | 5 |
| Y24 | 3.87 | 0.88 | 4 | 1 | 5 |
| Y25 | 2.23 | 0.97 | 2 | 1 | 5 |
| Y26 | 2.26 | 1.03 | 2 | 1 | 5 |
| Y27 | 3.86 | 0.92 | 4 | 2 | 5 |
| Y28 | 3.90 | 0.78 | 4 | 2 | 5 |
| Y29 | 4.00 | 0.78 | 4 | 2 | 5 |
| Y30 | 3.93 | 0.84 | 4 | 1 | 5 |
| Y31 | 3.93 | 0.91 | 4 | 1 | 5 |
| Y32 | 2.40 | 1.18 | 2 | 1 | 5 |
| Y33 | 3.83 | 0.94 | 4 | 1 | 5 |
| Y34 | 3.88 | 0.79 | 4 | 2 | 5 |
| Y35 | 3.88 | 0.82 | 4 | 2 | 5 |
| Y36 | 2.49 | 1.00 | 2 | 1 | 5 |
| Y37 | 4.00 | 0.75 | 4 | 2 | 5 |
| Y38 | 2.57 | 1.27 | 2 | 1 | 5 |
| Y39 | 3.77 | 1.02 | 4 | 1 | 5 |
| Y40 | 3.94 | 0.77 | 4 | 2 | 5 |
| Y41 | 2.64 | 1.19 | 3 | 1 | 5 |
| Y42 | 2.50 | 1.06 | 2 | 1 | 5 |
| Y43 | 3.90 | 0.88 | 4 | 1 | 5 |
| Y44 | 2.55 | 1.16 | 3 | 1 | 5 |
| Y45 | 3.91 | 0.78 | 4 | 2 | 5 |
| Y46 | 4.05 | 0.77 | 4 | 2 | 5 |
| Y47 | 2.49 | 1.20 | 2 | 1 | 5 |
| Y48 | 4.14 | 0.75 | 4 | 3 | 5 |
3.2 Inferential/ Inductive Analysis
After passing the screening stage, the researchers conducted a primary test by distributing questionnaires. Then, the researcher analyzed the questionnaire to filter the data that could be processed. The researcher finally used 100 respondents for the data, which was processed using SmartPLS 3.28 software.
Testing of the primary analysis requirements was carried out to ensure that the instruments used were suitable for measurement (valid and reliable). Testing with PLS begins with testing the measurement model (outer model) to test the construct validity and instrument reliability. The validity test was conducted to measure the ability of research instruments to measure what should be measured (Cooper, 2014; Namazi and Namazi, 2017). The construct validity test in the PLS indicator reflective model is carried out through convergent validity, discriminant validity, and Average Variance Extracted (AVE) tests. The reliability test was used to measure the consistency of the measuring instrument in measuring the concept, or it can also be used to measure the consistency of the respondent in answering the instrument. The instrument was said to be reliable if a person’s answer to the statement is consistent or stable over time. The reliability test in PLS can use the composite reliability method and Cronbach’s alpha (Abdullah and Hartono, 2015). The following was a discussion of the results of testing the measurement model (Outer Model) and structural measurement (Inner Model):
- Testing the Measurement Model (Outer Model)
- Convergent Validity
Convergent validity was the value of the loading factor on the latent variable with its indicators. Convergent validity was assessed based on the correlation between the item score/ compound score and the construct score calculated by PLS. The reflective measure was considered high if it correlated more than 0.70 with the construct to be measured. Below is a table of loading factors in which each indicator is calculated using PLS.
Table 6: Test Outer Loading
| X | loading | Y | loading |
| X01 | -0.726 | Y01 | -0.817 |
| X02 | -0.828 | Y02 | -0.798 |
| X03 | -0.865 | Y03 | -0.848 |
| X04 | -0.798 | Y04 | -0.839 |
| X05 | -0.852 | Y05 | 0.692 |
| X06 | -0.865 | Y06 | 0.746 |
| X07 | -0.763 | Y07 | 0.777 |
| X08 | -0.418 | Y08 | 0.750 |
| X09 | -0.784 | Y09 | 0.762 |
| X10 | -0.851 | Y10 | 0.773 |
| X11 | -0.777 | Y11 | 0.740 |
| X12 | -0.855 | Y12 | -0.879 |
| X13 | 0.798 | Y13 | -0.867 |
| X14 | 0.789 | Y14 | -0.876 |
| X15 | 0.820 | Y15 | -0.786 |
| X16 | 0.789 | Y16 | -0.840 |
| X17 | 0.898 | Y17 | -0.883 |
| X18 | 0.825 | Y18 | -0.904 |
| X19 | 0.839 | Y19 | -0.886 |
| X20 | 0.811 | Y20 | 0.752 |
| X21 | 0.804 | Y21 | 0.763 |
| X22 | 0.749 | Y22 | 0.180 |
| X23 | 0.241 | Y23 | -0.345 |
| X24 | -0.767 | Y24 | -0.759 |
| X25 | 0.783 | Y25 | 0.795 |
| X26 | 0.132 | Y26 | 0.781 |
| X27 | -0.769 | Y27 | 0.262 |
| X28 | 0.804 | Y28 | 0.792 |
| X29 | 0.626 | Y29 | 0.822 |
| X30 | 0.587 | Y30 | 0.766 |
| X31 | -0.792 | Y31 | 0.755 |
| X32 | -0.868 | Y32 | 0.782 |
| X33 | -0.840 | Y33 | -0.856 |
| X34 | -0.853 | Y34 | -0.786 |
| X35 | -0.893 | Y35 | -0.767 |
| X36 | 0.779 | Y36 | 0.774 |
| X37 | 0.786 | Y37 | 0.862 |
| X38 | 0.844 | Y38 | 0.797 |
| X39 | 0.292 | Y39 | -0.797 |
| X40 | 0.790 | Y40 | -0.470 |
| X41 | 0.810 | Y41 | 0.823 |
| X42 | 0.803 | Y42 | 0.804 |
| X43 | 0.769 | Y43 | 0.761 |
| X44 | 0.308 | Y44 | 0.831 |
| X45 | 0.780 | Y45 | -0.772 |
| X46 | -0.750 | Y46 | -0.915 |
| X47 | -0.806 | Y47 | -0.257 |
| X48 | -0.741 | Y48 | -0.895 |
| X49 | -0.738 | ||
| X50 | -0.857 | ||
| X51 | 0.631 | ||
| X52 | -0.718 | ||
| X53 | -0.859 | ||
| X54 | 0.817 | ||
| X55 | -0.684 | ||
| X56 | 0.857 | ||
| X57 | 0.785 | ||
| X58 | 0.836 | ||
| X59 | 0.855 | ||
| X60 | 0.797 | ||
| X61 | -0.725 | ||
| X62 | -0.746 | ||
| X63 | -0.840 | ||
| X64 | 0.789 | ||
| X65 | 0.786 | ||
| X66 | 0.393 | ||
| X67 | -0.817 | ||
| X68 | -0.474 | ||
| X69 | -0.820 | ||
| X70 | -0.709 | ||
| X71 | 0.803 | ||
| X72 | 0.845 |
Source: Output SmartPLS 3.28 results processed by researchers
From the results above, several indicators with outer values were below 0.7 (red color, so they were discarded in the model).
Table 7: Revised Outer Loading Test
| X | loading | Y | loading |
| X13 | 0.878 | Y06 | 0.843 |
| X14 | 0.799 | Y07 | 0.845 |
| X15 | 0.765 | Y08 | 0.820 |
| X16 | 0.884 | Y09 | 0.804 |
| X17 | 0.889 | Y10 | 0.863 |
| X18 | 0.863 | Y11 | 0.816 |
| X19 | 0.891 | Y20 | 0.816 |
| X20 | 0.775 | Y21 | 0.845 |
| X21 | 0.740 | Y25 | 0.719 |
| X22 | 0.842 | Y26 | 0.744 |
| X25 | 0.853 | Y28 | 0.850 |
| X28 | 0.889 | Y29 | 0.877 |
| X37 | 0.893 | Y30 | 0.826 |
| X38 | 0.902 | Y31 | 0.837 |
| X40 | 0.887 | Y32 | 0.726 |
| X41 | 0.883 | Y36 | 0.715 |
| X42 | 0.733 | Y37 | 0.917 |
| X43 | 0.863 | Y38 | 0.759 |
| X45 | 0.728 | Y41 | 0.820 |
| X54 | 0.750 | Y42 | 0.823 |
| X56 | 0.915 | Y43 | 0.761 |
| X57 | 0.837 | Y44 | 0.831 |
| X58 | 0.920 | ||
| X59 | 0.881 | ||
| X60 | 0.907 | ||
| X64 | 0.889 | ||
| X65 | 0.852 | ||
| X71 | 0.890 | ||
| X72 | 0.900 |
It can be seen that all indicators were valid because the loading value was > 0.7 (green).
-
- Discriminant Validity
Table 8: Discriminant Validity
| Cross Loadings | X | Y |
| X13 | 0.878 | 0.797 |
| X14 | 0.799 | 0.779 |
| X15 | 0.765 | 0.778 |
| X16 | 0.884 | 0.789 |
| X17 | 0.889 | 0.882 |
| X18 | 0.863 | 0.858 |
| X19 | 0.891 | 0.871 |
| X20 | 0.775 | 0.726 |
| X21 | 0.740 | 0.714 |
| X22 | 0.842 | 0.796 |
| X25 | 0.853 | 0.813 |
| X28 | 0.889 | 0.821 |
| X37 | 0.893 | 0.794 |
| X38 | 0.902 | 0.815 |
| X40 | 0.887 | 0.821 |
| X41 | 0.883 | 0.797 |
| X42 | 0.733 | 0.798 |
| X43 | 0.863 | 0.763 |
| X45 | 0.728 | 0.804 |
| X54 | 0.750 | 0.770 |
| X56 | 0.915 | 0.869 |
| X57 | 0.837 | 0.810 |
| X58 | 0.920 | 0.832 |
| X59 | 0.881 | 0.843 |
| X60 | 0.907 | 0.819 |
| X64 | 0.889 | 0.785 |
| X65 | 0.852 | 0.794 |
| X71 | 0.890 | 0.847 |
| X72 | 0.900 | 0.856 |
| Y06 | 0.833 | 0.843 |
| Y07 | 0.842 | 0.845 |
| Y08 | 0.768 | 0.820 |
| Y09 | 0.826 | 0.804 |
| Y10 | 0.814 | 0.863 |
| Y11 | 0.785 | 0.816 |
| Y20 | 0.803 | 0.816 |
| Y21 | 0.858 | 0.845 |
| Y25 | 0.608 | 0.719 |
| Y26 | 0.654 | 0.744 |
| Y28 | 0.804 | 0.850 |
| Y29 | 0.853 | 0.877 |
| Y30 | 0.804 | 0.826 |
| Y31 | 0.786 | 0.837 |
| Y32 | 0.657 | 0.726 |
| Y36 | 0.610 | 0.715 |
| Y37 | 0.914 | 0.917 |
| Y38 | 0.676 | 0.759 |
| Y41 | 0.721 | 0.820 |
| Y42 | 0.717 | 0.823 |
| Y43 | 0.809 | 0.761 |
| Y44 | 0.717 | 0.831 |
The cross-loading value was the most significant correlation between indicators and their latent variables.
-
- Average Variance Extracted (AVE)
Another method to assess discriminant validity is to compare the value of the square root of the average variance extracted (AVE) of each construct with the correlation between the construct and other constructs in the model. Suppose the square root value of the AVE for each construct is greater than the correlation value between the construct and other constructs in the model. In that case, it is said to have an excellent discriminant validity value Fornell and Larcker (1981) in Ghozali (2006). The formula for calculating AVE is as follows:
Referring to the study from (Fornell and Larcker, 1981; Imam Ghozali, 2014) stated that this measurement can measure the reliability of the latent variable component score, and the results are more conservative than composite reliability (ƿc). It is recommended that the AVE value should be greater than 0.50.
Table 9: AVE
| Average Variance Extracted (AVE) | |
| X | 0.729 |
| Y | 0.662 |
Source: Output SmartPLS 3.28 results processed by researchers
From the figure and table above, it can be seen that the AVE value was above 0.5 (green color). This means that all latent variables used in this study were valid because they met the recommended AVE value (>0.5).
-
- Composite Reliability and Cronbach Alpha > 0.7
In addition, to construct validity tests, construct reliability tests were also carried out as measured by composite reliability and Cronbach’s alpha from the indicator block measuring constructs. The following are the results of testing composite reliability and Cronbach’s alpha from Smart PLS:
Table 10: Composite Reliability and Cronbach Alpa
| Cronbach’s Alpha | Composite Reliability | |
| X | 0.986 | 0.987 |
| Y | 0.975 | 0.977 |
Source: Output SmartPLS 3.28 results processed by researchers
A construct was declared reliable if it has a composite reliability value above 0.70 and Cronbach’s alpha above 0.70. The results of the SmartPLS output in the table above showed that all constructs had a composite reliability value above 0.70 and Cronbach’s alpha above 0.70. Therefore, the construct had good reliability.
- Structural Model Testing (Inner Model)
The structural model is evaluated using R-square for the dependent variable and the path coefficient value for the independent variable, which is then assessed for significance based on the t-statistic value of each path.
-
- R Square
The value of R Square is the coefficient of determination on the endogenous construct. According to (Chin and Newsted, 1998), the value of R square is 0.67 (strong), 0.33 (moderate), and 0.19 (weak). The coefficient of determination (R square Adjusted) shows how much influence the influencing variable has on the affected variable.
Changes in the value of the R-square can be used to assess the effect of certain independent latent variables on the dependent latent variable and whether it has a substantive effect.
Table 11: R Square
| R Square | R Square Adjusted | |
| Y | 0.901 | 0.900 |
Source: Output SmartPLS 3.28 results processed by researchers.
Based on the table above, the R square value of each equation was already above 30 percent (0.3).
The value of r square of 0.901 means that the independent variable (X) could explain the dependent variable (Y) by 90 percent, the remaining 10 percent by other variables outside the model.
-
- Stone-Geisser Q-square test (Predictive Relevance)
Stone-Geisser Q-square test for predictive relevance and t-test and significance of the coefficients of structural path parameters. Q-square measures how well the model and its parameters generate the observed values. A Q-square value greater than 0 (zero) indicates the model has predictive relevance, while a Q-square value less than 0 (zero) indicates that the model lacks predictive relevance.
To calculate Q2, they can be used the formula:
Q2=1- (1-R12) (1-R22) …… (1-Rp2) …
Nilai Q2 = 1- (1-0.901) =0.901
Q2 = 0, 901
A q-square value greater than 0 (zero) indicates the model has predictive relevance.
-
- Effect Size (f2)
Table 12: f2
| f Square | Y |
| X | 9.142 |
The f-square value greater than 0.15 indicated that the model had predictive relevance.
-
- Goodness of Fit (gof)
The Goodness of Fit model was evaluated using the dependent latent variable R2 with the same interpretation as the regression. A model is good if the gof value is above 0.38.
= root (0.695*0.901)= root(0,626)= 0.791
Gof= 0.791, the gof was already big because it is above 0.38.
Hypotheses Testing
To test the hypothesis, the t-statistic value generated from the PLS output is compared with the t-table value; the PLS output estimates the latent variable, a linear aggregate of the indicators. The test criteria with a significance level of (a) 5% for a one-way test (positive/ negative effect) are determined as follows:
From the results of t table and t arithmetic, the comparison can be seen as follows:
- If t count > t table (1,64), then H0 is rejected and H1 is accepted.
- If t count < t table (1,64), then H0 is accepted, and H1 is rejected.
Meanwhile, to see the significant effect between variables for the one-way test, it can be seen on the following criteria:
- If the prob-value sig/2 < 0.05, then there is a significant effect.
- If the prob-value sig/2 > 0.05, then the effect is not significant.
| Influence | Original Sample (O) | T Statistics (|O/STDEV|) | P Values | Conclusion |
| X -> Y | 0.949 | 91.141 | 0.000 | Positive Influence |
Y=0.949 X
X directly and significantly positively affected Y with a coefficient of 0.949, t stat = 91.141 > t table = 1.64, and prob-value = 0.0000 < alpha = 0.05. This means that a 1-point increase in PPIK will increase KLK by 0.949 points assuming other variables are constant.
In complementing the result of inferential statistical analysis, this research also concluded the result of observations and interviews with several respondents. Based on the observations and interviews of researchers with several respondents, rule enforcement is a form of implementing Good University Governance. However, universities had a background experience of gradually enforcing rules; from the beginning, those rules had not been formed until the formation of rules, from simple problems and rules to complex things. All that changes gradually and is dynamic. The staffs value most when they find out that the management can make all the university staffs comply with the rules. This opinion fits the (Farazmand, 2022)statement that the constitution is a sketch and the most crucial structural dimension in sound governance. The journal supporting this research (NAZ, 2009) also concluded that e-governance can improve customer service and satisfaction.
4. Conclusion
According to this research, the implementation of sound university governance principles significantly and directly affected public service quality. The right actions should back correct principles. Public policies indicate the social, ethical, and economic principles upon which laws are based. Governance is governing or using force, shared values or personal traits, or some blend of the three, to cause people to do certain things. Without good public policy and good governance, the theory is backed by action. Officials cannot govern well. It is suggested to do qualitative research on this topic so the State Religious Universities will gain more information about the topics.
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