Abstract
Purpose
Improving service quality, student satisfaction and student loyalty is important to higher education institutions’ sustainable growth. The objectives of this study are a twofold: first, the study seeks to determine the dimensions of higher education service quality with a specific focus on Vietnam. Second, it examines how the service quality dimensions impact student satisfaction and student loyalty, with the moderating role of the university image.
Design/methodology/approach
This study followed a rigorous procedure, including interviews, a survey, exploratory factor analysis (EFA) and reliability analysis to identify higher education service quality dimensions and their measures. After that, using the data obtained from 1,550 university students in Vietnam, confirmatory factor analysis was used to validate the identified dimensions and structural equation modeling was used to test a proposed model explaining the outcomes of higher education service quality.
Findings
The findings reveal five dimensions of higher education service quality: academic aspect, nonacademic aspect, programming issues, facilities and industry interaction. Most of these factors have a positive influence on student satisfaction. In addition, the university image moderates the positive relationship between student satisfaction and student loyalty.
Practical implications
This study’s findings highlight the complexity of service quality in the higher education context and encourage higher education institutions to improve their service quality in image to enhance student satisfaction and loyalty.
Originality/value
This study suggests a unique measure of higher education service quality dimensions and provides fresh insights into how they impact student satisfaction and loyalty in Vietnam.
Keywords
Citation
Nguyen, H.V., Vu, T.D., Saleem, M. and Yaseen, A. (2024), "The influence of service quality on student satisfaction and student loyalty in Vietnam: the moderating role of the university image", Journal of Trade Science, Vol. 12 No. 1, pp. 37-59. https://doi.org/10.1108/JTS-12-2023-0032
Publisher
:Emerald Publishing Limited
Copyright © 2024, Hoang Viet Nguyen, Tuan Duong Vu, Muhammad Saleem and Asif Yaseen
License
Published in Journal of Trade Science. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Higher education, as a service sector (Galeeva, 2016), plays a significant role in personal, social and economic development globally (Marginson, 2010; Statista, 2023). Moreover, higher education institutions have increasingly been recognized as key contributors to the achievement of the United Nations Sustainable Development Goals (SDGs) (Žalėnienė and Pereira, 2021). Among the 17 SDGs quality education (SDG4) is identified as an important goal focusing on equal access to higher education and promoting lifelong learning opportunities for each individual. Notably, universities have an important responsibility: to become an essential driving force in achieving all SDGs through training, knowledge production and innovation (Chankseliani and McCowan, 2021). The higher education sector has been expanding substantially (Latif et al., 2019), with over 30,000 universities worldwide (Statista, 2023). This rapid growth has called more attention to the issue of service quality and its related impact on student satisfaction and loyalty (Teeroovengadum et al., 2019; Üstünlüoğlu, 2017). Given the intense competition in higher education, improving service quality, student satisfaction and loyalty is essential for the sustainable growth of universities (Chen, 2019; Qian et al., 2022).
Prior studies have examined the relationships among service quality, university image, student satisfaction and loyalty in higher education in several countries including Malaysia, Mauritius and Portugal (Alves and Raposo, 2007; Ali et al., 2016; Teeroovengadum et al., 2019; Annamdevula and Bellamkonda, 2016; Chandra et al., 2019). However, their results vary across different research contexts. For example, Chandra et al. (2019) reported that service quality improves student satisfaction but does not impact student loyalty. Annamdevula and Bellamkonda (2016) demonstrated that service quality could promote student satisfaction and loyalty. Furthermore, these studies conceptualize service quality in different ways and their findings are inconsistent or even conflicting. As an example, while Ali et al. (2016) found that five service quality dimensions (academic aspect, nonacademic aspect, program issues, reputation and access) positively enhance student satisfaction, Teeroovengadum et al. (2019) reported that, among two aspects of service quality including functional service quality and transformative service quality, the impact of the latter on student satisfaction is insignificant. These inconsistencies and variations suggest the need for further studies that clarify the dimensions of higher education service quality and the mechanism through which these dimensions generate outcomes, such as student satisfaction and loyalty. Similar to corporate image, university image is gradually becoming an essential element of universities when communication channels have become more diverse (Manzoor et al., 2021). Businesses with a good image can attract customers and improve business performance (Andreassen and Lindestad, 1998) and some empirical evidence implies that universities with a good image may be better at attracting learners, making learners more satisfied and enhancing learner loyalty (Schlesinger et al., 2023; Le et al., 2023). However, many scholars advocate that more research is needed to increase understanding of university image (Manzoor et al., 2021). In particular, limited studies investigate the moderating role of the university image in the relationship between student satisfaction and loyalty.
Recent policies that increase investment in higher education and university autonomy have brought many advantages to universities in Vietnam, such as improving financial resources, flexibility in operation and opportunities to expand. There are currently 237 universities in Vietnam, of which 172 are public universities and 65 are private institutions, with more than 1.6 m registered students. It should be noted that the demand for higher education is decreasing. As an illustration, 518,587 new students were enrolled in 217 universities during the 2014–2015 academic year, while the number of new enrollments was only 447,483 for the 2019–2020 academic year. This situation has led to an aggressive competition in the higher education sector and put much pressure on higher education institutions to attract potential students and retain current students and, consequently, improving service quality, image, student satisfaction and loyalty is of utmost importance and has become a top priority for many higher education institutions.
The objectives of this study are a twofold: first, it seeks to determine the dimensions of higher education service quality with a specific focus on Vietnam. Second, it examines how the service quality dimensions impact student satisfaction and student loyalty, with the moderating role of the university image. The current study contributes to the literature relating to service quality and its impact on students’ evaluations, gratifications and behaviors. The findings of this study assist researchers and higher education institutions that seek to understand service quality dimensions and promote student satisfaction and loyalty.
2. Literature review
2.1 Service quality in higher education
According to Parasuraman et al. (1988, p. 15), service quality is defined as “a form of attitude related but not equivalent to satisfaction, and results from the comparison of expectations with perceptions of performance.” Service quality is considered an essential factor that creates competitive advantage for enterprises (Ghobadian et al., 1994). Hence, the concept and measure of service quality has received much attention from researchers and managers (Nguyen et al., 2022). Notably, their mixed views exist on service quality dimensions due to the diversity of service types and characteristic variations.
Grönroos (1984) proposes to evaluate service quality based on technical quality and functional quality. Meanwhile, based on the theories of customer expectation and perception, Parasuraman et al. (1988) postulate the SERVQUAL (service quality) scale with five main dimensions, namely (1) Reliability, (2) Responsiveness, (3) Empathy, (4) Tangibility and (5) Assurance. The SERVQUAL scale has been widely recognized and applied in various studies on service quality. Cronin and Taylor (1992) argued that the assessment of service quality associated with expectation can cause confusion for customers when collecting data. They, therefore, propose the SERVPERF scale, with dimensions similar to the SERVQUAL scale but with reduced statements measuring customer expectations and instead a focus on customer perception.
Due to the nature of the services provided and the unique characteristics of the educational environment in each country, the dimensions used to evaluate quality of higher education services vary. Table 1 summarizes key higher education service quality dimensions from past studies.
According to Table 1, key service quality dimensions investigated in previous studies include nonacademic aspects, programming issues, facilities/physical evidence, location and industry interaction. In general, service quality dimensions are essential determinants of student satisfaction (Chandra et al., 2019; Alves and Raposo, 2007; Brown and Mazzarol, 2009; Teeroovengadum et al., 2019).
Based on the results summarized in Table 1, the selected dimensions for service quality in higher education are diverse, and there are differences in selection of dimensions from studies to studies. For example, studies that were conducted in India and Europe use career opportunities and enterprise interaction as a critical component of service quality in higher education (Jain et al., 2013; Tsinidou et al., 2010; Vanniarajan et al., 2011). Meanwhile, in other studies in Asian countries, academic factors are more common (Gamage et al., 2008; Abdullah, 2006). Differences in the development orientation of higher education and culture in different countries partly explain these differences.
2.2 Student satisfaction
Customer satisfaction has been defined in different ways (Nesset and Helgesen, 2009). Anderson (1994) described customer satisfaction as a generalized evaluation of a service based on the experience gathered during provision of the service. Giese and Cote (2000, p. 3) stated that customer satisfaction is a “summary, affective and variable intensity response centered on specific aspects of acquisition and/or consumption, and which takes place at the precise moment when the individual evaluates the object.” Customer satisfaction appears to be an important motivator of customer loyalty (El-Adly, 2019).
2.3 Student loyalty
Oh (1995) suggests that there are three main approaches to measuring customer loyalty including attitudinal, behavioral and integrated measures. While there are different definitions of customer loyalty, most focus on behavioral measurement, which thus far has included purchase rate (Lee and Cunningham, 1996), probability of purchase (Farley, 1964), probability of product repurchases (Kuehn, 1976), repurchase behavior (Brown, 1953), frequency of purchase (Brody and Cunningham, 1968), customer engagement (Hennig-Thurau et al., 2001) and positive word of mouth (Ferguson et al., 2006). Customer loyalty is considered to be a consequence of customer satisfaction (Fornell et al., 1996; Athanassopoulos et al., 2001) and in higher education studies, student loyalty is not only concerned with the frequency of use of the university services but also related perceptions, attitudes and motivations for behavior (Hennig-Thurau et al., 2001). Specifically, student loyalty refers to the extent to which students feel connected to the organization and how their attitudes and behaviors demonstrate this association (Nesset and Helgesen, 2009). According to Rojas-Méndez et al. (2009), student loyalty can be used to evaluate the university’s success and their efforts in retaining students. Loyal students tend to use additional services provided by the universities in the future. In the context of increasing competition in the higher education market, understanding student loyalty will assist the universities in attracting learners to continue using services in the future.
2.4 University image
Barich and Kotler (1991) stated that image was defined as a general impression of a company that anyone was familiar with in their mind. Corporate image is perceived to result from many factors, including beliefs, experiences, knowledge and feelings individuals give to an organization (Kazoleas et al., 2001). According to Nguyen and LeBlanc (2001, p. 303), the university image is related to “physical and behavioral attributes of the organization, such as business name, architecture, variety of products/services, tradition, ideology, and to the impression of quality communicated by each person interacting with the organization’s clients.” Each organization could create a message to the public, and the corporate image was an element that stood out and represented what was communicated (Kotler and Fox, 1995). According to Andreassen and Lindestad (1998), the communication and experience process is essential in forming the corporate image. In a theoretical basis, the university image included cognitive and emotional factors (Palacio et al., 2002).
3. Study 1: determining service quality dimensions for this study
3.1 Methodology
3.1.1 Research procedure
In the analysis of the diversity in the selection of dimensions to measure service quality in higher education, to select the components constituting the quality of higher education services in Vietnam, the group of authors followed the specific process as follows:
Stage 1: Extracting constructs and items from past studies and the Vietnamese legal documents system regarding higher education activities.
Stage 2: Conducting seven expert interviews and focus group discussions with 30 students to identify dimensions and items relevant to the research context in Vietnam.
Stage 3: Collecting data from 140 students using a survey method, which were then subjected to the exploratory factor analysis (EFA) and reliability analysis to explore the dimensions of higher education service quality.
3.1.2 Measurement scale
In determining the dimensions of the service quality scale, we referred to the items used in many different studies. The list of items was referenced and developed from past studies related to service quality, university image and student satisfaction. The initial item count included 46 statements extracted from the findings of previous studies (Ali et al., 2016; Hennig-Thurau et al., 2001; Nesset and Helgesen, 2009; LeBlanc and Nguyen, 1997; Kwan and Ng, 1999; Abdullah, 2006; Gamage et al., 2008; Tsinidou et al., 2010; Vanniarajan et al., 2011; Jain et al., 2013; Nguyen and LeBlanc, 2001; Peng and Samah, 2006). All items were selected from highly reliable studies that were conducted in countries with many similarities to the research context in Vietnam, and the scale of the study was adapted from past studies. To avoid linguistic problems when translating the scale statements, two linguists were invited to test the translation. The student loyalty scale included four items taken from Hennig-Thurau et al. (2001). The student satisfaction scale consisted of three items applied from the study of Nesset and Helgesen (2009). The university image scale included three items applied from the results of Nguyen and LeBlanc (2001). The service quality scale included 29 items representing five dimensions developed from studies by LeBlanc and Nguyen (1997), Kwan and Ng (1999), Abdullah (2006), Peng and Samah (2006), Gamage et al. (2008), Tsinidou et al. (2010), Vanniarajan et al. (2011) and Jain et al. (2013). The questions were applied on a five-point Likert scale, ranging from 1 (very poor) to 5 (excellent). Student satisfaction and loyalty items were designed with a slightly different scale: 1 (strongly disagree) to 5 (strongly agree).
3.2 Result of study 1
3.2.1 Item selection for measurement scale
After extracting the constructs and items from past studies and Vietnamese legal documents system regarding higher education activities, in-depth interviews were conducted with experts, including two doctors in business administration, a doctor in education, two university lecturers with over 10 years of teaching experience and two senior-level managers at the universities. The participating experts represent individuals with extensive knowledge of service marketing and higher education (two doctors in business administration and one in education). Senior lecturers represent individuals who are directly involved in delivering higher education services and have a wealth of knowledge about the university activities while university managers represent individuals involved in developing higher education services. Thus, the instrument development process will ensure a multi-dimensional perspective and high reliability. From the 46 selected items, the expert team reached a consensus on the finalized 41 items representing service quality, student satisfaction and university image and student loyalty. Next, the items were put into focus group discussions with 30 students (divided into three groups). As a result (Table 2), when evaluated from students' perspectives, 41 items were reduced to 39 items. The results of the qualitative study are described in Table 2.
3.2.2 Exploratory factor analysis and Cronbach’s alpha test
In order to explore the service quality dimensions, 29 items related to higher education service quality were identified through interviews were included in EFA) with sample size N = 140. Through the extraction of principal components, varimax rotation and Bartlett’s test, the results show that the KMO value = 0.814 (range 0.5–1). Sig of Bartlett test = 0.000 (less than 0.05), the eigenvalues of the item groups are all greater than 1; the total variance is 60.842%. Factor loadings value ranges from 0.558 to 0.842 (greater than 0.5). Meanwhile, the Cronbach’s alpha values ranged from 0.776 to 0.866 (greater than 0.7). Thus, the scale ensures reliability and is consistent with the collected data (Hair, 2009). The results of EFA and Cronbach’s alpha tests are described in Table 3.
In terms of EFA, there were 29 observed variables of higher education service quality selected from the interview, which were then categorized into five groups of factors. As illustrated in Table 4, we evaluated the reflected content of the items in the groups and named the dimensions accordingly.
Through the interview process and checking the reliability of items using the Cronbach’s alpha and EFA tests, the research has identified 29 items representing service quality in higher education in Vietnam. These items are guaranteed to be consistent from the perspective of both experts and students' perceptions. This result is an essential foundation for evaluating the outcome of higher education service quality in Study 2.
4. Study 2: the influence of higher education service quality on student satisfaction and student loyalty, with the moderating role of the university image
4.1 Hypotheses development and conceptual model
The relationship between service quality and customer satisfaction is an essential concept of marketing studies. The positive impact of service quality on customer satisfaction is confirmed in various studies (Cronin et al., 2000). Many researchers proposed evaluating the influence of dimensions of service quality on customer satisfaction (Slack et al., 2020). In the higher education context, investigating the impact of dimensions of higher education service quality on customer satisfaction is familiar approach of many studies (Ali et al., 2016; Teeroovengadum et al., 2019). This study focuses on five dimensions, namely, academic, nonacademic, programming issue, industry interaction and facilities, which are identified clearly in Study 1.
Several previous studies mentioned the path between the above dimensions and student satisfaction. Elliott and Shin (2002) and Thomas (2011) reported that academic aspect is essential attribute of higher education service quality and this component could enhance student satisfaction. Similarly, Van et al. (2020) provided empirical evidence, which demonstrated academic staff and academic environment promote significantly student satisfaction in Vietnam. Hence, the hypothesis H1a is proposed:
Academic aspect has a positive impact on student satisfaction.
Nonacademic factors positively impact the operation and activities of higher education institutions and influence student satisfaction (Abdullah, 2006) and the relationship between nonacademic factors and student satisfaction has also been tested in many studies (Arambewela et al., 2009; Gibson, 2010). The nature of educational services is not simply to provide academic values and knowledge but also to support students in developing skills. In Vietnam in recent years, with the increasing competition in the business environment, the universities have focused on student support services in addition to the core activity of providing knowledge to students. The benefits of recreational and extracurricular activities gradually show their importance to the development of students. From the above arguments, research hypothesis H1b is proposed as follows:
Non-academic aspect has a positive impact on student satisfaction.
In this study, the authors also evaluated the programming issues from the perspective of student access, focusing on flexibility, diversity and updates. Several empirical evidences highlighted the role of programming issues to customer satisfaction. For example, Huang (2010) implied that the suitable study program could improve student satisfaction. Hai (2022) pointed out education programs enhance service quality and has indirect effect on student satisfaction in Vietnam. Thus, hypothesis H1c was formulated and is proposed:
Programming issues have a positive impact on student satisfaction.
Facilities play an important role in supporting enterprises in producing and providing customer services (Parasuraman et al., 1988). In higher education services, issues of facilities such as classrooms, libraries, computer rooms and teaching equipment are highly essential. Picus et al. (2005) found a close relationship between facilities and student learning outcomes. The positive impact of facilities on student satisfaction is also mentioned in many studies (Douglas et al., 2006; Hanssen and Solvoll, 2015; Darawong and Sandmaung, 2019). From the above arguments, the hypothesis H1d is proposed:
Facilities have a positive impact on student satisfaction.
Industry interaction quality is relevant to the cooperation between universities, enterprises and students (Jain et al., 2013). In higher education, collaborative activities between schools and businesses are increasingly common. Furthermore, the labor market’s requirements for practical knowledge and job skills of students upon graduation are gradually being focused. Therefore, training cooperation between universities and enterprises is an important solution to this problem. The experience, updated knowledge and skills from natural working environments outside the business contribute significantly to helping students strengthen and develop themselves. Activities linking enterprises and schools in training and finding job opportunities for students bring much value and positively impact student satisfaction (Hussien and La Lopa, 2018; Jaradat, 2017). However, in Vietnam, studies have not yet to evaluate the impact of business interaction factors on student satisfaction. Thus, the hypothesis H1e is proposed:
Industry interaction has a positive impact on student satisfaction
In the higher education sector, students can be considered the main customers of universities (Oldfield and Baron, 2000). The concept of “student satisfaction” denotes “a student’s subjective evaluation of the various outcomes and experiences with education and campus life” (Elliott and Shin, 2002, p. 198). Several studies conducted by Alves and Raposo (2007), Brown and Mazzarol (2009) and Teeroovengadum et al. (2019) demonstrate a positive effect of student satisfaction on student loyalty. Hence, the following hypothesis is developed:
Student satisfaction has a positive impact on student loyalty.
Kipkirong Tarus and Rabach (2013) argued that enterprises with a good image would bring psychological satisfaction to customers and customers with psychological satisfaction were expected to be loyal to the business. There is an observable tendency for customers to want to be associated with good enterprises. Therefore, a positive business image will enhance customer loyalty to the business. The university’s image reflects its reputation and contributions to society in higher education and is an essential reference for educational institutions to attract learners. It can promote re-enrollment to use and encourage word-of-mouth behavior from learners, as they are satisfied with their learning experience. Kipkirong Tarus and Rabach (2013) through empirical research of data from 140 customers using mobile services, have demonstrated the role of image in enhancing the relationship between customer satisfaction and loyalty. Thus, the following hypothesis is proposed:
University image positively moderates the relationship between student satisfaction and student loyalty.
Figure 1 illustrates the proposed research model.
4.2 Methodology
4.2.1 Measures approach
In this phase, a conceptual model is proposed based on the hypotheses development to reflect the role of dimensions of higher education service quality on student satisfaction and student loyalty. Afterward, the structural modeling equation (SEM) is employed to test the hypotheses and paths analysis. Quantitative analysis for the primary data was performed by two applications: IBM SPSS 26 and IBM AMOS 26. The authors performed descriptive statistical tests, EFA and Cronbach’s alpha coefficients by SPSS 26. The construct reliability, SEM model analysis and a multigroup test to evaluate the moderator variable’s role were carried out by AMOS 26 software. Chi-square/df (chi-square to degree of freedom ratio), adjusted goodness of fit index (AGFI), goodness of fit index (GFI), comparative fit index (CFI), Tucker and Lewis index (TLI ), normed fit index (NFI) and Root mean square error of approximation (RMSEA) was used to test the suitability of the model. According to Hair (2009), the thresholds for the above indicators include Chi-square/df < 3; p-value <0.05; the value of AGFI, GFI, NFI, CFI, TLI> 0.9 and RMSEA <0.08.
Higher education service quality dimensions were measured by the 29 items identified in Study 1. The student satisfaction scale includes three items adopted from Nesset and Helgesen (2009): “The university I am studying is similar to the ideal university”, “The university met my expectations” and “Overall, I am satisfied with the university I attended.” Student loyalty was measured by four items from Hennig-Thurau et al. (2001): “I am very interested in keeping in touch with my faculty,” “I would recommend my university to someone else”, “I will attend other courses/further education at my university” and “I would become a member of any alumni organizations at my university”. Finally, the university image includes three items, which were adapted from Nguyen and LeBlanc (2001): “The university I am attending has a good reputation,” “My university contributes many values to society” and “The university I participated in has good social links”. It should be important to note that two items from the original scale were eliminated after in-depth interviews with four experts because they were not relevant to the Vietnamese context.
4.2.2 Data collection and sample
The convenience sampling method was applied in this study with the research sample collected from students studying at five public universities in Vietnam and face-to-face and online surveys were used to collect the data. For the face-to-face approach, researchers went to selected universities to collect data and before conducting interviews respondents were required to confirm they were not under any time or psychological pressure during the process. These interviews were then processed by a team of research assistants. For the online survey collection method, Google Forms was used, and the surveys were designed to simulate an equivalent question list that could otherwise have been delivered in person. Each question had an option that allowed the respondents to indicate if they lacked understanding about the content of the question (if available).
Data collection lasted from January to March 2022. After screening and removing invalid surveys, the number of validated questionnaires in the sample was 1,550, equivalent to a 77.5% rate of response. Details of the sample are described in Table 5.
4.3 Research results
4.3.1 Descriptive, construct reliability and validity
The confirmatory factor analysis (CFA) was conducted to validate the dimensions of higher education service quality, student satisfaction, student loyalty and university image. The results of descriptive statistical analysis and CFA are demonstrated in Table 6 and Table 7. Accordingly, the measurement model includes 645 degrees of freedom, χ2/df = 2,470 (less than 3); p-value = 0.000 (less than 0.05); AGFI = 0.939; GFI = 0.950; TLI = 0.959; CFI = 0.964; NFI = 0.942 (greater than 0.9); RMSEA value = 0.031 (less than 0.08). The loading factor values ranged from 0.641 to 0.845 (greater than 0.6). Average variance extracted (AVE) values > 0.5 and greater than maximum shared variance (MSV); composite reliability (CR) ranges from 0.782 to 0.868 (greater than 0.7). AVE’s square root values (SQRT AVE) were greater than the correlation values between the variables, so the thresholds for convergent and discriminant validity are accepted. In addition, the correlation values are less than 0.7, so multicollinearity does not appear in this study (Hair, 2009; Fornell and Larcker, 1981).
4.3.2 Common bias method
Choosing the dimensions and items from many different studies can lead to the common bias method problems. The common bias method can negatively affect the measurement performance of the research model (Podsakoff et al., 2012). To limit these potential risks, we followed the suggestions based on Podsakoff et al. (2012) in securing respondents' personal information during the interview process (demographics information of respondents is coded and the collected data will only be used for research purposes), shuffling the order of questions in the questionnaire to limit respondents' perceptions about the structure of the research model and testing. We also strictly controlled the research data collection process. Finally, Harman’s single test and common latent factor test were applied to assess the likelihood of problems related to the common bias method.
After data collection, quantitative analyses resulted in single-factor explanations of 23.909% of variables' variance for Harman’s single-factor test. For the latent common factor test performed by IBM AMOS 26 software, the latent common method variance factors test results demonstrated that the factor method accounted for less than 25% of the total variance. Furthermore, the differences between the standardized estimate of the measurement model and the common latent factor test model are less than 0.2. Therefore, according to the suggestion of Malhotra et al. (2006), the common bias method problems do not appear in this study.
4.3.3 Hypotheses testing
The SEM model analysis method was applied to test the research hypotheses. The model fit indexes of the SEM model such as: χ2/df = 2.810 (less than 3); p-value = 0.000 (less than 0.05); AGFI = 0.936; GFI = 0.947; TLI = 0.955; CFI = 0.960; NFI = 0.940 (greater than 0.9) and RMSEA value = 0.034 (less than 0.08). These values all meet the thresholds suggested by Hair (2009); therefore, the model is suitable for the collected data.
The results of testing the research hypotheses in Table 8 have shown that, except for the hypothesis of the relationship between the industry interaction and student satisfaction being rejected, the remaining hypotheses are accepted with p-value <0.05. Among the service quality factors, the academic aspect showed the strongest impact on student satisfaction with β = 0.219 (t-value = 5.093; p-value <0.001), followed by facilities with β = 0.183 (t-value = 5.944; p-value <0.001). The impact of programming issues is reflected in β = 0.152 (t-value = 3.736; p-value <0.001). Finally, the nonacademic aspect has β = 0.127 (t-value = 3.369; p-value <0.001). Student satisfaction also shows an important role in student loyalty with the coefficient β = 0.488 (t-value = 15.496; p-value <0.001). The independent variables explained 29.2% of the variation in student satisfaction (R2 = 0.292) and 23.8% of the variation in student loyalty (R2 = 0.238).
4.3.4 Moderating effect hypotheses testing
The study uses the multigroup test method in SEM (Hair, 2009) to evaluate the difference between two groups of samples divided by the perceived degree of university image. After applying the K-means cluster division method by IBM SPSS 26 software, two groups of samples were determined, including Group 1: 680 people (43.87%) have a low perception of university image. Group 2: 870 people (56.13%) have a high perception of university image.
The method applied in the multigroup analysis is multigroup SEM (Hair, 2009), which is performed in two steps: (1) perform an invariance test and (2) analysis of the structural model (structural invariance) to evaluate the different relationships.
The results of the evaluation of invariance through configurational invariance (CI) and metric invariance (MI) showed that the model has suitable fitting parameters such as AGFI, GFI, CFI and TLI greater than 0.9, χ2/df < 3; RMSEA <0.08 (see Table 9). Besides, the p-value reached 0.157 > 0.05. Thus, there is no difference in factor loadings coefficient between the groups. The structural model is considered to evaluate the differences in the effects of the independent variables with the dependent variable of the two sample groups.
Regarding the test, the difference between the structural weight model and the measurement weight mode indicated ∆ χ2 = 17.843 with p-value = 0.007 (less than 0.05), so the unconstrained model will be used to compare the difference in student impact satisfaction for student loyalty in two sample groups (Byrne, 2004).
Regarding the test for chi-square with one difference of degrees of freedom between two models, the restricted model showed the difference of chi-square in the support threshold for the H3 hypotheses with statistical significance p < 0.05. According to the results in Table 10, for the group with a low perception of university image, the relationship between satisfaction and loyalty is lower than that of the group with a high perception of university image (β = 0.401 compared to β = 0.573).
4.3.5 Indirect effect analysis result
The bootstrapping method, as introduced by Preacher and Hayes (2008) was used to test the indirect impact of service quality parameters on student loyalty. The IBM AMOS 26 software was utilized in this test. Table 11 summarizes the findings in detail.
Thus, except for industry interaction, all the remaining four factors of service quality indirectly influence student loyalty with statistical significance p-value <0.01. The order in descending order of magnitude is academic, facilities, program and nonacademic.
5. Discussion, implications and limitations
5.1 Discussion
The most important contribution of this study was the successful construction of a scale of higher education service quality in the context of research in Vietnam from the findings and development of past research results and assessing the impact of these dimensions on student satisfaction and loyalty (Alves and Raposo, 2007; LeBlanc and Nguyen, 1997; Jain et al., 2013; Abdullah, 2006; Gamage et al., 2008; Tsinidou et al., 2010). Based on the direct and indirect effect analysis, this study highlighted the role of academic aspects and facilities, dimensions that significantly enhanced student satisfaction and student loyalty. Subsequently, programming issues and nonacademic aspects also improved two independent variables, but the levels of impact was lower than the academic aspect and facilities.
In addition, the study also demonstrated the role played by the university image in enhancing the relationship between student satisfaction and loyalty and results reinforced the views on the role of service quality in customer satisfaction and loyalty (Fornell et al., 1996; Gong and Yi, 2018) and both contexts in higher education (Alves and Raposo, 2007; Ali et al., 2016). In addition, the moderating role of the university image was the new finding of this study. Some scholars have examined the role of corporate image in enhancing the relationship between customer satisfaction and customer loyalty in mobile services (Kipkirong Tarus and Rabach, 2013) but in higher education services there have not been many studies verifying the moderating role of the university image. These findings will add to the theories of customer behavior in the higher education service environment and enrich understanding of the causal relationship between customer satisfaction and loyalty.
In addition, beyond the essential roles of academic, programming issues, facilities or nonacademic factors in student satisfaction verified in many studies (Nesset and Helgesen, 2009; Teeroovengadum et al., 2019), concerning the relationship between service quality and student satisfaction, the effects of industry interaction were not apparent. This result was partly due to the joint activities of universities and enterprises in Vietnam in training and creating an internship environment for students having only been implemented in recent years. The results of descriptive statistics have shown the outstanding limitations of this activity: the universities are only focusing on organizing workshops and enterprise trips. However, they have not been able to promote such activities as on-the-job training or inviting experts to teach students directly. Therefore, the values gained from the industry interaction activities may not be able to satisfy the needs of students, which are to have access to efficient knowledge and skills to create competitive advantages when entering the labor market.
5.2 Implications
From the above-mentioned empirical research results and comparisons, the research contributes to some implications for the university management teams and higher education management in improving customer satisfaction. Student satisfaction and loyalty are based on service quality and university image.
Firstly, universities need to focus on improving the quality of human resources and facilities. These are the factors that have a significant influence on the quality of academic and nonacademic experiences. In particular, the academic staff needs to improve their qualifications, knowledge and teaching methods through training activities and worthy remuneration policies. For support activities, it will be necessary to create more extracurricular activities, improve the efficiency of administrative procedures and improve the skills of administrative staff to increase student satisfaction. In addition, the training program needs to be flexible and updated to keep up with the advancements in knowledge. The contents of the training program need to be selective to better match the labor market requirements.
Secondly, while the research results indicate that there was no significant impact on student satisfaction, developing links with enterprises is an inevitable development trend to increase the efficiency of training activities. Therefore, the universities need to promote linkages with enterprises to focus on practical skills training activities through on-the-job training methods combined with increasing the invitation of experts to participate directly in teaching. For higher education authorities, it will be necessary to conduct a comprehensive assessment of the effectiveness of policies to promote the industry interaction at higher education institutions. Management agencies need to support universities by issuing policies to create flexibility in learning and practicing at enterprises as well as remuneration mechanisms for learners when doing internships at enterprises. These solutions will contribute to increased benefits for universities, enterprises and students.
Thirdly, the role of university image in enhancing the relationship between satisfaction and student loyalty shows the importance of this factor for the sustainable development of universities. In order to improve the image of the university, the universities need to focus more on communication, activities serving the community’s interests and, most importantly, comprehensive improvement of the quality of training to improve academic reputation. Among the selected items for the interview, the item with content about the faculty’s research ability was excluded because the students did not perceive them. Therefore, developing a university reputation based on the quality of scientific research and international publication will be a solution that should be considered and promoted.
Finally, based on the research findings, several implications were suggested for policymakers. This study shows the vital role of higher education service quality in student satisfaction and loyalty, two essential criteria for achieving the goal of sustainable development in higher education. Therefore, policymakers need to focus on strategies to strengthen the assessment and control of the quality of higher education services. In addition, service-marketing theories are used effectively in this research, so higher education should be viewed as a pure service with the customer at the center.
5.3 Conclusion, limitations and future research
Improving the quality of higher education is the key to achieving the United Nations SDGs. This study provides empirical evidence on the role of higher education service quality in the university student satisfaction and loyalty. In addition, the analysis helps identify important service quality dimensions in higher education and their impact on learners. These findings help improve the understanding of higher education and the awareness of the importance of service quality in higher education. This study also provides new perspectives on higher education through the lens of marketing in Vietnam – an emerging country, which has many challenges in developing higher education toward achieving the SDGs’ goals. Thus, the universities need to continue to apply strategies to improve service quality to attract learners, improve the quality of student outcomes and provide learners with knowledge and skills that meet the needs of the enterprise. This study is one of the first attempts to identify the attributes of service quality of higher education in Vietnam as a Southeast Asian emerging economy. These findings highlight five critical dimensions: academic, nonacademic, facilities and industry interaction and programming issues. This study also investigates the influence of these dimensions on student satisfaction and student loyalty. Furthermore, our research explores the profound findings about the moderating role of the university image. Based on the results, several implications were suggested to stakeholders including universities, enterprises and policymakers.
While this study has achieved its objectives, there are still some limitations: first, it applied the convenience sampling method. While the sample size is relatively large at 1,550, it is still a small portion extracted from the overall population. In addition, this research only focused on students studying at the universities of economics and business administration. Hence, future research should use a more reliable sampling method (e.g. probability sampling) and seek to collect data from a more representative sample, including students from different majors. Second, the rates of explaining the variation of the dependent variables were relatively low (29.2% and 23.8%), indicating a need to improve the research model with possible modifications, including additional factors such as personal values and individual personality to increase the validity of the model. Finally, future research is encouraged to test this study’s model in other emerging countries.
Figures
The dimensions of higher education service quality
| Authors | Dimensions | Country |
|---|---|---|
| LeBlanc and Nguyen (1997) | Contact personnel: faculty; reputation; physical evidence; contact personnel: administration; curriculum; responsiveness and access to facilities | N/A. The survey was conducted at a small business school |
| Kwan and Ng (1999) | Course content; concern for student; facilities; assessment; instruction medium; social activities and people | Hong Kong and China |
| Abdullah (2006) | Academic aspect; nonacademic aspect; program issues; access and understanding | Malaysia |
| Gamage et al. (2008) | Academic aspect; nonacademic aspect andfacilities aspect | Thailand and Japan |
| Tsinidou et al. (2010) | Academic staff; administration services; library services; curriculum structure; location; infrastructure and carrier prospects | Greece |
| Vanniarajan et al. (2011) | Programming issues; physical aspect; academic reputation; career opportunities; location and promotion | India |
| Jain et al. (2013) | Input quality; curriculum; academic facilities; industry interaction; interaction quality; support facilities and nonacademic processes | India |
| Mattah et al. (2018) | Physical facilities; teaching staff; administrative/supporting staff; physical environment; services (teaching/counseling/mentoring, etc.) and programs/courses | Ghana |
| Teeroovengadum et al. (2019) | Functional service quality and transformative quality | Mauritius |
Source(s): Table by the authors
Summary qualitative research results
| Items | Literature support | Interview support | In-depth interview result | Focus group interview result |
|---|---|---|---|---|
| X1: Academic qualification | Tsinidou et al. (2010) | Yes | 7/7 | 30/30 |
| X2: Professional experience | Yes | 7/7 | 30/30 | |
| X3: Communication skills | Yes | 7/7 | 30/30 | |
| X4: Positive attitude of lecturers | Abdullah (2006) | Yes | 7/7 | 30/30 |
| X5: Support from lecturers outside of class time | Peng and Samah (2006) | Yes | 6/7 | 28/30 |
| X6: Lecturers research productivity | Gamage et al. (2008) | No | 6/7 | 9/30 |
| X7: Lecturers’ ability to use information technology | Leblanc and Nguyen (1997) | Yes | 7/7 | 28/30 |
| X8: Options available of the program | Vanniarajan et al. (2011) | Yes | 6/7 | 25/30 |
| X9: The usefulness of the course syllabus in fulfilling | Peng and Samah (2006) | Yes | 7/7 | 30/30 |
| X10: The program is useful for career development | Yes | 7/7 | 30/30 | |
| X11: The program is highly up-to-date | Gamage et al. (2008) | Yes | 7/7 | 30/30 |
| X12: Flexible syllabus and structure | Abdullah (2006) | Yes | 7/7 | 29/30 |
| X13: Specialist programs provided | Vanniarajan et al. (2011) | Yes | 6/7 | 25/30 |
| X14: Flexibility of program to more within campus | No | 2/7 | 12/30 | |
| X15: The availability of quiet places to study in the university | Kwan and Ng (1999) | Yes | 6/7 | 30/30 |
| X16: The amount and availability of library facilities | Yes | 7/7 | 30/30 | |
| X17: The places provided for students to relax and lounge | Yes | 7/7 | 29/30 | |
| X18: The amount and availability of sports and recreational facilities | Yes | 7/7 | 27/30 | |
| X19: Layout of classrooms | Leblanc and Nguyen (1997) | Yes | 7/7 | 26/30 |
| X20: Medical facilities | Tsinidou et al. (2010) | Yes | 7/7 | 30/30 |
| X21: Transport facilities | Vanniarajan et al. (2011) | No | 3/7 | 13/30 |
| X22: Directional signposts on campus | Gamage et al. (2008) | No | 3/7 | 10/30 |
| X23: Rapid service | Tsinidou et al. (2010) | Yes | 7/7 | 30/30 |
| X24: Opportunities to participate and organize social activities | Jain et al. (2013) | Yes | 7/7 | 30/30 |
| X25: Administrative process like registration, examination, etc | Yes | 7/7 | 26/30 | |
| X26: Convenient opening hours | Abdullah (2006) | Yes | 7/7 | 29/30 |
| X27: Counseling services | Yes | 6/7 | 28/30 | |
| X28: Positive attitude of nonacademic staff | Yes | 7/7 | 27/30 | |
| X29: Applying contemporary teaching methods | Jain et al. (2013) | Yes | 7/7 | 28/30 |
| X30: Industrial tours for the students | Yes | 6/7 | 22/30 | |
| X31: Guest lectures from industry experts are organized | Yes | 6/7 | 21/30 | |
| X32: Summer training for student | No | 2/7 | 9/30 | |
| X33: Seminars/workshops are organized by enterprises and university | Yes | 6/7 | 23/30 | |
| X34: The institute organizes for on-the-job training | Yes | 5/7 | 24/30 |
Source(s): Table by the authors
Exploratory factor analysis and Cronbach’s alpha test results for dimensions of higher education service quality (N = 140)
| Items | Component | ||||
|---|---|---|---|---|---|
| Academic | Industry interaction | Facilities | Nonacademic | Programming issue | |
| ACA4 (X4) | 0.787 | ||||
| ACA3 (X3) | 0.779 | ||||
| ACA6 (X7) | 0.765 | ||||
| ACA2 (X2) | 0.759 | ||||
| ACA1 (X1) | 0.715 | ||||
| ACA5 (X5) | 0.634 | ||||
| INI2 (X30) | 0.842 | ||||
| INI1 (X29) | 0.837 | ||||
| INI5 (X34) | 0.824 | ||||
| INI3 (X31) | 0.807 | ||||
| INI4 (X32) | 0.788 | ||||
| FACI4 (X18) | 0.775 | ||||
| FACI3 (X17) | 0.742 | ||||
| FACI5 (X19) | 0.729 | ||||
| FACI1 (X15) | 0.722 | ||||
| FACI6 (X20) | 0.713 | ||||
| FACI2 (X16) | 0.659 | ||||
| NACA5 (X27) | 0.727 | ||||
| NACA6 (X28) | 0.720 | ||||
| NACA3 (X25) | 0.696 | ||||
| NACA2 (X24) | 0.695 | ||||
| NACA4 (X26) | 0.678 | ||||
| NACA1 (X23) | 0.670 | ||||
| PROG1 (X8) | 0.790 | ||||
| PROG3 (X10) | 0.706 | ||||
| PROG5 (X12) | 0.680 | ||||
| PROG4 (X11) | 0.676 | ||||
| PROG2 (X9) | 0.624 | ||||
| PROG6 (X13) | 0.558 | ||||
| Eigenvalue | 7.660 | 3.321 | 2.856 | 2.186 | 1.622 |
| Cronbach’s alpha | 0.872 | 0.885 | 0.832 | 0.829 | 0.843 |
| KMO = 0.814; Sig Bartlett’s test = 0.000; Total explained variance = 60.842% | |||||
Source(s): Table by the authors
Summary of the identified dimensions
| Dimension | Number of items | Definition | References |
|---|---|---|---|
| Academic (ACA) | 6 | This dimension assesses the qualifications and attitudes of the teaching staff | Abdullah (2006), Gamage et al. (2008), Tsinidou et al. (2010), Peng and Samah (2006) |
| Nonacademic (NACA) | 6 | This dimension reflects support for administrative procedures, extracurricular activities and attitudes and skills of administrative staff | Abdullah (2006), Jain et al. (2013) |
| Programming issues (PROG) | 6 | This dimension refers to the structure of the training program, the updating of the training program and the ability to operate the training program | Abdullah (2006), Vanniarajan et al. (2011) |
| Facilities (FACI) | 6 | This dimension evaluates the quality of the equipment system for teaching, room conditions and the system of facilities for extracurricular activities and entertainment | LeBlanc and Nguyen (1997), Abdullah (2006), Tsinidou et al. (2010) |
| Industry interaction (INI) | 5 | This dimension denotes the corporate activities of universities and enterprises to serve training, creating a practice environment for students in the learning process, events about job opportunities and career skills | Jain et al. (2013), Joseph and Joseph (1997) |
Source(s): Table by the authors
Demographic profile of the respondents
| Demographic characteristic | Frequency | % |
|---|---|---|
| Gender | ||
| Male | 728 | 46.97 |
| Female | 822 | 53.03 |
| Academic year | ||
| Second-year student | 522 | 33.68 |
| Third-year student | 588 | 37.93 |
| Fourth-year student | 440 | 28.39 |
| University | ||
| Thuongmai University | 396 | 25.55 |
| National Economics University | 324 | 20.90 |
| Foreign Trade University | 258 | 16.65 |
| Academy Finance – University in Vietnam | 308 | 19.87 |
| University Of Economics Ho Chi Minh City | 264 | 17.03 |
| Major in | ||
| Business administration | 216 | 13.94 |
| Marketing | 204 | 13.16 |
| Accounting | 142 | 9.16 |
| Commercial law | 132 | 8.52 |
| Economics | 170 | 10.97 |
| International trade | 68 | 4.39 |
| Brand management | 112 | 7.23 |
| Finance and banking | 158 | 10.19 |
| Human resource management | 150 | 9.68 |
| Tourism services and tour management | 198 | 12.77 |
Source(s): Table by the authors
Descriptive, factor loadings, CR, AVE and MSV
| Items | Factor loadings | CR | AVE | MSV | Mean | Standard deviation |
|---|---|---|---|---|---|---|
| Academic aspect – ACA | ||||||
| ACA1 | 0.685 | 0.858 | 0.503 | 0.436 | 3.755 | 0.841 |
| ACA2 | 0.674 | 3.575 | 0.846 | |||
| ACA3 | 0.696 | 3.441 | 0.853 | |||
| ACA4 | 0.721 | 3.568 | 0.777 | |||
| ACA5 | 0.816 | 3.632 | 0.809 | |||
| ACA6 | 0.653 | 3.528 | 0.676 | |||
| Nonacademic aspect – NACA | ||||||
| NACA1 | 0.743 | 0.867 | 0.522 | 0.312 | 3.421 | 0.884 |
| NACA2 | 0.697 | 3.526 | 0.874 | |||
| NACA3 | 0.755 | 3.587 | 0.840 | |||
| NACA4 | 0.700 | 3.607 | 0.860 | |||
| NACA5 | 0.712 | 3.464 | 0.970 | |||
| NACA6 | 0.724 | 3.455 | 0.973 | |||
| Programming issues – PROG | ||||||
| PROG1 | 0.713 | 0.864 | 0.514 | 0.436 | 3.399 | 0.858 |
| PROG2 | 0.732 | 3.485 | 0.875 | |||
| PROG3 | 0.732 | 3.383 | 0.877 | |||
| PROG4 | 0.718 | 3.600 | 0.858 | |||
| PROG5 | 0.710 | 3.341 | 1.044 | |||
| PROG6 | 0.694 | 3.326 | 1.039 | |||
| Facilities – FACI | ||||||
| FACI1 | 0.754 | 0.868 | 0.524 | 0.169 | 3.226 | 0.891 |
| FACI2 | 0.668 | 3.169 | 1.041 | |||
| FACI3 | 0.765 | 3.200 | 0.931 | |||
| FACI4 | 0.732 | 3.211 | 0.879 | |||
| FACI5 | 0.696 | 3.017 | 0.841 | |||
| FACI6 | 0.724 | 3.015 | 0.827 | |||
| Industry interaction – INI | ||||||
| INI1 | 0.793 | 0.864 | 0.561 | 0.083 | 3.253 | 0.990 |
| INI2 | 0.728 | 3.283 | 0.987 | |||
| INI3 | 0.754 | 3.106 | 1.035 | |||
| INI4 | 0.741 | 3.350 | 1.073 | |||
| INI5 | 0.726 | 2.994 | 1.076 | |||
| University – IMA | ||||||
| IMA1 | 0.777 | 0.788 | 0.556 | 0.012 | 3.231 | 1.262 |
| IMA2 | 0.808 | 3.207 | 1.289 | |||
| IMA3 | 0.641 | 3.364 | 1.205 | |||
| Satisfaction – SAT | ||||||
| SAT1 | 0.744 | 0.782 | 0.545 | 0.218 | 3.222 | 0.826 |
| SAT2 | 0.801 | 3.257 | 0.811 | |||
| SAT3 | 0.664 | 3.059 | 0.834 | |||
| Student loyalty – LOY | ||||||
| LOY1 | 0.709 | 0.812 | 0.522 | 0.218 | 3.272 | 0.767 |
| LOY2 | 0.663 | 3.345 | 0.815 | |||
| LOY3 | 0.657 | 3.341 | 0.786 | |||
| LOY4 | 0.845 | 3.277 | 0.778 | |||
Source(s): Table by the authors
Correlation and discriminant validity
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
|---|---|---|---|---|---|---|---|---|
| (1) IMA | 0.746 | |||||||
| (2) NACA | 0.092 | 0.722 | ||||||
| (3) FACI | 0.067 | 0.411 | 0.724 | |||||
| (4) PROG | 0.081 | 0.532 | 0.318 | 0.717 | ||||
| (5) ACA | 0.056 | 0.559 | 0.346 | 0.660 | 0.709 | |||
| (6) INI | −0.023 | 0.288 | 0.185 | 0.236 | 0.242 | 0.749 | ||
| (7) LOY | 0.034 | 0.326 | 0.216 | 0.275 | 0.307 | 0.152 | 0.722 | |
| (8) SAT | 0.108 | 0.396 | 0.357 | 0.418 | 0.447 | 0.182 | 0.467 | 0.738 |
Source(s): Table by the authors
Hypotheses testing and direct effects
| Hypotheses | β | S.E. | t-value | p-value | Findings | ||
|---|---|---|---|---|---|---|---|
| Nonacademic aspect | → | Satisfaction | 0.127 | 0.035 | 3.369 | *** | Accepted |
| Programming issues | → | Satisfaction | 0.152 | 0.041 | 3.736 | *** | Accepted |
| Academic aspect | → | Satisfaction | 0.219 | 0.063 | 5.093 | *** | Accepted |
| Facilities | → | Satisfaction | 0.183 | 0.031 | 5.944 | *** | Accepted |
| Industry interaction | → | Satisfaction | 0.032 | 0.026 | 1.096 | 0.273 | Rejected |
| Satisfaction | → | Loyalty | 0.488 | 0.032 | 15.496 | *** | Accepted |
Note(s): ***p-value <0.001
Source(s): Table by the authors
Result of measurement invariance test
| χ2 | df | χ2/df | AGFI | GFI | CFI | TLI | RMSEA | ∆ χ2 | p-value | |
|---|---|---|---|---|---|---|---|---|---|---|
| CI | 1879.783 | 1,088 | 1.728 | 0.923 | 0.937 | 0.968 | 0.963 | 0.022 | ||
| MI | 1924.262 | 1,124 | 1.712 | 0.923 | 0.935 | 0.968 | 0.964 | 0.021 | 44.479 | 0.157 |
Source(s): Table by the authors
Moderating effect hypotheses testing
| Paths | Low | High | Unconstrained model | Constrained model | ||
|---|---|---|---|---|---|---|
| β | p-value | β | p-value | |||
| University image as moderator | ||||||
| Satisfaction → Loyalty | 0.401 | *** | 0.573 | *** | χ2(1,098) = 1922.104 | χ2(1,099) = 1929.601 |
Note(s): Chi-square difference test: ∆ χ2 (1) = 7.497, p = 0.006 < 0.05 (significant). H3 is supported
***p-value <0.001
Source(s): Table by the authors
Indirect effect analysis result
| Construct | Indirect effect |
|---|---|
| Facilities → Satisfaction → Loyalty | 0.089** |
| Programming issues → Satisfaction → Loyalty | 0.074** |
| Nonacademic → Satisfaction → Loyalty | 0.062** |
| Academic → Satisfaction → Loyalty | 0.107** |
| Industry interaction → Satisfaction → Loyalty | 0.015NS |
Note(s): ***p < 0.001; ** <0.01; * <0.05 and NS: Nonsignificant
Source(s): Table by the authors
References
Abdullah, F. (2006), “The development of HEdPERF: a new measuring instrument of service quality for the higher education sector”, International Journal of Consumer Studies, Vol. 30 No. 6, pp. 569-581, doi: 10.1111/j.1470-6431.2005.00480.x.
Ali, F., Zhou, Y., Hussain, K., Nair, P.K. and Ragavan, N.A. (2016), “Does higher education service quality effect student satisfaction, image and loyalty? A study of international students in Malaysian public universities”, Quality Assurance in Education, Vol. 24 No. 1, pp. 70-94, doi: 10.1108/qae-02-2014-0008.
Alves, H. and Raposo, M. (2007), “Conceptual model of student satisfaction in higher education”, Total Quality Management, Vol. 18 No. 5, pp. 571-588, doi: 10.1080/14783360601074315.
Anderson, E.W. (1994), “Cross-category variation in customer satisfaction and retention”, Marketing Letters, Vol. 5 No. 1, pp. 19-30, doi: 10.1007/bf00993955.
Andreassen, T.W. and Lindestad, B. (1998), “The effect of corporate image in the formation of customer loyalty”, Journal of Service Research, Vol. 1 No. 1, pp. 82-92, doi: 10.1177/109467059800100107.
Annamdevula, S. and Bellamkonda, R.S. (2016), “Effect of student perceived service quality on student satisfaction, loyalty and motivation in Indian universities: development of HiEduQual”, Journal of Modelling in Management, Vol. 11 No. 2, pp. 488-517, doi: 10.1108/jm2-01-2014-0010.
Arambewela, R. and Hall, J. (2009), “An empirical model of international student satisfaction”, Asia Pacific Journal of Marketing and Logistics, Vol. 21 No. 4, pp. 555-569, doi: 10.1108/13555850910997599.
Athanassopoulos, A., Gounaris, S. and Stathakopoulos, V. (2001), “Behavioural responses to customer satisfaction: an empirical study”, European Journal of Marketing, Vol. 35 Nos 5/6, pp. 687-707, doi: 10.1108/03090560110388169.
Barich, H. and Kotler, P. (1991), “A framework for marketing image management”, MIT Sloan Management Review, Vol. 32 No. 2, pp. 94-104.
Brody, R.P. and Cunningham, S.M. (1968), “Personality variables and the consumer decision process”, Journal of Marketing Research, Vol. 5 No. 1, pp. 50-57, doi: 10.2307/3149793.
Brown, G.H. (1953), “Brand loyalty-fact of fiction”, Trademark Reporter, Vol. 43, p. 251.
Brown, R.M. and Mazzarol, T.W. (2009), “The importance of institutional image to student satisfaction and loyalty within higher education”, Higher Education, Vol. 58 No. 1, pp. 81-95, doi: 10.1007/s10734-008-9183-8.
Byrne, B.M. (2004), “Testing for multigroup invariance using AMOS graphics: a road less traveled”, Structural Equation Modeling, Vol. 11 No. 2, pp. 272-300, doi: 10.1207/s15328007sem1102_8.
Chandra, T., Hafni, L., Chandra, S., Purwati, A.A. and Chandra, J. (2019), “The influence of service quality, university image on student satisfaction and student loyalty”, Benchmarking: An International Journal, Vol. 25 No. 5, pp. 1533-1549, doi: 10.1108/bij-07-2018-0212.
Chankseliani, M. and McCowan, T. (2021), “Higher education and the sustainable development goals”, Higher Education, Vol. 81 No. 1, pp. 1-8, doi: 10.1007/s10734-020-00652-w.
Chen, C.T. (2019), “The mediating effect of brand identity on brand knowledge and the operational development of universities”, South African Journal of Business Management, Vol. 50 No. 1, pp. 1-11, doi: 10.4102/sajbm.v50i1.416.
Cronin, J.J. Jr, Brady, M.K. and Hult, G.T.M. (2000), “Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments”, Journal of Retailing, Vol. 76 No. 2, pp. 193-218, doi: 10.1016/s0022-4359(00)00028-2.
Cronin, J.J. Jr and Taylor, S.A. (1992), “Measuring service quality: a reexamination and extension”, Journal of Marketing, Vol. 56 No. 3, pp. 55-68, doi: 10.1177/002224299205600304.
Darawong, C. and Sandmaung, M. (2019), “Service quality enhancing student satisfaction in international programs of higher education institutions: a local student perspective”, Journal of Marketing for Higher Education, Vol. 29 No. 2, pp. 268-283, doi: 10.1080/08841241.2019.1647483.
Douglas, J., Douglas, A. and Barnes, B. (2006), “Measuring student satisfaction at a UK university”, Quality Assurance in Education, Vol. 14 No. 3, pp. 251-267, doi: 10.1108/09684880610678568.
El-Adly, M.I. (2019), “Modelling the relationship between hotel perceived value, customer satisfaction, and customer loyalty”, Journal of Retailing and Consumer Services, Vol. 50, pp. 322-332, doi: 10.1016/j.jretconser.2018.07.007.
Elliott, K.M. and Shin, D. (2002), “Student satisfaction: an alternative approach to assessing this important concept”, Journal of Higher Education Policy and Management, Vol. 24 No. 2, pp. 197-209, doi: 10.1080/1360080022000013518.
Farley, J.U. (1964), “Why does ‘brand loyalty’ vary over products?”, Journal of Marketing Research, Vol. 1 No. 4, pp. 9-14, doi: 10.1177/002224376400100401.
Ferguson, R.J., Paulin, M. and Leiriao, E. (2006), “Loyalty and positive word-of-mouth: patients and hospital personnel as advocates of a customer-centric health care organization”, Health Marketing Quarterly, Vol. 23 No. 3, pp. 59-77, doi: 10.1080/07359680802086174.
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50, doi: 10.2307/3151312.
Fornell, C., Johnson, M.D., Anderson, E.W., Cha, J. and Bryant, B.E. (1996), “The American customer satisfaction index: nature, purpose, and findings”, Journal of Marketing, Vol. 60 No. 4, pp. 7-18, doi: 10.2307/1251898.
Galeeva, R.B. (2016), “SERVQUAL application and adaptation for educational service quality assessments in Russian higher education”, Quality Assurance in Education, Vol. 24 No. 3, pp. 329-348, doi: 10.1108/qae-06-2015-0024.
Gamage, D.T., Suwanabroma, J., Ueyama, T., Hada, S. and Sekikawa, E. (2008), “The impact of quality assurance measures on student services at the Japanese and Thai private universities”, Quality Assurance in Education, Vol. 16 No. 2, pp. 181-198, doi: 10.1108/09684880810868457.
Ghobadian, A., Speller, S. and Jones, M. (1994), “Service quality: concepts and models”, International Journal of Quality and Reliability Management, Vol. 11 No. 9, pp. 43-66, doi: 10.1108/02656719410074297.
Gibson, A. (2010), “Measuring business student satisfaction: a review and summary of the major predictors”, Journal of Higher Education Policy and Management, Vol. 32 No. 3, pp. 251-259, doi: 10.1080/13600801003743349.
Giese, J.L. and Cote, J.A. (2000), “Defining consumer satisfaction”, Academy of Marketing Science Review, Vol. 1 No. 1, pp. 1-22.
Gong, T. and Yi, Y. (2018), “The effect of service quality on customer satisfaction, loyalty, and happiness in five Asian countries”, Psychology and Marketing, Vol. 35 No. 6, pp. 427-442, doi: 10.1002/mar.21096.
Grönroos, C. (1984), “A service quality model and its marketing implications”, European Journal of Marketing, Vol. 18 No. 4, pp. 36-44, doi: 10.1108/eum0000000004784.
Hai, N.C. (2022), “Factors affecting student satisfaction with higher education service quality in Vietnam”, European Journal of Educational Research, Vol. 11 No. 1, pp. 339-351, doi: 10.12973/eu-jer.11.1.339.
Hair, J.F. (2009), “Multivariate data analysis”.
Hanssen, T.E.S. and Solvoll, G. (2015), “The importance of university facilities for student satisfaction at a Norwegian University”, Facilities, Vol. 33 Nos 13/14, pp. 744-759, doi: 10.1108/f-11-2014-0081.
Hennig-Thurau, T., Langer, M.F. and Hansen, U. (2001), “Modeling and managing student loyalty: an approach based on the concept of relationship quality”, Journal of Service Research, Vol. 3 No. 4, pp. 331-344, doi: 10.1177/109467050134006.
Huang, Q. (2010), “The relationship between service quality and student satisfaction in higher education sector: a case study on the undergraduate sector of Xiamen University of China”, AU Journal of Management, Vol. 8 No. 1, pp. 38-44.
Hussien, F.M. and La Lopa, M. (2018), “The determinants of student satisfaction with internship programs in the hospitality industry: a case study in the USA”, Journal of Human Resources in Hospitality and Tourism, Vol. 17 No. 4, pp. 502-527, doi: 10.1080/15332845.2018.1486600.
Jain, R., Sahney, S. and Sinha, G. (2013), “Developing a scale to measure students' perception of service quality in the Indian context”, The TQM Journal, Vol. 25 No. 3, pp. 276-294, doi: 10.1108/17542731311307456.
Jaradat, G.M. (2017), “Internship training in computer science: exploring student satisfaction levels”, Evaluation and Program Planning, Vol. 63, pp. 109-115, doi: 10.1016/j.evalprogplan.2017.04.004.
Joseph, M. and Joseph, B. (1997), “Service quality in education: a student perspective”, Quality Assurance in Education, Vol. 5 No. 1, pp. 15-21.
Kazoleas, D., Kim, Y. and Anne Moffitt, M. (2001), “Institutional image: a case study”, Corporate Communications: An International Journal, Vol. 6 No. 4, pp. 205-216, doi: 10.1108/eum0000000006148.
Kipkirong Tarus, D. and Rabach, N. (2013), “Determinants of customer loyalty in Kenya: does corporate image play a moderating role?”, The TQM Journal, Vol. 25 No. 5, pp. 473-491, doi: 10.1108/tqm-11-2012-0102.
Kotler, P. and Fox, K.F. (1995), Strategic Marketing for Educational Institutions, 2nd ed., Prentice Hall, Englewood Cliffs, New Jersey.
Kuehn, A.A. (1976), “Consumer brand choice–a learning process?”, Mathematical Models in Marketing, Lecture Notes in Economics and Mathematical Systems, Springer, Berlin, Heidelberg, Vol. 132, doi: 10.1007/978-3-642-51565-1_7.
Kwan, P.Y. and Ng, P.W. (1999), “Quality indicators in higher education‐comparing Hong Kong and China's students”, Managerial Auditing Journal, Vol. 14 Nos 1/2, pp. 20-27, doi: 10.1108/02686909910245964.
Latif, K.F., Latif, I., Farooq Sahibzada, U. and Ullah, M. (2019), “In search of quality: measuring higher education service quality (HiEduQual)”, Total Quality Management and Business Excellence, Vol. 30 Nos 7-8, pp. 768-791, doi: 10.1080/14783363.2017.1338133.
Le, Q.H., Fuller, R., Hoang, T.H. and Nguyen, N. (2023), “Branding in higher education: a bibliometric analysis and research agenda”, Journal of Marketing for Higher Education, pp. 1-24, doi: 10.1080/08841241.2023.2289020.
Leblanc, G. and Nguyen, N. (1997), “Searching for excellence in business education: an exploratory study of customer impressions of service quality”, International Journal of Educational Management, Vol. 11 No. 2, pp. 72-79, doi: 10.1108/09513549710163961.
Lee, M. and Cunningham, L.F. (1996), “Customer loyalty in the airline industry”, Transportation Quarterly, Vol. 50 No. 2, pp. 57-72.
Malhotra, N.K., Kim, S.S. and Patil, A. (2006), “Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research”, Management Science, Vol. 52 No. 12, pp. 1865-1883, doi: 10.1287/mnsc.1060.0597.
Manzoor, S.R., Ho, J.S.Y. and Al Mahmud, A. (2021), “Revisiting the ‘university image model’for higher education institutions' sustainability”, Journal of Marketing for Higher Education, Vol. 31 No. 2, pp. 220-239, doi: 10.1080/08841241.2020.1781736.
Marginson, S. (2010), “Higher education in the global knowledge economy”, Procedia-Social and Behavioral Sciences, Vol. 2 No. 5, pp. 6962-6980, doi: 10.1016/j.sbspro.2010.05.049.
Mattah, P.A.D., Kwarteng, A.J. and Mensah, J. (2018), “Indicators of service quality and satisfaction among graduating students of a higher education institution (HEI) in Ghana”, Higher Education Evaluation and Development, Vol. 12 No. 1, pp. 36-52, doi: 10.1108/heed-10-2017-0006.
Nesset, E. and Helgesen, Ø. (2009), “Modelling and managing student loyalty: a study of a Norwegian university college”, Scandinavian Journal of Educational Research, Vol. 53 No. 4, pp. 327-345, doi: 10.1080/00313830903043117.
Nguyen, N. and LeBlanc, G. (2001), “Image and reputation of higher education institutions in students' retention decisions”, International Journal of Educational Management, Vol. 15 No. 6, pp. 303-311, doi: 10.1108/eum0000000005909.
Nguyen, H.V., Vu, T.D., Nguyen, B.K., Nguyen, T.M.N., Do, B. and Nguyen, N. (2022), “Evaluating the impact of E-service quality on customer intention to use video teller machine services”, Journal of Open Innovation: Technology, Market, and Complexity, Vol. 8 No. 3, p. 167, doi: 10.3390/joitmc8030167.
Oh, H.C. (1995), An Empirical Study of the Relationship between Restaurant Image and Customer Loyalty, Virginia Polytechnic Institute and State University.
Oldfield, B.M. and Baron, S. (2000), “Student perceptions of service quality in a UK university business and management faculty”, Quality Assurance in Education, Vol. 8 No. 2, pp. 85-95, doi: 10.1108/09684880010325600.
Palacio, A.B., Meneses, G.D. and Pérez, P.J.P. (2002), “The configuration of the university image and its relationship with the satisfaction of students”, Journal of Educational Administration, Vol. 40 No. 5, pp. 486-505, doi: 10.1108/09578230210440311.
Parasuraman, A.B.L.L., Zeithaml, V.A. and Berry, L. (1988), “SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality”, Vol. 64 No. 1, pp. 12-40.
Peng, P.J. and Samah, A. (2006), “Measuring students’ satisfaction for quality education in a e-learning university”, UNITAR e-Journal, Vol. 2 No. 1, pp. 11-21.
Picus, L.O., Marion, S.F., Calvo, N. and Glenn, W.J. (2005), “Understanding the relationship between student achievement and the quality of educational facilities: evidence from Wyoming”, Peabody Journal of Education, Vol. 80 No. 3, pp. 71-95, doi: 10.1207/s15327930pje8003_5.
Podsakoff, P.M., MacKenzie, S.B. and Podsakoff, N.P. (2012), “Sources of method bias in social science research and recommendations on how to control it”, Annual Review of Psychology, Vol. 63, pp. 539-569.
Preacher, K.J. and Hayes, A.F. (2008), “Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models”, Behavior Research Methods, Vol. 40 No. 3, pp. 879-891, doi: 10.3758/brm.40.3.879.
Qian, H., Ye, M., Liu, J. and Gao, D. (2022), “Evaluation of and policy measures for the sustainable development of national experimental teaching demonstration centers in Chinese universities and colleges”, Sage Open, Vol. 12 No. 1, 21582440211068517, doi: 10.1177/21582440211068517.
Rojas-Méndez, J.I., Vasquez-Parraga, A.Z., Kara, A.L.I. and Cerda-Urrutia, A. (2009), “Determinants of student loyalty in higher education: a tested relationship approach in Latin America”, Latin American Business Review, Vol. 10 No. 1, pp. 21-39, doi: 10.1080/10978520903022089.
Schlesinger, W., Cervera-Taulet, A. and Wymer, W. (2023), “The influence of university brand image, satisfaction, and university identification on alumni WOM intentions”, Journal of Marketing for Higher Education, Vol. 33 No. 1, pp. 1-19, doi: 10.1080/08841241.2021.1874588.
Slack, N., Singh, G. and Sharma, S. (2020), “The effect of supermarket service quality dimensions and customer satisfaction on customer loyalty and disloyalty dimensions”, International Journal of Quality and Service Sciences, Vol. 12 No. 3, pp. 297-318, doi: 10.1108/ijqss-10-2019-0114.
Statista (2023), “Education worldwide - statistics and facts”, available at: https://www.statista.com/topics/7785/education-worldwide/#topicOverview
Teeroovengadum, V., Nunkoo, R., Gronroos, C., Kamalanabhan, T.J. and Seebaluck, A.K. (2019), “Higher education service quality, student satisfaction and loyalty: validating the HESQUAL scale and testing an improved structural model”, Quality Assurance in Education, Vol. 27 No. 4, pp. 427-445, doi: 10.1108/qae-01-2019-0003.
Thomas, S. (2011), “What drives student loyalty in universities: an empirical model from India”, International Business Research, Vol. 4 No. 2, p. 183, doi: 10.5539/ibr.v4n2p183.
Tsinidou, M., Gerogiannis, V. and Fitsilis, P. (2010), “Evaluation of the factors that determine quality in higher education: an empirical study”, Quality Assurance in Education, Vol. 18 No. 3, pp. 227-244, doi: 10.1108/09684881011058669.
Üstünlüoğlu, E. (2017), “Teaching quality matters in higher education: a case study from Turkey and Slovakia”, Teachers and Teaching, Vol. 23 No. 3, pp. 367-382.
Van, T.D., Thi, K.C.N. and Thi, H.P.T. (2020), “Data survey on the factors affecting students' satisfaction and academic performance among private universities in Vietnam”, Data in Brief, Vol. 33, 106357, doi: 10.1016/j.dib.2020.106357.
Vanniarajan, T., Meharajan, T. and Arun, B. (2011), “Service quality in education: students' perspective”, European Journal of Social Sciences, Vol. 26 No. 2, pp. 297-309.
Žalėnienė, I. and Pereira, P. (2021), “Higher education for sustainability: a global perspective”, Geography and Sustainability, Vol. 2 No. 2, pp. 99-106, doi: 10.1016/j.geosus.2021.05.001.
Acknowledgements
The authors would like to sincerely thank Thuongmai University for creating a favorable working condition for academics to exchange and cooperate in researching, and enthusiastically support us to accomplish research.