Keywords

1 Introduction

Over the past decades, one of the fastest growing and competitive industries around the world is the higher education industry. The higher education industry is a very important industry for a country’s economic progress [1]. Good management practices and high-level performance are needed to every higher education organisation. The higher education industry currently faces considerable strategic challenges to become more responsive to students’ demands by simultaneously improving efficiency and quality. In recent years it has become apparent that modern HEI place more emphasis on students’ feedback on issues related to satisfaction levels as well as service quality [2]. More and more universities around the world recognize the importance of students’ satisfaction as a strategic tool and determinant of long-term sustainability and economic growth. Quality and university students’ satisfaction are key drivers of university performance. University performance is an important component in determining the successful organization and operation of a HEI. The quality management approach at higher education services discloses important particularities, creating a symbiosis between prospects, theoretical concepts, and the necessity of fundamental decisions to be undertaken [3].

As the production and delivery of a service heavily depends on the people who provide those, fluctuations in the quality of the service will be evident [4]. The difference, for instance, between two HEI probably lies in the service quality that academic and administrative staff are delivering to students. Equally important is the fact that services which offered to the university students cannot be “checked for conformance with quality standards before reaching the customer as they are usually produced and consumed under real-time conditions”. Inconsistency of services, therefore, can only be penetrated through evaluating university students’ satisfaction [5]. The main objective of this study is to determine students' satisfaction with the services which they receive from a public university in Greece and the factors that influence their satisfaction.

2 Literature Review

The concept of university students’ satisfaction, has puzzled researchers since the beginning of the twentieth century. The opportunity to understand the causes of university students’ behaviour and predict future outcomes presents itself as the ideal proposition for any university across the globe [6]. Although the definitions of student’s satisfactions in higher education vary depending on the aspect the researcher has chosen to focus on, researchers tend to agree that the term refers to the attitude of the students towards to the services which receive from the university. The quality of a HEI services provided, are determined indirectly but also essentially by the nature of the services provided. It is therefore to be expected that student’s satisfaction with the quality of a HEI service will be determined indirectly by various factors [7]. At the same time, a HEI has its own special features which, with all the above, affect the final result and determine students' satisfaction. Such factors can be about building installations, lecture halls, hall equipment, laboratories, variety of courses, semester workload, study material, internet access and administrative services, secretariat operating hours, ease of access to student services [8].

Students’ satisfaction in higher education means that students need is met, services are considered satisfactory and therefore the experience is positive. It is especially important to denote that the management of a HEI should use measuring tools for university students’ satisfaction and loyalty. All the above satisfaction dimensions give the service, a form of flexibility and subjectivity the quality of which is difficult to be examined or judged individually either by the recipient of the service (student) or by even the service provider (HEI) [9]. Thus, quality students’ service, is a necessity for the satisfaction of increasingly demanding customers and the build-up of closer and more enduring relationships with them. Moreover, students; satisfaction can be used as “a competitive weapon in order to differentiate a service and gain competitive advantage” [10]. Concerning university students’ satisfaction, there are several scientific papers that try to discover the factors associated with students’ satisfaction within an educational institute. As shown, the factors related to university students’ satisfaction are clearly multi-factorial and differ from person to person, and from university to university [11]. Browne et al. [12] in his research found that total satisfaction from a HEI was driven by a student’s assessment of the quality of the course and the curriculum. Mai [13] finds that the impression of the quality of education, the lecturers’ responses towards complaints/suggestions and the availability of study areas for students have a positive influence on university student total satisfaction. The results of a study by Kärnä et al. [14] find that University facilities, and the management of these facilities play an important role on university student total satisfaction. Wach et al. [15] measured university students’ satisfaction using the following dimensions: the content of learning, the conditions of learning and the personal coping with learning.

3 Methodology

For the purposes of this research, an electronic questionnaire was used for data collection. The survey ran during October to November 2021. A total of 387 questionnaires were collected from students at the University of West Attica, Greece—Business Administration Department. In this paper, university students’ satisfaction criteria and subcriteria are selected based on an extensive review of the relevant literature [16,17,18,19,20]. The main criteria which selected to determine students’ satisfaction were associated with Study Program, Academic Staff, Infrastructures, Secretariat, Web page. For each of these criteria, a number of sub-criteria were selected. The satisfaction criteria and subcriteria are provided in Table 1.

Table 1 University students’ satisfaction criteria and subcriteria
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The satisfaction survey results were based on MUSA's multi-criteria model (Multi-criteria Satisfaction Analysis). The Multi-criteria Satisfaction Analysis (MUSA) method was used to measure customer satisfaction. The technique is an ordinal–regression-based approach used to assess a set of collective satisfaction functions so that the global satisfaction criterion becomes as consistent as possible with customers’ judgments [21]. This method inferred an additive collective value function Y* and a set of partial satisfaction (value) functions Xi*, given customers’ global satisfaction Y and partial satisfaction Xi according to the i–th criterion (ordinal scaling). The main research objective was to achieve the maximum consistency between the value function Y*and the customers’ judgments Y. Based on the modelling of preference disaggregation approach [22], the ordinal regression equation was termed as follows:

$$ \left\{ {\begin{array}{*{20}l} {{\text{Y}}^{*} = \sum\limits_{{{\text{i}} = 1}}^{{\text{n}}} {{\text{b}}_{{\text{i}}} {\text{x}}_{{\text{i}}}^{*} } } \hfill \\ {\sum\limits_{{{\text{i}} = 1}}^{{\text{n}}} {{\text{b}}_{{\text{i}}} = 1} } \hfill \\ \end{array} } \right. $$
(1)

where \({\tilde{\text{Y}}}^{*}\) represents the estimation of the global value function, n represents the number of criteria, bi is a positive weight of the i–th criterion, σ + and σ − are the overestimation and the underestimation errors, respectively, and the value functions Y* and Xi are normalized in the interval [0, 100]. The global and partial satisfaction Y* and Xi* are monotonic functions normalized in the interval [0, 100]. Thus, in order to reduce the size of the mathematical program, removing the monotonicity constraints for Y* and Xi*, the following transformation equations were utilized:

$$ \left\{ {\begin{array}{*{20}l} {{\text{z}}_{{\text{m}}} = {\text{y}}^{{ * {\text{m + 1}}}} - {\text{y}}^{{*{\text{m}}}} } \hfill & {{\text{for m}} = 1,2 \ldots {\text{a}} - 1} \hfill \\ {{\text{w}}_{{{\text{ik}}}} = {\text{b}}_{{\text{i}}} ({\text{x}}_{{\text{i}}}^{{ * {\text{k + 1}}}} - {\text{x}}_{{\text{i}}}^{{ * {\text{k}}}} )} \hfill & {{\text{for k}} = 1,2, \ldots {\text{a}}_{{\text{i}}} - 1\;\upkappa \upalpha \upiota \,{\text{i}} = 1,2, \ldots ,{\text{n}}} \hfill \\ \end{array} } \right. $$
(2)

where y*m is the value of the ym satisfaction level, xi*k is the value of the xik satisfaction level, and α and αi are the number of global and partial satisfaction levels. According to the aforementioned definitions and the assumptions, the basic estimation model can be written in alignment with the following linear program formulation:

$$ \left\{ {\begin{array}{*{20}l} {[\min ]{\text{F}} = \sum\limits_{{{\text{j}} = 1}}^{{\text{M}}} {\sigma_{{\text{j}}}^{ + } + \sigma_{{\text{j}}}^{ - } } } \hfill \\ {\text{under the constraints:}} \hfill \\ {\sum\limits_{{{\text{i}} = 1}}^{{\text{n}}} {\sum\limits_{{{\text{k}} = 1}}^{{{\text{t}}_{{{\text{ji}}}} - 1}} {{\text{w}}_{{{\text{ik}}}} } - \sum\limits_{{{\text{m}} = 1}}^{{{\text{t}}_{{\text{j}}} - 1}} {{\text{z}}_{{\text{m}}} - \sigma_{{\text{j}}}^{ + } + \sigma_{{\text{j}}}^{ - } = 0\forall {\text{j}} = 1,2, \ldots ,{\text{M}}} } } \hfill \\ {\sum\limits_{{{\text{m}} = 1}}^{{{\text{a}} - 1}} {{\text{z}}_{{\text{m}}} = 100} } \hfill \\ {\sum\limits_{{{\text{i}} = 1}}^{{\text{n}}} {\sum\limits_{{{\text{k}} = 1}}^{{{\text{a}}_{{\text{j}}} - 1}} {{\text{w}}_{{{\text{ik}}}} } = 100} } \hfill \\ {{\text{z}}_{{\text{m}}} ,{\text{w}}_{{{\text{ik}}}} ,\sigma_{{\text{j}}}^{ + } ,\sigma_{{\text{j}}}^{ - } \ge \forall {\text{m}},{\text{i}},{\text{k}},{\text{j}}} \hfill \\ \end{array} } \right. $$
(3)

where M is the number of customers, n is the number of criteria, and xi * j, y * j are the jth level on which variables Xi and Y were estimated. Τhe method involves three main steps: (1) identifying the criteria that influence satisfaction, (2) collecting data on the perceived levels of satisfaction for each criterion, and (3) analyzing the data using mathematical techniques to generate an overall satisfaction score and identify the most influential criteria.

4 Results and Discussion

The results provide evidence about the students’ global satisfaction in a HEI. Based on Fig. 1 the students seem to be very satisfied (86.3%) with the services they receive from the university.

Fig. 1
A line and a bar graph of global satisfaction function. The line graph plots value versus total satisfaction scale. It is as follows. Dissatisfied, 71.04. Neutral, 77.85. Satisfied, 84.36, very satisfied, 100. The bar graph plots satisfaction percentage versus methods with MUSA 1 at 86.3.

Global satisfaction function

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Furthermore, based on Fig. 2, we see that the most important criterion for students’ satisfaction is this of the “web page” (35.97%), followed by the criteria of “study program” with a percentage of 30.6%, the “academic staff” (14%), and the “infrastructures” (10.22%). Finally, the criterion with the lowest performance is this of “secretariat” (9.22%).

Fig. 2
A horizontal bar graph of satisfaction. It plots criteria versus weight. The bars are for MUSA 1. The data is as follows. Web page, 35.97. Secretariat, 9.22. Infrastructures, 10.22. Academic staff, 14. Study program, 30.6.

Satisfaction criteria weights

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Based on Fig. 3, it is obvious that the university students are totally satisfied regarding the “web page” quality (93.09%), and the “study program” (89.77%), while they are also very satisfied with the criterion of “academic staff” (80.76%). On the other hand, university students are not very satisfied with the criteria of “infrastructures” (69.64%) and the “secretariat” (57.04%).

Fig. 3
A bar graph of customer satisfaction. It plots satisfaction in percent versus criteria. The data is as follows. Study program, 89.77. Academic staff, 80.76. Infrastructures, 69.64. Secretariat, 57.04. Web page, 93.09.

Customer satisfaction with the main criteria

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The results of the action diagram (Fig. 4) indicate that the strong points of the ser-vices which offers the business administration department are the “web page” and “study program”. The criteria “web page” and “study program” are located in the leverage opportunity area of the action diagram, which means that these criteria are of high performance and importance. On the other hand, according action diagram the weak points of the services which offers the department to the students are the “infrastructures” and “secretariat”. The criteria “infrastructures” and “secretariat” are located in the Status quo area of the action diagram, which means that these criteria are of low performance and importance.

Fig. 4
A 4 quadrant graph of satisfaction criteria. It plots satisfaction versus weights. Web page has the highest satisfaction and weights, and lies in quadrant 1 slightly above study program. Academic stuff lies in quadrant 2 and has a low satisfaction. Infrastructures and secretariat have negative values.

Satisfaction criteria action diagram

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5 Conclusions

In view of the foregoing analysis, we may terminate that the measurement of students’ satisfaction in higher education is a strategic tool of university long-term sustainability and growth. The provision of high-quality services is ultimately to be understood as being a priority that is of the utmost importance for the sustainability of a competitive university. Thus, the provision of top-quality services over time is basically a kind of investment in the linkage between the customer of the service, but also the provider so that the relationship between them is founded on strong-fundamental values such as trust, faith and loyalty. All the above are essentially confirmed by the fact that the provision of high-quality services and the maintenance of it has become more subjects of research, study and analysis. There are quality systems assurances that are based on the features that are essential for a service be considered of high quality. Thus, the student’s satisfaction and the service quality in higher education industry is now very composite and is influenced by many factors.