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Service Quality and Student Satisfaction: The Moderating Role of University Culture, Reputation and Price in Education Sector of Pakistan.


Addressing quality in Higher Educational Institutions (HEIs) is a complex phenomenon. There are two important points to focus, academic quality and service quality. Academic quality emphasizes on the learning outcome, that is, the acquiring of knowledge and abilities in subject domains. While, service quality generally emphasizes the institutional services. The service aspects include both tangible and non-tangible features. The weaker comprehension of the service provision notion in HEIs has made them to concentrate more on measureable features like equipment, physical environment and assets. As a result, HEC generally funds universities more on these perceptions and hence evaluates them on the same too. There is a strong need of Pakistani HEIs to concentrate on the promotion of minds they employ and spend more on the human resources, in addition to their infrastructure.

The number of educational institutes in Pakistan has been increased during 2000-2015. In 2000, 45 universities and degree awarding institutions were operative which increased to 173, according to the figure of Higher Education Commission (HEC) of Pakistan in 2015. This increase in number of universities raised the need for the service quality in order to attract and retain students. Since, education is considered to be an important source in any economy and there exists a significant association between education and growth in economy of any country (Husain et al., 2009).

Number of researches have been done around the globe pertaining the impact of service quality and student satisfaction. Very few investigated price and university reputation as determinants of service quality or as independent variables. But, scarce literature is available in the context of Pakistani HEIs about the effect of mediator or moderator variables. The current research emphasized on three factors, which are price, university reputation, and university culture, as moderators and investigates the extent to which service quality impacts satisfaction of students, particularly in Pakistani context.

The key objective of this research is to identify the effect of service quality on student satisfaction with moderating effect of university culture, price and university reputation. This study investigated the following two research questions:

1. To what extent does the service quality impact on student satisfaction?

2. What role do university culture, university reputation and price have between the association of service quality and student satisfaction?

Literature Review

Service quality

Service quality (Parasuraman et al., 1985; Zeithaml, 1988; Chang, 2009) is difficult to be measured because of its intangible nature (Eshghi et al., 2008). It is essential to take all aspects of service quality in consideration that justifiy the situation in which specific industry is working (Lagrosen, 2001). Parasuraman et al. (1988) suggested that 'SERVQUAL', which measures service quality, is based on five aspects, these are tangibles, reliability, responsiveness, assurance, and empathy (Andam et al., 2015). Researchers are of the view that the dimensions used by SERVQUAL requires more generalization (Iwaarden & Van der Wiele, 2002).

Student satisfaction

Student satisfaction is "the extent to which an institutions' service performance meets the reckoning of the students" (Weirs-Jenssen et al., 2002; Salami, 2005; Roberts-Lombard, 2009). Satisfaction of students is affected by students' expectations and their perception about services and quality of services provided (Hu et al., 2009; Babaei, 2015).

Satisfaction of the students with university has institutional, individual, and social welfare. According to an institutional argument, contented students are much more probable to endure their studies and prosper academically. Student satisfaction can easily be achieved by outstanding service standards. This will help HEIs to attain a sustainable competitive edge in today's higher educational environment (Huang et al., 2012).

Helgesen and Nesset (2007) strengthened the findings of Mavondo et al. (2000), by stating that reputation of institutions is also an important consideration in determining satisfaction level of students. Those students who are satisfied, generate optimistic views about institution and endorse the same institute to other students.

University culture

With the passage of time, leadership involved in these institutions is getting aware of the culture (Ahangaran et al.. 2016) and its role in diversity towards university's development. Each university has its own elements which are interconnected with its cultural aspects (Bartell, 2003; Sporn, 1996). In short, university culture works as a web, where administration plays its role by keeping everyone connected (Bartell, 2003).

University culture can be regarded as a strong or weak culture (Sporn, 1996). Universities with strong cultures have same set of constant beliefs and ethics shared by both managers and the staff (Kotter & Heskett, 1992). If the links present between groups are lost then the culture is said to be weak. Strong university culture is distinguished by collective standards, strong behavioral values, compliance and readiness of the faculty to conform to these values. Weak culture is distinguished by the divergence from collective standards, lack of values (both written and oral) at the university (Antic & Ceric, 2008). It is imperative to comprehend university culture to promote student satisfaction with the university.

University reputation

The combined representation of university's multiple elements (internal and external elements), including the media-hold of the university over the period of time is known as university reputation. Fombrun and Van Riel (2003), and Van den Bosch et al. (2005) discussed five aspects of corporate reputation (visibility, distinctiveness, authenticity, transparency and consistency) by proposing a closed association between "corporate visual identity" and "corporate reputation". Researchers reported that a university's reputation can be made by the direct or indirect mediated experiences and the information that is obtained through various channels of communication and symbols (Bromley, 2000; Gotsi & Wilson, 2001; Grunig & Hung, 2002).

Numerous researches identified and recognized the importance of university reputation (Nguyen & LeBlanc, 2001; Hoyt & Brown, 2003). According to Sevier (1994), mostly students chose universities based on the perception they have about the reputation of the university. He further added that reputation is mostly perceived in two directions; horizontal and vertical. Horizontal context of reputation deals with the comparison of institutions on the basis of specific characteristics and dimension that a student would love to mention it to anyone. While vertical context of reputation deals with the student's perception of positive or negative emotions concerning institutes.


Price is the purchase of commodities and services by spending money or commodities (Hanif et al., 2010; Kotler & Amstrong, 2010). Zeithaml (1988) narrated price in the context of consumer perception as letting go of something to attain a particular service or commodity. Price can be defined as a concept of quality and contentment in the context of service (Zeithaml & Bitner, 2000). Bolton et al. (2003) defined the concept of fairness as the procedure through which rational outcomes can be attained. If the price is fair in the view of consumer, then it can be perceived as fairly charged (Clemes et al., 2008).

Customer contentment can be determined through the perceived price (Anderson et al., 1994; Zeithaml & Bitner, 1996). The factors that are used for identifying the price are reported by many researchers including Kao (2007). He described that perceived price of student can be examined with the help of different factors like, tuition charges, books cost and the coaching material. If customers get satisfied from the received services or product in return of what they had paid then they considered the product as quality product and feel satisfied (Bei & Chiao, 2001). There is dissimilarity between all these concepts including price, consumer contentment and service quality (Parasuraman et al., 1994). These interrelationships among the concepts of satisfaction, quality and price make it interesting to study all of these three concepts.

Research Methodology

It is a cross-sectional study, using quantitative approach to obtain primary data from undergraduate students of HEIs in Pakistan. Convenience sampling technique was used to save expenses and time.

Theoretical framework

Since, service quality showed an enhancing impact on student satisfaction (Parasuraman et al., 1985; Buttle, 1996), there are rare studies specifically measuring the effect of service quality on student satisfaction by using university culture, price and university reputation as moderators. The theoretical model is shown in Figure 1.


The research hypotheses for this research are as follows:

H1. Service quality significantly impacts student satisfaction.

H2. University culture has a moderating relation between service quality and student satisfaction.

H3. University reputation has a moderating relation between service quality and student satisfaction.

H4. Price has a moderating relation between service quality and student satisfaction.


Data were collected from students of 20 HEIs as a sample through a questionnaire. Personal visits and responses via email were collected. Students were mostly from business administration, telecommunication engineering, computer engineering, electrical engineering, software engineering and computer sciences programs. Total questionnaires rotated were 900. A response rate of 83% was achieved as 747 questionnaires were received.

Questionnaire statements were adopted form different empirical studies, shown in Table 1.

Data were analyzed using different techniques through SPSS-21 and AMOS-18 including reliability analysis, confirmatory factor analysis and correlation analysis. The impact of the independent variable was investigated on the dependent variable in presence of moderators through Structural Equation Modeling (SEM) to test the proposed association in order to find the impact of each constructs, directly or indirectly and through moderator.

Reliability and Validity Analysis

Reliability analysis

Reliability of the instrument is the essential pillar of the research process. Reliability of any method or experiment is achieved, when "a test, an experiment or method gives the same results repeatedly" (Carmines & Zeller, 1979). Normally, Cronbach's coefficients value of above 0.70 is considered suitable and scale with this value or greater is considered reliable (Murphy & Balzer, 1989). According to Nunnally (1978), the value above 0.6 is also acceptable. Table 2 contains alpha values of instrument and shows that all the values are above 0.6 which shows that scale/instrument used in this research is acceptable and highly reliable.

Validity analysis

Validity is "when measurement procedures truly identify what it aims to identify and ensures reliability through similar findings of the investigation if the measurement procedure is repetitive". Validity is an imperative feature of the instrument because it ensures suitability of construct in particular context and more importantly measures what it is supposed to measure (Suter, 2006).

Validity is classified as "content validity, construct validity and criterion-related validity" by Creswell (2002), Gay and Airasian (2003), and Muijs (2004). Criterion-related validity embraces simultaneous and analytical validity. Hypothesized constructs upon which researchers build their study, are validated through measuring their construct validity.

Construct validity

The degree to which a construct is precisely operational is known as construct validity (Yin, 2003). This study obtained a greater construct validity by devising the procedures after detailed study of research works available regarding variables used. These procedures have high validity as they are imitated in the number of studies carried out earlier. The improvement in the questionnaire is completed by consulting skilled experts in the field of service quality and education. After pilot survey, language of the questionnaire is reviewed to have further operational form of the questionnaire and its consideration amongst the respondents. The questionnaire is confirmed subsequently incorporating the inputs of the experts and the respondents of the pilot survey, thus leaving no grounds for the misrepresentation of the instruments.

Confirmatory Factor Analysis (CFA)

According to Schreiber et al. (2006), CFA is normally used to analyze the validity of the experimental variables in association with latent constructs. Factors were loaded with their items and standardized estimates were calculated for validity analysis. Estimates showed that factor loading is greater than 0.3, ensuring no item is needed to be removed from measuring instrument and all the items used in the scale are valid in the particular context in which data were collected. Furthermore, goodness-of-fit indices are used to assess the model.

In Table 3, CMIN/DF for the default model is 2.105 < 5; it reveals an acceptable range of model fitness (Tabachnick & Fidell, 2007) showing a significant model at p-value 0.00<0.05.

Baseline Comparisons (NFI, TLI, and CFI), Parsimony-Adjusted Measures (PRATIO, PNFI and PCFI), and RMSEA showed a good model fit as per standards set by Byrne (1998), Mulaik et al. (1989) and Hair et al. (1998), respectively.


Demographic profiles and frequencies

The percentage and frequency of each of the demographic variable is given in Table 4.

Correlation analysis

Normality of data is checked using non-parametric test of One-Sample Kolmogorov-Smirnov Test. Data are found to be normal. Further, the strength of interrelationships using Pearson correlation is checked.

Table 5 illustrates p-values of all variable less than 0.05 and 0.01, which depicts that all the causal relationships are significant. Analysis revealed that there exists a positive association among service quality, student satisfaction and three moderating variables, indicating if service quality improves then the student satisfaction also improves.

Moderation Analysis using Structure Equation Model (SEM) SEM is more diverse and multipurpose as compared to most of other multivariate procedures as it permits to analyze multiple relationships between dependent and independent variables simultaneously.

As shown in Table 6 of CMIN, p-value is 0.034 and CMIN/DF is 4.508, representing a good model fit as described by Tabachnick and Fidell (2007). Findings suggested that the research framework proposed in this study can be utilized to identify the effect of service quality on satisfaction. Since moderators used in this relationship are the major contributors, future research can be initiated in any service industry using this framework. Baseline comparisons shows that NFI statistics is 1.000, representing a very perfect model fit. TLI is 0.990, CFI statistics is 1.000, showing a standard model fit. All these model fit indices clearly depict the causal model. RMSEA value at .069 also showed a model fit within reasonable assortments (Hair et al., 1998).

Results in Table 7 of Structural Equation Modeling (SEM) showed that service quality has a substantial positive impact on student satisfaction (p-value of 0.00<0.05). Beta value for direct relation is 0.671 showing a strong effect of service quality on student satisfaction.

Estimates and Findings

In the presence of university reputation (as a moderator), there exists a significant negative moderating association of university reputation and price between service quality and student satisfaction. In contrast, university culture plays a substantial positive moderator in relationship of service quality and student satisfaction.


This research investigated the relationship between service quality and student satisfaction with the moderating role of university reputation, price, and university culture. Results identified that service quality strongly and significantly impacts the students' satisfaction in education sector. Findings suggested that currently it is imperative for the Higher Education Institutes (HEIs) to guarantee quality of service for students to augment students' satisfaction. Findings are consistent with the studies accomplished by Kordshouli et al. (2016), Ferdousipour (2016), Azizi et al. (2014), Ali and Mohamed (2014), Shah (2013), Enayati et al. (2013), Rahim (2012), and Dado et al. (2012).

Along with service quality, strong university culture can bring student satisfaction into account. Findings of the study suggested that there is a substantial moderating association of university culture between service quality and student satisfaction. Findings are in accordance with the results of Uprety and Chhetri (2014).

Reputation of any university or HEI is important in determining the worth of that institute. Findings suggested that there is a substantial moderating association of university reputation between service quality and student satisfaction. Results are consistent with the studies conducted by Dib and Alnazer (2013), and Ntabathia (2013).

Outcomes suggested that there is a substantial moderating association of price between service quality and student satisfaction. These conclusions supported the study conducted by Tuan (2012).


This study focused on the service quality of Higher Educational Institutes (HEIs) to attain student satisfaction through the moderating effect of university reputation, price and university culture. Conclusively, the association between service quality and student satisfaction is positively strengthened by the university culture, while price and university reputation negatively strengthened the relationship.

Although it was tried to collect data from all the four provinces of Pakistan, because of logistics and financial restrictions, the study is limited to only 20 Universities/HEIs out of 173.

Education sector managers are suggested to extend the quality enhancement scope to student satisfaction because satisfaction is the very next predictor influencing student's future behavioral intentions. HEIs should improve overall service for a long-term revenue generation. To meet the international standards of education, special attention is required to upgrade infrastructure and other allied facilities, as well as to improve the communication skills of the students, to establish efficient quality enhancement cells and job placement cells, and to arrange industrial visits and study tours to enhance practical skills of the students.

This research has been conducted on much wider scale but it only measures the perceptions of the students. Further studies can be done to explore the perception of other stakeholders including the academic, non-academic staff and parents. This study is a cross-sectional survey. In future, a longitudinal investigation approach will offer a foundation for more valuable clarifications and explanations. Future researchers should advance inspection of the impact of service quality on student satisfaction in the presence of customer value or on HEIs performance.


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Shahzadi Saima Saleem (1), kamran Moosa (2), Abeer Imam (3*), Rashid Ahmed Khan (4)

(1.) COMSATS Institute of Information Technology, Lahore, Pakistan

(2.) PIQC Institute of Information Technology, Lahore, Pakistan

(3.) Department of Management Sciences, COMSATS Institute of Information Technology, Lahore, Pakistan

(4.) COMSATS Institute of Information Technology, Lahore, Pakistan

(Received: November 21, 2016; Revised: January 9, 2017; Accepted: January 11, 2017)

(*) Corresponding Author, Email:

DOI: 10.22059/ijms.2017.217335.672304
Table 1. Measure

Sr. No.   Construct              Author                    No. of Items

 1        Tangibles              Owlia &                    7
                                 Aspinwall (1998)
 2        Competence             Owlia &                    6
                                 Aspinwall (1998)
 3        Outcome                Gronroos (1990)            3
 4        Content                Owlia &                    4
                                 Aspinwall (1998)
 5        Reliability            Owlia &                    5
                                 Aspinwall (1998)
 6        Empathy                Parasuraman                3
                                 et al. (1991)
 7        Assurance              Parasuraman                4
                                 et al. (1991)
 8        Responsiveness         Parasuraman                4
                                 et al. (1991)
                                 Quinn & Rohrbaugh
                                 (1983); Elliott &
 9                               Shin (2002); Ijaz         17 (*)
                                 et al. (2011); Abdullah
          University culture     (2006); Zindi (1994);
                                 Tinklin et al.
                                 (2004); Presley
                                 et al. (1993)
10        Price                  Sweeney &                  2
                                 Soutar (2001)
11        University             Petrick (2002);            5
          reputation             Sweeney & Soutar
12        Student satisfaction   Athiyaman (1997);          8
                                  Cronin et al. (2000);
                                 McDougall &
                                 Levesque (2000)

(*) All of these questions were modified as per Pakistani context.

Table 2. Reliability analysis

Sr. No.   Dimension              No. of items  Cronbach Alpha ([alpha])

1         Service Quality        36             0.930
2         Price                   2             0.679
3         Reputation              5             0.772
4         University Culture     17             0.892
5         Student Satisfaction    8             0.866

Table 3. Model fit summary

Model             P       CMIN/DF   NFI      TLI     CFI     PRATIO
                                    Deltal   rho2

  Default model   0.000   2.105     0.796    0.872   0.881   0.935
Saturated model                     1.000            1.000   0.000
  Independence    0.000   9.663     0.000    0.000   0.000   1.000

Model             PNFI    PCFI    RMSEA

  Default model   0.745   0.824   0.038
Saturated model   0.000   0.000   0.108
  Independence    0.000   0.000

Table 4. Demographic characteristics

Category                 Percentage   Frequency

Male                     68.9%        515
Female                   31.1%        232
Age Group
Below 20 years           19.3%        144
20- 22 years             65.1%        486
Above 22 years           15.7%        117
Urban area               64.8%        484
Rural area               35.2%        263
Program of study
Business                 22.8%        170
Computer science         21.6%        161
Electrical engineering   16.5%        123
Software engineering     14.6%        109
Telecommunication        14.3%        107
Computer engineering     10.3%         77
Year of Study
1st year                 11.5%        123
2nd year                 30.9%        231
3rd year                 30.8%        230
4th year                 21.8%        163
Status of the HEIs
Public sector            70%          523
Private sector           30%          224

Table 5. Summarized correlation analysis (N-747)

                              Price       UR          SS

Price   Pearson correlation   1
        Sig. (2-tailed)
UR      Pearson correlation   0.427 (*)   1
        Sig. (2-tailed)       0.000
SS      Pearson correlation   0.477 (*)   0.662 (*)   1
        Sig. (2-tailed)       0.000       0.000
UC      Pearson correlation   0.438 (*)   0.645 (*)   0.692 (*)
        Sig. (2-tailed)       0.000       0.000       0.000
SQ      Pearson correlation   0.430 (*)   0.652 (*)   0.720 (*)
        Sig. (2-tailed)       0.000       0.000       0.000

                              UC          SQ

Price   Pearson correlation
        Sig. (2-tailed)
UR      Pearson correlation
        Sig. (2-tailed)
SS      Pearson correlation
        Sig. (2-tailed)
UC      Pearson correlation   1
        Sig. (2-tailed)
SQ      Pearson correlation   0.755 (*)   1
        Sig. (2-tailed)       0.000

(*) Correlation is significant at the 0.01 level (2-tailed).

Table 6. Model fit summary and baselines comparisons

Model               P      CMIN/DF  NFI Deltal  TLI rho2   CFI     RMSEA

Default model       0.034    4.508  1.000       0.990      1.000   0.069
Saturated model                     1.000                  1.000   0.684
Independence model  0.000  350.200  0.000       0.000      0.000

Table 7. Model summary and coefficients (with moderator)

Dependent                        Independent  Estimate  SE     Critical
variable                         variable                      ratio

Student                          Service       0.671    0.143    4.706
satisfaction   [left arrow] --                 quality
as moderator
Student                          University   -0.221    0.009  -23.761
satisfaction   [left arrow] --   reputation
Price as
Student                          Price        -0.258    0.006  -44.169
satisfaction   [left arrow] --
culture as
Student                          University    0.400    0.041    9.771
satisfaction   [left arrow] --   culture

Dependent                        P-values   Result

Student                          0.000      Accept
satisfaction   [left arrow] --
as moderator
Student                          0.000      Accept
satisfaction   [left arrow] --
Price as
Student                          0.000      Accept
satisfaction   [left arrow] --
culture as
Student                          0.000      Accept
satisfaction   [left arrow] --
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Article Details
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Author:Saleem, Shahzadi Saima; Moosa, kamran; Imam, Abeer; Khan, Rashid Ahmed
Publication:Iranian Journal of Management Studies
Article Type:Report
Geographic Code:9PAKI
Date:Jan 1, 2017
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