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American Journal of Scientific Research
ISSN 1450-223X Issue 48 (2012), pp. 135-148
© EuroJournals Publishing, Inc. 2012
http://www.eurojournals.com/ajsr.htm
Relationship between “Higher Education and Training” and
“Technological Readiness”: A Secondary Analysis of
Countries Global Competitiveness
Saeed Safari
Assistant Professor, Faculty of Human Sciences
Shahed University, Tehran, Iran
E-mail: safari_saeed@yahoo.com
Tel: +98-912-6200766
Rohollah Ghasemi
Corresponding Author, Ph.D
Student of Production and Operations Management
University of Tehran, Tehran, Iran
E-mail: ghasemir@ut.ac.ir
Tel: +98-935-8070906
Akram Elahi Gol
M.Sc. Candidate of MBA, University of Tehran, Tehran, Iran
E-mail: a_elahi_gol@yahoo.com
Tel: +98-912-5960073
Yousef Mirzahossein Kashani
M.Sc. Candidate of Industrial Management
Islamic Azad University (Central Tehran Branch), Tehran, Iran
E-mail: yousef.m.kashani@gmail.com
Tel: +98-936-3700534
Abstract
The concept of competitiveness has attracted abundant attentions of both scholars
and governors during the past decade. During this period, the World Economic Forum has
published its annual reports which encompass Global Competitiveness Index (GCI) in
order to measure national competitiveness in different countries. This paper aims at
investigating the interaction between the two sets of “Higher Education and Training” and
“Technological readiness” as the two basic pillars of national competitiveness in order to
provide information for improving national competitiveness in countries that are in stage II
of development (efficiency-driven economies). In our study, we used descriptive-
correlation methodology. The statistical population was 139 countries whose GCI data
were included in GCI 2010 report. Also, we employed Canonical Correlation Analysis
(CCA) to investigate interaction between two sets of “Higher education and training” and
“Technological readiness”. Our findings show that there is a significant and positive
relationship between the set of “Higher education and training” and that of “Technological
readiness”.
Relationship between “Higher Education and Training” and “Technological Readiness”:
A Secondary Analysis of Countries Global Competitiveness 136
Keywords: Global Competitiveness, Higher education and training, Technological
readiness, Canonical Correlation Analysis.
1. Introduction
In globalization age, the economic competition among countries and economic enterprises has
increased globally. The concept of competitiveness has been applied by Michael Porter at a wide
extend of competitiveness of enterprise and industry to national and global competitiveness (Porter and
Schwab, 2008).
In global economy, the competitiveness means the ability of obtaining suitable and constant
situation at international markets. In view of Organization for Economic Cooperation and
Development (OECD), the ability of a country in producing commodities and services for presentation
in international markets is one of the most important dimensions of competitiveness. The
competitiveness means reaching of internal commodities and services to international markets. The
competitiveness has been also defined as the ability of an economy for stabilization of its share in the
market and in all these definitions, the concept of competitiveness attracts attention as obtaining a
suitable place in international markets for products of a country (Karimi-Hesenijeh, 2007).
The countries and enterprises and industrial organizations have justified the relationship
between innovation and economical success. The development of technology helps the innovators to
move at first line of market. Therefore, the application of technology (in addition to its development) is
one of the key factors of success in global competition (Khalil, 1999).
The changes in globalization process means that the nations cannot reach suitable development
just from producing commodity and services for national markets. In 21st century, the degree of
development of nations depends on their political, national and economical capacity, their leaders and
also the speed of their national institutions in adjustment and use from globalization process. Therefore
the exact identification of globalization process and exact scrutiny of this trend is necessary in different
countries especially in developing countries which have entered into this scene (Safari and
Asgharizadeh, 2008).
Globalization increasingly affects “higher education and training” sector around the world (Li-
Hua et al., 2011). In other hand, researchers believe that there is a common agreement about the
importance of change rate of technology in determination of the economic growth rate (Feldman,1999).
On the other hand, the variability of competition rules in business world shows the process of new
product presentation into markets with a special importance. Today, most organizations more than any
other time have found that it is not enough to depend on just traditional competition leverages such as
quality improvement, decrease of cost or differentiation in presentation of products and services and
instead concepts such as speed and flexibility in competition have raised and the trend toward the
presentation of new products and services into market itself is the adequate reason of this change of
attitude (Jafarnejad et al., 2010).
The technology is the factor of wealth creation. The more effective use of technology severely
affects the competition conditions. Technology management urges the invention and the innovation
management and these two are the main parts of every system that creates and uses the technology
(Khalil, 1999).
The rapid progress of globalization has alarmed nation states worldwide to develop stable
macroeconomic policies in order to enhance the competitiveness of domestic markets. The State has an
important role to play in this process. This also means greater efforts to reform education and science,
to promote advanced technologies and to strengthen the private sector (Ivaniashvili-Orbeliani, 2009).
Economic management agenda in many economies around the world is transition from factor-driven
economy to efficiency-driven. To this end, their economic policy making should benefit valid
orientation and indicators for this stage. Utilizing comparative approach and benchmarking from
successful economic experiences around the world can help the policy makers and business leaders
137 Saeed Safari, Rohollah Ghasemi, Akram Elahi Gol and Yousef Mirzahossein Kashani
manage economy and achieve a higher level of prosperity. In this regard, improving the national
competitiveness is a key factor.
The concept of competitiveness has attracted abundant attentions of both scholars and
governors during the past decade to the extent that World Economic Forum (WEF) has developed GCI
to measure competitiveness of countries around the world (Vares et al., 2011).
The purpose of WEF of issuing the annual GCI reports is to provide benchmarking tools for
business leaders and policymakers to identify obstacles to improved competitiveness, thus stimulating
discussion on the best strategies and policies to overcome them (Schwab, 2010).
However, before adopting the GCI as a benchmark or spending any resources and efforts to
improve national competitiveness, policymakers must determine their country priorities for improving
national competitiveness. In our study, we seek to provide information for countries which are in stage
II of development (efficiency-driven economies) in order to improve their national competitiveness
(Vares et al., 2011).
2. Literature Review
2.1. Competitiveness
According to Dutta (2007) in today’s perspectives, competitiveness has become a fundamental force in
economics like gravity in physics. Competitiveness is a concept, which tries to explain why some
countries develop faster than others. Also, it connects the macro- and micro-economic views of social-
economic development (Kovacˇic, 2007).
McFetridge (1995) identified competitiveness at three levels: firm, industry and national. In the
context, Porter (1990) believed that “the only meaningful definition of competitiveness at the national
level is national productivity”. Furthermore, Heap (2007) point out that “improving productivity is the
only way of baking a bigger cake – most other changes simply give us different sized slices”.
From a macro policy perspective, the primary goal of competitiveness is the well being of the
citizens of a country, be it through individual income, standard of living, human development, or social
justice (Kovacˇic, 2007).
2.2. Measuring Nation’s Competitiveness
Since 1979, annual Global Competitiveness Reports of World Economic Forum (WEF) have examined
the many factors enabling national economies to achieve sustained economic growth and long-term
prosperity. In these reports competitiveness has been defined as the set of institutions, policies, and
factors that determine the level of productivity of a country (Porter and Schwab, 2008); Also, since
2005, the WEF has developed the Global Competitiveness Index (GCI). As a highly comprehensive
index, GCI captures the microeconomic and macroeconomic foundations of national competitiveness.
According to GCI reports, “a nation’s level of competitiveness reflects the extent to which it is able to
provide rising prosperity to its citizens” (Schwab, 2009).
The GCI captures the open-ended dimension of competitiveness by providing a weighted
average of many different components, each of which reflects one aspect of the complex concept of
competitiveness (Schwab, 2009). The GCI contains 12 pillars which are classified as following table:
Table 1: GCI pillars in three main sub-indexes
Main sub-indexes Pillars of GCI
Basic requirements
1. Institutions
2. Infrastructure
3. Macroeconomic stability
4. Health and primary education
Relationship between “Higher Education and Training” and “Technological Readiness”:
A Secondary Analysis of Countries Global Competitiveness 138
Table 1: GCI pillars in three main sub-indexes - continued
Efficiency enhancers
5. Higher education and training
6. Goods market efficiency
7. Labor market efficiency
8. Financial market sophistication
9. Technological readiness
10. Market size
Innovation and sophistication factors 11. Business sophistication
12. Innovation
It is important to keep in mind that These 12 pillars of competitiveness are not independent:
they tend to reinforce each other, and a weakness in one area often has a negative impact on other
areas. For example, innovation (pillar 12) will be very difficult without a well-educated and trained
workforce (pillars 4 and 5) that are adept at absorbing new technologies (pillar 9), and without
sufficient financing (pillar 8) for R&D or an efficient goods market that makes it possible to take new
innovations to market (pillar 6) (Schwab, 2010).
2.3. Three Stages of Development and GCI
Another useful classification of GCI reports considers three different stages of development in which
each country falls. In this regard, Schwab (2009) pointed out that:
Different pillars of GCI affect different countries differently: the best way for
Burkina Faso to improve its competitiveness is not the same as the best way for
Switzerland. According to the GCI, in the first stage, the economy is factor-driven and
countries compete based on their factor endowments, primarily unskilled labor and
natural resources. Companies compete on the basis of price and sell basic products or
commodities, with their low productivity reflected in low wages. Maintaining
competitiveness at this stage of development hinges primarily on well-functioning
public and private institutions (pillar 1), well-developed infrastructure (pillar 2), a stable
macroeconomic framework (pillar 3), and a healthy and literate workforce (pillar 4). As
wages rise with advancing development, countries move into the efficiency-driven stage
of development, when they must begin to develop more efficient production processes
and increase product quality. At this point, competitiveness is increasingly driven by
higher education and training (pillar 5), efficient goods markets (pillar 6), well-
functioning labor markets (pillar 7), sophisticated financial markets (pillar 8), a large
domestic or foreign market (pillar 10), and the ability to harness the benefits of existing
technologies (pillar 9). Finally, as countries move into the innovation-driven stage, they
are able to sustain higher wages and the associated standard of living only if their
businesses are able to compete with new and unique products. At this stage, companies
must compete through innovation (pillar 12), producing new and different goods using
the most sophisticated production processes (pillar 11) (p. 7).
As mentioned in Introduction, this research seeks to investigate interactions between pillars of
“Higher education and training” and “Technological readiness” in GCI in order to provide information
for countries that are in stage II of development (efficiency-driven economies) in order to improve their
national competitiveness.
Within the time span, we reviewed related literature we found studies (such as Bremer, 1990;
Willis et al., 2003;Uys et al., 2004; Georgina et al., 2008; Georgina and Hosford, 2009; Bucciarelli et
al., 2010).
Bremer (1990) investigated impediments to technology transfer from the higher education
sector.
139 Saeed Safari, Rohollah Ghasemi, Akram Elahi Gol and Yousef Mirzahossein Kashani
Uys et al. (2004) analyzed and suggested some possible technological innovation strategies in
higher education institutions in Africa. Their paper described management issues in the
implementation of eLearning with particular reference to its usage in higher education abroad and in
Africa, and also suggested appropriate approaches for technological innovation of higher education in
Africa.
Georgina et al. (2008) were investigating integration of technology in higher education. Also
Georgina and Hosford (2009) demonstrated that how faculty technology literacy and technology
training impact the integration of technology into their pedagogy.
In addition, Bucciarelli et al. (2010) studied role of education and training in technology
adoption under an advanced socio-economic perspective. They proved the usefulness of the
dissemination and application of modern technology to education and training processes emphasizing
that the relationship between the process of education and the adoption of ICT is essential for
competitiveness and sustainable development of the European countries analyzed whose economy is
based on advanced knowledge and highly trained human capital.
However, few studies have addressed “Higher education and training” and “Technological
readiness” (Lee, 2001).
Lee (2001) said that the technology gap between developing and advanced countries has been
increasing during the last few decades. In the process of technology development human capital plays a
critical role as an absorption capacity for new technologies in developing countries. The cross-country
regression shows that human capital interacts with inflows of foreign technology embodied in
machinery imports as well as FDI, and thereby contributes to technology growth in developing
countries. He also found that the stock of human capital, at the secondary and tertiary levels of
education in particular, plays a key role in determining the development of information and
communication technology. His paper discusses the measures in building appropriate human capacities
for the adaptation of new technologies in developing countries by focusing on the education strategies
of East Asian economies.
Any way this lack of study motivated us to consider relationship between “Higher Education
and Training” and “Technological readiness” for improving national Competitiveness in efficiency-
driven economies.
2.4. Higher Education and Training
Today’s globalizing economy requires economies to nurture pools of well-educated workers who are
able to adapt rapidly to their changing environment. This pillar measures secondary and tertiary
enrollment rates as well as the quality of education as assessed by the business community. The extent
of staff training is also taken into consideration because of the importance of vocational and continuous
on-the-job training (Porter and Schwab, 2008).
As mentioned earlier, each pillar of GCI Report includes a set of sub-indexes. The “Higher
education and training” sub-indexes are:
1. Secondary education enrollment rate;
2. Tertiary education enrollment rate;
3. Quality of the educational system;
4. Quality of math and science education;
5. Quality of management schools;
6. Internet access in schools;
7. Local availability of research and training services;
8. And, Extent of staff training (Schwab, 2010).
2.4.1. Secondary Education Enrollment Rate
According to Schwab (2010) the reported value for secondary education enrollment rate, “corresponds
to the ratio of total secondary enrollment, regardless of age, to the population of the age group that
Relationship between “Higher Education and Training” and “Technological Readiness”:
A Secondary Analysis of Countries Global Competitiveness 140
officially corresponds to the secondary education level. Secondary education completes the provision
of basic education that began at the primary level, and aims at laying the foundations for lifelong
learning and human development, by offering more subject- or skills-oriented instruction using more
specialized teachers.” Korpershoek et al. (2010) argued that, “the subject choices that students make in
secondary education are important decisions, because they limit the options in entering specific studies
in higher education”.
2.4.2. Tertiary Education Enrollment Rate
According to Schwab (2010) the reported value for Tertiary education enrollment rate, “corresponds to
the ratio of total tertiary enrollment, regardless of age, to the population of the age group that officially
corresponds to the tertiary education level. Tertiary education, whether or not leading to an advanced
research qualification, normally requires, as a minimum condition of admission, the successful
completion of education at the secondary level.”
2.4.3. Quality of the Educational System
Based on previous studies of several authors, Chapman et al. (2005) mentioned education quality as,
“the extent that an education system is able to achieve the generally accepted goals of education,
central to which is knowledge and skill development”. They continued, “However, most observers
recognize that education systems have multiple goals, many of which go beyond the transmission of
cognitive knowledge, such as the development of relevant employment skills and attitudes that
facilitate civic engagement”. Quality of the educational system examines how well the educational
system in countries meets the needs of a competitive economy (Schwab, 2010).
2.4.4. Quality of Math and Science Education
Jita (2010) claimed that, “the need for improvement in education is often more pronounced in such
scarce skills areas such as science and mathematics”. This factor assesses the quality of math and
science education in target countries’ schools (Schwab, 2010).
2.4.5. Quality of Management Schools
Pariseau and McDaniel (1997) based on studies of Parasuraman et al., (1988) argued that, “the
consumer’s opinion of quality is formed by an internal comparison of performance with expectations.
Quality service is defined as that in which the consumer’s perception of service performance meets or
exceeds their expectation of what the service firm should do. The key to service quality then is to meet
or exceed consumer expectations.” Based on findings of Parasuraman et al. (1988), Pariseau and
McDaniel (1997) studied five dimensions of service quality in the context of business schools. These
dimensions are shown in Table 2:
Table 2: Dimensions of service quality
Factor Description
Assurance Knowledge and courtesy of employees and their ability to inspire trust and confidence
Responsiveness Willingness to help customers and provide prompt service
Empathy Caring, individualized attention the firm provides its customers
Reliability Ability to perform the promised service dependably and accurately
Tangibles Condition of facilities, equipment, and appearance of personnel
Source: Pariseau and McDaniel (1997)
To measure these factors, they asked both students and faculty to answer the questionnaires;
although “both groups think all dimensions of service quality are important and both groups rank
assurance as the most important factor”, but these two groups had different opinion about the rest of
141 Saeed Safari, Rohollah Ghasemi, Akram Elahi Gol and Yousef Mirzahossein Kashani
factors. “Students want assurance, responsiveness, empathy, reliability and tangibles in that order,
while faculty believes that assurance, tangibles, reliability, empathy and responsiveness is the correct
rank order” (Pariseau and McDaniel, 1997).
2.4.6. Internet Access in Schools
In the early 2000s, schools at all levels in many countries began to prepare all students’ for the Internet
literacy and to promote Internet-based learning for life-long learning (Tsai and Tsai, 2010). Based on
the 1996 report of International Technology Education Association in 1996, Tsai et al. (2001) argued
that, “preparing students for the knowledge and skills of Internet applications has also been recognized
as an important goal of computer literacy education in the school curriculum”.
Also Zhao et al. (2010) said that “for those students who used the Internet at school and home,
higher Internet self-efficacy related to better academic performance”. The Internet self-efficacy is a
cognitive concept in the research of ICT which was used to investigate the effects of training, the new
technology or new service adoption and Internet use.
The Internet access in schools factor in Schwab’s 2010 report is being measured by, “How
would you rate the level of access to the Internet in schools in your country?” question (Schwab,
2010).
2.4.7. Local Availability of Research and Training Services
According to Schwab (2010), local availability of research and training services is about this question,
“In your country, to what extent are high-quality, specialized training services available?”
2.4.8. Extent of Staff Training
According to Hallier and Butts (1999), “there is now broad agreement among commentators that skills
training does improve organizational productivity and national performance”. “When line managers
own and drive training and development, performance improvements usually result” (Mindell, 1995).
“Inattention to skills development thus is found to hamper organizational performance in a number of
ways including limiting the take-up and use of new technology, lengthening delivery times, increasing
scrap levels and reducing the ability of the organization to meet increases in demand and to exploit
market opportunities” (Hallier and Butts, 1999).
2.5. Technological Readiness
In today’s globalized world, technology has increasingly become an important element for firms to
compete and prosper. The technological readiness pillar measures the agility with which an economy
adopts existing technologies to enhance the productivity of its industries, with specific emphasis on its
capacity to fully leverage information and communication technologies (ICT) in daily activities and
production processes for increased efficiency and competitiveness. Whether the technology used has or
has not been developed within national borders is irrelevant for its ability to enhance productivity. The
central point is that the firms operating in the country have access to advanced products and blueprints
and the ability to use them. (Porter & Schwab, 2008). The “Technological readiness” sub-indexes are:
1. Availability of latest technologies;
2. Firm-level technology absorption;
3. Foreign Direct Investment (FDI) and technology transfer;
4. Internet users;
5. Broadband Internet Subscriptions;
6. and Internet bandwidth (Schwab, 2010).
Relationship between “Higher Education and Training” and “Technological Readiness”:
A Secondary Analysis of Countries Global Competitiveness 142
2.6. Canonical Correlation Analysis (CCA)
CCA is a multi variables statistical approach for measuring linear relationship between different groups
of variables. This approach can play an important role in exploratory mean when multi attribute
variables have some relations to an analytical category (Lima et al, 2004). CCA is obtaining linear
composition of predicting variables that has the most correlation with linear combination of criteria
variables. These combinations are shown as follow: (LeClere, 2006).
W =a
1
x
1+
a
2
x
2
+…+ a
p
x
p
V= b
1
y
1 +
b
2
y
2
+… + b
q
y
q
The number of dependent variables (eight) or the number of independent variables (six),
whichever is smaller, determines the maximum number of canonical functions. Thus the analysis is
based upon the derivation of seven canonical functions (Mai and Ness, 1999). Table 3 is showing some
researches in CCA field.
Table 3: Some previous research which applied CCA technique
Author(s)
Methodology
Mohaghar et al. (2011)
Using by Canonical Correlation Analysis, this study examined the interdependencies
between supply chain relation quality and supply chain performance in automotive
industry in Iran.
Asghari-zade et al. (2011) Using by Canonical Correlation Analysis, this study examined the interdependencies
between Enabler and results in EFQM model in TAVANIR Company in Iran.
Tutuncu and Kucukusta (2009) They demonstrated a meaningful relationship between Job satisfaction and EFQM by
utilizing CCA.
Macinati (2008) They used CCA to study relationships between TQM and organizational performance.
Jang and Ryu (2006) Using Canonical Correlation Analysis, this study examined the interdependencies in
investing And financing decisions of restaurant firms.
Bou-Llusar et al. (2005) They used CCA to study relationships between enablers and results in EFQM.
Baloglu et al. (1998) This study utilized a canonical correlation approach to segment the senior pleasure
traveler market.
3. Proposed Model
This Proposed model is composed of two kinds of variables: “Higher education and training” and
“Technological readiness” as in the following figure.
Figure 1: Research proposed model
Secondary
education
enrollment rate
Tertiary education
enrollment rate
Quality of the
educational system
Quality of math and
science education
Quality of
management
schools
Internet access in
schools
Local availability of
research and
training
services
Extent of staff
training
Availability of
latest
technologies
Firm-level
technology
absorption
FDI and
technology
transfer
Internet users
Broadband
Internet
Subscriptions
Internet
bandwidth
Technological
readiness
Higher education
and training
143 Saeed Safari, Rohollah Ghasemi, Akram Elahi Gol and Yousef Mirzahossein Kashani
According to the Above-Mentioned Figure Research Question is
Is there any meaningful relationship between “Higher education and training” and “Technological
readiness”?
And Research Sub Questions are
1. Is there any correlation between “Higher education and training” sub-index and “Technological
readiness” sub-index?
2. In the set of “Higher education and training”, which pillar has the most and which one has the
least impact on creating a meaningful relationship between “Higher education and training”
and “Technological readiness”?
3. In the set of “Technological readiness”, which pillar has the most and which one has the least
impact on creating a meaningful relationship between “Higher education and training” and
“Technological readiness”?
4. Research Methodology
4.1. Research Method
Research method used for this study is descriptive-correlation. Secondary analysis method was also
used for analyzing secondary data source. First, we studied literature of Competitiveness, GCI,
“Higher education and training”, “Technological readiness”, and CCA. Then, we used the GCI report
data in 2010 for doing our secondary analysis. The Statistical population in this study was 139
countries whose data was included in GCI report in 2010. Finally, we utilized Canonical Correlation
Analysis (CCA) by SAS9 software; thereafter, analysis output was obtained.
4.2. Information Gathering Tools
According to De Vaus (2002) it would be appropriate to use data collected by other people or agencies
to address the relevant research questions. Such data is called secondary data resource. So we utilized
the data published by World Economic Forum (GCI report in 2010) as our secondary data resource.
5. Data Analysis
Using SAS9 software, we investigated correlation between two sets of “Higher education and training”
and “Technological readiness” by using CCA.
For answering the first sub question, based on table 4, we can see a meaningful positive
correlation in significance level of 0.05 between “Higher education and training” sub-indexes and
“Technological readiness” sub-indexes. “Internet access in schools” and “Broadband Internet
subscriptions” have the strongest correlation and “Tertiary education enrollment rate” and “FDI and
technology transfer” have the least correlation in this table.
Several interesting relationships were detected in Table4. For example in “Higher education
and training” sub-indexes, “Local availability of research and training services” has the most
correlation and “Tertiary education enrollment rate” has the least correlation with “Availability of
latest technologies”. Also “Extent of staff training” has the most correlation and “Tertiary education
enrollment rate” has the least correlation with “FDI and technology transfer”.
Relationship between “Higher Education and Training” and “Technological Readiness”:
A Secondary Analysis of Countries Global Competitiveness 144
Table 4: Correlation coefficient between “Higher education and training” and “Technological readiness”.
Technological readiness
Higher education and training
Availability
of latest
technologies
Firm-level
technology
absorption
FDI and
technology
transfer
Internet
Users
Broadband
Internet
subscriptio
ns
Internet
bandwidth
Secondary education enrollment rate 0.6857
0.5797
0.3722
0.8546
0.8389
0.8027
Tertiary education enrollment rate 0.5382
0.4224
0.1953
0.7619
0.8110
0.7455
Quality of the educational system 0.6323
0.6356
0.4630
0.5754
0.5486
0.4785
Quality of math and science
education 0.5498
0.5194
0.3422
0.6552
0.6323
0.5957
Quality of management schools 0.7776
0.7219
0.5527
0.6130
0.6669
0.5891
Internet access in schools 0.8339
0.7611
0.5974
0.8591
0.8672
0.8251
Local availability of research and
training services 0.8554 0.8257 0.6133 0.7250 0.7688 0.7475
Extent of staff training 0.7873 0.8193 0.6671 0.5693 0.5872 0.5433
Table 5: Canonical Correlation Analysis summary
N=139 Higher education and training Technological readiness
Number of variables 8 6
Extracted variance 89.47% 100%
Redundancy index 66.97% 79.38%
Variables: 1 Secondary education enrollment rate Availability of latest technologies
2 Tertiary education enrollment rate Firm-level technology absorption
3 Quality of the educational system FDI and technology transfer
4 Quality of math and science education Internet Users
5 Quality of management schools Broadband Internet subscriptions
6 Internet access in schools Internet bandwidth
7 Local availability of research and
training services
8 Extent of staff training
Table 5 is showing enveloped data variation by CCA. The extracted variance for “Higher
education and training” and “Technological readiness” is showing that 89.47% of canonical roots are
covered by internal “Higher education and training” variation and also 100% of canonical roots are
covered by internal “Technological readiness” variation. These statistics are very considerable and
support CCA utilization.
Table 6: Statistical tests
Canonical
roots
Chi-square Tests With Successive Roots Removed
Canonical R Canonical R
2
Chi-sqr df P-value Lambda
Prime
0 0.9665 0.9341 515.539 48 0.0000 0.018
1 0.7596 0.5770 163.241 35 0.0000 0.283
2 0.3893 0.1515 51.79 24 0.0008 0.670
3 0.3384 0.1145 30.50 15 0.0102 0.790
4 0.2880 0.082 14.74 8 0.64 0.89
5 0.1630 0.026 3.52 3 0.316 0.973
Usual Canonical Correlation Analysis meaningful level for interpretation is 0.05. As it's shown
in table 6, P-value is used for this research; first, second, third and forth canonical variables are
statistically meaningful.
145 Saeed Safari, Rohollah Ghasemi, Akram Elahi Gol and Yousef Mirzahossein Kashani
In addition, other statistical tests like “Lambda Prime” and “
2
χ
“ are proofing our results. We
considered first canonical variable and ignored interpretation of second, third and forth variables
because of their weak canonical cross loading and redundancy index.
For answering research question, we focus on table 5 and 6. Relationship importance between
“Higher education and training” and “Technological readiness” is determined by canonical correlation
(Rc) and Eigen value (Rc
2
).
Based on table 6, first variable R
C
is 96.65% and R
C2
is 93.41%. Because R
C
cannot directly
prepare the shared variation, we utilize redundancy index. Redundancy index for R
c2
is in multiple
regression analysis.
Table 5 is showing that we can predict more than 79.3%of changes in “Technological
readiness” by studying changes in “Higher education and training”. These findings are mentioning a
meaningful relationship between “Higher education and training” pillar and “Technological readiness”
pillar. Also “Higher education and training” pillar has a positive effect on “Technological readiness”
pillar.
Table 7: Canonical loading and canonical cross loading for meaningful canonical variables in “Higher
education and training” & “Technological readiness”
Canonical variable 1 Canonical variable 2
loading Cross loading loading Cross
loading
Higher education and training
Secondary education enrollment rate
0.8558 0.8312 0.3497 -0.1125
Tertiary education enrollment rate 0.7445 0.7236 0.5539 -0.3141
Quality of the educational system 0.6665 0.6445 -0.2024 0.2627
Quality of math and science education 0.6771 0.6547 0.1509 -0.0946
Quality of management schools 0.7744 0.7421 -0.2539 0.0667
Internet access in schools 0.9510 0.9104 0.0295 0.1638
Local availability of research and training services 0.8939 0.8657 -0.2122 -0.1320
Extent of staff training 0.7654 0.7311 -0.4964 0.2252
Extracted variance (%) 63.48 10.62
Technological readiness
Availability of latest technologies 0.9230 0.8623 -0.3009 0.2448
Firm-level technology absorption 0.8508 0.8002 -0.4676 0.1430
FDI and technology transfer 0.6386 0.6034 -0.5698 0.3405
Internet Users 0.9163 0.8595 0.3060 0.0941
Broadband Internet subscriptions 0.9386 0.8734 0.2789 0.3958
Internet bandwidth 0.8958 0.8324 0.2496 -0.2119
Extracted variance (%) 75.11 14.46
Redundancy index (%) 59.3% 6.13%
For answering second and third sub-questions, we used canonical cross loading for evaluating
the importance of every criterion in meaningful canonical variable. In general the researcher faces the
choice of interpretation of the functions using canonical weights (standardized coefficients), canonical
loadings (structure correlations) or, canonical cross loadings. Given a choice, it is suggested that cross
loadings are superior to loadings, which are in turn superior to weights (Hair, 1998).
According to table 7, all variables in both sets have a high canonical cross loading in creating a
canonical variable in their sets. So they are very effective in creating a meaningful relationship
between “Higher education and training” and “Technological readiness”. In “Higher education and
training” sub-indexes, “Internet access in schools”, “Local availability of research and training
services”, and “Secondary education enrollment rate” have the highest effect and “Quality of the
educational system” has the lowest effect in creating this relationship. Furthermore, in the
“Technological readiness” sub-indexes, “Broadband Internet subscriptions”, “Availability of latest
technologies” and “Internet Users” have the highest effect and “FDI and technology transfer” has the
Relationship between “Higher Education and Training” and “Technological Readiness”:
A Secondary Analysis of Countries Global Competitiveness 146
lowest effect in creating this relationship. In addition, based on high amount of canonical cross loading
in both sets, we can conclude that “Higher education and training” pillar have a positive impact on
“Technological readiness” pillar. Also, for CCA validity, we used sensitivity analysis on independent
variables. For this validation, we eliminate one of “Higher education and training” sub-indexes every
time and utilize CCA. Outputs depicted no impression change in construct coefficient of variables. So
we assured that data were valid.
6. Summary and Concluding Remarks
This research intended to investigate the relationship between “Higher education and training” and
“Technological readiness” by using CCA for GCI 2010 data. First, we studied literature of
Competitiveness, GCI, Higher education and training, Technological readiness, and CCA. Then, we
used the Global Competitiveness report data in 2010 to do our secondary analysis. The population in
this study was 139 countries whose data was included in GCI report in 2010. Eventually, we utilized
Canonical Correlation Analysis (CCA) through SAS 9 software then analysis output was obtained.
According to research findings, there is a meaningful relationship between “Higher education
and training” pillar and “Technological readiness” pillar and “Higher education and training” pillar
have a positive effect on “Technological readiness” pillar. In “Higher education and training” sub-
indexes, “Internet access in schools”, “Local availability of research and training services”, and
“Secondary education enrollment rate” and in “Technological readiness” sub-indexes, “Broadband
Internet subscriptions”, “Availability of latest technologies” and “Internet Users” have the most impact
on creating a meaningful relationship.
Being familiar with national competitiveness indexes provides a suitable ability for different
industry agents to analyze their country environment with regional countries and even world countries.
Generally, the findings of this research increased our knowledge about the relationship between
indexes of “Higher education and training” and “Technological readiness”.
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