1. Quality Assurance in Education
Emerald Article: Improving service quality in technical education: use of
interpretive structural modeling
Roma 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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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
http://dx.doi.org/10.1108/09684881211264019
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
http://dx.doi.org/10.1108/09684881211264019
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2. Improving service quality in
technical education: use of
interpretive structural modeling
Roma Mitra Debnath
Indian Institute of Public Administration, New Delhi, India, and
Ravi Shankar
Department 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 to
technical education. It seeks to critically analyze the relationship amongst them so that policy makers
can 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 are
categorized under “enablers” and “barriers”. The enablers would help policy makers to improve and
develop the curriculum of the technical education and the identifying barriers would help the decision
maker to improve upon those variables.
Findings – The major findings of this study are to prioritize the strategic parameters in reducing the
risks associated with technical education. The model also proposes a hierarchical structure classifying
the parameters as drivers and enablers.
Research limitations/implications – The study proposes a scientific way to model the enablers
and barriers to become a progressive institution in the emerging era of globalization and
modernization. This would help to prioritize the issues as the enablers and barriers are hierarchically
structured and categorized.
Practical implications – The paper maps out a course of action and the adoption of the proposed
framework would provide a competitive edge for India over others. Also, the various stakeholders
would 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 in
the field of technical education in India. The integrated framework of policy related parameters would
contribute towards overall growth and development.
Keywords Technical education, Curriculum planning, Structural analysis, Modelling, India,
Technical training
Paper type Research paper
Introduction
In twenty-first century, with the emergence of knowledge and technology driven
economies, there is a huge demand for a highly skilled and technically qualified
competent workforce. As a result, the global demand for higher education is constantly
rising, likely to be 160 million by 2025 (Glakas, 2003) and technical institutes are trying
to 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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Improving
service quality
387
Received 20 March 2012
Accepted 26 June 2012
Quality Assurance in Education
Vol. 20 No. 4, 2012
pp. 387-407
q Emerald Group Publishing Limited
0968-4883
DOI 10.1108/09684881211264019
3. also growing. The education sector is catching up with this trend. The number of
institutes has gone up to meet the demand in the field of technical education. The
government of India has encouraged the private sector to invest in technical
educational institutes to provide high-quality education. Around 85 per cent of
technical education is now being delivered by the private sector.
However, quality in education is constantly being debated. Quality in education has
been referred to in terms of customer focus, efficiency, high standards, etc. if quality is to
be embedded, a high level of involvement of various stakeholders is essential. Technical
institutes are facing challenges to improve the quality of education. Sustainability and
striving for excellence for the education sector has become indispensable.
Two major approaches to quality improvement are quality assurance and quality
enhancement. Quality assurance in education can be achieved when the barriers to
quality are removed from the system and simultaneously, quality in education can be
enhanced 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 of
learning, teaching inputs, outputs, governance issues, etc. To be successful, a focus on
barriers, which are creating hindrances to the development of technical education,
becomes a necessary step. Enablers are equally significant, as focusing on them would
help to plan a strategy for future growth. The process of globalization in technical
education has its own challenges and barriers. The issues of fair access and affordable
participation in technical education are important as India has to develop technical
manpower 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) and
issues (barriers) from the perspective of growth of technical education in India. This
paper adopts an empirical analysis of technical institutes to identify the enablers and
barriers within the education system. This study proposes an evolutionary way to
become a progressive institution in the emerging era of globalization and
modernization. The paper also maps out a course of action that would help the
educational institutions to achieve competitive advantage.
Theoretical background
In the past few decades, there has been growing concern about quality in higher
education. Most studies have focused on customer satisfaction and overall satisfaction
with the education system. Doherty (2008) focused on “quality”, “TQM” and
“Autonomy” in the education sector. The paper involved a discussion among the
academicians of the relevance of the three concepts in education. Mergen et al. (2000) and
Grant et al. (2002) presented a model with three components: quality of design, quality of
conformance and quality of performance as a framework to identify opportunities for
improvement in higher education. The authors also dealt with the measurement of the
parameters. Shank et al. (1995) discussed the fact that higher education possesses all of
the characteristics of a service: it is intangible, heterogeneous and inseparable from the
person delivering it. Kanji (1998) studied and proposed an excellence model for higher
education, which focused on four principles viz delight the customer, management by
fact, people-based management and continuous improvement.
Telford and Masson (2005) investigated the relationship between the congruence of
the quality values and the level of student satisfaction. This paper involves several
stakeholders like students, faculty and the senior management. The authors also
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4. 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), Peat
et 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 all
critical areas of higher education in terms of faculty, staff and infrastructure, academic
life, management’s policy towards employees, curriculum design, pedagogy, admission
processes, non-academic processes, etc.
Viswanadhan and Rao (2005) studied nine parameters, which were affected by
privatization viz commitment of top management and leadership, customer focus,
course delivery, communication, campus facilities, congenial learning environment and
continuous assessment and improvement in the context of India. Sakthivel et al. (2005)
studied five parameters viz commitment of top management, course delivery, campus
facilities, courtesy and customer feedback and improvement. They then developed a
TQM model of academic excellence for technical institutions of India.
Dotchin and Oakland (1994) and Asubonteng et al. (1996) opined that most of the
studies are customer focused. However, it is also necessary to identify the requirements
of the customers (Parasuraman et al., 1988 and Babakus and Boller, 1992). Hence,
defining quality in higher education means including the quality of inputs, the quality
of 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 institutes
providing technical courses in India is also very large. There are approximately 2,400
technical institutions across India of which less than 8 percent of public institutions are
autonomous (World Bank, working paper, 2010). A sudden change in the
demand-supply in the technical education sector makes it mandatory to ensure that
the institutes are efficiently and effectively managed and governed to satisfy the needs
of industry and society. In order to maintain the standard of technical education, a
statutory authority- The All India Council for Technical Education (AICTE) was set
up, which is responsible for planning, formulation and maintenance of norms and
standards, quality assurance through accreditation, funding in priority areas,
monitoring and evaluation, maintaining parity of certification and awards and
ensuring coordinated and integrated development and management of technical
education in India.
Technical education not only involves career preparation but also intellectual
development, which should have a lifelong impact on individuals as quoted by Norris
(1978). In 1994 The American Society for Engineering Education suggested that
engineering education needs to be relevant, attractive and connected to the lives and
careers of students.
According to Powar (2001), the Indian education system has not been able to take
the advantage of the possibilities for improving the quality of education for economic
benefit. Though the opportunities are available to the youth, it would be worthwhile to
highlight them. As well as infrastructure facilities, equality in participation having
adequate control related to quality and financial arrangements are main conditions laid
down by the author.
Natarajan (2007) mentioned some enablers like growing employment opportunities
in the IT sector and the popularity of IT tools for Technology-Enhanced Learning in
the field of technical education. Distance Education possibilities, especially for
Continuing technical Education is also one of the significant enablers in technical
Improving
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389
5. education. Networking of technical institutions with R&D labs and industry and the
role of Technology and Engineering Education for national development and
prosperity are widely acknowledged. Wani et al. (2007) suggested developing
entrepreneurship in the technical education, as it would enable students to consider
self-employment as a career option. Webster (2000) and Sanghi (1996) considered the
role of technical entrepreneurship as an enabler in the process of liberalization of
technical education.
Nanda and Ahuja (2003) discussed some of the barriers in the technical education
system. Diverse requirements of society, and users in particular, may be seen as a
major barrier in the field of technical education. Rapid changes in requirements of
industries are a serious barrier in the field of technical education in India. The need for
skilled manpower like faculty and staff to develop new technical programs is also a
major concern. They also emphasized other factors like rapid evolution of technologies
and technological innovations, the need to upgrade infrastructure facilities regularly to
keep pace with ever changing technologies. Natarajan (2000) concluded that growing
global competition and development of information and communications technology
has challenged the trend in engineering education. Rhodes (2002) presented a
comparative study between US and Indian higher education institutes. One of the
challenges mentioned by the author is the expectation of the students in terms of
student service resources, like support staff, counseling services, housing, etc. Sohail
and Shaikh (2004) explored students’ expectations of quality in higher education and
identified 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 the
quality of technical education. The present research is motivated by the desire to
understand the relation between the various enablers and barriers, to know their
degree of dependence and driving power. The main purpose of this study is to provide
a source of information to ensure meaningful communication regarding challenges and
new opportunities faced by educators, institutions and industries. In this paper the
process of technical education is being examined with a view to improve the quality of
provision.
Interpretive structural modeling (ISM): an overview
ISM offers a methodology for structuring complex issues and it is a combination of
three modeling languages: words, digraphs and discrete mathematics. It differs
significantly from many traditional modeling approaches, which use quantifiable
variables. ISM incorporates elements measured on ordinal scales of measurement and
provides a modeling approach, which permits qualitative factors to be retained as an
integral part of the model.
Conventional methods like the Delphi method is also a structured technique used for
forecasting in various disciplines. It is a decision making process where a group of
experts reaches a consensus after a brainstorming session. However, collecting
information from the respondents was extremely difficult due to lack of time. Soft
systems methodology (SSM) can only deal with ill-defined parts of the system. It is
unable 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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6. approach, which requires statistical data. It involves hypothetical tests to determine
the extent of the proposed model (Wisner, 2000).
On the other hand, ISM has advantages over other methods. It was proposed by
Warfield (1976) to analyze complex socioeconomic systems. Since then ISM has often
being used to help understand complex situations and to enable a strategy for solving
problem. Sage (1977) stated that ISM is used for identifying the relationship between
various 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 contextual
relationships among a set of variables. ISM acts as a tool for imposing order and
direction 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 has
been used in the context of technical education institutes. ISM has been used to model
the enablers of and barriers to curriculum in technical education. To develop the
overall quality of technical education, a number of variables play a significant role.
This paper mentions two models involving the variables and parameters that could be
of great significance to top management. ISM can explain the relationship between
variables that can be extracted from the system under study.
The steps of ISM have been described as a process within the present context. The
process starts with the identification of the relevant elements of the problem. Group
solving techniques have been used to address this step. In the next stage, a
contextually relevant subordinate relation is chosen. Based on this relation, a
self-structural self-interaction matrix (SSIM) is developed. In the next step, the SSIM is
converted into a reachability matrix and its transitivity is checked. Once this is done, a
well-defined representation system in the form of matrix model is obtained. In the final
step the partitioning of the elements and the extraction of the structural model is done
to complete the ISM. Mandal and Deshmukh (1994), Soti et al. (2010) presented the
process in the form of a flow chart. Figure 1 represents the various stages of ISM in a
form of a flow chart.
Empirical analysis
The 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 the
workforce.
.
To identify the barriers to technical education to meet the challenges of today’s
economy.
.
To recommend strategic decisions for industry to look for trained and skilled
resources.
To address the generic issue, this paper identifies specific issues faced by industry,
skill training providers, technical education institutes and educators. Using this data as
a baseline, an action plan may be developed for re-conceptualizing the linkage between
industry and the educators, namely institutions.
The present discussion is based on 11 variables under the “enabler” category and 12
variables under “barrier” category. The selection of parameters is done through the
literature review and discussion with two experts: one from academia and one from
industry.
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7. Methodology
Since 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 applied
to achieve the research objectives. The methodology is now developed. The initial step
in this study was to facilitate experts in developing a relationships matrix. The survey
included two parts. In Part I, respondents were asked to provide information on
various parameters on enablers to technical education. Part II of the survey included a
series of questions regarding the barriers to technical education. These questions were
Figure 1.
Flow chart of ISM
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8. scaled on a Likert scale of 1 (no importance) to 5 (very high importance). The variables
were selected through discussion with educators, through interactions with industry
representatives and from the existing literature.
A structured questionnaire was developed and administered with companies who
visited the campus during the placement and to the faculty members. The respondents
were asked to indicate the importance of 11 enablers and 12 barriers. These parameters
are listed in Tables I and II and have been numbered as 1, 2, 3, [. . .]. Out of 700
questionnaires used, 200 usable responses were received, which resulted in a response
rate of 28.57 per cent. Cronbach’s coefficient (a) was calculated to test the reliability
and internal consistency of the responses. The value of a was found to be 0.89. This
value is considered to be consistent as reported by Cronin and Taylor (1992) and
Parasuraman et al. (1988). The descriptive statistics for enablers and barriers are
exhibited in Tables I and II respectively.
Survey
no. Barriers Mean SD
Rank as
per mean
1 Lack of long-term goals of the technical institutes 3.2 1.2237 4
2 Lack of qualified instructors 4.23 0.7566 1
3 Lack of industry-institute interaction 2.22 0.8633 11
4 Lack of industry focus and emphasizing on short-term remedies 2.39 0.9285 10
5 Lack of insufficient teaching space (labs, classrooms) 2.80 1.0736 7
6 Lack of providing practical skills needed for employment 3.09 1.3010 5
7 Lack of financial resources 2.97 1.1001 6
8 Lack of credibility of the institute for not getting the accreditation/
approval 3.38 1.0683 3
9 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 9
11 Lack of distance education in technical field 4.05 0.8094 2
12 Lack of support staff 2.63 1.0990 8
Table II.
Survey results related to
barriers in the technical
education
Survey
no. Enablers of technical education in India Mean SD
Rank as
per mean
1 Benchmarking of technical education 4.41 0.6668 6
2 Ubiquitous technology and technical literacy 4.19 0.7705 8
3 Real world/practical applications in technical education 4.42 0.5954 5
4 Soft skill development 4.49 0.5398 4
5 Career orientation among parents, educators and students to create
the interests 3.52 0.9941 10
6 Next generation industry specific technical knowledge 4.72 0.4612 1
7 Need for effective technical professionals for successful companies 4.62 0.5542 3
8 Strategic planning. 4.01 0.8082 9
9 Technical training for the employees 3.05 0.9367 11
10 Specialization and customization to meet new industry demands 4.63 0.5710 2
11 Innovation from technical and domain specific knowledge/
experience 4.28 0.7100 7
Table I.
Survey results related to
enablers in the technical
education
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9. The ISM methodology suggests the use of expert opinions, based on various
management techniques such as brainstorming in developing the contextual relationship
among the variables. In the present case, two experts from industry were consulted to
identify the nature of the contextual relationship among the enablers of technical
curriculum. The results of their discussion are not discussed to avoid bias. It was
observed that variation between the two experts was not significant. In order to analyze
the relationship among the variables, a contextual relationship was developed by using
V, 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 and
barriers 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 technical
jobs. Hence the relationship is depicted as “V” in Table III. The need for effective
technical professionals (enabler 7) would help to have practical applications in
technical education (enabler 3), as there would be a demand from industry to increase
the skill and knowledge of the workforce. Hence the relation is “A”. Enabler 5 and 11
are unrelated. Career orientation among parents, educators and students to create the
interests (enabler 5) is uncorrelated with innovation from technical and domain specific
knowledge/experience (enabler 11). Thus, “O” represents their relation in Table III.
Enabler 6, namely next generation, requires industry specific technical knowledge and
enabler 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 implies
that, if an effort is made to increase the focus on industry and on long-term remedies,
EnablersSurvey
no. 11 10 9 8 7 6 5 4 3 2
1 Benchmarking of technical education A A O O X A O V X O
2 Ubiquitous technology and technical literacy A A O A A O O O A
3 Real world/practical applications in technical education O O V O A O O V
4 Soft skill development O A O A O O O
5 Career orientation among parents, educators and students
to create the interests O O O O O O
6 Next generation industry specific technical knowledge O V V O X
7 Need for effective technical professionals for successful
companies O V V O
8 Strategic planning O V V
9 Technical training for the employees O A
10 Specialization and customization to meet new industry
demands A
11 Innovation from technical and domain specific
knowledge/experience
Table III.
Structural self-interaction
matrix (SSIM) of the
enablers in technical
education
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10. 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 be
alleviated by barrier 7 i.e. removal of financial restrictions would help to alleviate
barrier 2 i.e. qualified instructors required in technical education. Thus “A” denotes the
relationship between barriers 2 and 7 in SSIM. Barrier 3 viz lack of industry-institute
interaction and barrier 6 i.e. lack of providing practical skills needed for employment
would help achieve each other. Thus “X” denotes the relationship between these two
barriers in the SSIM. No relationship exists between barrier 11, i.e. lack of distance
education and barrier 12, i.e. lack of support staff. Thus, “O” denotes the relationship
between barrier 11 and 12 in Table IV.
Tables V and VI represent the final reachability matrix for enablers and barriers
respectively. Substituting V, A, X, O by 1 and 0 as per the following rule, a binary
matrix is achieved.
.
If the (i, j) entry in the SSIM is V, then the (i, j) entry in the reachability matrix
becomes 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 matrix
becomes 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 matrix
becomes 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 matrix
becomes 0 and the ( j, i) entry also becomes 0.
Tables V and VI also shows the “driving power” and the “dependence” of the enablers
and barriers of technical education respectively. The higher the driving power, the higher
the 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 of
variables, which may help achieving it. For instance, enabler 7 (need for effective technical
BarriersSurvey
no. 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 V
2 Lack of qualified instructors A O A X V A V X V V
3 Lack of industry-institute interaction O O A X O A X O X
4 Lack of industry focus and emphasizing on short-
term remedies O A A V O A V O
5 Lack of insufficient teaching space (labs,
classrooms) V O X O X A V
6 Lack of providing practical skills needed for
employment O X A X X O
7 Lack of financial resources V V X V X
8 Lack of credibility of the institute for not getting the
accreditation/approval V O X O
9 Lack of technical awareness to understand the
customers’ need O X A
10 Lack of strategic planning of the technical institutes V O
11 Lack of distance education in technical field O
12 Lack of support staff
Table IV.
Structural self-interaction
matrix (SSIM) of the
barriers in technical
education
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13. professionals for successful companies) and enabler 3 (real world/practical applications in
technical 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 maximum
dependence. Hence they are given the first rank. Enabler 5, 8 and 11 namely, “career
orientation among parents, educators and students to create the interests”, “strategic
planning” and “Innovation from technical and domain specific knowledge/experience”
respectively have the least dependence and hence are given the fourth rank in the list of
enablers of technical education.
Table VI exhibits that lack of financial resources (barrier 7) and lack of strategic
planning of the institutes (barrier 10) as first rank holder in terms of driving power. Lack
of support staff (barrier 12) has scored the least in terms of driving power. Hence, it is the
last rank holder in the list of barriers of technical education considered in the present
context. In terms of dependence power, Lack of providing practical skills needed for
employment (barrier 6) has been ranked first. Lack of financial grant (barrier 7) is the last
rank holder in terms of dependence power among the barriers under study.
The reachability matrix has been partitioned on the basis of the reachability and
antecedent set (Warfield, 1976). From the final reachability matrix, the reachability and
antecedent set for each factor are found. The reachability set consists of the element
itself and other elements which it may help achieve, whereas the antecedent set
consists 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 which
the reachability and intersection sets are same is the top-level element in the ISM
hierarchy. The top-level element of the hierarchy would not help achieve any other
element above their own. Once the top-level element is identified, it is separated out
from 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 VII
and VIII show the level of enablers and barriers respectively. The enablers are barriers
have been grouped in various levels such as level 1, 2, 3 [. . .]. The levels identified aids
in 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 2
2 2 2, 3, 7, 8, 10, 11 2 Level 1
3 1, 2, 3, 4, 9 1, 3, 7 1,3 Level 2
4 4 1, 3, 4, 8, 10 4 Level 1
5 5 5 5 Level 1
6 1, 6, 7, 9, 10 6, 7 6, 7 Level 4
7 1, 2, 3, 6, 7, 9, 10 1, 6, 7 1, 6, 7 Level 4
8 2, 4, 8, 9, 10 8 8 Level 4
9 9 3, 6, 7, 8, 9, 10 9 Level 1
10 1, 2, 4, 9, 10 6, 7, 8, 10, 11 10 Level 3
11 1, 2, 10, 11 11 11 Level 4
Table VII.
Partition of reachability
matrix for enablers under
study
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14. From Table VII, it can be seen that enablers 2, 4, 5 and 9 namely, “ubiquitous
technology and technical literacy”, “soft skills development”, “career orientation
among parents, educators and students to create the interests” and “technical training
for the employees” respectively are found at level 1 (dependents). Thus, these would be
positioned at the top of the ISM hierarchy. Similarly, enablers 1 and 3 are found at level
2, 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 of
providing practical skills needed for employment (barrier 6) and lack of technical
awareness to understand the customers’ needs (barrier 9) have been identified as level 1
barriers. 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 of
qualified faculty in the technical institutes (barrier 5), lack of industry focus of the
institutes (barrier 4) and lack of support staff in the technical institutes (barrier 12). At
the third level, barriers 1, 5, 7, 8 and 10 have been identified. These barriers occupy the
bottom the hierarchy of the barriers of technical education in India.
The structural model is generated from the final reachability matrix and the
digraph is drawn. If there is a relationship between the parameters i and j, it is shown
by an arrow which points from i to j. The resultant graph is called directed graph. After
removing the transitivity, the digraph is finally converted into the ISM as shown in
Figure 2 for enablers and Figure 3 for barriers.
As is evident from Figure 3, the independent variables, which are at the bottom of
the model viz “Lack of long-term goals of the technical institutes”, “lack of sufficient
teaching space”, “lack of financial resources”, “lack of credibility for not getting
accreditation process” and “lack of strategic planning of the institutes” are some of the
important barriers of technical education that emerged from the model. The nature of
these barriers represents major hindrances and these are the responsibility of the
organization and the authorities. As we follow the hierarchy, we observe that the
quality of the technical education is suffering because the organization is not able to
retain 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 bottom
of 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 3
2 2, 3, 4, 5, 6, 8, 9 1, 2, 5, 7, 9, 10, 12 2, 9 Level 2
3 3, 4, 6, 9 1, 2, 3, 4, 6, 7, 9, 10 3, 4, 6, 9 Level 1
4 1, 3, 4, 6, 9, 11 1, 2, 3, 4, 7, 10, 11 1, 3, 4, 11 Level 2
5 2, 5, 6, 8, 10, 12 1, 2, 5, 7, 8, 10 2, 5, 8, 10 Level 3
6 3, 6, 8, 9, 11 1, 2, 3, 4, 5, 6, 8, 9, 10, 11 3, 6, 8, 9, 11 Level 1
7 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12 7, 8, 10 7, 8, 10 Level 3
8 1, 5, 6, 7, 8, 10, 12 1, 2, 5, 6, 7, 8, 10 1, 5, 6, 7, 8, 10 Level 3
9 2, 3, 6, 9, 11 1, 2, 3, 4, 6, 7, 9, 10, 11 2, 3, 6, 9, 11 Level 1
10 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12 1, 5, 7, 8, 10 1, 5, 7, 8, 10 Level 3
11 4, 6, 9, 11 4, 6, 9, 11 4, 6, 9, 11 Level 1
12 2, 12 1, 5, 7, 8, 10, 12 12 Level 2
Table VIII.
Partition of reachability
matrix for barriers under
study
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15. 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 to
meet the new industry demands”, “real world application”, “benchmarking of the
technical institutes”, etc. the enablers found at top of the model are dependent and do
not have any driving power for instance, “ technical literacy”, “career orientation
among the parents and students to create the interest”.
In Figures 4 and 5, the enablers and barriers have been classified into four
categories by MICMAC analysis based on driving power and the dependence. The
objective behind this classification is to analyze the driving power and dependency of
the enablers and barriers. The driver power and dependence of each of the enablers and
barriers are shown in Tables V and VI. The diagrammatic representation is shown in
Figures 4 and 5 for enablers and barriers respectively. Figure 4 exhibits the categories
of the various enablers on technical curriculum. The dependence is plotted on X-axis
and the driving power is plotted on Y-axis. As an illustration, enabler 3 has a
dependence power of three and the driving power of seven. Therefore, in Figure 4, it
has been positioned in the fourth quadrant corresponding to high driving power and
low dependence power.
Figure 2.
ISM-based model for the
enablers of technical
education
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16. The first cluster in Figure 4 includes “autonomous enablers” that have weak driving
power and weak dependence. These parameters are relatively disconnected from the
system. In the present study, five enablers are in the first quadrant and they are
enablers 4, 5, 8, 10 and 11. These enablers have few links, which may be strong. The
second cluster consists of the dependent variables that have weak driving power but
strong 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 also
strong dependence. Any action on these variables will affect others and there will be a
feedback effect on them. This makes them unstable in the system. Finally the fourth
cluster is known as independent variables with low dependence and high driving
power. It has been found that a variable with very strong driving power called the key
variable, falls into the category of independent or linkage variable. There are two
enablers (3 and 7) in this section.
Figure 3.
ISM-based model for the
barriers of technical
education
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17. Among the barriers, two barriers are in the first quadrant and they are enablers 5
and 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 the
dependent variables that have weak driving power but strong dependence. In the
present scenario, barriers 3, 4, 6 and 9 are in the second category. The third cluster
includes linkage variables that have strong driving power and also strong
dependence. 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, one
should take care regarding the consequential changes in other variables. Finally the
fourth cluster has only key parameters, also known as independent variables, with
low dependence and high driving power. There are four barriers in this section and
they are 1, 7, 10 and 12.
Figure 4.
Driving power and
dependence matrix of the
enablers of technical
education
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18. Discussion and conclusion
As the results show, a call to action is required to address various issues (barriers)
identified by industry representatives and faculty fraternity. The barriers identified
need to be taken seriously by policy makers to enable necessary actions to be taken to
improve the quality of education. Knowing the barriers is equally important when
seeking to undertake a new project or introduce a new course.
As discussed before, the technical education sector is large and complex in
nature in India. In the present scenario, a large number of families find it difficult to
meet the costs of technical education. The government of India is yet to develop a
Figure 5.
Driving power and
dependence matrix of the
barriers of technical
education
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19. mechanism to overcome this issue. “Lack of financial resources” is identified as a
major barrier, which should be addressed as fee structures becomes very high in
absence ofsome sort of intervention. This is a governance issue playing a major role
in the education sector. “Lack of distance education” and “lack of credibility of the
institutes” show major barriers of the system which are leading the other issues (see
Figure 3). Hence, co-operation is required by the government to make technical
education available.
Barriers 1 (lack of long-term goals of the technical institutes), 7 (lack of financial
resources) and 10 (lack of strategic planning of the technical institutes) have been
idenfied as key barriers (see Figure 5) pertain to governance and policy issues. Good
governance acts as a buttress to the mission and purpose of the institute. It helps to
create an ethical and sustainable strategy, acceptable to all the stakeholders of the
system as it formulates transparent and honest strategies and oversees the
implementation of these to the benefit of all the stakeholders.
Lack of financial resources is leading to another issue related to instructors in the
institute. 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. Lack
of qualified faculty, who can integrate industrial knowledge with academic knowledge,
is led by management who lack long-term goals for technical education. Due to
non-availability of the qualified faculty, who are considered to be the interface between
the industry and academics, a direct impact can be seen on the delivery of the
curriculum (see Figure 3). To address this issue, sharing of resources like faculty or
laboratories and libraries among the institutes can be done. With the advent of
technology, use of technology to deliver courses or to support courses can be
considered. The model suggests, instead of improving the curriculum or emphasizing
industry-institute interaction, there should be a focus on the policies to have a clear
mission and vision so that the long-term goals becomes achievable.
Getting accreditation is one of the significant barriers as identified in the model (see
Figure 3). This in turn is leading to lack in long-term goals of the institutes. In absence
of 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 in
gaining admission to an unapproved institute and industry would not like to recruit the
students 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, the
role of technical education in the international market has increased its importance in
terms of visibility and applicability. It is worth mentioning on the basis of existing
literature, that the education sector has a strong effect on the skilled labour market and
has become an integral part of economic development. Opportunities (enablers) emerging
with new technologies need to be taken into account. The successful leaders in the
education sector need to know characteristics such as the present opportunities prevailing
in 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 effective
technical 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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20. required to achieve and sustain this growth. A proper policy and formal mechanisms
related to industry investment in technical education, inviting experts from private
industry to serve as faculty and researchers to provide training for students for future
would 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 effective
technical professionals for successful industries (see Figure 2). Since, the industry
needs technical qualified manpower for their sustainability and existence; they are
dependent on trained working people in their organization. However, to keep pace
with innovation and requirements, the curriculum in institutes need to be kept up to
date. This is mainly achieved by benchmarking the institutes. As discussed before,
benchmarking is a process based on certain quality related parameters of academic
and non-academic activities. Through benchmarking, a common framework for
standards specifying the knowledge and skills specific to industry requirements,
credible employable skills, assessment of the students and guide to curriculum
development can be achieved.
Like any other sector, the education sector also demands change and scrutiny. This
could be done by making changes in accreditation standards and several government
procedures to improve the quality of the technical education. The present study could
prove a catalyst for planners of technical education. Although there are opportunities
in the field of technical education, the results show that a call for action is required to
address various issues/barriers identified by the experts. The barriers identified need
to be taken seriously by the policy makers and necessary actions can be taken to
improve the quality of education. This study could be insightful for the strategic
decision makers and policy makers of technical education.
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Corresponding author
Roma Mitra Debnath can be contacted at: roma.mitra@gmail.com
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