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Quality Assurance in EducationEmerald Article: Improving service quality in technical education: use of interpretive structural modelingRoma Mitra Debnath, Ravi Shankar

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To cite this document: Roma Mitra Debnath, Ravi Shankar, (2012),"Improving service quality in technical education: use of interpretive structural modeling", Quality Assurance in Education, Vol. 20 Iss: 4 pp. 387 - 407

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Improving service quality intechnical education: use of

interpretive structural modelingRoma Mitra Debnath

Indian Institute of Public Administration, New Delhi, India, and

Ravi ShankarDepartment of Management Studies, Indian Institute of Technology,

Delhi, India

Abstract

Purpose – The purpose of this paper is to identify the relevant enablers and barriers related totechnical education. It seeks to critically analyze the relationship amongst them so that policy makerscan focus on relevant parameters to improve the service quality of technical education.

Design/methodology/approach – The present study employs the interpretive structural modeling(ISM) approach to model the crucial parameters of technical education. The parameters discussed arecategorized under “enablers” and “barriers”. The enablers would help policy makers to improve anddevelop the curriculum of the technical education and the identifying barriers would help the decisionmaker to improve upon those variables.

Findings – The major findings of this study are to prioritize the strategic parameters in reducing therisks associated with technical education. The model also proposes a hierarchical structure classifyingthe parameters as drivers and enablers.

Research limitations/implications – The study proposes a scientific way to model the enablersand barriers to become a progressive institution in the emerging era of globalization andmodernization. This would help to prioritize the issues as the enablers and barriers are hierarchicallystructured and categorized.

Practical implications – The paper maps out a course of action and the adoption of the proposedframework would provide a competitive edge for India over others. Also, the various stakeholderswould be satisfied, which would be beneficial for the system as a whole.

Originality/value – The application of ISM to the decision making process is the unique feature inthe field of technical education in India. The integrated framework of policy related parameters wouldcontribute towards overall growth and development.

Keywords Technical education, Curriculum planning, Structural analysis, Modelling, India,Technical training

Paper type Research paper

IntroductionIn twenty-first century, with the emergence of knowledge and technology driveneconomies, there is a huge demand for a highly skilled and technically qualifiedcompetent workforce. As a result, the global demand for higher education is constantlyrising, likely to be 160 million by 2025 (Glakas, 2003) and technical institutes are tryingto create new programs to meet the requirements of the industry and society.

India has witnessed a phenomenal growth in the education sector in last 20 years.As India is the fastest growing economy in the world, the demand from other sectors is

The current issue and full text archive of this journal is available at

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Improvingservice quality

387

Received 20 March 2012Accepted 26 June 2012

Quality Assurance in EducationVol. 20 No. 4, 2012

pp. 387-407q Emerald Group Publishing Limited

0968-4883DOI 10.1108/09684881211264019

also growing. The education sector is catching up with this trend. The number ofinstitutes has gone up to meet the demand in the field of technical education. Thegovernment of India has encouraged the private sector to invest in technicaleducational institutes to provide high-quality education. Around 85 per cent oftechnical education is now being delivered by the private sector.

However, quality in education is constantly being debated. Quality in education hasbeen referred to in terms of customer focus, efficiency, high standards, etc. if quality is tobe embedded, a high level of involvement of various stakeholders is essential. Technicalinstitutes are facing challenges to improve the quality of education. Sustainability andstriving for excellence for the education sector has become indispensable.

Two major approaches to quality improvement are quality assurance and qualityenhancement. Quality assurance in education can be achieved when the barriers toquality are removed from the system and simultaneously, quality in education can beenhanced once the opportunities present in the system are identified. At national level,a continuous effort has been made to identify the key issues to improve the quality oflearning, teaching inputs, outputs, governance issues, etc. To be successful, a focus onbarriers, which are creating hindrances to the development of technical education,becomes a necessary step. Enablers are equally significant, as focusing on them wouldhelp to plan a strategy for future growth. The process of globalization in technicaleducation has its own challenges and barriers. The issues of fair access and affordableparticipation in technical education are important as India has to develop technicalmanpower to boost its growth and secure a place in the international arena.Addressing these issues is imperative.

The purpose of the paper is to investigate and examine opportunities (enablers) andissues (barriers) from the perspective of growth of technical education in India. Thispaper adopts an empirical analysis of technical institutes to identify the enablers andbarriers within the education system. This study proposes an evolutionary way tobecome a progressive institution in the emerging era of globalization andmodernization. The paper also maps out a course of action that would help theeducational institutions to achieve competitive advantage.

Theoretical backgroundIn the past few decades, there has been growing concern about quality in highereducation. Most studies have focused on customer satisfaction and overall satisfactionwith the education system. Doherty (2008) focused on “quality”, “TQM” and“Autonomy” in the education sector. The paper involved a discussion among theacademicians of the relevance of the three concepts in education. Mergen et al. (2000) andGrant et al. (2002) presented a model with three components: quality of design, quality ofconformance and quality of performance as a framework to identify opportunities forimprovement in higher education. The authors also dealt with the measurement of theparameters. Shank et al. (1995) discussed the fact that higher education possesses all ofthe characteristics of a service: it is intangible, heterogeneous and inseparable from theperson delivering it. Kanji (1998) studied and proposed an excellence model for highereducation, which focused on four principles viz delight the customer, management byfact, people-based management and continuous improvement.

Telford and Masson (2005) investigated the relationship between the congruence ofthe quality values and the level of student satisfaction. This paper involves severalstakeholders like students, faculty and the senior management. The authors also

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proposed a framework of quality values in education which includes course design,course marketing, student recruitment, induction, course delivery, course content,assessment monitoring, miscellaneous and tangibles. Mustafa and Chiang (2006), Peatet al. (2005), Srdoc et al. (2005), Alashloo et al. (2005), Sahney et al. (2004), Bath et al.(2004), Koch and Fisher (1998) studied and documented the view that TQM covers allcritical areas of higher education in terms of faculty, staff and infrastructure, academiclife, management’s policy towards employees, curriculum design, pedagogy, admissionprocesses, non-academic processes, etc.

Viswanadhan and Rao (2005) studied nine parameters, which were affected byprivatization viz commitment of top management and leadership, customer focus,course delivery, communication, campus facilities, congenial learning environment andcontinuous assessment and improvement in the context of India. Sakthivel et al. (2005)studied five parameters viz commitment of top management, course delivery, campusfacilities, courtesy and customer feedback and improvement. They then developed aTQM model of academic excellence for technical institutions of India.

Dotchin and Oakland (1994) and Asubonteng et al. (1996) opined that most of thestudies are customer focused. However, it is also necessary to identify the requirementsof the customers (Parasuraman et al., 1988 and Babakus and Boller, 1992). Hence,defining quality in higher education means including the quality of inputs, the qualityof processes and the quality of outputs as advocated by various researchers (Sallis,1993; Green, 1994; Cheng and Tam, 1997; Kanji et al., 1999).

As technical education courses in India are quite diverse, the number of institutesproviding technical courses in India is also very large. There are approximately 2,400technical institutions across India of which less than 8 percent of public institutions areautonomous (World Bank, working paper, 2010). A sudden change in thedemand-supply in the technical education sector makes it mandatory to ensure thatthe institutes are efficiently and effectively managed and governed to satisfy the needsof industry and society. In order to maintain the standard of technical education, astatutory authority- The All India Council for Technical Education (AICTE) was setup, which is responsible for planning, formulation and maintenance of norms andstandards, quality assurance through accreditation, funding in priority areas,monitoring and evaluation, maintaining parity of certification and awards andensuring coordinated and integrated development and management of technicaleducation in India.

Technical education not only involves career preparation but also intellectualdevelopment, which should have a lifelong impact on individuals as quoted by Norris(1978). In 1994 The American Society for Engineering Education suggested thatengineering education needs to be relevant, attractive and connected to the lives andcareers of students.

According to Powar (2001), the Indian education system has not been able to takethe advantage of the possibilities for improving the quality of education for economicbenefit. Though the opportunities are available to the youth, it would be worthwhile tohighlight them. As well as infrastructure facilities, equality in participation havingadequate control related to quality and financial arrangements are main conditions laiddown by the author.

Natarajan (2007) mentioned some enablers like growing employment opportunitiesin the IT sector and the popularity of IT tools for Technology-Enhanced Learning inthe field of technical education. Distance Education possibilities, especially forContinuing technical Education is also one of the significant enablers in technical


389

education. Networking of technical institutions with R&D labs and industry and therole of Technology and Engineering Education for national development andprosperity are widely acknowledged. Wani et al. (2007) suggested developingentrepreneurship in the technical education, as it would enable students to considerself-employment as a career option. Webster (2000) and Sanghi (1996) considered therole of technical entrepreneurship as an enabler in the process of liberalization oftechnical education.

Nanda and Ahuja (2003) discussed some of the barriers in the technical educationsystem. Diverse requirements of society, and users in particular, may be seen as amajor barrier in the field of technical education. Rapid changes in requirements ofindustries are a serious barrier in the field of technical education in India. The need forskilled manpower like faculty and staff to develop new technical programs is also amajor concern. They also emphasized other factors like rapid evolution of technologiesand technological innovations, the need to upgrade infrastructure facilities regularly tokeep pace with ever changing technologies. Natarajan (2000) concluded that growingglobal competition and development of information and communications technologyhas challenged the trend in engineering education. Rhodes (2002) presented acomparative study between US and Indian higher education institutes. One of thechallenges mentioned by the author is the expectation of the students in terms ofstudent service resources, like support staff, counseling services, housing, etc. Sohailand Shaikh (2004) explored students’ expectations of quality in higher education andidentified six factors, namely contact with personnel, physical environment,reputation, responsiveness, access to facilities and curriculum.

Empirical studies are scarce in the literature that investigated the challenges(barriers) and opportunities (enablers) to improve and assess the dimensions of thequality of technical education. The present research is motivated by the desire tounderstand the relation between the various enablers and barriers, to know theirdegree of dependence and driving power. The main purpose of this study is to providea source of information to ensure meaningful communication regarding challenges andnew opportunities faced by educators, institutions and industries. In this paper theprocess of technical education is being examined with a view to improve the quality ofprovision.

Interpretive structural modeling (ISM): an overviewISM offers a methodology for structuring complex issues and it is a combination ofthree modeling languages: words, digraphs and discrete mathematics. It differssignificantly from many traditional modeling approaches, which use quantifiablevariables. ISM incorporates elements measured on ordinal scales of measurement andprovides a modeling approach, which permits qualitative factors to be retained as anintegral part of the model.

Conventional methods like the Delphi method is also a structured technique used forforecasting in various disciplines. It is a decision making process where a group ofexperts reaches a consensus after a brainstorming session. However, collectinginformation from the respondents was extremely difficult due to lack of time. Softsystems methodology (SSM) can only deal with ill-defined parts of the system. It isunable to build the complete problem and is not able to build the system as a whole(Anonymous, 2002). Structural Equation Model (SEM) is a confirmatory statistical

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approach, which requires statistical data. It involves hypothetical tests to determinethe extent of the proposed model (Wisner, 2000).

On the other hand, ISM has advantages over other methods. It was proposed byWarfield (1976) to analyze complex socioeconomic systems. Since then ISM has oftenbeing used to help understand complex situations and to enable a strategy for solvingproblem. Sage (1977) stated that ISM is used for identifying the relationship betweenvarious factors, which define a specific issue or problem. It is an interactive process,which also uses the notion of graph theory to explain the complex pattern of contextualrelationships among a set of variables. ISM acts as a tool for imposing order anddirection on the complexity of relationships among the variables as discussed by Sage(1977), and Singh et al. (2003); Jharkharia and Shankar (2004). In this paper ISM hasbeen used in the context of technical education institutes. ISM has been used to modelthe enablers of and barriers to curriculum in technical education. To develop theoverall quality of technical education, a number of variables play a significant role.This paper mentions two models involving the variables and parameters that could beof great significance to top management. ISM can explain the relationship betweenvariables that can be extracted from the system under study.

The steps of ISM have been described as a process within the present context. Theprocess starts with the identification of the relevant elements of the problem. Groupsolving techniques have been used to address this step. In the next stage, acontextually relevant subordinate relation is chosen. Based on this relation, aself-structural self-interaction matrix (SSIM) is developed. In the next step, the SSIM isconverted into a reachability matrix and its transitivity is checked. Once this is done, awell-defined representation system in the form of matrix model is obtained. In the finalstep the partitioning of the elements and the extraction of the structural model is doneto complete the ISM. Mandal and Deshmukh (1994), Soti et al. (2010) presented theprocess in the form of a flow chart. Figure 1 represents the various stages of ISM in aform of a flow chart.

Empirical analysisThe objectives of this paper are:

. To identify the enablers of technical education for growth in this sector.

. To recommended strategies to improve technical education and training for theworkforce.

. To identify the barriers to technical education to meet the challenges of today’seconomy.

. To recommend strategic decisions for industry to look for trained and skilledresources.

To address the generic issue, this paper identifies specific issues faced by industry,skill training providers, technical education institutes and educators. Using this data asa baseline, an action plan may be developed for re-conceptualizing the linkage betweenindustry and the educators, namely institutions.

The present discussion is based on 11 variables under the “enabler” category and 12variables under “barrier” category. The selection of parameters is done through theliterature review and discussion with two experts: one from academia and one fromindustry.


391

MethodologySince ISM is an interactive process to explain the relationship among a set of variables,a questionnaire was developed where various steps of the ISM technique were appliedto achieve the research objectives. The methodology is now developed. The initial stepin this study was to facilitate experts in developing a relationships matrix. The surveyincluded two parts. In Part I, respondents were asked to provide information onvarious parameters on enablers to technical education. Part II of the survey included aseries of questions regarding the barriers to technical education. These questions were

Figure 1.Flow chart of ISM

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scaled on a Likert scale of 1 (no importance) to 5 (very high importance). The variableswere selected through discussion with educators, through interactions with industryrepresentatives and from the existing literature.

A structured questionnaire was developed and administered with companies whovisited the campus during the placement and to the faculty members. The respondentswere asked to indicate the importance of 11 enablers and 12 barriers. These parametersare listed in Tables I and II and have been numbered as 1, 2, 3, [. . .]. Out of 700questionnaires used, 200 usable responses were received, which resulted in a responserate of 28.57 per cent. Cronbach’s coefficient (a) was calculated to test the reliabilityand internal consistency of the responses. The value of a was found to be 0.89. Thisvalue is considered to be consistent as reported by Cronin and Taylor (1992) andParasuraman et al. (1988). The descriptive statistics for enablers and barriers areexhibited in Tables I and II respectively.

Surveyno. Barriers Mean SD

Rank asper mean

1 Lack of long-term goals of the technical institutes 3.2 1.2237 42 Lack of qualified instructors 4.23 0.7566 13 Lack of industry-institute interaction 2.22 0.8633 114 Lack of industry focus and emphasizing on short-term remedies 2.39 0.9285 105 Lack of insufficient teaching space (labs, classrooms) 2.80 1.0736 76 Lack of providing practical skills needed for employment 3.09 1.3010 57 Lack of financial resources 2.97 1.1001 68 Lack of credibility of the institute for not getting the accreditation/

approval 3.38 1.0683 39 Lack of technical awareness to understand the customers’ need 2.11 0.9231 12

10 Lack of strategic planning of the technical institutes 2.43 1.0541 911 Lack of distance education in technical field 4.05 0.8094 212 Lack of support staff 2.63 1.0990 8

Table II.Survey results related tobarriers in the technical

education

Surveyno. Enablers of technical education in India Mean SD

Rank asper mean

1 Benchmarking of technical education 4.41 0.6668 62 Ubiquitous technology and technical literacy 4.19 0.7705 83 Real world/practical applications in technical education 4.42 0.5954 54 Soft skill development 4.49 0.5398 45 Career orientation among parents, educators and students to create

the interests 3.52 0.9941 106 Next generation industry specific technical knowledge 4.72 0.4612 17 Need for effective technical professionals for successful companies 4.62 0.5542 38 Strategic planning. 4.01 0.8082 99 Technical training for the employees 3.05 0.9367 11

10 Specialization and customization to meet new industry demands 4.63 0.5710 211 Innovation from technical and domain specific knowledge/

experience 4.28 0.7100 7

Table I.Survey results related toenablers in the technical

education


393

The ISM methodology suggests the use of expert opinions, based on variousmanagement techniques such as brainstorming in developing the contextual relationshipamong the variables. In the present case, two experts from industry were consulted toidentify the nature of the contextual relationship among the enablers of technicalcurriculum. The results of their discussion are not discussed to avoid bias. It wasobserved that variation between the two experts was not significant. In order to analyzethe relationship among the variables, a contextual relationship was developed by usingV, A, X and O. The symbols denote the direction of relationship between i and j:

. V for enabler/barrier i will help achieve/alleviate enabler/barrier j;

. A for enabler/barrier j will be achieved/alleviate by enabler/barrier i;

. X for enabler/barrier i and j will help achieve each other; and

. O for enablers/barriers i and j are unrelated.

The following statements explain the use of symbols V, A, X and O for enablers andbarriers in SSIM as presented in Tables III and IV respectively.

Enabler 1 i.e. benchmarking of technical education will have a focus on curriculum,which would be able to achieve enabler 4 namely, soft skills development for technicaljobs. Hence the relationship is depicted as “V” in Table III. The need for effectivetechnical professionals (enabler 7) would help to have practical applications intechnical education (enabler 3), as there would be a demand from industry to increasethe skill and knowledge of the workforce. Hence the relation is “A”. Enabler 5 and 11are unrelated. Career orientation among parents, educators and students to create theinterests (enabler 5) is uncorrelated with innovation from technical and domain specificknowledge/experience (enabler 11). Thus, “O” represents their relation in Table III.Enabler 6, namely next generation, requires industry specific technical knowledge andenabler 7 viz need for effective technical professionals will help each other to achieve.Therefore “X” depicts the relationship.

A similar logic holds for barriers. Barrier 4 helps to alleviate barrier 9. It impliesthat, if an effort is made to increase the focus on industry and on long-term remedies,

EnablersSurveyno. 11 10 9 8 7 6 5 4 3 2

1 Benchmarking of technical education A A O O X A O V X O2 Ubiquitous technology and technical literacy A A O A A O O O A3 Real world/practical applications in technical education O O V O A O O V4 Soft skill development O A O A O O O5 Career orientation among parents, educators and students

to create the interests O O O O O O6 Next generation industry specific technical knowledge O V V O X7 Need for effective technical professionals for successful

companies O V V O8 Strategic planning O V V9 Technical training for the employees O A

10 Specialization and customization to meet new industrydemands A

11 Innovation from technical and domain specificknowledge/experience

Table III.Structural self-interactionmatrix (SSIM) of theenablers in technicaleducation

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then it would help to understand the customers’ needs and requirements (enabler 9).Hence “V” in Table IV denotes the relationship between 4 and 9. Barrier 2 can bealleviated by barrier 7 i.e. removal of financial restrictions would help to alleviatebarrier 2 i.e. qualified instructors required in technical education. Thus “A” denotes therelationship between barriers 2 and 7 in SSIM. Barrier 3 viz lack of industry-instituteinteraction and barrier 6 i.e. lack of providing practical skills needed for employmentwould help achieve each other. Thus “X” denotes the relationship between these twobarriers in the SSIM. No relationship exists between barrier 11, i.e. lack of distanceeducation and barrier 12, i.e. lack of support staff. Thus, “O” denotes the relationshipbetween barrier 11 and 12 in Table IV.

Tables V and VI represent the final reachability matrix for enablers and barriersrespectively. Substituting V, A, X, O by 1 and 0 as per the following rule, a binarymatrix is achieved.

. If the (i, j) entry in the SSIM is V, then the (i, j) entry in the reachability matrixbecomes 1 and the ( j, i ) entry becomes 0.

. If the (i, j) entry in the SSIM is A, then the (i, j) entry in the reachability matrixbecomes 0 and the ( j, i ) entry becomes 1.

. If the (i, j) entry in the SSIM is X, then the (i, j) entry in the reachability matrixbecomes 1 and the ( j, i ) entry also becomes 1.

. If the (i, j) entry in the SSIM is O, then the (i, j) entry in the reachability matrixbecomes 0 and the ( j, i ) entry also becomes 0.

Tables V and VI also shows the “driving power” and the “dependence” of the enablersand barriers of technical education respectively. The higher the driving power, the higherthe rank. The driving power of a particular variable is the total number of variables(including itself), which it may help achieve while the dependence is the total number ofvariables, which may help achieving it. For instance, enabler 7 (need for effective technical

BarriersSurveyno. 12 11 10 9 8 7 6 5 4 3 2

1 Lack of long-term goals of the technical institutes V O X V X A V V X V V2 Lack of qualified instructors A O A X V A V X V V3 Lack of industry-institute interaction O O A X O A X O X4 Lack of industry focus and emphasizing on short-

term remedies O A A V O A V O5 Lack of insufficient teaching space (labs,

classrooms) V O X O X A V6 Lack of providing practical skills needed for

employment O X A X X O7 Lack of financial resources V V X V X8 Lack of credibility of the institute for not getting the

accreditation/approval V O X O9 Lack of technical awareness to understand the

customers’ need O X A10 Lack of strategic planning of the technical institutes V O11 Lack of distance education in technical field O12 Lack of support staff

Table IV.Structural self-interaction

matrix (SSIM) of thebarriers in technical

education


395

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Table V.Final reachability matrixof the enablers oftechnical education

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396

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IIV

Table VI.Final reachability matrix

of the barriers oftechnical education


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professionals for successful companies) and enabler 3 (real world/practical applications intechnical education) have the maximum “driving power” of 7, are given the first rank.Enabler 4, 5 and 9 namely “Soft skill development”, “Career orientation among parents,educators and students to create the interests” and “Technical training for the employees”have the least driving power and hence are given the fifth rank.

Similar justification is given for dependence. Enabler 1, 2 and 9 namely,“benchmarking technical education”; “Ubiquitous technology and technical literacy”and “Technical training for the employees” respectively have the maximumdependence. Hence they are given the first rank. Enabler 5, 8 and 11 namely, “careerorientation among parents, educators and students to create the interests”, “strategicplanning” and “Innovation from technical and domain specific knowledge/experience”respectively have the least dependence and hence are given the fourth rank in the list ofenablers of technical education.

Table VI exhibits that lack of financial resources (barrier 7) and lack of strategicplanning of the institutes (barrier 10) as first rank holder in terms of driving power. Lackof support staff (barrier 12) has scored the least in terms of driving power. Hence, it is thelast rank holder in the list of barriers of technical education considered in the presentcontext. In terms of dependence power, Lack of providing practical skills needed foremployment (barrier 6) has been ranked first. Lack of financial grant (barrier 7) is the lastrank holder in terms of dependence power among the barriers under study.

The reachability matrix has been partitioned on the basis of the reachability andantecedent set (Warfield, 1976). From the final reachability matrix, the reachability andantecedent set for each factor are found. The reachability set consists of the elementitself and other elements which it may help achieve, whereas the antecedent setconsists of the element itself and the other elements which may help in achieving it.Then the intersection of these sets is derived for all elements. The element for whichthe reachability and intersection sets are same is the top-level element in the ISMhierarchy. The top-level element of the hierarchy would not help achieve any otherelement above their own. Once the top-level element is identified, it is separated outfrom the other elements. Then by the same process, the next level of elements is found.

These identified levels help in building the diagraph and final model. Tables VIIand VIII show the level of enablers and barriers respectively. The enablers are barriershave been grouped in various levels such as level 1, 2, 3 [. . .]. The levels identified aidsin building the final model of ISM.

Enablers Reachability set Antecedent set Intersection set Level

1 1, 3, 4, 7 1, 3, 6, 7, 10, 11 1, 3, 7 Level 22 2 2, 3, 7, 8, 10, 11 2 Level 13 1, 2, 3, 4, 9 1, 3, 7 1,3 Level 24 4 1, 3, 4, 8, 10 4 Level 15 5 5 5 Level 16 1, 6, 7, 9, 10 6, 7 6, 7 Level 47 1, 2, 3, 6, 7, 9, 10 1, 6, 7 1, 6, 7 Level 48 2, 4, 8, 9, 10 8 8 Level 49 9 3, 6, 7, 8, 9, 10 9 Level 110 1, 2, 4, 9, 10 6, 7, 8, 10, 11 10 Level 311 1, 2, 10, 11 11 11 Level 4

Table VII.Partition of reachabilitymatrix for enablers understudy

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From Table VII, it can be seen that enablers 2, 4, 5 and 9 namely, “ubiquitoustechnology and technical literacy”, “soft skills development”, “career orientationamong parents, educators and students to create the interests” and “technical trainingfor the employees” respectively are found at level 1 (dependents). Thus, these would bepositioned at the top of the ISM hierarchy. Similarly, enablers 1 and 3 are found at level2, enabler 10 is at level 3 and enabler 6, 7, 8 and 11 are at level 4 (drivers).

Similarly, in Table VIII, lack of industry-institute interaction (barrier 3), lack ofproviding practical skills needed for employment (barrier 6) and lack of technicalawareness to understand the customers’ needs (barrier 9) have been identified as level 1barriers. Therefore, they occupy the top position in the hierarchy of the ISM model.Barriers 2, 4 and 12 have been identified as level 2. These barriers are related to lack ofqualified faculty in the technical institutes (barrier 5), lack of industry focus of theinstitutes (barrier 4) and lack of support staff in the technical institutes (barrier 12). Atthe third level, barriers 1, 5, 7, 8 and 10 have been identified. These barriers occupy thebottom the hierarchy of the barriers of technical education in India.

The structural model is generated from the final reachability matrix and thedigraph is drawn. If there is a relationship between the parameters i and j, it is shownby an arrow which points from i to j. The resultant graph is called directed graph. Afterremoving the transitivity, the digraph is finally converted into the ISM as shown inFigure 2 for enablers and Figure 3 for barriers.

As is evident from Figure 3, the independent variables, which are at the bottom ofthe model viz “Lack of long-term goals of the technical institutes”, “lack of sufficientteaching space”, “lack of financial resources”, “lack of credibility for not gettingaccreditation process” and “lack of strategic planning of the institutes” are some of theimportant barriers of technical education that emerged from the model. The nature ofthese barriers represents major hindrances and these are the responsibility of theorganization and the authorities. As we follow the hierarchy, we observe that thequality of the technical education is suffering because the organization is not able toretain qualified faculty resulting in difficulty in understanding the customer’s need.“Lack of distance education” and “lack of support staff” is at the top level in the model,indicating that these are dependent on other barriers.

Figure 3 represents the relationship among the enablers. The enablers at the bottomof the model are known as drivers viz “next generation industry specific technical

Barriers Reachability set Antecedent set Intersection set Level

1 1, 2, 3, 4, 5, 6, 8, 9, 10, 12 1, 4, 7, 8, 10 1, 4, 8, 10 Level 32 2, 3, 4, 5, 6, 8, 9 1, 2, 5, 7, 9, 10, 12 2, 9 Level 23 3, 4, 6, 9 1, 2, 3, 4, 6, 7, 9, 10 3, 4, 6, 9 Level 14 1, 3, 4, 6, 9, 11 1, 2, 3, 4, 7, 10, 11 1, 3, 4, 11 Level 25 2, 5, 6, 8, 10, 12 1, 2, 5, 7, 8, 10 2, 5, 8, 10 Level 36 3, 6, 8, 9, 11 1, 2, 3, 4, 5, 6, 8, 9, 10, 11 3, 6, 8, 9, 11 Level 17 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12 7, 8, 10 7, 8, 10 Level 38 1, 5, 6, 7, 8, 10, 12 1, 2, 5, 6, 7, 8, 10 1, 5, 6, 7, 8, 10 Level 39 2, 3, 6, 9, 11 1, 2, 3, 4, 6, 7, 9, 10, 11 2, 3, 6, 9, 11 Level 110 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12 1, 5, 7, 8, 10 1, 5, 7, 8, 10 Level 311 4, 6, 9, 11 4, 6, 9, 11 4, 6, 9, 11 Level 112 2, 12 1, 5, 7, 8, 10, 12 12 Level 2

Table VIII.Partition of reachability

matrix for barriers understudy


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knowledge”, “need for effective technical professionals for successful companies”,“strategic planning” and “innovation from technical domain specific knowledge”.These enablers are leading the other enablers like “specialization and customization tomeet the new industry demands”, “real world application”, “benchmarking of thetechnical institutes”, etc. the enablers found at top of the model are dependent and donot have any driving power for instance, “ technical literacy”, “career orientationamong the parents and students to create the interest”.

In Figures 4 and 5, the enablers and barriers have been classified into fourcategories by MICMAC analysis based on driving power and the dependence. Theobjective behind this classification is to analyze the driving power and dependency ofthe enablers and barriers. The driver power and dependence of each of the enablers andbarriers are shown in Tables V and VI. The diagrammatic representation is shown inFigures 4 and 5 for enablers and barriers respectively. Figure 4 exhibits the categoriesof the various enablers on technical curriculum. The dependence is plotted on X-axisand the driving power is plotted on Y-axis. As an illustration, enabler 3 has adependence power of three and the driving power of seven. Therefore, in Figure 4, ithas been positioned in the fourth quadrant corresponding to high driving power andlow dependence power.

Figure 2.ISM-based model for theenablers of technicaleducation

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The first cluster in Figure 4 includes “autonomous enablers” that have weak drivingpower and weak dependence. These parameters are relatively disconnected from thesystem. In the present study, five enablers are in the first quadrant and they areenablers 4, 5, 8, 10 and 11. These enablers have few links, which may be strong. Thesecond cluster consists of the dependent variables that have weak driving power butstrong dependence. In the present case, enablers 1 and 2 are in the second category.The third cluster includes linkage variables that have strong driving power and alsostrong dependence. Any action on these variables will affect others and there will be afeedback effect on them. This makes them unstable in the system. Finally the fourthcluster is known as independent variables with low dependence and high drivingpower. It has been found that a variable with very strong driving power called the keyvariable, falls into the category of independent or linkage variable. There are twoenablers (3 and 7) in this section.

Figure 3.ISM-based model for the

barriers of technicaleducation


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Among the barriers, two barriers are in the first quadrant and they are enablers 5and 11 in Figure 5. These two barriers have very few links, which may be strong.These are also disconnected from the system. The second quadrant consists of thedependent variables that have weak driving power but strong dependence. In thepresent scenario, barriers 3, 4, 6 and 9 are in the second category. The third clusterincludes linkage variables that have strong driving power and also strongdependence. There are two barriers in this category and they are barriers 2 and 8.These kinds of barriers are unstable and before making any changes in them, oneshould take care regarding the consequential changes in other variables. Finally thefourth cluster has only key parameters, also known as independent variables, withlow dependence and high driving power. There are four barriers in this section andthey are 1, 7, 10 and 12.

Figure 4.Driving power anddependence matrix of theenablers of technicaleducation

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Discussion and conclusionAs the results show, a call to action is required to address various issues (barriers)identified by industry representatives and faculty fraternity. The barriers identifiedneed to be taken seriously by policy makers to enable necessary actions to be taken toimprove the quality of education. Knowing the barriers is equally important whenseeking to undertake a new project or introduce a new course.

As discussed before, the technical education sector is large and complex innature in India. In the present scenario, a large number of families find it difficult tomeet the costs of technical education. The government of India is yet to develop a

Figure 5.Driving power and

dependence matrix of thebarriers of technical

education


403

mechanism to overcome this issue. “Lack of financial resources” is identified as amajor barrier, which should be addressed as fee structures becomes very high inabsence ofsome sort of intervention. This is a governance issue playing a major rolein the education sector. “Lack of distance education” and “lack of credibility of theinstitutes” show major barriers of the system which are leading the other issues (seeFigure 3). Hence, co-operation is required by the government to make technicaleducation available.

Barriers 1 (lack of long-term goals of the technical institutes), 7 (lack of financialresources) and 10 (lack of strategic planning of the technical institutes) have beenidenfied as key barriers (see Figure 5) pertain to governance and policy issues. Goodgovernance acts as a buttress to the mission and purpose of the institute. It helps tocreate an ethical and sustainable strategy, acceptable to all the stakeholders of thesystem as it formulates transparent and honest strategies and oversees theimplementation of these to the benefit of all the stakeholders.

Lack of financial resources is leading to another issue related to instructors in theinstitute. Sometimes, due to lack of proper infrastructure like library, classroom,laboratory, etc. new courses cannot be started even if there is a demand for them. Lackof qualified faculty, who can integrate industrial knowledge with academic knowledge,is led by management who lack long-term goals for technical education. Due tonon-availability of the qualified faculty, who are considered to be the interface betweenthe industry and academics, a direct impact can be seen on the delivery of thecurriculum (see Figure 3). To address this issue, sharing of resources like faculty orlaboratories and libraries among the institutes can be done. With the advent oftechnology, use of technology to deliver courses or to support courses can beconsidered. The model suggests, instead of improving the curriculum or emphasizingindustry-institute interaction, there should be a focus on the policies to have a clearmission and vision so that the long-term goals becomes achievable.

Getting accreditation is one of the significant barriers as identified in the model (seeFigure 3). This in turn is leading to lack in long-term goals of the institutes. In absenceof approval or accreditation, the institutes lack in the credibility from the students’point of view as well as from industry’s point of view. Students will not be interested ingaining admission to an unapproved institute and industry would not like to recruit thestudents from an unrecognized institute. This is a serious problem for the institutes.Once an institute gets accreditation from the government, it gains credibility in society,as accreditation is an indicator of many quality aspects.

Although there are many challenges, as discussed earlier, there exist opportunities too.Technical education caters for a substantial segment of students. In last few years, therole of technical education in the international market has increased its importance interms of visibility and applicability. It is worth mentioning on the basis of existingliterature, that the education sector has a strong effect on the skilled labour market andhas become an integral part of economic development. Opportunities (enablers) emergingwith new technologies need to be taken into account. The successful leaders in theeducation sector need to know characteristics such as the present opportunities prevailingin the market and how to exploit those to develop and expand the scale of operation.

“Real world/practical applications of technical education” and “need for effectivetechnical professionals for successful companies” have been identified as “key”enablers as these two are high in driving power and low in dependence (see Figure 4).As India is growing, its technical fields are also growing like applications of IT,manufacturing technology, infrastructure technology, etc. Skilled manpower is

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required to achieve and sustain this growth. A proper policy and formal mechanismsrelated to industry investment in technical education, inviting experts from privateindustry to serve as faculty and researchers to provide training for students for futurewould be beneficial for India.

However, enabler 1 (benchmarking of technical education), 2 (technical literacy)and 9 (technical training for employees) have been led by the need for effectivetechnical professionals for successful industries (see Figure 2). Since, the industryneeds technical qualified manpower for their sustainability and existence; they aredependent on trained working people in their organization. However, to keep pacewith innovation and requirements, the curriculum in institutes need to be kept up todate. This is mainly achieved by benchmarking the institutes. As discussed before,benchmarking is a process based on certain quality related parameters of academicand non-academic activities. Through benchmarking, a common framework forstandards specifying the knowledge and skills specific to industry requirements,credible employable skills, assessment of the students and guide to curriculumdevelopment can be achieved.

Like any other sector, the education sector also demands change and scrutiny. Thiscould be done by making changes in accreditation standards and several governmentprocedures to improve the quality of the technical education. The present study couldprove a catalyst for planners of technical education. Although there are opportunitiesin the field of technical education, the results show that a call for action is required toaddress various issues/barriers identified by the experts. The barriers identified needto be taken seriously by the policy makers and necessary actions can be taken toimprove the quality of education. This study could be insightful for the strategicdecision makers and policy makers of technical education.

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Broome, B.J. (2002), “Giving voice to diversity: an interactive approach to conflict managementand decision-making in culturally diverse work environments”, Journal of Business andManagement, Vol. 8 No. 3, pp. 239-54.

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Corresponding authorRoma Mitra Debnath can be contacted at: [email protected]


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