A model proposal for measuring service quality of eduaction with fuzzy rule-based approach and fuzzy ranking and an application

Abstract

In today’s understanding, the universities are considered as service providers besides their institutional functions. Because the universities shape the future of the country via the services they provide, it is a necessity that their service quality must be assessed by using scientific analyses, and their service quality must be improved based on such scientific findings. The Generation Z, whose members are currently receiving university education carries unique features that distinguish them from the previous generations. When this fact is considered, it is understood that the constant research and monitoring of the learning environment of the Generation Z is important. In this study, as a result of a detailed literature search, a scale consisting of 7 dimensions and 36 indicators was developed in order to measure the higher education service quality of the Z generation. The validity and reliability tests of this scale are completed via the convergent and divergent validity analyses, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Because the answers provided to the surveys reflect the personal evaluation of the participants, the Fuzzy Logic is employed, and the study is conducted by using the fuzzy modelling and fuzzy ranking. As a result of this study, the General Satisfaction Index is created, and improving recommendations are carried out based on the scores.

Keywords

Service quality fuzzy logic artificial intelligence higher education generation-z

1 Introduction

In the past, when the first production activities started the concept of quality emerged and this quality concept was defined differently in time depending on the technological advancements, management related differences, changes in the demands and perceptions of the consumers, and competition environment. Quality is taking the demands of the customers into consideration currently and in the future and satisfying the needs [20]. Besides the quality concept, different methods are devised in order to control quality and measure quality. In the services sector that grows day by day, the most important factor that distinguishes one business from another is its ability to provide better and more quality service than its competitors. When we look at the definitions of the concepts of service, quality and service quality in the literature, although there is no common concept, producing quality service plays an important role in the success of service providers [65]. Services defined as all kind of activities that meet the needs fast, satisfactorily, and synchronous referring to consumption coinciding with production [3]. The service concept is not related to only one type of business group, but it is related to all institutions that produce service [12]. The role of education in increasing life quality for the society is continuously increasing. When the education system is not high quality, its effects are seen at all sectors. This indicates how high the responsibility of the higher education institutions is. Service quality at the higher education institutions must be considered as a subject that is not only related to the universities; but also, employers, and even the whole of society. Higher education service quality can be managed by assessing all dimensions of quality in a system as a whole [51]. The higher education system consists of dynamic factors that affect the process. Features related to students, teaching staff, course contents and schedules, infrastructure and support services are the inputs; applications are the processes, and success is the output. We can see the quality of the system by having a look at the quality of the graduates and their level of success in the business life. The thoughts on the success of the institution will be more positive depending on the results of the outputs. When the thoughts on an institution are positive, the prestige of that institution will be higher, it will be preferred more, and more skilled students will be accepted into that institution. Moreover, when the input-process-output system is supported by feedback, positive success will be inevitable. When this mentioned process is applied for all input factors, the quality of higher education will rise continuously. The service sector and quality concept at the service sector are evaluated within the literature in a comprehensive way regarding different fields. Compared to the earlier periods economic and social mobility, the exchange speed of information changes the expectations and needs of the people. This situation creates a difference between today’s people from the people of earlier or later times, and the concept of generation emerges.

Generation Z individuals, who generally include those born in 1995 and later, are intelligent and resourceful individuals who can easily access information, use the data they have learned by blending them with each other, and are intertwined with technology. Generation Z will create the employees of the future and the customers of the future. In this context, they are very important in terms of shaping the future. Generation Z will play a major role in electing political authority today and some in the future [76].

One of the advantages of our study is that the scales are used with a viewpoint suitable for the Generation-Z, who have different qualities, expectations and goals from all previous generations. In this study, Generation- Z was examined in detail and in this context, “Generation-Z Higher Education Service Quality Scale (ZGEN-QUAL)” was developed. Almost all the events faced by human beings in the world are complex. This complexity generally arises from uncertainty, certainty, or inability to decide [30]. The concept of fuzzy logic is a logic system that overlaps with people’s ability to think in imprecise terms. The service quality concept of the universities emerges as the result of the interaction among the students, academic personnel, and administrative personnel. This situation makes the assessments on the quality fuzzy and makes achieving exact results harder [5]. For this reason, fuzzy logic approach was used in the study. With this study we bridge the gap in decreasing subjectivity of student evaluations on satisfaction points and we have a more objective evaluation advantage of student satisfaction.

Apart from the fact that the study is conducted on the Generation-Z, the other innovation point is that the values obtained by fuzzy ranking are weighted and included in the general index score calculation.

The study is organized as follows: In the second section, a comprehensive literature review is presented. In the third section, the methodology of the study is revealed, and this is shown with a process diagram. In this section, data acquisition and statistical analyses were carried out. In the fourth section, the data were analysed with fuzzy logic approach. In the last section, the findings and results of the study are shown.

2 Literature review

In the literature regarding service quality measurement, we frequently encounter two methods: “Service Quality (Servqual)” developed by Parasuruman et al [50] and “Service Performance (Servperf)” developed by Cronin and Taylor [17]. These scales are used for quality measurement in general meaning. Higher education service quality measurement has some different features than service quality measurement at the other sectors [67]. It is seen that studies focusing on the measurement of higher education service quality in the literature are grouped under two main headings [75]. i. Studies based on Servqual and Servperf and ii. Scales developed to measure higher education service quality.

Kocapinar [33] gathered the dimensions used in the study under the headings of non-educational service quality and service quality dimensions related to education. Ozgul and Devebakan [49], Donlagic [21], Shekarchizadeh and Rasli [53], Celik [14], Dursun [22], Ong and Nankervis [48], Cerri [15], Calvo-Porral et al. [13], Yousapronpaiboon [71], Turkel [61], Aytac [8], Ayaz and Karakaya [47], Dag [19], Alsheyadi and Albalushi [5], Nojovan et al. [46] used use five dimensions as: reliability, tangibility, responsibility, security and empathy.

Yilmaz et al. [69], Bayrak [9], Soganci [56], Yuce [72], Aygun [7], Galeeva [24], Rashid and Rahman [40] used responsibility, physical characteristics, qualifications, assurance, attitude and communication, lecturer, curriculum, material, physical environment, student’s approach, interest, security, learning and teaching environment in their study. Although some dimensions were revised for higher education service quality, these measures are not mainly directed to measure the service quality in the higher education service quality.

Table 1 shows the scales developed in higher education and the dimensions used in these scales. Kokoc and Ersoz [34] conducted detailed research on all scales and explained them in their study.

Table 1
Higher education service quality scales [34]

Author Scale Dimensions

Abdullah [3] Bektas and Uluturk [10] HedPERF Academic Aspects, Non-Academic Aspects, Reputition, Programs Issues, Access, Understanding

Mahapatra and Khan [39] EduQUAL Learning Outcomes, Responsiveness, Physical Facilities, Personality Development, Academics

Sahin [52] EFÖMÖ Academic Personel, Consulting, Administration, Resources, Computer Facilities, Curriculum

Yildiz and Kara [67] PESPERF Academic Service, Empathy, Accessibility

Sultan and Wong [57] PHEd Dependability,Effectiveness, Capability, Efficiency, Competencies, Assurance, Unusual Situation Management, Semester and Syllabus

Tsinidou et al. [60] HQAA Academic Staff, Administration Services, Library Services, Curriculum Structure, Location, Infrastructure, Carrier Prospects

Annamdevuta and Bellamkonda [6] HiEdQUAL Teaching and Course content, Administrative Services, Academic Facilities, Campus Infrastructure, Internationalization, Support Services

Icli and Anil [29] HedQUAL Academic Quality, Administrative Service Quality, Library Services Quality, Quality of Providing Career Opportunities, and Supporting Services Quality

Hernandez and Ibara [27] SERVQUALITC Reliability, Responder Capacity, Security, Empathy, Physical Properties

Amin Y.Noaman et al. [42] HEQAM Curriculum, Staff, Career Prospects, Infrastructure, E-Services, Library Services, Management Services, Location

Khawaja Fawad Latif et al. [36] HiEduQUAL Teacher Quality, Administrative Services, Knowledge Services, Activities, Continuous Improvement, Leadership Quality

Zhoc et al. [74] HESES Academic Engagement, Coginitive Engagement, Social Engagement with Peers,Social Engagement with Teachers, Affective Engagement

Teeroovengadum et al. [59] Yılmaz [70] HESQUAL Administrative Quality, Support Facilities Quality, Core Educational Quality, Transformative Quality, Physical Enviroment Quality

Marimon et al. [41] UnivQUAL Curriculum, Skills Development, Services and Facilities

Abbas [1] HEISQUAL Teachers’ Profile, Curriculum, Infrastructure and Facilities, Management and Support Staff, Employment Quality, Safety and Security and Students’ Skills Development.

Gokcen and Gok [26] DE-SERVQUAL E-learning Environment,Trust, Accesibility, Enthusiasm

Author	Scale	Dimensions
Abdullah [3] Bektas and Uluturk [10]	HedPERF	Academic Aspects, Non-Academic Aspects, Reputition, Programs Issues, Access, Understanding
Mahapatra and Khan [39]	EduQUAL	Learning Outcomes, Responsiveness, Physical Facilities, Personality Development, Academics
Sahin [52]	EFÖMÖ	Academic Personel, Consulting, Administration, Resources, Computer Facilities, Curriculum
Yildiz and Kara [67]	PESPERF	Academic Service, Empathy, Accessibility
Sultan and Wong [57]	PHEd	Dependability,Effectiveness, Capability, Efficiency, Competencies, Assurance, Unusual Situation Management, Semester and Syllabus
Tsinidou et al. [60]	HQAA	Academic Staff, Administration Services, Library Services, Curriculum Structure, Location, Infrastructure, Carrier Prospects
Annamdevuta and Bellamkonda [6]	HiEdQUAL	Teaching and Course content, Administrative Services, Academic Facilities, Campus Infrastructure, Internationalization, Support Services
Icli and Anil [29]	HedQUAL	Academic Quality, Administrative Service Quality, Library Services Quality, Quality of Providing Career Opportunities, and Supporting Services Quality
Hernandez and Ibara [27]	SERVQUALITC	Reliability, Responder Capacity, Security, Empathy, Physical Properties
Amin Y.Noaman et al. [42]	HEQAM	Curriculum, Staff, Career Prospects, Infrastructure, E-Services, Library Services, Management Services, Location
Khawaja Fawad Latif et al. [36]	HiEduQUAL	Teacher Quality, Administrative Services, Knowledge Services, Activities, Continuous Improvement, Leadership Quality
Zhoc et al. [74]	HESES	Academic Engagement, Coginitive Engagement, Social Engagement with Peers,Social Engagement with Teachers, Affective Engagement
Teeroovengadum et al. [59] Yılmaz [70]	HESQUAL	Administrative Quality, Support Facilities Quality, Core Educational Quality, Transformative Quality, Physical Enviroment Quality
Marimon et al. [41]	UnivQUAL	Curriculum, Skills Development, Services and Facilities
Abbas [1]	HEISQUAL	Teachers’ Profile, Curriculum, Infrastructure and Facilities, Management and Support Staff, Employment Quality, Safety and Security and Students’ Skills Development.
Gokcen and Gok [26]	DE-SERVQUAL	E-learning Environment,Trust, Accesibility, Enthusiasm

Abdullah [3] developed a new measurement tool, “Higher Education PERFormance-only” (HEdPERF), which deals with the unique dimensions that determine quality in the higher education sector. HedPERF consists of 41 sub-factors and 6 dimensions. In the scale, not only the academic components, but also the characteristics of the service environment where the student spends time are taken into consideration. Bektas and Uluturk [10] adapted this study for another sample and analyzed its validity and reliability.

Annamdevula and Bellamkonda [6] developed a new tool called “Higher Education Quality (HiEdQUAL)”, which covers various service dimensions from the point of view of students who are considered as primary customers, to evaluate the service quality in the higher education sector. 27 sub-factors and 5 dimensions were used in the study. A significant relationship was found with the dimensions considered to have a significant positive effect on students’ overall perceived service quality.

Teeroovengadum [59] used HESQUAL scale, which consists of 5 dimensions. In this study, the transformative quality of higher education on students, that is, the improving quality of university on students, was evaluated as a measurement dimension, unlike previous studies.

Noaman et al. [45] propose an advanced higher education quality assessment model (HEQAM) that can be applied to improve university services in their study. 8 dimensions and 53 sub-factors were used in the model. With the help of AHP, the model criteria were prioritized and weighted.

The aim of the study by Latif et al. [36] is to develop and validate the HiEduQual (Higher Education Service Quality) structure to measure the level of service quality in higher education institutions. Since this study focused on inputs from students, parents, teachers and employers to develop a reliable and valid scale to measure service quality in higher education institutions, it was evaluated that future studies could focus on different groups.

Abbas [1] developed the HEISQUAL scale to evaluate the quality of service in higher education from the students’ perspective, Yıldız and Kara [67] developed PESPERF (Physical Education and Sports Sciences PERFormance) to measure the service quality in the school of physical education and sports sciences; Sahin [52] used the EFÖMÖ (Education Faculty Student Satisfaction Scale) to measure the level of satisfaction with the service received by the students studying at the faculty of education; Hernéndez and Ibarra [25] developed scales called SERQUALITC to evaluate the service quality perceived by technology institute students; Mahapatra and Khan [39] developed EDUQUAL (EDUcation QUALity) to evaluate service quality in technical education.

In the literature, it has been seen that fuzzy logic is used in studies to measure the quality of higher education.

Darvish et al. [18] used the fuzzy logic method in statistics to evaluate the satisfaction of the students at the university. Gagliardi [23] used 3-dimensional scale and fuzzy logic approach to determine satisfaction in higher education institutions in Albania. Darsono and Eduardo [43] used fuzzy Mamdani method to measure students’ satisfaction with academic services. Kom et al. [35] used fuzzy c-means method to see the effect of online learning process on student satisfaction in his study. Vera [62] developed a student satisfaction analysis system using fuzzy logic to reveal administrative services and student satisfaction. Mendonca et al. [42] developed a fuzzy cognitive map that can understand concepts and causal relationships in order to evaluate students’ perceptions of quality, thus identifying positive and negative points that affect the level of satisfaction. Najib and Afida [44] developed a fuzzy logic inference system for students’ satisfaction in distance education. Wang and Tseng [64] conducted a fuzzy importance-performance analysis to measure students’ perceptions of satisfaction with universities in Taiwan in the international arena. Siham et al. [55] developed a model by combining fuzzy inference system, support vector machines and natural language processing techniques in order to understand and analyze students’ thoughts about the university and included the emotion factor in the analysis. Lubis et al. [38] used 5-dimensional scale with the fuzzy servqual method to measure the satisfaction of the students towards the campus. Yifan et al. [68] in this study, a hybrid evidential reasoning algorithm was developed by combining the heuristic fuzzy set and multi-criteria decision-making method to measure higher education service quality.

3 Research methodology

The process of the study carried out in order to determine the service quality expectations of the Generation-Z and to develop the quality improvement model proposal is shown in Fig. 1. In this study, “Generation-Z Higher Education Service Quality Scale (ZGEN-QUAL)”, which is developed as a result of data collection with comprehensive literature review and interviews with the students and expert opinions. review and interviews with the students and expert opinions.

Fig. 1

Process diagram.

3.1 Data collection method

The first and the most important step while developing a scale is determining the features and limitations of the concept or structure of the subject of measurement. This step is hard and duration is long and the weakness in this step directly affects the following steps. Therefore, the first step must be focused well, and enough time must be spent on it.

The scale developed must be able to measure what factors comprise the service quality at the higher education institutions, at what factors the institution performs well, and at what factors the institution can improve its performance.

In other words, the indicator pool must not exceed the conceptual framework. ZGEN-QUAL Scale is designed in a way that has 7 dimensions and 36 indicators. The dimensions used are: “Academic Services (A)”, “Innovative Teaching Approach and Method (B)”, “Administrative Services (C)”, “Information System Services (D)”, “Adequacy of University Facilities and Social Life (E)”, “Career Planning (F)”, “Reputation (G)”. Indicators of the scale are levelled by the 5 Point Likert [75].

Firstly, the number of samples that must be used in the research is calculated via the Equation (1) [66]. In the equation the representations are as follows: n: Sample Size, N: Population Size, p: Encounter Rate of Research Unit in the Population, q: Non-Encounter Rate of Research Unit in the Population, z: Z Value, d: Sampling Error Percentage. When N: 24603, p and q: 0.50. z:1.96 and d: 5% are put into equation the sample size is required as 379. The sample size is re-calculated by using the program given at “http://www.surveysystem.com” and the same result is achieved. Moreover, the sample size is more than 10 times of the total indicators used in the Generation- Z Higher Education Service Quality Scale. $n = \frac{N . p . q . z^{2}}{p . q . z^{2} + (N - 1) . d^{2}}$ (1)

According to demographic analysis, 45.4% of students are male, 54.6% are female. 39.9% of the students are 1st grade, 32.8% are 2nd grade, 11.9% are 3rd grade, 15.4% are 4th grade. In addition, 12.7% of students are from Faculty of Science-Literature, 54.4% from Faculty of Engineering and Architecture,

9.4% are from Faculty of Health Sciences, 23.5% are from Faculty of Social Sciences. As another finding, the average age of the students is calculated as 20.375. For the sample to be homogeneous; A questionnaire was applied to 1-2-3-4th grade students in 8 different academic departments in 6 faculties, paying attention to the female-male ratio. In this vein, face-to-face surveys are conducted with 412 students of a state-funded university in order to test the validity and reliability of the scale that has 7 dimensions and 36 indicators. Among these 412 surveys, 401 are included in the study because 11 survey forms do not satisfy the required criteria because of blank answers.

3.2 The analysis of data and findings

In this study, the analysis of data is conducted by using the R Project software [58]. During the analysis process, psych and lavaan packages of the R software are used. In order to determine the sub-dimensions and main structure of the data matrix Exploratory Factor Analysis (EFA) is applied [73]. Kaiser-Meyer-Olkin (KMO) sample sufficiency statistics is investigated. Correlation regarding the variables in the data set is tested via the Bartlett globality test. In the structure validity studies, determining the dimension number of the developed measuring instrument requires making critical decisions, and these decisions are based on several techniques and methods. In order to make the structure studies properly, the researchers must use techniques and methods that have statistical background, rather than making their own decisions. For that reason, Horn’s Parallel Method, which is frequently used in the latest studies, and said to be better compared to other EFA methods is used for determining the number of dimensions [28]. EFA is applied firstly for scale development and structure validity determination [11]. To test the inner consistency of the scale that is result of the EFA, Cronbach Alfa reliability analysis is conducted. And in order to test validity. Confirmatory Factor Analysis (CFA) is applied. For the estimation of CFA, Diagonal Weighted Least Squares (DWLS) technique is preferred because the data is Likert typed. Factor structure was revealed with EFA. Then the factor structure was tested with CFA.

In the final part of the analysis, coherence validity analysis is conducted for the Generation- Z higher education service quality scale. In this coherence validity analysis, a “University Student Satisfaction Index (USSI)” which is like the scale in our study and “Covid-19 Fear Scale (CFS) Kaya et al. [31] which is not like the scale we developed. All three surveys are applied to the same students. For divergent validity analysis, Generation- Z higher education service quality scale and USSI scale, and for divergent validity analysis Generation- Z higher education service quality scale and CFS relations are investigated via Pearson correlation analysis, and the coefficients are evaluated. The reason behind this analysis is, verifying the validity of the scale that we develop by using a different statistical analysis method. According to Horn Parallel analysis, the scale is represented under 7 sub-dimensions. According to results, B4, B5, B6, D1, D2, D3, E1 and E2 indicators are omitted from the analysis because their factor is below 0.40 at all factors. It is seen that all other factor loads are above 0.50. In addition, variance explanation rates are respectively found as 0.107, 0.066, 0.151, 0.151, 0.078, 0.066 and 0.117. According to EFA results, the scale indicators of Generation- Z higher education service quality scale are grouped under 7 factors with 73.6% variance explanation rate.

In Table 2 the results of Bartlett sphericity test that includes main assumptions regarding the EFA findings, and Kaiser Meier Olkin (KMO) statistics are shown. According to the analysis results, it is seen that KMO statistics is above 0.7. Regarding KMO values, 0.60-0.70 interval means weak, 0.70-0.80 interval means medium, 0.80-1.00 interval means sufficient finding in terms of the sample. In addition, when Bartlett Sphericity test is looked at it is seen that there is statistically significant correlation between the indicators of the scale (p < 0.05).

Table 2
Main assumption results of generation-z higher education service quality scale regarding EFA process

KMO 0.910

Bartlett Sphericity Test 6639.7

p <0.001

KMO	0.910
Bartlett Sphericity Test	6639.7
p	<0.001

In the light of these findings, Cronbach Alfa coefficients of 7 dimensions are calculated respectively as 0.886, 0.780, 0.890, 0.814, 0.712, 0.797 and 0.865. In addition, general Cronbach Alfa coefficient that represents Generation- Z higher education service quality is calculated as 0.933. At Table 3 Generation- Z higher education service quality scale’s fitness of coherence values that are found as the result of confirmatory factor analysis are shown. According to these findings, Chi-square/df=0.697 is less than 2, and RMSEA value is less than 0.05. As another finding, SRMR value is less than 0.05, and GFI, CFI, and AGFI values are more than 0.975. The validity results of the Generation- Z higher education service quality scale represent perfect fit.

Table 3

Coherence indexes of CFA findings of the generation- z higher education service quality scale

Fit index	Acceptable value	Value found
χ²/df	≤5.00	0.697
GFI	≥0.90	0.988
AFGI	≥0.85	0.985
CFI	≥0.90	1
SRMR	≤0.10	0.047
RMSEA	≤0.085	0.012

In Table 4 the correlation results between Generation-Z higher education service quality scale adjusted points based on fit validity and its Pearson convergent and divergent scales are shown. According to the test results, it is seen that there is a statistically significant relation between the Generation-Z higher education service quality scale and its convergent scale (p < 0.05).

Table 4

Convergent - divergent validity results of the generation- z higher education service quality scale

	USSI	CFS
r	0.019	-0.009
p	0.321	0.819

In addition, it is found that there is no statistically significant relation between Generation-Z higher education service quality scale and its divergent scale (p > 0.05). It is seen that Generation-Z Higher Education Service Quality Scale’s coherence validity is satisfied because the coefficient achieved as the result of the correlation analysis between the scale and its divergent scale is negative and statistically non-significant.

4 Conceptual model and method

It is seen that the scale developed in this study can be utilized as a valid and reliable mean of measurement in order to measure the expectations of the Generation-Z regarding higher education service quality and student satisfaction index. The answers to the survey questions are subjective reflecting personal views. Therefore, the evaluation should be based on Fuzzy Logic rather than the classical methods. For that reason, the inputs derived from participants’ answers based on 5 Point Likert scale are processed to fuzzification-defuzzification process and used as inputs into the model designed via the MATLAB Fuzzy Toolbox [25].

As the output of the model, satisfaction scores for all dimensions are found. The second usage of fuzzy approach was carried out with fuzzy ranking [54]. In the 2nd part of the survey that participants are asked to make a priority list of the designed quality dimensions. Using priority list as input to fuzzy ranking, weight values for each factor (dimension) are calculated. Next, general index scores are found by using the scores found by the first fuzzy model and weights of second fuzzy ranking model. The process of the model is shown in Fig. 2.

Fig.2

Process modelling.

4.1 Calculation of satisfaction scores based on dimensions

The data used in the research are gathered by using the survey that consists of 7 dimensions and 36 questions. This Five Point Fuzzy Likert Scale style survey is applied to the students at a public university in a face-to-face way. “Optimistic-Pessimistic Combination” is utilized to fuzzy student evaluations as inputs into the model [4]. In this combination as it is seen at Fig. 3, there are pessimistic (Pi) and optimistic (Oi) points that belong to triangular fuzzy number. “Qi” indicates the values that are found as the result of the defuzzification, and these values are calculated by geometric average. While calculating the importance of fuzzy criteria and creating the dual comparison matrix prepared for the group decisions, the geometric average of the inputs provided by the participants/decision makers is used.

Fig. 3

Pessimistic-optimistic representation on triangular fuzzy number.

In the combination developed by Aktepe et al. [4], the points that are determined according to the fuzzy Likert scale inputs of the participants fuzzy mid-point values reflections to both sides that are found by using the Equations (2) and (3). Defuzzification values are calculated by using the Equation (4) [4]. $P_{i} = a_{1} . α + a_{2} . (1 - α)$ (2) $O_{i} = a_{3} . α + a_{2} . (1 - α)$ (3) $Q_{i} = \sqrt{P_{i} * Q_{i}}$ (4)

The membership function values matching the verbal expressions in the questionnaires were represented with the equations given above, and the mean Q_i values of the dimensions were found as follows; administrative services; (3.6) academic services; (3.8), innovative teaching approach; (3.5), information system services; (3.2), adequacy of university facilities and social life; (2.8), career planning; (2.9), reputation; (3.1).

After this point, Qi values are used as input variables into the fuzzy modelling created via MATLAB Fuzzy Toolbox Model [23] is created for all dimensions according to the answers regarding the sub dimensions. Membership functions are selected and finally scores are found for all dimensions. For example, for Academic Services dimension 5 sub-factors are used as inputs in order to get its quality score. Figure 4 illustrates this. While modelling, as the method of defuzzification “centroid” method is used.

Fig. 4

An example of fuzzy score calculation for academic services.

In the survey, the preference labels for the inputs are selected as membership functions as “Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree”. Range values for the inputs are [1 5]. Membership function parameters are: “Strongly Disagree” [1 1 2], “Disagree” [1 2 3], “Neutral” [2 3 4], “Agree” [3 4 5], “Strongly Agree” [4 5 5]. 3 membership functions are used for outputs as “Not satisfied, Neutral and Satisfied”. Range values are selected as [1 100] in order to turn results into scores out of 100. The parameters for the outputs are determined as: Not satisfied [1 1 35], Neutral [25 50 75], and Satisfied [65 100 100].

If (A1 is Neutral) and (A2 is Strongly Agree) and (A3 is Strongly Agree) and (A4 is Strongly Agree) and (A5 is Strongly Agree) then (Academic Service Quality is Satisfied)

If (A1 is Strongly Agree) and (A2 is Neutral) and (A3 is Neutral) and (A4 Neutral) and (A5 Strongly Agree) then (Academic Service Quality is Neutral)

If (A1 is Strongly disagree) and (A2 is Neutral) and (A3 is Disagree) and (A4 Strongly Disagree) and (A5 Strongly Agree) then (Academic Service Quality is Disagree)

If (A1 is Agree) and (A2 is Strongly Agree) and (A3 is Strongly Agree) and (A4 is Strongly Agree) and (A5 is Neutral) then (Academic Service Quality is Satisfied)

If (A1 is Disagree) and (A2 is Disagree) and (A3 is Neutral) and (A4 Strongly Disagree) and (A5 Neutral) then (Academic Service Quality is Disagree)

Similar to the academic services dimension, all steps are executed for all other dimensions. The scores obtained by creating a total of 2186 rules are shown in Fig. 5. (The indices from A to G represent dimensions.)

Fig. 5

Dimension scores.

4.2 Determining the weight of dimensions via the method of fuzzy ranking

Because the fuzzy numbers are used for the digitizing of unclear situations, in some cases, comparing or ranking these numbers may become necessary. Especially this becomes more important when making decision by using fuzzy logic in which the criteria’s and alternatives’ final values are fuzzy numbers [16]. Fuzzy optimization and ranking of fuzzy numbers, which is the fundamental problem of fuzzy decision-making methods, can be conducted differently based on different features such as gravity centre of fuzzy clusters, the area below the membership degree functions or some intersection points [74]. Fuzzy numbers do not constitute a natural line unlike the real numbers. Therefore, various methods are utilized in order to rank the fuzzy numbers (Liou and Wang [37] and Abdel-Kader and Dugdale [2]). The model developed by Liou and Wang [37] that is utilized in this study that uses the total integral value method. α ∈[0 1] as optimism index total integral value for the Ã=(m n u) triangular fuzzy number is as shown at Equation (5) which is developed by Liou and Wang [37]. α is called as decision maker’s optimism index gets a value within the range of [0 1]. A higher α represents a more optimistic decision maker while a lower α represents a more pessimistic decision maker. $I_{T}^{α} = \frac{1}{2} [α . u + m + (1 - α) . l]$ (5)

The participants are asked to rank dimensions from one to seven according to the level of importance. Firstly, the ranking numbers were turned into fuzzy numbers as in Table 5. The first rank has the highest fuzzy value and the last rank has the lowest fuzzy value.

Table 5

Fuzzy values of importance ranks

Importance Rank	Transformation
of Respondent	to Fuzzy Number
First	(7,8,9)
Second	(6,7,8)
Third	(5,6,7)
Fourth	(4,5,6)
Fifth	(3,4,5)
Sixth	(2,3,4)
Seventh	(1,2,3)

The rankings are turned into fuzzy numbers and their geometric averages are calculated [63]. For each dimension i, (l m u) triangular fuzzy numbers are calculated. Then lower, medium and upper values of fuzzy numbers in Fig. 4 are calculated with Equations (6)-(8) [53]. Here, in these set of equations l, m and u are the lower, medium and upper values of fuzzy numbers respectively. i represents the dimension and n is sample size. $x_{{geo}_{l_{ij}}} = \sqrt[n]{l_{i 1} * l_{i 2} * l_{i 3} * \dots * l_{in}}$ (6) $x_{{geo}_{m_{ij}}} = \sqrt[n]{m_{i 1} * m_{i 2} * m_{i 3} * \dots * m_{in}}$ (7) $x_{{geo}_{u_{ij}}} = \sqrt[n]{u_{i 1} * u_{i 2} * u_{i 3} * \dots * u_{in}}$ (8)

Figure 6 indicates these triangular fuzzy values of each dimension.

Fig. 6

Fuzzy rankings of importance evaluation of dimensions.

The weights, listed in order of importance in Table 6, were found by the integral values equation of Liou and Wang with 0.6 alpha value and shown by normalizing between 0 and 1.

Table 6

Weights for each dimension

Dimension	W	Weight
Innovative Teaching Approach	W_B	0.52
Academic Services	W_A	0.52
Career Planning	W_F	0.46
Adequacy of Uni. Fac. and Social Life	W_E	0.39
Information Systems Services	W_D	0.37
Reputation	W_C	0.34
Administrative Sciences	W_G	0.31

4.3 Calculating the general satisfaction scores

General Satisfaction Score is calculated with weighted average index of defuzzified scores given at Equation (9) [32]. This shows the result of the participants’ answers to the Five Point Likert Scale and weights obtained via ranking the dimensions. $GSS = \frac{w_{A} . x_{A} + w_{B} . x_{B} + \dots + w_{G} . x_{G}}{w_{A} + w_{B} + \dots + w_{G}} = 64, 768$ (9)

The distance from the score obtained as a result of the calculation to 100 points is 35.232 points. This shows the expectation gap in higher education service quality.

5 Conclusion and discussion

In this study, a fuzzy evaluation approach is developed for evaluation of service quality for Generation-Z. Fuzzy rule-based approach is used to determine the scores and fuzzy ranking approach is used to determine the weights. A general satisfaction index score is also calculated. This enabled us to make a more comprehensive evaluation of service quality. The scale was developed by considering the personal characteristics and expectations of the Generation-Z in order to measure the service quality they expect from higher education. The developed scale was applied to the participants and the lowest score based on the received dimension was 53.9 and the highest score was 75.8. Higher scores indicate higher student satisfaction regarding the quality of services offered in higher education. Similarly, a decrease indicates that students’ satisfaction with the quality of services offered in higher education has decreased. With the fuzzy evaluation model used in the study, the difference of human judgments was considered and analyses were carried out by digitizing the verbal data. When the data obtained as a result of the analysis were evaluated, it was seen that the higher education service quality satisfaction index score of the Generation- Z was approximately 64.768. In other words, an expectation gap of 35.232 percent has emerged. When analysed based on dimensions, respectively, “Academic Services (75.8)”, “Administrative Services (71.2)”, “Innovative Teaching Approach (69.7)”, “Image (62)”, “Information System Services (61.1)”, “Career Planning (56)”, “Adequacy of University Facilities and Social Life (53.9)” were obtained. It has been seen that the areas where the expectations of the Generation-Z in the higher education process are not met the most are the university facilities, the adequacy of the social life and career planning, while their expectations in the fields of academic services, administrative services and innovative teaching approach are more met. Considering that the Generation-Z, unlike previous generations, has adopted social learning environments in which they can be involved directly in the learning process by doing and experiencing, and is more career-oriented than other generations, the existing expectation gaps can be eliminated with improvements to be made in this context. In the last part of the applied questionnaire, the participants were asked to list the dimensions of the developed scale in order of importance. The answers were analyzed with the fuzzy ranking method and the participants ranked the service quality dimensions in higher education in order of importance as academic services, innovative teaching approach, career planning, adequacy of university facilities and social life, information system services, administrative services and image. Different from the studies in the literature evaluating the service quality and satisfaction perceptions of university students (HiEdQUAL, HiEduQUAL, HESQUAL, HEISQUAL etc.), in our study, dimensions and sub-factors specific to Generation Z were developed.

The dimensions of “Innovative Teaching Approach” and “Information System Services” used in our study and sub-factors that provide different approaches have contributed to the literature. In the satisfaction scale (ZGEN-QUAL) used in our study and developed by us, the characteristics specific to the Z generation were taken into account.

In future studies, the scale can be applied in universities and different faculties in different cultures, geographies and regions, and the results can be compared. The results can be compared and interpreted by using different membership functions, including trapezoidal and Gaussian membership function types, instead of triangular fuzzy sets used in the study. Fuzzy logic can be analyzed intuitively, considering that the answers given by the participants are undecided and hesitant questions. Results can be compared by comparing the fuzzy sorting methods.

Footnotes

Appendix

Dimensions	Code	Sub-factors
Academic Services	A1	Lecturers communicate with students at a sufficient level during the lesson.
	A2	Lecturers communicate adequately with students during extracurricular times.
	A3	Lecturers are in a guiding attitude towards the subjects on which they are undecided.
	A4	The general performance of the lecturers in education and training is at a sufficient level.
	A5	Lecturers use the technological infrastructure to support education.
Innovative Teaching Approach	B1	Course contents are such that students can benefit from their goals in the future and support their abilities.
	B2	In the lessons, the subjects are explained in relation to real life.
	B3	Unlike classical and passive learning methods, group work, joint project work and student-centered active learning methods in which the student is active are used in the lessons.
	B4	In the lessons, approaches that enable students to learn by exploring and analyzing are used.
	B5	The ideas of the students are taken in determining the working methods of the course.
	B6	The number of elective courses is in number and variety to meet the needs.
Administrative Services	C1	Administrative staff have a caring and helpful attitude towards students.
	C2	The behavior of the administrative staff towards students is courteous.
	C3	Administrative personnel have sufficient knowledge about their duties.
	C4	Administrative services fast and effective.
	C5	Administrative services are transparent.
	C6	Administrative staff can also be contacted by e-mail during out-of-school times.
Information System Services	D1	Internet infrastructure is sufficient at the university and internet access can be provided from most places.
	D2	The university’s website is up-to-date and adequate.
	D3	Classrooms and laboratories are supported by technological infrastructure with internet access.
	D4	Information system tools in classrooms and laboratories can be used during extracurricular times.
	D5	Virtual communication tools that provide continuous and rapid feedback are also used in extracurricular times. (Such as applications where students can ask questions and get answers, access lecture notes and videos of the course with tools such as mobile phones and tablets.)
	D6	Distance online education infrastructure is sufficient and there is no disruption in matters such as exams, homework, project presentations.
	D7	Technical support regarding the information system infrastructure is carried out without interruption. (Internet access, malfunction etc.)
	D8	By providing access to the electronic library, free access to national and international academic databases and licensed programs can be provided.
Adequacy of University Facilities and Social Life	E1	Social areas and service units (library, cafeteria, market, canteen, stationery, bank, cargo company, clinic, sports fields etc.) in the university are at a sufficient level.
	E2	There is an environment with suitable conditions and sufficient capacity to study at the university.
	E3	The university has enough accommodation facilities (dormitories, guesthouses, etc.).
	E4	Social activities (art and sports activities, student clubs) provided by the university are sufficient.
	E5	The transportation facilities of the university are sufficient.
Career Planning	F1	Seminars for graduate students are organized to provide information about business life.
	F2	The university is supportive of the student and collaborative with the business world in order to facilitate the employment of students after graduation.
	F3	Coordinated studies are carried out with different institutions and organizations for the internship studies of the students at the university.
Repututaion	G1	The scientific contribution of the university to society is at a good level.
	G2	The social contribution of the university to the society is at a good level.
	G3	The level of recognition of the university in the external environment is at a good level.

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A model proposal for measuring service quality of eduaction with fuzzy rule-based approach and fuzzy ranking and an application

Abstract

Keywords

1 Introduction

2 Literature review

Table 2 Main assumption results of generation-z higher education service quality scale regarding EFA process KMO 0.910 Bartlett Sphericity Test 6639.7 p <0.001

Footnotes

Appendix

References

Table 2
Main assumption results of generation-z higher education service quality scale regarding EFA process

KMO 0.910

Bartlett Sphericity Test 6639.7

p <0.001