International Journal of Comparative Education and Development The MEQUAL scale: measure of service quality in management education Sanjeev Verma, Ram Komal Prasad,

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Article information: To cite this document: Sanjeev Verma, Ram Komal Prasad, (2017) "The MEQUAL scale: measure of service quality in management education", International Journal of Comparative Education and Development, Vol. 19 Issue: 4, pp.193-206, https://doi.org/10.1108/IJCED-12-2016-0024 Permanent link to this document: https://doi.org/10.1108/IJCED-12-2016-0024 Downloaded on: 17 November 2017, At: 07:54 (PT) References: this document contains references to 74 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 8 times since 2017* Access to this document was granted through an Emerald subscription provided by emeraldsrm:514693 []

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The MEQUAL scale: measure of service quality in management education Sanjeev Verma Department of Marketing, National Institute of Industrial Engineering, Mumbai, India, and

Ram Komal Prasad

The MEQUAL scale

193 Received 26 December 2016 Revised 8 October 2017 Accepted 13 October 2017

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ICCMRT, Lucknow, India Abstract Purpose – The purpose of this paper is to develop an empirically validated scale to measure the students’ perception of service quality in management education. Design/methodology/approach – In this study, a three-stage systematic procedure of scale development has been adopted. Initially, extant literature review delineated the construct. Exploratory study techniques like focus group study and expert opinion helped in purifying the scale. In the second stage, principal component analysis with varimax rotation and Kaiser normalization (exploratory factor analysis) was used to refine the scale. Finally, multi-trait-multimethod matrix analysis was done to test the reliability and validity of the scale. Findings – A 25-item multi-dimensional construct with six factors (academic aspect, professional assurance, behavioral responses and supports, industry institute interaction, non-academic aspects and physical support) was derived scientifically for measuring service quality in management education. Psychometrically, the scale exhibits internal consistency and remains consistent across the samples. The scale passes the requisite reliability and validity tests (construct, convergent, discriminant, nomological, predictive) with all values within limits. Practical implications – Scientific and structured multi-dimensional construct for service quality in management education will help academicians, administrators and regulators in designing a process-oriented system for enhanced student satisfaction and performance. Originality/value – This study is an incremental attempt to develop an empirically validated scale for measuring the service quality level and resultant satisfaction in management education. Keywords Satisfaction, Scale development, Service quality, Management education Paper type Research paper

1. Introduction Globalization and liberalization have made the business vulnerable to global competitions and challenges. Thus there has been an emphasis on producing industry-ready graduates in last few decades (Conger and Fulmer, 2003). As the business environment is rapidly changing due to the development of new technology and liberalization, it is essential for management students to refine their managerial competencies for new challenges (Vinten, 2000). Management education and management practices are at a cusp of convergence (Martin and Butler, 2000) and realization of the fact that knowledge and action are inseparable, students should be developed to handle unforeseen critical challenges of the world (Dymsza, 1982). The technological developments, globalization and integrative approach demands for better-prepared management students for global competitiveness (Friga et al., 2003). Given that transformations are happening around, education service is getting translated into a statue of industry, and growing competition has led to business schools to work as a business house with “student as customer” approach for educational delivery (Mahapatra and Khan, 2007). Under changed circumstances, the quality of services plays a crucial role in the success of an institute (Sharma and Kaur, 2004) and becomes

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an essential choice criterion for students (Mishra, 2013; Sahney et al., 2004). The quality of educational services determines the learning outcome and employment opportunities (Lynne and Ross, 2007). India has nearly 4,000 business schools with an average annual intake of 3,50,000 students. However, out of this, merely 25 are globally benchmarked. In the recent years, there have been various transformations in methodology, curriculum and content of the management program to keep pace with the current changes in the corporate and industrial world (Dayal, 2002). Recently management education has tumbled to criticism and skepticism regarding its service quality. Due to the lack of desired quality, there is a sharp decline in the number of students opting for management education. According to the latest report, 147 management and engineering institutions across the country have closed down in the academic year 2013-2014 (All India Council for Technical Education, 2015). Corporates are relying more on well-established management institutions for hiring while university departments and other autonomous organization are facing problems to place their graduates. On-campus placement of management graduates in institutes other than the IIMs is hardly 20 percent. Recently, management education has been questioned for its value and relevance (Baruch, 2009; Datar et al., 2011). Service quality has been studied extensively in the past, and various measurement instruments like SERVQUAL (Parasuraman et al., 1988), service performance (SERVPERF) (Cronin and Taylor, 1992), normed quality model and evaluated performance model (Teas, 1993) were offered to gauge the level of service quality in varied context. Quality in higher education has also drawn the attention of previous researchers and different scales like HEDPERF (Abdullah, 2006), HETQMEX (Ho and Wearn, 1996) and EDUQUAL (Mahapatra and Khan, 2007) were developed to measure the status of service quality in higher education institutions. Higher education includes teaching, research and professional program at undergraduate and postgraduate level. The learning style and career choice of students pursuing teaching, research and professional postgraduate degree may differ widely. The generic measurement scale may not capture the essence of program-specific intricacies, and thus, there is a need to develop service quality scale for management education. This study is an attempt to develop a refined and validated scale for measuring the service quality of management education. A better understanding of service quality dimensions valued by management students may help educators to design systems for enhanced satisfaction (Seymour, 1992). This paper has five sections. Introduction section outlines the latest trend, the need of study and significance of the study. Subsequently, an extant literature review theorized the service quality attributes and their role in overall student’s satisfaction. Next, design and development of new scale followed by the conclusion, implications for future research and limitations are presented. 2. Literature review Service quality in management education has become a prime concern to the aspirants of management education, whereby overall development in various dimensions of professional competencies is vital. For the present study, a systematic literature review informed the factor structure. Keywords like service, service quality, service quality in higher education, service quality in management education and their synonyms were used to draw the relevant literature from online databases. Hermeneutics approach with funneling is used for the extant literature review. The hermeneutic circle was formed by moving between the micro and macro concept related to service quality in management education. There are varied service quality definitions available in literature and researchers argued the distinctive nature of service due to differential quality standards. Since there is no

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universal, all-encompassing definition of quality (Reeves and Bednar, 1994), the quality is defined as conformance to requirement (Crosby, 1984), fitness for use ( Juran, 1988), customers’ judgment for entity’s overall excellence and superiority (Zeithaml, 1988). The degree of expectations matched with service level can be an indicator of service quality (Lewis and Booms, 1983). Consumers compare their expectations with experience attained to evaluate the service quality (Gronroos, 1982). The direction and degree of disconfirmation of initial expectations with experience decide the level of customer satisfaction (Churchill and Suprenaut, 1982). Due to significant importance of service quality in the determination of customer satisfaction, researchers have developed various generic and sector-specific scales for its measurement. Parasuraman et al. (1985) did an exploratory study in four service business cases (retail banking, credit card, securities brokerage and product repair/maintenance) and proposed gap model of service quality. There are four possible gaps between perceptions of consumers and service marketers (Parasuraman et al., 1985). Gap 1 focus on the gap between consumer expectation-management perceptions, Gap 2 is management perception-service specification, Gap 3 is service specification-service delivery, and Gap 4 is service delivery-external communication gap. SERVQUAL initially assessed the customer perception of service quality in four independent service organizations (bank, credit card company, repair, and maintenance, long distance telephone company) and it covered five significant service dimensions, namely, reliability, responsiveness, tangibles, empathy and assurance (Parasuraman et al., 1988). Cronin and Taylor (1992) learned the importance of performance in an organization, and therefore, developed an improved version of the service quality measurement model, christening it service performance (SERVPERF) model. According to SERVPERF model, the main dimensions that influence service quality are customer appreciation and purchasing power. Teas (1993) had doubts regarding the applicability of SERVQUAL and observed that the difference in expectation and perception is a state of consumers’ mind. So, evaluated performance model links the relationship between the consumers’ perception of quality and the likelihood that the actual performance will exceed consumers’ expectation (Teas, 1993). Teas (1993) proposed the concept of revised expectation (the expectation formed by post-purchase experience) that led to the creation of the normed quality model. Teas (1993) addressed it as an excellence norm leading to the creation of SERVQUAL-E* (revised expectation). Here, the excellence norm is a result of the positive experience of the consumer. The excellence norm compared with an ideal standard set in the mind of the consumer (revised in comparison with the expectation). The difference in the two expectations termed as the “normed quality gap.” If there is no difference between the excellence norm and the expectation, then the normed quality is equal to the perception of the consumer (Teas, 1993). Voon (2006) integrated the idea of applying market orientation in the service industry by creating a service market orientation (SERVMO) model. Market orientation dimensions included in the SERVMO model are customer orientation, competitor orientation, inter-functional orientation, performance orientation, long-term orientation and employee orientation. Since every employee in the organization must realize the sensitivity of the consumers’ needs, the author developed this model as the set of beliefs, behaviors and cross-functional processes that focusses on the comprehensive understanding of the current and future need of the target customers. Previous researchers have delineated many service quality dimensions for evaluation of customers’ preferences and satisfaction, but researchers agree that no single dimension applies uniformly to all the service sectors (Cronin and Taylor, 1992). Generic service quality scales may provide the broader view of service quality status of any service sector

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organization, but none of them defines sector-specific dimensions. Thus, sector-specific scales may provide a crystallized view for strategic interventions. Previous researchers have developed many sector-specific scales for different types of services. Some of the examples of sector-specific scales are: •

Retail Service Quality scale was developed for capturing customer’s perceptions for retail stores (Dabholkar et al., 1995);

•

Logistics Service Quality scale for logistics companies (Mentzer et al., 1999);

•

Banking Service Quality scale for the banking sector (Bahia and Nantel, 2000);

•

Online Service Quality scale for measuring online service quality (Yang et al. 2004);

•

E-Service quality scale for electronic services (Ladhari, 2010); and

•

Hospital Service Quality Scale for hospitals and healthcare (Vandamme and Leunis, 1993).

Development of a service quality model to measure students’ perception in management education context is a complicated and tedious task because students are co-producer as well as the customer in the education context (Hadikoemoro, 2002). Carney (1994) opined that the service quality in the education sector is an essential issue for the academicians and identified a comprehensive set of 19 variables to measure the service quality in academia. Athiyaman (1997) evaluated service quality in higher education with the help of eight dimensions while Hadiokeomoro (2002) used only five dimensions to evaluate student preferences. Sahney et al. (2004) identified five dimensions (competence, attitude, content, delivery and reliability) for evaluating service quality in the Indian higher education context. Brook (2005) opines that service quality in education should encompass university activities for differentiation. Ho and Wearn (1996) developed a quality measurement model (HETQMEX), especially for the higher education institutions. The authors emphasized the importance of quality in higher education institutions and used TQM as the central theme in higher education. Quality maintenance in service requires a change in the entire system shifting from traditional methods to quick and innovative techniques. Ho and Wearn (1996) emphasized the importance of inclusive participation of stakeholders (students, staff, teachers, administration) in managing the service performance. Oldfield and Baron (2000) proposed that service experience in higher education is a complicated phenomenon and students pursuing higher education have a complex set of expectations. For a refined view of student’s perspective, Abdullah (2006) treated students as customers to develop the HEDPERF scale. HEDPERF scale included multi-construct (non-academic aspects, academic aspects, reputation, ambiance and program issues) scale for evaluation of the performance of higher education quality and emphasized on the vital significance of differentiation as critical dimensions for the competitive edge. Tsinidou et al. (2010) used multi-criteria decision making and hierarchical analytical process to measure the relative importance of quality determinants in higher education institutions and emphasized on internal quality assessment. Sumaedi et al. (2012) conducted a study on students’ overall perceived service quality and suggested that curriculum, faculty, education counseling, assessment and instruction medium have a positive effect on students overall perceived quality. Sultan and Yin Wong (2012) developed an integrated model incorporating the antecedents and consequences of service quality and posited relative importance of information over experience as the antecedents of service quality. Nenadál (2015) used EFQM excellence model and emphasized the importance of comprehensive quality assessment. Martínez-Caro et al. (2015) used performance evaluation model for e-learning quality in higher education and suggested that instructors must adopt an active role in making a difference.

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Multiple studies have been attempted in the past to identify the antecedents, moderators, mediators and consequences of service quality in higher education in general but much attention has not been towards management education specifically. Extant literature review pointed at generic service quality scales, industry-specific scales including higher education service but researcher could not find any comprehensive measurement scale for management education. Management education (professional course like MBA) is significantly different from other higher education degrees (taught and the research-based course like MA, MSc or MPhil), so existing scales cannot be used for assessment of service quality in management education. This study attempts to develop scientific scale (MEQUAL) to assess the status of service quality in management education domain.

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3. Design and development of the measurement scale As advised by Churchill (1979), the scientific and systematic procedure for scale development guides this study. Step by step method ensured the rigor and sanctity. Step 1 involves item generation and selection, Step 2 focusses on scale refinement, and finally, in Step 3 scale was validated using multi-trait-multimethod (MTMM) technique. Phase 1: item generation and selection Underlying dimensions affecting students’ perception of service quality and overall satisfaction in management institutions were captured with the help of extant literature review and focus group study. Initially, 112 items were identified from the extant literature review and distributed in a homogenized manner under 15 dimensions (Table I). Table I presents the distribution of items dimension wise. Further, expert opinion was taken to fine tune the measurement scale. A series of discussion with senior academicians having more than 15 years of experience in management education solicited for their feedback on the measurement scale. Expert opinion transpired into minor changes in the nomenclature of a few dimensions and deletion of 23 overlapping items. Hence, the original scale has 15 dimensions and 89 items at this stage.

Dimensions Academic aspects-faculty

No. of items Source of reference from literature 11

Pedagogy

6

Assurance/conformance

7

Attitude Support staff Non-academic activities Course curriculum Delivery Functional value Image Industry institute interaction

6 7 9 10 6 5 10 7

Reliability

10

Responsiveness

6

Perceived quality of outcome Physical evidence

4 8

Sahney et al. (2010), Smith et al. (2007), Soutar and McNeil (1996), Rajpurihit and Latwal (2012) Rajpurihit and Latwal (2012), Iñigo and Boix (2012), Carney (1994), Sahney et al. (2004) Hadikoemoro (2002), Smith et al. (2007), Owlia and Aspinwall (1996), Ramseook-Munhurrun et al. (2010), Jung (2005) Sahney et al. (2010), Soutar and McNeil (1996), Hadikoemoro (2002) LeBlanc and Nguyen (1999), Smith et al. (2007) Jain et al. (2013), Jung (2005) Jain et al. (2013), Owlia and Aspinwall (1996) Sahney et al. (2010) LeBlanc and Nguyen (1999) LeBlanc and Nguyen (1999), Joseph and Joseph (1997) Jain et al. (2013), Kolachi and Mohammad (2013), Joseph and Joseph (1997) Owlia and Aspinwall (1996), Barnes (2007), Sahney et al. (2010), Smith et al. (2007) Smith et al. (2007), Ramseook-Munhurrun et al. (2010), LeBlanc and Nguyen (1997) Brooks (2005), Hadikoemoro (2002), Jung (2005) Soutar and McNeil (1996), Smith et al. (2007), Jung (2005)

Table I. Item generation

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Phase 2: scale refinement Pilot study helped in getting the refined scale for measurement of service quality in management institutions. Probabilistic sampling was used to identify respondents for the pilot study. In total, 120 respondents were randomly selected from the different format of management institution, namely, public universities (30), private universities (30), affiliated institutions (30) and autonomous institutions (30) to capture the reasonable representation of different formats of management institutions. There are varied views in the literature regarding sample size determination during the pilot study. Different researchers have different opinions on ideal sample size during the pilot study. For example, Isaac and Michael (1995) suggested 10-30 percent of the participants while Hill (1998) suggested 10-30 percent of participants for the pilot study phase in a survey research. Julious (2005) and Van Belle (2002) suggested 12 percent of total sample size during the pilot study. Given the studies mentioned above, a sample size of 120 respondents met the recommended size as it is more than 10 percent of the project total sample size (1,040). The respondents were asked to rate the items on a five-point Likert scale (1 ¼ strongly disagree to 5 ¼ strongly agree). The original scale (15 dimensions and 89 items) retained after Phase 1, was put to the test using exploratory factor analysis (EFA). The data obtained were executed with factor analysis using principal component analysis utilizing varimax rotation method with Kaiser normalization to extract factors with weighted linear combinations of variables (Costello and Osborne, 2005). EFA helped in identifying the latent variables, which were contributing to the common variance in a set of measured variables. Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was used to examine the appropriateness of factor analysis. Since KMO value was higher than 5, it was decided to use factor analysis (Tabachnick and Fidell, 1996). Bartlett’s test of sphericity was also used to test the hypothesis that the items with each dimension are uncorrelated in the population. A high value of the test statistic favored the rejection of the null hypothesis and supported the use of EFA for data analysis. The widely accepted norms for deletion of items like factor loading less than 0.50 (Karatepe et al., 2005), cross-loading more than 0.40 or communalities less than 0.30 (Hair et al., 1998), were followed for scale refinement. Initial communalities were calculated for correlation analyses, i.e., to find out the proportion of variance accounted for in each variable by rest of the variables. Extraction communalities were calculated to estimate the variance in each variable accounted for by the factors in the factor solution. The communalities ranged from 0.610 to 0.757, suggesting that the standard factors reasonably explained the variance of the original values. Factor analysis output suggested six-factor solutions and explained more than 67 percent of the variance in the data with eigenvalues greater than 1. The results of the EFA produced a six-factor structure and 25 items (Table II). Phase 3: scale reliability and validation The instrument was tested for its reliability and validity to assess the goodness-of-use. According to Kline (1986), Cronbach’s coefficient α is the most efficient measure of reliability and items should have values higher than 0.7 for further testing. The internal consistency Cronbach’s α value for the reliability of the questionnaire was found to be 0.966. All items were well above the value of 0.70, which is the commonly accepted threshold (Table II). George and Mallery (2003) argue that if the value of Cronbach’s α equals or exceeds 0.70, the scale is acceptable; thus, the questionnaire passed the test of internal consistency. All individual scale items had statistically significant (at p o 0.05 level) item-to-total correlations. Hence, all items were deemed reliable.

Dimension Items AA

PA1

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BRS

III NAA

PE

AA 1 AA 2 AA 3 AA 4 AA5 PA 1 PA 2 PA 3 PA 4 BRS 1 BRS 2 BRS 3 BRS 4 III 1 III 2 III 3 NAA 1 NAA 2 NAA 3 NAA 4 NAA 5 PE 1 PE 2 PE 3 PE 4

Eigen value Percentage of variation Corrected item-to-total correlation Coefficient α 9.732

38.9

1.873

7.5

1.211

4.8

1.069

4.2

1.015

4.06

1.010

3.9

0.830 0.719 0.798 0.877 0.806 0.809 0.758 0.722 0.719 0.803 0.847 0.808 0.873 0.899 0.850 0.804 0.792 0.811 0.818 0.727 0.630 0.730 0.712 0.659 0.643

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0.789

0.822

199

0.812

0.796 0.831

0.792

The instrument was put to content validity, construct validity (convergent and discriminant) and cross-validity tests. As the variables under different dimensions drawn from relevant secondary and primary sources, the questionnaire is appropriate for the task at hand and thus, passes the test of face validity. The construct validity has two aspects-convergent validity and discriminant validity, and each one of them was tested individually with the help of MTMM matrix analysis (Table III). Convergent validity indicates the agreement between various measures of the same construct and divergent validity shows the disagreement between measures of unrelated constructs (Campbell and Fiske, 1959). Intercorrelation values have been used to identify the convergent and discriminant validity. The high correlation value between measures of the same construct indicates convergent validity while low correlation value between measures of unrelated constructs indicates discriminant validity (Campbell and Fiske, 1959). The loadings of each item to corresponding measures were checked individually and found to be higher than 0.5 at the significance level of po0.01.Thus, item loading more than 0.5factor wise indicates at convergent validity. Further, the MTMM matrix compared the correlations between items of different factors. The non-zero higher correlation value amongst items of the same measure certifies convergent validity. The lowest within factor correlation value for different items was identified (AA: 0.421, PA: 0.403, BRS: 0.514, 0.459, NAA: 0.439, PE: 0.383) and compared with the correlation values of unrelated factors. The number of violations (correlation between unrelated measure is higher than the correlation between related measures) was calculated and contrasted with a total number of comparisons possible in MTMM analysis. Campbell and Fiske (1959) suggested that the number of violations should be less than 50 percent of the total number of comparisons possible for the presence of discriminant validity. As evident from Table III, the number of violations (37) are lesser than 50 percent of possible comparisons, and hence, the scale has discriminant validity.

Table II. The finalized factors and items with item-to-total correlation and alpha value

Table III. Multi-trait multimethod matrix analysis

AA1 AA2 AA3 AA4 AA5 PA 1 PA 2 PA 3 PA 4 BRS 1 BRS 2 BRS 3 BRS 4 III 1 III 2 III 3 NAA 1 NAA 2 NAA 3 NAA 4 NAA 5 PE 1 PE 2 PE 3 PE 4

1.000 0.446 0.443 0.465 0.421 0.413 0.323 0.320 0.318 0.328 0.314 0.280 0.259 0.260 0.312 0.262 0.162 0.214 0.275 0.202 0.269 0.252 0.311 0.176 0.250

1.000 0.463 0.458 0.461 0.434 0.440 0.454 0.428 0.456 0.412 0.410 0.376 0.286 0.372 0.355 0.242 0.293 0.344 0.357 0.343 0.265 0.356 0.248 0.338

1.000 0.575 0.431 0.375 0.414 0.440 0.347 0.355 0.345 0.400 0.394 0.352 0.400 0.356 0.183 0.250 0.278 0.315 0.316 0.241 0.325 0.234 0.274

1.000 0.535 0.456 0.455 0.438 0.347 0.419 0.362 0.405 0.410 0.375 0.424 0.390 0.270 0.321 0.290 0.359 0.423 0.342 0.375 0.314 0.286

1.000 0.432 0.442 0.446 0.389 0.418 0.417 0.401 0.359 0.273 0.365 0.314 0.168 0.268 0.320 0.273 0.310 0.296 0.386 0.203 0.264 1.000 0.454 0.456 0.403 0.471 0.398 0.413 0.445 0.376 0.424 0.323 0.347 0.346 0.354 0.348 0.393 0.337 0.405 0.229 0.295 1.000 0.476 0.407 0.498 0.454 0.416 0.430 0.306 0.329 0.315 0.201 0.318 0.285 0.356 0.335 0.318 0.330 0.288 0.309 1.000 0.430 0.445 0.424 0.466 0.402 0.347 0.395 0.422 0.194 0.255 0.298 0.298 0.346 0.252 0.316 0.262 0.287 1.000 0.444 0.496 0.445 0.414 0.271 0.359 0.278 0.247 0.257 0.348 0.269 0.301 0.314 0.417 0.229 0.349 1.000 0.581 0.547 0.530 0.344 0.389 0.371 0.297 0.340 0.340 0.379 0.421 0.336 0.399 0.257 0.366

BRS 1

1.000 0.592 0.514 0.370 0.419 0.363 0.252 0.267 0.319 0.331 0.351 0.367 0.379 0.295 0.360

BRS 2

1.000 0.582 0.395 0.462 0.387 0.286 0.329 0.392 0.385 0.372 0.350 0.411 0.282 0.366

BRS 3

1.000 0.435 0.447 0.388 0.291 0.317 0.312 0.402 0.389 0.309 0.364 0.253 0.273

BRS 4

1.000 0.459 0.553 0.335 0.373 0.329 0.374 0.434 0.346 0.268 0.372 0.308

III 1

1.000 0.501 0.349 0.374 0.411 0.348 0.470 0.323 0.388 0.288 0.325

III 2

1.000 0.365 0.413 0.391 0.414 0.508 0.285 0.302 0.366 0.308

III 3

1.000 0.626 0.528 0.439 0.472 0.267 0.212 0.323 0.303

NAA 1

1.000 0.581 0.501 0.484 0.295 0.272 0.382 0.311

NAA 2

1.000 0.451 0.469 0.274 0.350 0.265 0.302

NAA 3

1.000 0.584 0.300 0.315 0.352 0.332

NAA 4

1.000 0.338 0.369 0.404 0.336

1.000 0.483 1.000 0.408 0.399 1.000 0.418 0.383 0.518 1.000

NAA 5 PE 1 PE 2 PE 3 PE 4

200

AA1 AA2 AA3 AA4 AA5 PA 1 PA 2 PA 3 PA 4

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4. Discussion and implications The present study aims to develop a multi-item scale for the measurement of service quality in management education in an empirically validated way. Factor analysis with rotated component matrix identified the underlying dimensions of service quality in management education. A multi-item scale with six dimensions and 25 items has emerged from the vast pool of variables and items drawn from extant literature and qualitative study. Scale showed internal consistency and remains consistent across the samples. The scale passes all the reliability and validity tests (construct, convergent, discriminant, nomological, predictive) and all values were within limits. The measurement scale includes academic aspects, behavioral responses and supports, professional assurance, non-academic aspects, industry institute interaction and physical evidence. Measurement factors for service quality in management education behave differently for different student groups based on their demographic and psychographic profiles. Academic aspect explained 38.9 percent variation in service quality of management education and emerged as the most significant contributor in the determination of management education service quality. Academic aspect entails course design, teaching pedagogy and learning support in the form of the induction program, balance between theory and practice, and reading the material. Contemporary course content and teaching pedagogy act as an essential differentiator. Student’s latitude improves with the perfect blend of these three aspects of academic aspects. Professional assurance also emerged as an essential factor for student satisfaction. The process orientation, adherence to schedule and standards infuse confidence in the students. The chances of error reduce to a greater extent in the presence of set processes and develop professional assurance with transparency in students’ minds. Students can plan their activities more systematically if schedules adhere religiously. For efficient utilization of resources, both hard and soft components of a system must function optimally. The soft component of an education institute includes human behavior and response while hard component includes physical infrastructure to augment the learning process. Both behavioral response and physical infrastructure support emerged as important factors in enhancing service quality for better student satisfaction. Student expects to have proper attention to their needs, empathy, proper conduct and effective grievance handling in case of any disgruntlement. The behavioral expectations of students from the management of any institute are hygiene factors and necessary minimum to be offered for mutual respect and harmonious environment. Students also expect to have proper infrastructural support to augment their learning. The state-of-art classroom, laboratories, recreational and accommodation facilities are must for better learning experience. Management graduates are expected to play dynamic leadership roles in varied capacities in the corporate world. Thus their overall development along with intellectual spurt is a must. In this study also, non-academic aspect was one of the critical factors in student satisfaction. Non-academic aspects like sports events, social events, cultural events, counseling services, soft skill and personality development are vital for the holistic development of management graduates. The hybrid of health and mind with these non-academic aspects can bring much-appreciated results. Finally, practical exposure to the contemporary business world can be immensely useful for management graduates in gearing themselves for future opportunities and challenges. The enduring institute-industry interaction was another factor arisen in the MEQUAL scale. Management graduates much appreciate the continuous dialogue between academia and industry in the form of guest lectures, industry visit, workshop and seminars. These interaction opportunities unlock enormous potential in management graduates, and it helps in building channel gateways for placement opportunities.

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Academically, the scale made the theoretical contribution to the existing service quality measurement studies by providing a validated tool to measure the service quality of management institutions. To the best of the knowledge, this study is the first attempt to offer the scientific tool of its kind. This scale offers the sector-specific tool to measure strength and gap areas of a management institute for strategic planning and resource allocation. The student’s expectations and perceptions can drive more meaningful use of resources and forecasting. A comparative service assessment of different formats of management institutions (public, private or autonomous) can offer competitive benchmarking and differentiation. The present study may help administrators and regulators to design business processes focused on student-centric preferences for better results. This scale may also be useful in making the educational policy and framework for management education. The management institutions can use these findings to design their administrative and academic policy for the betterment of academic services in their institutions. 5. Limitations and directions for future research In educational institutions, students are a primary stakeholder, but the education industry serves multiple stakeholders, for instance, industry, parents and society. Since this study focuses only on management students, future studies may attempt to develop a measuring instrument from the perspective of other stakeholders. This scale addresses sector-specific (management education) issues in developing country but under developing countries and developed countries may have different needs so that this scale may be validated and customized in other countries. This scale measures service quality for management education in general and does not address any specific type of management institution, so future studies may attempt to do a comparative study between different formats of management institutions with the help of this scale. 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. All India Council for Technical Education (2015), “Annual report”, All India Council for Technical Education, New Delhi. Athiyaman, A. (1997), “Linking student satisfaction and service quality perceptions: the case of university education”, European Journal of Marketing, Vol. 31 No. 7, pp. 528-540. Bahia, K. and Nantel, J. (2000), “A reliable and valid measurement scale for the perceived service quality of banks”, International Journal of Bank Marketing, Vol. 18 No. 2, pp. 84-91. Barnes, B.R. (2007), “Analysing service quality: the case of post-graduate Chinese students”, Total Quality Management & Business Excellence, Vol. 18 No. 3, pp. 313-331. Baruch, Y. (2009), “To MBA or not to MBA”, Career Development International, Vol. 14 No. 4, pp. 388-406. Brooks, R. (2005), “Measuring university quality”, The Review of Higher Education, Vol. 29 No. 1, pp. 1-21. Brook, R.L. (2005), “Measuring university quality”, The Review of Higher Education, Vol. 29 No. 1, p. 1-21. Campbell, D.T. and Fiske, D.W. (1959), “Convergent and discriminant validation by the multitrait-multimethod matrix”, Psychological Bulletin, Vol. 56 No. 2, pp. 81-105. Carney, R. (1994), “Building an image”, paper presented at the Proceedings Symposium for the Marketing of Higher Education, American Marketing Association, New Orleans, LA.

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Further reading Marsh, H. and Hocevar, D. (1985), “Application of confirmatory factor analysis to the study of self-concept: first and higher order factor models and their invariance across groups”, Psychological Bulletin, Vol. 97 No. 3, pp. 562-582. Petruzzellis, L., D’Uggento, A.M. and Romanazzi, S. (2006), “Student satisfaction and quality of service in Italian universities”, Managing Service Quality: An International Journal, Vol. 16 No. 4, pp. 349-364.

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Corresponding author Sanjeev Verma can be contacted at: [email protected]

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