An Empirical Study on the Student Experience of Higher Education Service Quality in Taiwan

Transcription

1 International Journal of Management Sciences Vol. 6, No. 12, 2016, An Empirical Study on the Student Experience of Higher Education Service Quality in Taiwan Yu-Chuan Chen 1 Abstract This study develops a multidimensional scale to measure the quality of the service offered to students in higher education. It explores theories on service quality and Importance-performance analysis in a literature review in order to discuss the nature of service quality scales. A questionnaire consisting of 31 items was developed to measure the service quality in higher education. A total of 100 students (in the pilot study) and 474 students (in the main study) answered the questionnaire using Likert scales. A construct validation using exploratory factor analysis displays a structure consisting of three factors. The study provides valuable insights into students perception of the quality of the services offered by universities and their satisfaction s. The final section summarizes the main conclusions and discusses the limitations of the research. This study not only provides specific analyses, but also suggests strategies; its results will help university managers identify areas of strategic focus and develop service quality strategies. Keywords: higher education; service quality; importance-performance analysis; Taiwan 1. Introduction In an era of global competition, quality service is the key to survival and success. Many experts concur that the most powerful, competitive marketing and business trend is service quality (Abdullah 2005: 305). Service quality has emerged as a key strategic management issue (Nejati and Nejati 2008). Service quality is crucial for achieving success; it helps managers assess how well the organization is doing both over time and relative to others in the industry. Managers must therefore constantly keep track of information about whether their organization is meeting its customers needs (Aigbedo and Parameswaran 2004). To attract customers, serve their needs, and retain them, service providers and researchers seek to understand consumers' expectations and perceptions of service quality. Service quality research in higher education is of more recent origin than research in the commercial sector, but higher education has been a top priority on the policy agendas of many nations (Sultan and Wong 2010). Service quality has emerged as a pervasive strategic force and key strategic issue in educational institutions. The pressures driving successful organizations toward top quality service make measuring service quality and its subsequent management extremely important (Abdullah 2006). The higher education sector can be considered a marketplace, and university education, a marketable service. In higher institutions such as universities, the delivery performance of service quality will determine the size of student enrollment. Students tend to select schools that provide high quality services and high satisfaction (Tahir, Bakar, and Ismail 2010: 133). Many researchers argue that it is important for the public sector to adopt marketing concepts (Gudlaugsson and Eysteinsson 2011; Lee, Wadood, Som, and Alzaidiyeen 2010; Malik, Danish, and Usman 2010). Other scholars have focused specifically on service quality in higher education (Abdullah 2005; Abdullah 2006; Abouchedid and Nasser 2002; Gallifa and Batallé 2010; Hill 1995; Nadiri, Hussain, and Kandampully 2011; Nejati and Nejati 2008; Yeo 2008). Even though service quality has 1 Associate professor Graduate Institute of Educational Policy and Administration National Taiwan Normal University 162, Sec.1, Heping E. Rd., Taipei City 10610, Taiwan ROC 2016 Research Academy of Social Sciences 582

2 International Journal of Management Sciences become an important issue for universities, few studies have explored this issue in the context of Taiwan or Asia. Mangers are increasingly realizing that higher education can be regarded as a business-like service industry, and are beginning to focus on meeting or even exceeding their students needs (Gruber, Fuß, Voss, and Gläser-Zikuda 2010). In a highly competitive environment, students have become more discriminating and more demanding of their colleges and universities. Universities must thus understand their expectations (Jain, Sinha, and De 2010). The purpose of this study is to develop a multidimensional scale to measure the quality of the services offered by higher education to students. As an IPA analysis of education service quality is a prerequisite for its improvement in higher education, this paper explores some theories on service quality and IPA in a literature review in order to examine the nature of service quality scales and develop a new one. This survey collects student views on the services provided by their universities. A questionnaire consisting of 31 items was developed to measure service quality in higher education. The final section summarizes the study s main conclusions and discusses its limitations. Service Quality While the dimensions of service quality vary depending on the industry, many consider the overall service quality construct to be unresolved (Caruana, Ewing, and Ramaseshan 2000). The most well-known dimensions have been proposed by Parasuraman, Zeithaml, and Berry (1985). As the relevant items differ depending on the type of service, uniform service quality dimensions had to be defined for all types of services (Aigbedo and Parameswaran 2004). Parasuraman, Zeithaml, and Berry (1988) have proposed five dimensions and developed a 22-item instrument for assessing service quality. The well-known SERVQUAL instrument is said to measure the relevant dimensions of perceived quality across all service industries (Abdullah 2006). The SERVQUAL model, used by many researchers, includes financial services (Host and Andersen 2004), education (Tahir, Bakar, and Ismail 2010), the service industry (Eastwood, Brooker, and Smith 2005; Fareena and Merlin 2000), and public services (Brysland and Curry 2001). The five dimensions of service quality are the following (Parasuraman, Zeithaml, and Berry 1988): A. Tangibility: physical facilities, equipment, and appearance of personnel B. Reliability: ability to perform the promised service dependably and accurately C. Responsiveness: willingness to help customers and provide prompt service D. Assurance: knowledge and courtesy of employees and their ability to inspire trust and confidence E. Empathy: the caring, individualized attention provided to customers The best way to measure service quality is still unknown, however. Quality in higher education is a complex and multifaceted concept lacking a single correct definition; there is no consensus concerning the best way to define and measure service quality. Each higher education stakeholder has a unique view of quality conditioned by his or her own needs (Gruber, Fuß, Voss, and Gläser-Zikuda 2010), with factors that might include good communication with staff, convenient opening hours, and good teaching. Abdullah (2006) has proposed the HEdPERF (Higher Education PERFormance-only) scale, which uses six dimensions (41 items) non-academic aspects, academic aspects, reputation, access, program issues, and understanding that are distinct and conceptually clear. These six factors can be described as follows: A. Non-academic aspects are the items essential for students fulfillment of their study obligations and relate to the duties carried out by non-academic staff. B. Academic aspects are the factors that are solely the responsibility of the academics. C. Reputation items suggest the importance of projection of a professional image by higher learning institutions. D. Access comprises items relating to such issues as approachability, ease of contact, availability, and convenience. E. Program issues relate to the importance of offering wide ranging and reputable academic specializations with flexible structures and syllabi. F. Understanding comprises items related to understanding students health services and counseling needs. 583

3 U. C. Chen The definition of customer and stakeholder in higher education differs considerably from that in other industries. This paper is concerned with only one higher education stakeholder: students. Allen and Davis (1991) and Holdford and Patkar (2003) define educational service quality as students overall evaluation of the services received as part of their educational experience. The merged HEdPERF- SERVQUAL moderating scale assesses the relative strengths and weaknesses of each instrument. Given the wide variety of cultures and countries requiring study, the author has modified HEdPERF-SERVQUAL and has deleted, increased, and merged some of its items. As a result, the 31-item instrument has been empirically tested using both the exploratory and confirmatory factor analyses, which, the author hopes, will provide a better perspective on higher education in Asian countries. Importance-Performance Analysis The importance-performance analysis (IPA) provides management with a useful marketing strategy development tool (Martilla and James 1977). The IPA is a simple but effective technique for studying customer satisfaction as a function of expectations related to both importance and performance (Wu and Shieh 2010). The two-dimensional IPA model is divided into four quadrants, with importance on the y-axis and performance on the x-axis (the four-quadrant IPA matrix is shown in Figure 1). Results are displayed in the form of a four-quadrant matrix in the following way (Martilla and James 1977; Tahir, Bakar, and Ismail 2010; Wong, Hideki, and George 2011; Yavas and Shemwell 2001): A. Keep up the good work: Quadrant I is evaluated high in both importance and performance. What is needed here is to keep up the good work. B. Concentrate here: All the attributes falling into this quadrant represent key top-priority areas that need to be improved. Customers feel that low service prices are very important but indicate low satisfaction with the dealer s performance. C. Low priority: The attributes falling into this quadrant are not important and pose no threat to the organization. Customers are not satisfied with the performance of the attribute, but it is relatively unimportant. D. Possible overkill: The dealer is judged as doing a good job of sending out maintenance notices, but customers attach only slight importance to them. Thus, customers are satisfied with the performance, but the attribute is relatively unimportant. Extremely Important Fair Concentrate Here Quadrant Ⅱ Keep Up the Good Work Quadrant Ι Excellent performance performance Low Priority Quadrant Ⅲ Possible Overkill Quadrant Ⅳ Slightly important Figure 1: Importance-Performance Analysis Source: Matrilla and James (1977) This paper s technique is designed to identify the weaknesses and strengths of higher education. The first factor is the attributes relative importance to students, and the second is students assessment of the school s performance in terms of those attributes. The IPA is thus a simple but effective technique that can provide higher education managers with effective strategies. 584

4 2. Methods Research Instrument International Journal of Management Sciences This study s main objective is measuring service quality in higher education by surveying students perceptions of their schools. A self-administrated questionnaire with two sections was used to collect information. The author informed the students and teachers of the study s intentions and presented the service quality instrument as a viable measurement technique. After several discussions, two of the questions were deleted, as they were not considered relevant to service quality at higher education institutions. As a result, the questionnaire comprises 31 questions. The first section consists of 31 attributes concerning the primary means (as indicated in the literature) by which the service quality construct may be realized; the 31 attributes were based on the literature review outlined above and on the author s practical experience. The importance of each attribute was rated using a five-point Likert scale with descriptive equivalents ranging from 5 ( very important ) to 1 ( very unimportant ). The performance of each attribute was rated from 5 ( very satisfied ) to 1 ( very unsatisfied ). The second section consisted of three questions on respondent demographics: gender, grade, and college type. Data Collection and Response Rate The author contacted teachers at universities and was granted permission to survey students over a period of two months. Data were collected from universities in Taiwan from April to May The author distributed 600 questionnaires and received 474 usable questionnaires in response, for a valid return rate of 82%. Data Analysis The author computed the relevant descriptive statistics, including the number of respondents and sample percentage, using the respondents demographic profiles. The author then applied an exploratory factor analysis (EFA) with varimax rotation to study the perceptions of importance and calculated Cronbach s alpha coefficients to check the service quality factors. To examine the factors structures and inter-correlations, a confirmatory factor analysis (CFA) via structural equation modeling was carried out with LISERAL. A One Way Analysis of Variance (ANOVA) was then used to test the responses to the research question to determine how students perceived their schools service quality, after allowing for gender, grade, and college type. The hypothesis was tested at a 0.05 of significance. The author used the IPA to compare the importance and performance of higher education service quality. 3. Statistical Analysis Demographic Profile of the Respondents This research focused on students attitudes to the quality of services provided by higher education. Data were collected at universities in Taiwan from April to May Some of the relevant demographic information, such as gender, grade, and college, are shown in Table 1. The sample comprised 28.5 percent males and 71.5 percent females; 37.8 percent were freshmen, 30.8 percent were sophomores, and 31.4 percent were juniors; 34.0 percent were in the College of Education, 9.7 percent were in the College of Management, 19.8 percent were in the College of Science, and 36.5 percent were in the College of Humanities and Liberal Arts, as shown in Table

5 Pilot Study U. C. Chen Table 1: The Demographic Information from the Survey Item Classification Number of times Percentage (%) Gender Male Female Grade Freshman Sophomore Junior College Education Management Science Humanities and Liberal Arts A pilot study was used to determine whether the study s instrument was valid and reliable. Hundred students participated in the pilot study over two weeks. A factor analysis was conducted prior to the finalization of the questionnaire. An EFA was conducted to assess the construct validity of the instrument. The factor analysis identified the dimensional structure of the service quality of Taiwan s higher education sector. The author applied a factor analysis to respondents responses on Importance without specifying the number of factors to be extracted. The results were significant at x² = (p = 0.01), a clear indication of suitability. A Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was computed to quantify the degree of variable inter-correlation; the results indicated an index of 0.941, which is considered good, as the KMO value exceeds the recommended value of 0.6 and the Barllett Test of Sphericity results support the factorability of the correlation matrix (Pallant 2005). These results indicate that the sample data were suitable for factor analysis. The 31 items were rotated by using a principal component analysis with varimax rotation, while retaining factors with eigenvalues greater than 1 (Kaiser 1960). Attributes with rotated factor loadings greater than 0.50 in absolute value were considered very significant (Hair, Anderson, Ronald, Tatham, and Black 1998) and were retained as constitutive attributes (Linda and To 2010). The decision to include a variable in a factor was based on whether the factor loading was greater than 0.5; all factors whose eigenvalues were greater than 1 were retained in the factor solution. This result explains the change to a 71.77% cumulative percentage of variance, with factor 1 contributing 25.12%, factor 2 contributing 24.07%, and factor 3 contributing 22.58%, respectively (see Table 2). The results presented in Table 2 are clearly related to the determinants of service quality. The factor analysis revealed three factors. The first (eigenvalue = 7.788) was labeled School and accounted for % of the covariance. Fifteen items defined this factor, with loadings ranging from 0.72 to The second factor (eigenvalue = 7.460) was labeled Staff and accounted for percent of the covariance. The nine items describing this factor, with loadings ranging from 0.75 to 0.59, were strongly related to staff issues. For example, this factor included the staff giving personal attention to students, showing a sincere interest in solving problems, and keeping promises in a timely way. The third factor (eigenvalue = 7.001) was labeled Teacher and accounted for % of the covariance. Seven items comprised this factor, with loadings ranging from 0.69 to This factor underscored teachers concerns for the importance of devoting time to teaching (e.g., of being caring and courteous with students, being knowledgeable of course content, and giving feedback on progress). A full list of the 31 refined scale items is shown in Table 2. The factor analysis revealed that each item had good construct validity. The measures reliability was assessed using Cronbach s alpha. The closer the Cronbach s alpha coefficient is to 1.0, the greater the internal consistency of the items in the scale. A Cronbach s alpha of 0.7 or above can be considered adequate (Watson and Thompson 2006). The Cronbach s alpha coefficients of Importance ranged from to 0.965, well above the accepted of 0.70 (Nunnally 1978). The Cronbach s alpha coefficient was in factor 1, in factor 2, and in factor 3. The factor analysis thus demonstrated that each item had satisfactory reliability and validity. 586

6 International Journal of Management Sciences Table 2: Results of Factor Analysis Item Factor loadings Communalities Factor 1 Factor 2 Factor 3 School 1.To get feedback for improvement To have knowledge of systems/procedures To have service delivery procedures To give counseling services To give reputable academic programs To have a flexible syllabus and structure To give health services To encourage students to establish student unions 9.To provide students with academic facilities To give students equal treatment and respect To respect the confidentiality of information To give students a fair amount of freedom To supply a variety of programs To make students feel secure and confident To provide services within a reasonable time frame Staff 16.To have a positive attitude To have accurate and retrievable records To give convenient opening hours To respond to requests for assistance To provide good communication To give students personal attention To deal efficiently with complaints To show a sincere interest in solving problem To keep promises in a timely way Teacher 25.To be caring and courteous with students To have a positive attitude toward students To provide good communication To show a sincere interest in solving problems To respond to requests for assistance immediately To be knowledgeable of course content To give feedback on progress Eigenvalue Percentage of variation Percentage of cumulative variance explained Cronbach s Alpha Note: n=

7 Confirmatory Factor Analysis U. C. Chen The Confirmatory Factor Analysis (CFA), the next step after the EFA, determines the factor structure of the data (n=474). The CFA is a special form of factor analysis, most commonly used in social research (Kline 2010). Using a CFA after an EFA is a common method of measuring validity (Worthington and Whittaker 2006). The CFA is a multivariate statistical procedure used to test how well the measured variables represent the number of constructs. The author conducted a CFA using LISERAL to evaluate how well the 31 service quality items fit the previously observed, theoretically based, three-factor structure (school, staff, and teacher). Various fit indices are used to test the adequacy of the CFA models. Most researchers use chi-square goodness, the goodness of fit index (GFI), normed fit index (NFI), relative fit index (RFI), comparative fit index (CFI), incremental fit index (IFI), root mean square residuals (RMR), or root mean square error of approximation (RM-SEA) (Ŝahin and Yilmaz 2011). This study uses the parsimony goodness of fit index (PGFI) and the parsimony normed fit index (PNFI) as its criteria. Traditional statistical methods normally use a single statistical test to determine the significance of an analysis. However, structural equation modeling processes, such as CFA, rely on several statistical tests to determine the adequacy of a model fit to the data (Suhr 2006). This paper uses the following criteria to determine good model fit: GFI, NFI, RFI, CFI, IFI above 0.90 (values close to 1 indicate perfect fit while values close to zero indicate bad fit), RMSEA below 0.80 (close to 0), RMR below 0.05, and a relative χ2 /df ratio less than 5 (Bentler 1990; Browne and Cudek 1993; Eisen, Wilcox, and Leff 1999; Halim, Meerah, Zakaria, Abdullah, and Tambychik 2012; Schumacker and Lomax 2004; Ŝahin and Yilmaz 2011; Meyers, Gamst, and Guarino 2006; Raked and Marcoulides 2006). The data were analyzed by CFA using LISREL. The p-value reached a significant (p = 0.000), and the relative chi-square (χ2/df 5) indicated a good fit. The test results revealed that the NFI (0.981), RFI (0.980), IFI (0.986), and CFI (0.986) all exceed 0.90, the standard value representing how well a model fits the population. Both GFI and AGFI were slightly lower than 0.90 because the GFI (0.816) and AGFI (0.788) values are strongly influenced by variations in sample size and measure non-normality. Some researchers have recommended GFI and AGFI as alternative measures of fit (Burton, Lichtenstein, and Garretson 1998). The RMR index is , lower than the recommended maximum of The PNFI (0.709) and PGFI (0.909) all exceed 0.50, well within accepted guidelines. Hence, it was concluded that the tree-factor model fits well and represents a reasonably close approximation to the population. Analysis of Variance Analysis of variance (ANOVA) is used to evaluate whether there are differences between the average value, and mean, across several population groups. A one-way ANOVA evaluates the effect of a single factor on a single response variable, including gender, grade, and college type. The author completed a series of ANOVAs on the survey items to determine if statistically significant differences existed among the different treatment groups. The author performed Scheffé s analysis, perhaps the most popular of the post hoc procedures, for all the significant main effects of students colleges and grades. The designated of statistical significance was p <.05 for all statistical analyses. For importance, comparisons between the two mean values for the two groups (male and female) for the gender variables produced F-values of for Teacher, for Staff, and 0.64 for School. Each F-value was lower than the critical F-value of 2.99 at the 0.05 of significance with 1 and 472 degrees of freedom. There were no statistically significant differences (p <. 05) in any of the important factors. For performance, the comparison between the two mean values for the two groups for the gender variables produced F-values of for Teacher, for Staff, and for School. Only one variable ( School ) was greater than the critical F-value of 2.99 at the 0.05 of significance with 1 and 472 degrees of freedom. Thus, the null hypothesis was rejected, suggesting a significant influence of School in the performance. This result indicates that the difference was in the female and male cohorts (p <.05) and that females grades were greater than the males (see Table 3). 588

8 International Journal of Management Sciences For importance, comparisons among the three mean values for the grade variables (freshman, sophomore, and junior grades) produced F-values of for Teacher, for Staff, and for School. Each F-value was lower than the critical F-value of 2.99 at the 0.05 of significance with 2 and 471 degrees of freedom. There were no statistically significant differences (p <.05) between the scores on perceptions of service quality in higher education institutions. For performance, comparisons among the three mean values for groups for the grade variables produced F-values of for Teacher, for Staff, and for School, all greater than the critical F-value of 2.99 at the 0.05 of significance with 2 and 471 degrees of freedom. Thus, the null hypothesis was rejected. The overall NOVA was significant at the < The Scheffé test indicated that freshman scores were greater than the sophomore scores for Teacher and that freshman scores were greater than the sophomore and junior scores for Staff and School (see Table 3). For importance, comparisons among the three mean values for the grade variables (freshman, sophomore, and junior grades) produced F-values of for Teacher, for Staff, and for School. Each F-value was lower than the critical F-value of 2.99 at the 0.05 of significance with 3 and 470 degrees of freedom. There were no statistically significant differences (p <. 05) between the scores for perceptions of service quality in higher education institutions. For performance, comparisons among the four mean values for groups for the grade variables produced F-values of for Teacher, for Staff, and for School. Only one variable, Teacher, was greater than the critical F-value of 2.99 at the 0.05 of significance, suggesting a significant influence of Teacher in the performance. The Scheffé test indicated that the only difference was in the colleges of Education and Science (see Table 3). Factor genders Table 3: ANOVA Results for Gender, Grade, and College Type for Performance Variable Source of Sum of Mean df variations Square Square F Scheffé Teacher Between Groups Within Groups Total grades colleges Staff School Teacher Staff School Teacher Between Groups Within Groups Total Between Groups ** Female>male Within Groups Total Between Groups ** Freshman>Sophomore Within Groups Total Between Groups ** Freshman>Sophomore Within Groups Freshman>Junior Total Between Groups ** Freshman>Sophomore Within Groups Freshman>Junior Total Between Groups ** Education > Science Within Groups Total

9 U. C. Chen Staff Between Groups Within Groups Total School Between Groups Within Groups Total **significant at 0.01, n=474 Importance - Performance Analysis The next stage of the analysis was examining the response across the scale items to assess students perceptions of service quality and the relative importance assigned by respondents to each item. All importance scores showed that the importance means for all items differed from the performance scores and were greater than the performance means. A paired-samples t-test was run to indicate where the mean importance scores differed significantly from the mean performance scores. This reflected a service quality gap between the perceptions of students and of the school. While respondents considered each item to be significantly important to their overall evaluations of service quality, the school was not performing at a reflective of the importance assigned. Performance Concentrate Here School Keep Up the Good Work Teacher Staff 4.40 Low Priority 4.39 Possible Overkill 4.38 Importance Figure 2: The IPA Grid As Figure 2 shows, three factors fell into quadrants one to three. The y-axis represents student perception of Importance, and the x-axis represents their perception of Performance. The crosshairs (vertical and horizontal lines), representing a relative judgment rather than an absolute measure (Martilla and Jame 1977), are located at the grand mean scores (3.48 and 4.44). Figure 2 shows the results generated by the IPA method. The paragraphs below describe the results in detail. A. The Keep up the good work quadrant: the Teacher factor fell into this quadrant. Students believe this attribute to be very important and are satisfied with its performance; thus, the school is doing well and simply needs to keep up the good work in this area. B. The Concentrate here quadrant: students believe this to be very important, but performance is lacking in this quadrant. Items in this quadrant represent key priority areas that need to be improved urgently and one factor, School, falls within this zone. 590

10 International Journal of Management Sciences C. The Low priority quadrant: students are not satisfied with the performance of this attribute, but the attribute is relatively unimportant. One factor, Staff, falls within this zone, but is not important and poses no threat to the school. It is thus not necessary for management to focus additional effort here. D. The Possible overkill quadrant contains items excessively emphasized by the school. The school needs to allocate more resources to the items in the Concentrate here quadrant, which reflects the misuse of resources, but no factor fell into this quadrant. 4. Conclusion and Discussion The author will now derive conclusions from our multi-method statistical approach in order to provide universities with service quality information for ensuring improvement. The author applied the EFA to study the perceptions of importance. A CFA via Structural Equation Models (SEM) was carried out using LISERAL to examine the structure of the relevant factors and their inter-correlations. Then, an ANOVA was used to test the responses to the research question; the author used an IPA to compare the importance and performance of service quality in higher education. A set of 31 items was rotated by using a principal component analysis with varimax rotation, retaining factors with eigenvalues greater than 1. The factor analysis revealed three factors (teacher, staff, and school). A CFA was used after an EFA to determine the factor structure of the data. The three-factor model was found to fit well and represent a reasonably close approximation to the population. The ANOVA, used to evaluate whether there are differences among average values across several population groups, indicated that there was a difference in the female and male cohorts (p <.05), and that females grades were greater than the males. A Scheffé test indicated that freshman scores were greater than sophomore scores for the Teacher and that freshman scores were greater than sophomore and junior scores for the Staff and School. Only one variable ( Teacher ) was greater than the critical F-value of 2.99 at the 0.05 of significance, and a Scheffé test indicated that the only difference was in the colleges of Education and Science. Finally, the IPA technique was used to measure service quality scores from the perspective of students to find focus areas for resource allocation. All importance scores showed that the importance means for all items differed from the performance scores and were greater than the performance means. A paired-samples t-test evaluated where the mean importance scores differed significantly from the mean performance scores, reflecting a service quality gap. The Teacher factor fell into the Keep up the good work quadrant, the Staff factor into Low priority, and School into Possible overkill. The gap analysis using the paired-samples t-test indicates differences between student perceptions of importance and performance in the service quality sector, reflecting a service quality gap between students and the school. School managers thus need to narrow the gap between importance and performance. The gap analysis through the ANOVA indicates the need for improvement across the grades. The study found that freshmen were satisfied but that sophomores and juniors were not, indicating that the more students learn, the more unsatisfied they become. Service quality must be improved immediately to resolve this problem. The IPA identifies how university services are being performed and helps the management target improvement efforts. The Concentrate here quadrant indicates the need to allocate resources for improvement; university managers will achieve this improvement by enhancing students Importance judgments. The School factor fell into this quadrant, with which managers need to be more concerned. The one factor ( Teacher ) in Keep up the good work has both high importance and high relative performance; university managers should therefore keep up their good work, and the managers responsible for this good work should receive positive reinforcement and recognition. As Staff is in a low priority area, school management need not focus any additional effort there. 591

11 U. C. Chen The IPA has indicated the factors required to satisfy the students. Of the many stakeholders in higher education, students are the most important. They are customers who can choose among many universities. This paper has provided further evidence of the importance of service quality to higher education managers. The paired-samples t-test, ANOVA, and IPA are all effective ways of measuring students perspectives on service provision. Managers should undertake extensive service quality research and identify the factors that students deem most important. A significant limitation of the study is its use of data from universities. Although the survey received a good response (from 474 participants), further studies should make the findings more generalizable. Despite its limitation, this research provides complementary information that facilitates a better understanding of service quality in East Asia s education industry. References Abdullah, F. (2005). HEdPERF versus SERVPERF: The quest for ideal measuring instrument of service quality in higher education sector. Quality Assurance in Education, 13(4), Abdullah, F. (2006). Measuring service quality in higher education: HEdPERF versus SERVPERF. Marketing Intelligence & Planning, 34(1), Abouchedid, K., & Nasser, R. (2002). Assuring quality service in higher education: Registration and advising attitudes in a private university in Lebanon. Quality Assurance in Education, 10(4), Aigbedo, H., & Parameswaran, R. (2004). Importance-performance analysis for improving quality of campus food service. The International Journal of Quality & Reliability Management, 21(8), Allen, J., & Davis, D. (1991). Searching for excellence in marketing education: The relationship between service quality and three out-come variables. Journal of Marketing Education, 13, Bentler, P. M. (1990). Comparative fit indices in structural models. PsychologicalBulletin, 107, doi: / Browne, M. W., & Cudek, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (pp ). Newbury Park, CA: Sage. Brysland, A., & Curry, A. (2001). Service improvements in public services using SERVQUAL. Managing Service Quality, 11(6), Caruana, A., Ewing, M. T., & Ramaseshan, B. (2000). Assessment of the three-column format SERVQUAL: An experimental approach. Journal of Business Research, 49, Eastwood, D. B., Brooker, J. R., & Smith, J. D. (2005). Developing marketing strategies for green grocers: An application of SERVQUAL. Agribusiness, 21(1), Eisen, S. V., Wilcox, M., & Leff, H. S. (1999). Assessing behavioural health outcomes in outpatient programs: Reliability and Validity of the BASIS-32. Journal of Behavioural Health Sciences and Research, 26, Fareena, S., & Merlin, C. S. (2000). International service variants: Airline passenger expectations and perceptions of service quality. Journal of Service Marketing, 14(3), Gallifa, J. & Batallé, P. (2010). Student perceptions of service quality in a multi-campus higher education system in Spain. Quality Assurance in Education, 18(2), Gruber, T., Fuß, S., Voss, R., & Gläser-Zikuda, M. (2010). Examining student satisfaction with higher education services: Using a new measurement tool. International Journal of Public Sector, 23(2),

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