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Canonical Correlation Analysis between Technological readiness
and Labor market efficiency: A Secondary Analysis of Countries
Global Competitiveness in 2011-2012
Abbas Ali Rastegar
Assistant Professor, Faculty of Economics & Management, Semnan University, Semnan, Iran
E-mail: a_rastgar_2005@yahoo.com
Tel: +98-912-1793760
Bahareh Mahbanooei
M.Sc. Student of Human Resource Management, University of Tehran, Tehran, Iran
Email: b.mahbanooi@ut.ac.ir
Tel: +98-21-61117735
Rohollah Ghasemi
Corresponding Author, Ph.D. Student of Production and Operations Management, University of
Tehran, Tehran, Iran
Email: ghasemir@ut.ac.ir
Tel: +98-935-8070906
Abstract
The concept of competitiveness has attracted abundant attentions of both scholars and
governors during the past decade. 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.
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. 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.
The GCI contains 12 pillars which are: 1st. Institutions, 2nd. Infrastructure, 3rd.
Macroeconomic stability, 4th. Health and primary education (Basic requirements), 5th.
Higher education and training, 6th. Goods market efficiency, 7th. Labor market efficiency,
8th. Financial market sophistication, 9th. Technological readiness, 10th. Market size
(Efficiency enhancers), 11th. Business sophistication, 12th. Innovation (Innovation and
sophistication factors).
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. In addition, 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. Also, The efficiency and flexibility of the labor
market are critical for ensuring that workers are allocated to their most efficient use in the
economy and provided with incentives to give their best effort in their jobs. Labor markets
must therefore have the flexibility to shift workers from one economic activity to another
rapidly and at low cost, and to allow for wage fluctuations without much social disruption.
This paper aims at investigating the interaction between the two sets of “Technological
readiness” and “Labor Market Efficiency” as the two basic pillars of national
competitiveness in order to provide some policy advices for countries that are going to
improving their Labor market efficiency and Technology readiness.
In our study, we used descriptive-correlation methodology. The statistical population was
142 countries whose GCI data were included in GCI 2011-2012 report. Also, we employed
Canonical Correlation Analysis (CCA) to investigate interaction between two sets of
“Technological readiness” and “Labor Market Efficiency”. 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. CCA is obtaining linear
composition of predicting variables that has the most correlation with linear combination
of criteria variables.
Our findings show that there is a significant and positive relationship between the set of
“Technological readiness” and that of “Labor Market Efficiency”. According to research
findings, there is a meaningful relationship between “Technological readiness” sub-
indexes and “Labor market Efficiency” sub-indexes and “Technological readiness” sub-
indexes have a positive effect on “Labor market Efficiency” sub-indexes. In
“Technological readiness” sub-indexes “Firm-level technology absorption”, “Availability
of latest technologies”, and “Internet users”, and in “Labor market Efficiency” sub-
indexes, “Reliance on professional management”, “Cooperation in Labor- employer
relation” and “Pay and productivity” 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 “technology readiness” and “Labor market
Efficiency”.
Keywords: Global Competitiveness, Canonical Correlation Analysis, Multivariate
Statistics.
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 & 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 a 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 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).
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 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 efficiency-driven economy to innovation-driven. To this end, their economic
policy making should benefit valid orientation and indicators for this transition. Utilizing
comparative approach and benchmarking from successful economic experiences around the
world can help the policy makers and business leaders manage economy and achieve a higher
level of prosperity. In this regard, improving the national competitiveness is a key factor (Vares
et al., 2011).
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 some policy advices for
countries that are going to improving their Labor market efficiency and Technology readiness.
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 &
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:
Table1. 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
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).
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).
Also Jain and Triandis (1990) said that the innovation process requires the combination
of technology and available inventions for creation of a new and modern product, new process or
system. Technological innovation consists of a series of processes and activities focusing on
knowledge, and its purpose is mobilization of scientific and mechanical sources to make
successful and innovative products or productive processes (García-Muiña et al., 2009) .
Technological innovation means the creation of a concept, combination or a special knowledge
that is hardly repeatable by competitors (Xin et al.,2010).
As mentioned in Introduction, this research seeks to investigate interactions between
pillars of “Technological readiness”and “Labor market efficiency” in GCI in order to provide
some policy advices for countries that are going to improving their Labor market efficiency and
Technology readiness.
2.4. Technological readiness (TR)
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. (Schwab, 2010). 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 (Porter & Schwab, 2008).
2.5. Labor Market Efficiency (LME)
The efficiency and flexibility of the labor market are critical for ensuring that workers are
allocated to their most efficient use in the economy and provided with incentives to give their
best effort in their jobs. Labor markets must therefore have the flexibility to shift workers from
one economic activity to another rapidly and at low cost, and to allow for wage fluctuations
without much social disruption. The importance of the latter has been dramatically highlighted
by the recent events in Arab countries, where high youth unemployment sparked social unrest in
Tunisia that spread across the region. Efficient labor markets must also ensure a clear
relationship between worker incentives and their efforts to promote meritocracy at the
workplace, and they must provide equity in the business environment between women and men.
Taken together these factors have a positive effect on worker performance and the attractiveness
of the country for talent, two aspects that are growing more important as talent shortages loom
on the horizon. The “Labor Market Efficiency” sub-indexes are:
1. Cooperation in labor-employer relations;
2. Flexibility of wage determination;
3. Rigidity of employment index, 0–100 (worst);
4. Hiring and firing practices;
5. Redundancy costs, weeks of salary;
6. Pay and productivity;
7. Reliance on professional management;
8. Brain drain;
9. Women in labor force, ratio to men (Schwab, 2011).
Although substantial gains can be obtained by improving other 11 pillars, all these pillars
eventually seem to run into diminishing returns. For example Seker (2012) has been shown that:
“There have been significant improvements in traditional trade policies in the past few decades”.
However, these improvements can only be fully effective when they are complemented with a
favorable investment climate. This study focuses on a particular aspect of investment climate,
namely labor regulations, and shows that how these regulations can be discouraging from
exporting. He used firm level data from 26 countries in Eastern Europe and Central Asia region,
the paper empirically shows that firms that cannot create new jobs due to stringent labor
regulations are less likely to export. Firms that plan to export expand their sizes before they start
to export. However, the rigidities in labor markets make this adjustment process costly. Higher
costs of employment decrease operating profits and lead to a higher productivity threshold level
required for entering export markets. As a result, a smaller fraction of firms can afford to export
(Seker, 2012).
3. Proposed model
This Proposed model is composed of two kinds of variables: “Technological readiness”and
“Labor Market Efficiency” as in the following figure.
Figure1. Research proposed model
Availability
of latest
technologies
Firm-level
technology
absorption
FDI and
technology
transfer
Internet users
Broadband
Internet
Subscript
ions
Internet
bandwidth
Labor Market
Efficiency
Technological
readiness
Cooperation in
labor-employer
relations
Flexibility of
Wage determination
Rigidity of employment
index
Hiring and
Firing practices
Redundancy costs,
weeks of salary
Pay and productivity
Reliance on professional
management
Brain drain
Women in labor force,
ratio to men
According to the above-mentioned figure research question is:
Is there any meaningful relationship between “Labor market Efficiency” and “Technological
readiness” efficiency”?
And Research Sub questions are:
1. Is there any correlation between “Technological readiness”sub-index and “Labor market
Efficiency” sub-index?
2. In the set of “Technological readiness”, which pillar has the most and which one has the
least impact on creating a meaningful relationship between “Technological readiness”
and “Labor market Efficiency”?
3. In the set of “Labor market Efficiency”, which pillar has the most and which one has the
least impact on creating a meaningful relationship between “Technological readiness”
and “Innovation”?
4. Research Methodology
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, “Technological readiness”, “Labor market Efficiency”, and CCA. Then, we used the GCI
report data in 2011 for doing our secondary analysis. The Statistical population in this study was
142 countries whose data was included in GCI report in 2011-2012. Finally, we utilized
Canonical Correlation Analysis (CCA) by SAS9 software; thereafter, analysis output was
obtained.
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 =a1x1+ a2x2+…+ apxp
V= b1y1 + b2y2+… + bqyq
The number of dependent variables (six) or the number of independent variables (nine),
whichever is smaller, determines the maximum number of canonical functions. Thus the analysis
is based upon the derivation of four canonical functions (Mai and Ness, 1999).
Also 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 2011-
2012) as our secondary data resource.
5. Data Analysis
Using SAS9 software, we investigated correlation between two sets of “Technological
readiness”and “Labor market Efficiency” by using CCA.
For answering the first sub question, based on table 2, we can see a meaningful positive
correlation in significance level of 0.05 between some “Technological readiness” sub-indexes
and “Labor market efficiency” sub-indexes. Such as, “Reliance on professional management”
and “Firm-level technology absorption” have the strongest correlation and “Firm-level
technology absorption” and “Hiring and firing practices” have the least correlation in this table.
For example in “Technology readiness” sub-indexes, “Firm-level technology absorption” has the
most correlation and “Internet bandwidth” has the least correlation with “Cooperation in Labor-
employer relation”. Also “Firm-level technology absorption” has the most correlation and
“Internet bandwidth” has the least correlation with “Pay and productivity”.
Table 2. Correlation coefficient between TR and LME
Women
in labor
force,
ratio to
men
Brain
drain
Reliance on
professional
management
Pay and
productivity
Redundancy
costs,
weeks of
salary
Hiring
and firing
practices
Rigidity of
emplacemen
t index
Flexibility of
wage
determination
Cooperation
in Labor-
employer
relation
LME
TR
0.0280.2400.7990.471-0.156-0.076-0.0840.0630.646
Availability of
latest
technologies
-0.0360.20.8010.527-0.065-0.012-0.1120.1090.711
Firm-level
technology
absorption
-0.0050.1620.6110.477-0.0790.053-0.1120.0030.52
FDI and
technology
transfer
0.1480.1220.6680.501-0.27-0.083-0.0270.1080.555Internet users
0.3040.110.6540.414-0.268-0.1040.0200.1370.522
Broadband
Internet
Subscriptions
0.2160.0750.4530.308-0.2050.155-0.079-0.0460.448
Internet
bandwidth
Table 3. Canonical Correlation Analysis summary
Table 3 is showing enveloped data variation by CCA. The extracted variance for
“Technological readiness” and “Labor market efficiency” is showing that 71.19% of canonical
roots are covered by internal “Technological readiness” variation and also 100% of canonical
roots are covered by internal “Labor market efficiency” variation. These statistics are very
considerable and support CCA utilization.
142=N Technology readiness Labor market efficiency
Number of variables 6 9
Extracted variance 100% 71.19%
Redundancy index 57.21% 25.88%
Variables: 1 Availability of latest technologies Cooperation in Labor- employer relation
2 Firm-level technology absorption Flexibility of wage determination
3 FDI and technology transfer Rigidity of emplacement index
4 Internet users Hiring and firing practices
5 Broadband Internet Subscriptions Redundancy costs, weeks of salary
6 Internet bandwidth Pay and productivity
7Reliance on professional management
8Brain drain
9Women in labor force, ratio to men
Table 4. Statistical tests
Usual Canonical Correlation Analysis meaningful level for interpretation is 0.05. As it's
shown in table 4, P-value is used for this research; 1st , 2nd , 3rd, 4th, and 5th canonical variables
are statistically meaningful.
In addition, other statistical tests like "Lambda Prime" and " 2
" are proofing our results.
we considered first canonical variable and ignored interpretation of 2nd , 3rd, 4th, and 5th variables
because of their weak canonical cross loading and redundancy index.
For answering research question, we focus on table 3 and 4. Relationship importance
between “Technological readiness” and “Labor market efficiency” is determined by canonical
correlation (Rc) and Eigen value (Rc2).
Based on table 4, first variable Rcis 87.96% and Rc2is 77.36%. Because Rc cannot directly
prepare the shared variation, we utilize redundancy index. Redundancy index for Rc2is in
multiple regression analysis.
Table 3 is showing that we can predict more than 25.88% of changes in “LME” by
studying changes in “Technological readiness”. Also, this table is showing that we can predict
more than 57.21% of changes in “TR” by studying changes in “LME”.
These findings are mentioning a meaningful relationship between “Technological
readiness” sub-indexes and “Labor market efficiency” sub-indexes, also “Technological
readiness” sub-indexes has a positive effect on “Labor market efficiency” sub-indexes.
Table 5. Canonical loading and canonical cross loading for meaningful canonical variables in TR & LME
Canonical variable 1 Canonical variable 2
loading Cross loading loading Cross loading
Technological readiness
Availability of latest technologies 0.9616 0.8440 0.0445 0.0125
Firm-level technology absorption 0.9800 0.8863 -0.1227 -0.0967
FDI and technology transfer 0.7341 0.6228 -0.0891 -0.0439
Internet users 0.8434 0.7119 0.3544 0.2442
Broadband Internet Subscriptions 0.7844 0.6902 0.5899 0.4439
Internet bandwidth 0.5054 0.4385 0.2248 0.1367
Extracted variance (%) 66.77 9.15
Labor market efficiency
Cooperation in Labor- employer relation 0.7992 0.6854 -0.1140 -0.0773
Flexibility of wage determination 0.1189 0.0634 0.1662 0.0984
Rigidity of emplacement index -0.1097 0.0576 0.2792 0.1958
Hiring and firing practices -0.0406 -0.0295 -0.2405 -0.1760
Redundancy costs, weeks of salary -0.1483 -0.0869 -0.4149 -0.3345
Pay and productivity 0.6241 0.5322 -0.0291 -0.0132
Reliance on professional management 0.9243 0.8448 0.0519 0.0342
Brain drain 0.2264 0.1418 -0.1176 -0.0654
Canonical
roots
Chi-square Tests With Successive Roots Removed
Canonical R Canonical R2Chi-sqr df P Lambda
Prime
0 0.8796 0.7736 320.846 54 0.00000 0.089602
10.6052 0.3663 123.251 40 0.00000 0.395859
20.4063 0.1651 62.5780 28 0.00019 0.624683
30.3619 0.1309 38.5795 18 0.00326 0.748210
40.3487 0.1216 19.9056 10 0.03019 0.860996
5 0.1409 0.0198 2.6659 4 0.61520 0.980155
Women in labor force, ratio to men 0.0115 0.0044 0.8288 0.7654
Extracted variance (%) 22.04 11.7
Redundancy index (%) 51.66 3.35
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 6, some variables in both sets have a high canonical cross loading in
creating a canonical variable in their sets. So they are effective in creating a meaningful
relationship between “Technological readiness” and “Labor market efficiency”. In
“Technological readiness” sub-indexes, “Firm-level technology absorption”, “Availability of
latest technologies”, and “Internet users” have the highest effect and “Internet bandwidth” has
the lowest effect in creating this relationship. Furthermore, in the “Labor market efficiency” sub-
indexes, “Reliance on professional management”, “Cooperation in Labor- employer relation”
and “Pay and productivity” have the highest effect and “Women in labor force, ratio to men” has
the lowest effect in creating this relationship. In addition, based on high amount of canonical
cross loading in both sets, we can conclude that “Technological readiness” sub-indexes have a
positive impact on “Labor market efficiency” sub-indexes. Also, for CCA validity, we used
sensitivity analysis on independent variables. For this validation, we eliminate one of
“Technological readiness” 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 “Technological readiness”and
“Innovation” by using CCA for GCI 2011-2012 data. First, we studied literature of
Competitiveness, GCI, “Technological readiness”, “Labor market Efficiency”, and CCA. Then,
we used the Global Competitiveness report data in 2011-2012 to do our secondary analysis. The
population in this study was 142 countries whose data was included in GCI report in 2011-2012.
Eventually, we utilized Canonical Correlation Analysis (CCA) through SAS9 software then
analysis output was obtained.
According to research findings, there is a meaningful relationship between
“Technological readiness” sub-indexes and “Labor market Efficiency” sub-indexes and
“Technological readiness” sub-indexes have a positive effect on “Labor market Efficiency” sub-
indexes. In “Technological readiness” sub-indexes “Firm-level technology absorption”,
“Availability of latest technologies”, and “Internet users”, and in “Labor market Efficiency”
sub-indexes, “Reliance on professional management”, “Cooperation in Labor- employer
relation” and “Pay and productivity” have the most impact on creating a meaningful relationship.
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