Industrial Management & Data Systems
A comparative study on industrial spillover effects among Korea, China, the USA,
Germany and Japan
Yong-Ki Min, Sang-Gun Lee, Yaichi Aoshima,
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A comparative study on industrial
spillover effects among Korea,
China, the USA, Germany
and Japan
Yong-Ki Min and Sang-Gun Lee
Business School, Sogang University,
Seoul, Korea, and
Industrial
spillover
effects
Received 26 May 2018
Revised 6 August 2018
Accepted 1 September 2018
Downloaded by 1.251.17.59 At 09:27 10 October 2018 (PT)
Yaichi Aoshima
Institute of Innovation Research, Hitotsubashi University,
Tokyo, Japan
Abstract
Purpose – Starting from industry 4.0 in Germany and followed by the New Strategy for American Innovation
in the USA and the smartization strategy in Japan, developed countries are pushing nation-wide innovation
strategies. Similarly, China is pursuing the Made in China 2025, and Korea announced the Manufacturing
Industry Innovation 3.0 strategy. However, few researchers have identified the industrial structure that
establishes the foundation of the 4th Industrial Revolution or have derived strengths and weaknesses to provide
implications on policy formulation through quantitative comparison with developed countries. Therefore, the
purpose of this paper is to analyze the spillover effect of the information and communication technology (ICT)
industry (the foundation of the 4th Industrial Revolution) and machinery·equipment industry (the foundation of
smart manufacturing through convergence with ICT industry).
Design/methodology/approach – This study examines the industrial spillover effects of the ICT industry
and machinery·equipment industry in the USA, Germany, Japan, China and Korea by using the World
Input–Output Table from 2000 to 2014.
Findings – The results showed that backward linkage effect of the ICT Industry are high in the order of
Korea≑ChinaWJapanWthe USA≑Germany, and forward linkage effect of the ICT industry are high in the order
of Japan ≑W the USA≑Korea ≑W China ≑W Germany. Backward linkage effects of the machinery·equipment
industry are high in the order of ChinaWJapan≑KoreaWthe USAWGermany, and forward linkage effects of the
machinery·equipment industry are high in the order of ChinaWKoreaWGermany≑Japan≑the USA.
Practical implications – China and Korea encourage active government investment in ICT and
machinery·equipment industries, especially the intentional convergence between ICT and machinery·equipment
industries is expected be generate higher synergy. The “innovation in manufacturing” strategy in the USA that
utilizes its strength in ICT services seems appropriate, whereas Germany needs to revitalize the ICT industry to
strengthen its manufacturing industry. Japan’s strategy is to focus its ICT capabilities on robot sector. While the
scope of innovation is limited, its synergy is worth expecting.
Originality/value – This study attempted to provide a theoretical approach to the determination of national
policy strategies and provide practical implications for response to the impacts of the 4th Industrial Revolution,
by comparing the inducement effects of ICT and machinery·equipment industries between major countries.
Keywords Industry 4.0, Industrial innovation 4.0, Industrial spillover effect, ICT industry,
Machinery·equipment industry
Paper type Research paper
1. Introduction
Industry 4.0 was first used in the 2011 Hannover Expo, and assigned as a core future project
for the 4th Industrial Revolution by German Government in 2012 (Drath and Horch, 2014).
During the opening ceremony of the 2015 Hannover Exhibition Ground, Angela Merkel, the
Chancellor of Germany, emphasized integrated industry, as well as the necessity of
integrating all production processes and the close cooperation with information and
communication technology (ICT) and machinery industries. In addition, Klaus Schwab, the
Industrial Management & Data
Systems
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-05-2018-0215
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IMDS
Founder and Executive Chairman of the World Economic Forum, stated the arrival of the
4th Industrial Generation, which is distinctly different from the 3rd Industrial Generation
(Schwab, 2016). Although some people argue that it is too early to call the development 4th
Industrial Generation, we are reaching an age of not only digitalization but also “digital
transition,” where the entire society utilizes digital technologies (OECD, 2017).
To respond to such changes, countries including the USA, Germany and Japan are
promoting a “nation-wide innovation” strategy that utilizes their individual strengths. The
USA developed “A Strategy for American Innovation” for creating new jobs and
strengthening ICT industry leadership, and promoted advanced manufacturing partnership
for reviving the manufacturing industry based on the private sectors’ capability of Big Data,
IoT, etc. Germany contributes to “Industry 4.0,” the industrial transformation with
automation, by converging general machinery and outstanding labor in manufacturing, in
order to maintain its lead in manufacturing. Japan is the first country that set “the 4th
Industrial Revolution” as a national strategy, actively utilizing its strong robotics
technology in order to increase industrial competitiveness and accelerate the socio-economic
system. Similarly, China is promoting the “Made in China 2025 Plan” by benchmarking
Germany’s Industry 4.0 in its 13th five-year plan. Korea enforces various strategies such as
the “Mid- to Long-Term Master Plan in Preparation for the Intelligent Information Society”
and “Manufacturing Innovation 3.0.”
The cyber-physical system (CPS) is the foundation of the 4th Industrial Revolution and
refers to the convergence between ICT and other industry (Schwab, 2016), which can
increase the competitiveness of manufacturing industry by optimizing the production
process. ICT, machinery·equipment and biomedical industries are directly related to the core
technologies of 4th Industrial Revolution, including AI, IoT, Big Data, automation and
sensors. Many studies and policies are available at present, when it is necessary to develop
active strategies that utilize the 4th Industrial Revolution as a new growth and leap.
However, few studies have quantitatively compared the infrastructure of the 4th Industrial
Revolution with the “nation-wide innovative” countries that lead the 4th Industrial
Revolution, and the corresponding strategic directions.
Therefore, this study aims to use input–output (IO) analysis to analyze the spillover
effect of the ICT industry (the foundation of the 4th Industrial Revolution) and the
machinery·equipment industry (the foundation of smart manufacturing through
convergence with ICT industry), by using the World Input–Output Table (WIOT) during
2000–2014. By quantitatively comparing the forward and backward linkage effects of the
ICT industry and machinery·equipment industry between China, Korea and the three
countries that are promoting nation-wide innovation – the USA, Germany and Japan – this
study examines each country’s global strengths and weaknesses in managing the 4th
Industrial Revolution and suggests national innovation strategies for each country.
2. Literature review
2.1 Arrival of the 4th Industrial Revolution
Although Jeremy Rifkin and Rober Gordon argue that the statement by Klaus Schwab (2016)
on the arrival of the 4th Industrial Revolution from the perspectives of speed, scope and
system impact is untimely and inappropriate. But all countries are accelerating innovation to
respond to the new world with different management of society and human role.
Schwab (2016) defined the 4th Industrial Revolution as CPS. CPS was first suggested by
the USA in the mid-2000s, but Germany is leading in manufacturing innovation by Industry
4.0 (Drath and Horch, 2014).
The most noticeable point of the 4th Industrial Revolution is “manufacturing innovation,”
which uses the ICT technology to automate the production process and intelligently
implement inter-process communication systems, in order to achieve “digitalization of
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manufacturing process” and “servitization of products,” leading to innovative changes including
customized small-quantity production and IoT-based customer service. Germany’s “Industry 4.0”
is a representative policy of such changes. At the enterprise level, the industrial internet model of
GE can be a reference. OECD focuses on not only digitization but also “digitalization/digital
transformation,” where digital technology is utilized over the whole community, demonstrating
that the widespread economic and social changes along with technology revolution will make a
huge difference to our lifestyle (OECD, 2017).
As presented in Table I, most existing studies on the 4th Industrial Revolution have
focused on employment and manufacturing innovation.
Industrial
spillover
effects
2.2 ICT industry and machinery·equipment industry
ICT industry is the driving force of economic and social growth (Fransman, 2009, and has
a strong impact on all industries because it grows rapidly and induces innovation of
enterprises. ICT industry has a higher forward linkage than backward linkage (Mattioli
and Lamonica, 2013); in particular, telecommunication industry is a core industry on the
supply side with its high forward linkage (Garcia and Vincente, 2014). In the USA, ICT
manufacturing industries took up 9.5 percent of the whole manufacturing from 2011
to 2014, which was lower than that between 1981 and 1985, while the revolution of ICT
service industry runs better than that in Europe and other developing
countries (Fransman, 2009). The percentage of ICT manufacturing in Germany
decreased the most from 13.0 to 9.6 percent, whereas that of ICT manufacturing in
Japan increased from 12.3 to 15.7 percent. China, the world’s largest exporter of ICT goods,
has been mainly relying on quantitative growth of HW based on its manufacturing
technology as a global manufacturer. Along with the globalization of manufacturing
companies in China, internet companies in China start expanding their impacts in the
global market after steady growth in the domestic market. That is, the traditional
Type
Researcher
General
Porter and
The USA is leading and obtaining benefits from smart connectivity
Heppelmann (2014)
Drath and Horch (2014) Addressed an easy approach to the core idea of Industry 4.0 and
industrial requirements for success
Alemanno (2017)
The problems of information control and imbalance of use due to
data retention
Weiss et al. (2016)
Showed the limitations on US ability of three case studies through
experiments on the cooperation between humans and robots
(Suganya, 2017)
Global higher education changes for the 4th Industrial Revolution
Smithers and James
Three pillars for effective communication according to
(2017)
technological change of the 4th Industrial Revolution: English for
Business Purposes (EBP), Computer-mediated Communication
(CMC), English as a Business Lingua Franca (BELF)
Arntz et al. (2017)
Future labor research on the 4th Industrial Revolution, which
examined the influence of automation technology on the total
labor market
Graetz and Michaels
Although the growth rate of industrial robotics increased
(2015)
in 17 countries, it did not have new impact on total employment
Mosterman and
Address concrete examples of CPS
Zander (2016)
Lee et al. (2015)
Proposal of an integrated five-level architecture as a guideline for
implementing CPS: smart connection – data to information
conversion – cyber – cognition – configuration
Monostori (2014)
CPS may lead to the 4th Industrial Revolution
Application
Education
Employment
Manufacturing
innovation
Contents
Table I.
Existing research on
the 4th Industrial
Revolution
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IMDS
HW-oriented industrial structure is changing to a new SW- and internet-oriented
industrial structure. On the other hand, Korea has an export-oriented structure in ICT
manufacturing, which held 22.2 percent of the whole manufacturing industry between
2011 and 2014. On the other hand, the growth in ICT service is slow, and foreign direct
investment increased rapidly by 46.9 percent from 2012 to 2015, while capital investment
decreased (Lee, 2017).
Machinery·equipment industry, including the manufacturing of machine tools, industrial
robots, automation, mechanical elements and equipment, and each industry’s specialized
machines tools, is a key industry that provides the facilities infrastructure for
manufacturing industry, such as automation, new materials and sensor technology
development (Arnold, 2001). Moreover, machinery industry is a key industry for
implementing smart factory, which has high linkage effects, especially backward linkage
effect, on other industries (Kwak and Park, 2009). Machinery industry is one of the top five
manufacturing industries in the USA in 1984, occupying 6.4 percent of the total
manufacturing outputs, but it then disappeared without a trace during 2011–2014. In
contrast, machinery secured second place among all domestic manufacturing industries in
Germany, and its proportion increased from 12.8 percent in 1984 to 15.2 percent in 2014. The
proportion of machinery to all domestic manufacturing industries in Japan increased from
8.2 to 10.4 percent, moving up from the fifth to third place in 2014. Since the Chinese
Government promotes the “Zizhu chuangxin(自主創新)” policy in its 11th five-year plan in
2006, it implemented policies to foster general industrial machinery in earnest. Machinery
production has been expanding rapidly until 2011 and then slowed down, but it remains as
the absolute no. 1 producer and importer of the world. As of 2014, it accounted for
11.5 percent of the global exports. Korea’s machinery industry accounts for 7.42 percent of
the total manufacturing industry, takes the fifth place in production amount, third place in
number of businesses and second place in number of employees. Korea’s
machinery·equipment industry has high proportions of small businesses and employees,
and thus is a very important industry from not only the economic perspective but also the
employment perspective (Statistics Korea, 2017) (Table II).
The manufacturing industry was reinvented as the central axis of the twenty-first
century economic growth and job creation after losing its leading position to the service
industry. The USA desired for “manufacturing renaissance” by constructing
manufacturing innovative networks, accelerating digitalization and establishing R&D
centers. On the other hand, Germany, a manufacturing powerhouse based on its
traditional machinery industry, started promoting “Industry 4.0” strategy to “construct
complete automation production systems and optimize all manufacturing productions” in
earnest since 2013. Similarly, Japan aims to transform to the most advanced economic and
social system by using its strength, such as robotics, IoT, Big Data and AI. China
promoted the “Made in China 2025 Plan” and mapped out a specific plan in May 2015
to boost manufacturing industry. This is a key strategy of the Chinese Government to
pursue industrial advancement, and China is striving to become a manufacturing
powerhouse across three stages over the next 30 years. In Korea, because the rate of
increase of ICT convergence is the steepest within machinery industry, policy
contributions are made to establish Smart Factory through convergence between ICT
and machinery (Song and Kwak, 2012). As of 2012, the USA, China, Germany, Japan and
Korea account for 48 percent of the global GDP and 35 percent of export, and the
economies are constantly expanding. The five countries are leading the 4th Industrial
Revolution, and each of them is promoting the convergence between ICT and machinery
equipment industries for smart Society and smart manufacturing. Therefore, it is
meaningful to examine the inducement effect of ICT and machinery·equipment industries
of each of the country, and provide implications on their national strategic directions.
Industry
Type
Researcher
Contents
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ICT industry
General Yang et al. (2013) The entry barriers of ICT firms are lower than those of
non-ICT firms, and the manufacturing sector finds a
stronger innovation effect than the service sector
Garcia and
Analyzed the capabilities of ICT in Europe using the
Vincente (2014) structure hole theory
Country Jung et al. (2013) Verified the hypothesis that technology convergence is
the main motivation of the recent productivity
increment in Korea, and estimated the effect of ICT on
total factor productivity (TFP)
Xing et al. (2011) Analysis of convergence status and roles of ICT
manufacture and ICT service industries of China for the
period of 2002
Fransman (2009) Showed that the USA operated the innovation process
of ICT ecosystem better than Europe and other
developing countries
Kim (2014)
Strategies for improving global competitiveness and
strengthening overseas expansion of the ICT industry
through fundamental changes and innovations for
revitalize the Japanese economy
Machinery·equipment General Arnold (2001)
Took a general view of the history of machine tool industry,
industry
and explained and noted the industry’s sharp changes
caused by the adoption of numerical control system
Richter and streb German machine tool traders blamed China on patent
(2011)
infringement, while Germany itself used imitation
strategy until the 1920s
Country Shinno et al.
A pair-wise comparison and quantitative SWOT
(2006)
analysis of the global competitiveness of Japan’s
machine tool industry using a matrix
Kalafsky and
Most American manufacturers in the machine tool
Macpherson
industry are small businesses, but small-sized
(2002)
enterprises can hardly flourish in the long term
Ernst (1995)
Systematically evaluated 50 businesses within
Germany’s mechanical engineering industry
Fransman (1986) Addressed how the machine tool fields in Taiwan
and Japan were affected by changes in technology and
production, the mechanism of growth and the role of
the nation
2.3 World Input–Output Tables
WIOT is the output of the World Input–Output Database (WIOD) project, which is
organized by scholars and professionals from 11 organizations and sponsored by European
Commission. This project, published in May 2009, reports IO tables of 27 EU countries and
13 other major countries in the world, and is the first inter-industry relation table based on
other national account statistics. Subsequently, WIOD released a revised edition in
November 2016, which provides a time-series IO table from 2000 to 2014 of EU country and
major countries. WIOT uses the supply-use tables of individual countries as basic data, but
it also uses both international trade data and socio-economic or environmental data
composed based on industrial standards to compile statistics more intuitively and
elaborately than directly linking IO tables of each country. In addition, this study used the
frequently updated statistics of national accounts as comparative data in order to eliminate
breaks in time-series data. Timmer et al. (2013) explained WIOT’s country and industry
classification and the process of deriving each country’s IO tables.
Industrial
spillover
effects
Table II.
Research on the
ICT and
machinery·equipment
industries
IMDS
3. Research methodology
3.1 Research model
At the center of the 4th Industrial Revolution changes is “manufacturing innovation,” which
constructs systems for digitalization, servicitization of products and customized production
through an automatic and smart production process using ICT technology. The convergence of
the machinery·equipment industry and the ICT industry is a prerequisite for the implementation
of smart factories. Thus, it is necessary to compare the inter-industry linkage effect of the ICT and
machinery·equipment industries in the USA, Germany, Japan, China and Korea, the five countries
that are promoting national innovative strategies to prepare for the 4th Industry Revolution and
to provide political implications by examining the global competitiveness of each country and the
spillover effect on all industries. Figure 1 presents the research model of this study.
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3.2 Hypothesis
In 2014, the US domestic market size of the ICT industry was the largest in the world, which
was valued at $1.11 trillion and took up 30 percent of the world’s total. China’s ICT domestic
market has grown with the focus of device and communication services, which is reaching
$377.9bn and accounts for 8.5 percent. Japan accounted for 8 percent and ranked third,
following by Germany, which accounted for 3.9 percent and ranked fourth. The market size
of Korea was $77.5bn, accounted for 2.1 percent of the world’s total and ranked 9th in the
world (Gartner, 2014). China was the largest ICT exporter of the world in 2013; the amount
of exports was $508.4bn accounted for 31.8 percent of global exports. The USA ranked
the second place with the amount of $140bn and accounted for 8.7 percent. Korea ranked the
fourth place with $107.1bn and accounted for 6.7 percent. Germany ranked the seventh
place, and Japan ranked the eighth, both accounted for 3.9 percent.
Since ICT industries in Korea, China, the USA, Germany and Japan have different
domestic market sizes, export scales, growth rates, and manufacturing and service
H1(H1a, H1b)
KOR ICT
CHN ICT
USA ICT
GER ICT
JPN ICT
Backward
Linkage
Backward
Linkage
Backward
Linkage
Backward
Linkage
Backward
Linkage
Forward
Linkage
Forward
Linkage
Forward
Linkage
Forward
Linkage
Forward
Linkage
H3(H3a, H3b)
Figure 1.
Research model
KOR Machinery
KOR Machinery
KOR Machinery
KOR Machinery
KOR Machinery
Backward
Linkage
Backward
Linkage
Backward
Linkage
Backward
Linkage
Backward
Linkage
Forward
Linkage
Forward
Linkage
Forward
Linkage
Forward
Linkage
Forward
Linkage
H2(H2a, H2b)
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productions, their influence on different domestic industries is expected to differ. Particularly,
the innovation process of the ICT ecosystem is operated better in the USA than in EU and
other developing countries (Fransman, 2009), and the accelerated productivity in the USA has
been based on ICT service instead of ICT manufacturing since the mid-1990s (Basu and
Fernald, 2006). Thus, the USA is expected to have high forward linkage effect but low
backward linkage effect. In Korea, ICT industry captured 8.7 percent of the GDP in 2013 and
took up 23 percent of the real growth rate of GDP in the second quarter of 2014, which is
expected to have a considerably high inter-industry linkage effect. Besides, since ICT
manufacturing has shown its strong points, its backward linkage effect is expected to be
higher than that in other countries. Because ICT manufacturing in China is strong, its
backward linkage effect is expected to be higher than other countries. In contrast, both
the backward and forward linkage effects of ICT is expected to have low in Germany, since
the proportion of ICT manufacturing decreased, and ICT service sector does not stand out. We
propose the following hypotheses:
H1. Inter-industry linkage effects of ICT industry are different in Korea, China, the USA,
Germany and Japan.
H1a. Backward linkage effects of ICT industry are different in Korea, China, the USA,
Germany and Japan.
H1b. Forward linkage effects of ICT industry are different in Korea, China, the USA,
Germany and Japan.
UN Comtrade showed Germany, the USA, Japan and China at the first, second, third and
fourth places, respectively, in the world in exports of general machinery. Korea ranked
eighth by accounting for 3.4 percent of the world’s total. In terms of imports, the USA
ranked first, China second, Germany third, Korea eighth and Japan tenth worldwide.
Annual average increase in the demand for general machinery in the global market was
9.8 percent between 2001 and 2011. During the same period, the annual average increase
in export amount in China, Korea, the USA, Japan and Germany was 10.5 percent. China
recorded rapid growth in export with an annual average growth rate of 27.8 percent,
followed by Korea with an annual average growth rate of 17.7 percent. According to
OECD, machinery·equipment investment in Japan was very high, whereas that in the USA
was not high compared to its economic scale. Machinery is the production-based
sector of manufacturing industry and the key of manufacturing facility. It has a high
backward linkage effect since it is a B2B industry that manufactures various materials
and components.
The inter-industry linkage effects of the machinery·equipment industry are different in
Korea, China, the USA, Germany and Japan, due to the distinctions between each country’s
proportion of the machinery·equipment industry, exports, industrial structure, etc.
Moreover, backward linkage effects in China and Korea is expected to be even higher than
those in the other countries since its manufacturing share is exceedingly high. In contrast,
manufacturing in the USA has little importance, so the forward and backward linkage
effects of the machinery·equipment industry are expected to be lower than those in other
countries. We propose the following hypotheses:
H2. Inter-industry linkage effects of the machinery·equipment industry are different in
Korea, China, the USA, Germany and Japan.
H2a. Backward linkage effects of the machinery·equipment industry are different in
Korea, China, the USA, Germany and Japan.
H2b. Forward linkage effects of the machinery·equipment industry are different in
Korea, China, the USA, Germany and Japan.
Industrial
spillover
effects
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IMDS
The proportion and spillover effect of the ICT industry and the machinery·equipment
industry differ among Korea, China, the USA, Germany and Japan since each country has
different industrial structures. In general, the ICT industry has higher forward linkage effect
than backward linkage effect, and thus provides many supports and supplies to the overall
production activities. Therefore, forward linkage effect is expected to be higher than
backward linkage effect in all of Korea, China, the USA, Germany and Japan. Since the
machinery·equipment industry is the key industry that provides the equipment base to
manufacturing industry (Arnold, 2001). In other words, because the machinery·equipment
industry uses various materials and components to boost production activities, Korea,
China, the USA, Germany and Japan are all expected to have high backward linkage effect.
In general, the ICT industry has high forward linkage effect, while machinery·equipment
industry has high backward linkage effect. Especially, since the USA has a strong ICT
service, which helps boost the productivity of other industries, the difference between the
forward linkage effects of ICT and that of the machinery·equipment industry is expected to
be greater than that in other countries. We propose the following hypotheses:
H3. The inter-industry linkage effects between the ICt and machinery·equipment
industries are different in Korea, China, the USA, Germany and Japan.
H3a. The backward linkage effects between the ICT and machinery·equipment
industries are different in Korea, China, the USA, Germany and Japan.
H3b. The forward linkage effects between the ICT and machinery·equipment industries
are different in Korea, China, the USA, Germany and Japan.
3.3 Research process
The research process involves: collecting the amount of intermediate consumption of each
industry from WIOT of Korea, China, the USA, Germany and Japan released by WIOD;
classifying the machinery·equipment industry and re-classifying the ICT industry by
adding all ICT-related industries into one; using Excel to compute backward and forward
linkage effects of the ICT industry and the machinery·equipment industry; using SPSS 18.0
to conduct one-way ANOVA to verify each hypothesis; and analyzing the difference
between the two industries and the five countries (Figure 2).
Step 1: Data
Data Collection
Data Collection
Step 2: Industrial Classification
Machinery·
Equipment
ICT
Step 3: Methodology
Step 4: Test
Figure 2.
Research process
Step 5: Analysis
Linkage Effect
Hypothesis
Results Analysis
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3.3.1 Data collection. This study uses WIOT that covers 56 industries in 43 countries.
Although the amount of inputs in both the ICT industry and machinery equipment industry
decreased in the USA, Korea, Germany and Japan in 2009 due to the global financial crisis in
2008, the ICT and machinery equipment industries of all countries except Germany’s ICT
industry returned to recovery in 2010. China reduced the amount of inputs in ICT industry
but then recovered in 2010, while the machinery equipment industry did not decrease even
in 2009. Although each country has different industrial structures and growth patterns that
may affect the results, there was no distortion of the data that have been used to compare
between the countries because all the countries were affected by the financial crisis in 2008.
Industries C26 (manufacture of computer, electronic, and optical products), J61
(telecommunications) and J62_63 (computer programming, consultancy and related
activities; information service activities) on WIOT are classified as the ICT industry
according to OECD’s ICT industry classification. On the other hand, industry C28
(manufacture of machinery and equipment n.e.c.) on WIOT is classified as the
machinery·equipment industry according to the international standard International
Standard Industrial Classification (ISIC) Rev.4 (UN, 2006).
3.3.2 Industry classification. The data of WIOT use each country’s released statistics,
considering the ease of verification and possibility of composing additional statistics in the
future. The supply-use tables of each country were standardized to 59 products and
35 industries. Each of the product and industry classifications were determined based on
Classification of Products by Activity and Nomenclature statistique des activités
économiques dans la Communauté européenne, the classification system established in
EU. UN’s ISIC is composed by UNSD and is used as the industrial standard in most
countries in the world. Rev.4 published in 2006 classified industries into four broad and
detailed structures (UN, 2006).
In 2007, OECD’s Working Party on Indicators for the Information Society improved the
classification standard of ICT industry in ISIC Rev.4.0 by dividing it into manufacturing and
service sectors, which enabled cross-national comparison on economic activities of ICT
industry (OECD, 2006, 2009). Both ISIC Rev.4.0 and WIOT-classified machinery·equipment
industry is the same as Division 28 (manufacture of machinery and equipment n.e.c.).
3.3.3 Analysis method. Inter-industry analysis, which is also called IO analysis, uses IO
tables to quantitatively examine the inter-relationship between industries. IO tables describe
the overall economic activities within an economy for a certain period (generally one year)
using matrices to clearly organize and record how the products and services of a particular
industry are distributed to other industries or sectors and how outputs of other industries or
sectors are inputted to each industry for production based on certain rules, so that people
can view the inter-industry relationships within an economy at a glance (Kin, 2015). IO
analysis uses Leontief inverse matrix coefficients and equations to derive each industry’s
inducement coefficient that induces the demand of raw materials and intermediary goods to
examine each industry’s spillover effect. The production inducement coefficient refers to the
total units of outputs required, both directly and indirectly, when the final demand
of production is increased by one unit. The inter-industry linkage effect refers to the result of
dividing each industry’s production inducement effect by the average inducement effect of
all industries. Such inter-industry linkage effect, or spillover effect, can be divided into
backward and forward linkage effects (Hirschman, 1958). Although there are various
methodologies for measuring backward and forward linkage effects, this study adopted the
commonly used Rasmussen method (Rasmussen, 1957).
Changes in production activities in one industry create demand for input in related
productions, and productions of the related industries will be influenced simultaneously due
to such indirect inducement. The total of direct and indirect inducement effect is represented
Industrial
spillover
effects
IMDS
by demand balanced formula X ¼ (I–A)−1d. That is, the IO matrix (marked as A) is derived
by dividing each column in the IO table by the total output of the corresponding industry. In
order to find out the demand balanced condition of each production when the final demand
is determined externally, the vector of production (X ), vector of final demand (d ), and the
identity matrix I are used together with A to form the formula (I−A)x ¼ d. Thus, if (I−A) is a
non-singular matrix, it will have an inverse matrix. The formula is X ¼ (I–A)−1d and is
called the Leontief inverse matrix. The result represents the coefficient of inducement effect
of final demand and composition change on the production activities P
of each industry.
P
Therefore, the formula to compute backward linkage effect is BLj ¼ 1=n i Bij =1=n2 ij Bij ,
P
where i Bij is the sum of the column elements of the Leontief inverse matrix (I–A)−1.
Similarly, the forward linkages can
P from the rows
P be obtained
Pof the Leontief inverse matrix,
the formula being FLi ¼ 1=n i Bij =1=n2 ij Bij , where
j Bij is the sum of the row
elements of the Leontief inverse matrix.
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4. Results
4.1 Spillover effect of ICT
This study used one-way ANOVA to test all hypotheses and ran Levene’s test to verify the
assumption of homogeneity of variance. Levene test showed that the value of ICT backward
linkage effect is 3.468 and the significance probability is 0.012, whereas the result of
Forward linkage effect is 5.135 and the significance probability is 0.001. The results indicate
that the null hypothesis is rejected and the homogeneous variance assumption in invalid,
thus Tamhane’s T2 was conducted after Welch’s ANOVA test (Figure 3 and Table III).
The difference between Korea and China, the USA and Germany is not statistically
significant, with an F-value of 51.015 and po0.001, whereas one-way ANOVA found
significant differences between all other countries, in the order of Korea≑ChinaWJapanWthe
USA≑Germany. In the USA, backward linkage effect continued to decrease from 1.0652 in
2001 to 0.8893 in 2014. Korea has the highest backward linkage effect, which increased from
1.1028 in 2000 to 1.1494 in 2009, but then started decreasing and eventually dropped to 1.0546
in 2014. Backward linkage effect in China exceeded Korea since 2010, and it was the highest
among all the five countries in 2014 with the value of 1.1307. ICT industry has a high forward
linkage effect since it continuously creates new demands in the market. Likewise, the results
in this study showed that the forward linkage effects of the ICT industry in Korea, China, the
USA, Germany and Japan were all higher than 1 in the order of Japan ≑W the USA≑Korea
≑W China ≑W Germany. Germany’s ICT industry was found to have lower forward linkage
effect than Japan, the USA and Korea. Forward linkage effect in the USA kept reducing from
2.2297 in 2000 to 1.7953 in 2014, while that in Japan remained stably high from 2.0471 in 2000
to 1.9337 in 2014. In comparison with forward linkage effect in the USA, the forward linkage
effect in Japan was higher and more stable.
CHN
USA
GER
JPN
20
20
20
20
KOR
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
1
20
0.7
00
1.5
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
0.9
00
2
01
1.1
20
Figure 3.
Comparison of
backward and
forward linkage
effects of ICT industry
ICT Forward linkage effect
2.5
01
ICT Backward linkage effect
1.3
KOR
CHN
USA
GER
JPN
1.1028
1.0940
1.1229
1.1164
1.1319
1.1308
1.1465
1.1352
1.1183
1.1494
1.0878
1.0588
1.0682
1.0593
1.0546
KOR
1.0923
1.0852
1.0559
1.0257
1.0416
1.0854
1.0552
0.9972
1.0328
1.0729
1.1193
1.1464
1.1426
1.1378
1.1397
1.0318
1.0652
1.0434
1.0062
0.9741
0.9458
0.9392
0.9174
0.8991
0.9192
0.9146
0.9209
0.9225
0.8855
0.8893
F ¼ 51.015***
0.9133
0.9140
0.9058
0.9594
0.9377
0.9563
0.9353
0.9428
0.9530
0.9758
0.9542
0.9299
0.9038
0.8912
0.8925
1.0644
1.0631
1.0416
1.0334
1.0259
1.0156
1.0037
0.9954
0.9887
0.9550
0.9676
0.9740
0.9760
0.9684
0.9685
Backward linkage effect (C26+J61+C62_3)
CHN
USA
GER
JPN
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
ICT in five
countries
ICT
1.7941
1.8921
1.9615
1.8996
1.9162
1.9244
2.0012
1.9534
1.7931
1.9420
1.7425
1.5150
1.6624
1.6641
1.6621
KOR
1.6587
1.7556
1.7496
1.7248
1.8757
2.0548
1.8627
1.5567
1.4499
1.5019
1.7603
1.8005
1.8204
1.8391
1.8250
2.2297
2.1663
2.0806
1.9918
1.8973
1.8456
1.7882
1.7783
1.7720
1.8013
1.8204
1.8434
1.8620
1.7553
1.7953
F ¼ 17.241***
1.4996
1.5683
1.6016
1.6570
1.6469
1.6716
1.6433
1.6825
1.6998
1.7748
1.6871
1.6641
1.6114
1.6116
1.5940
Forward linkage effect (C26+J61+C62_3)
CHN
USA
GER
JPN
2.0471
2.0327
2.0222
2.0353
2.0036
1.9223
1.9140
1.9105
1.9825
1.9353
1.9819
1.9935
1.9938
1.9888
1.9337
Korea≑China WJapan Wthe USA≑Germany
Japan ≑W the USA≑Korea ≑W China ≑W Germanya
Notes: a≑Wmeans A country≑B country or A countryWB country. *p o0.05; **p o0.01; ***p o0.001
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
ICT in five
countries
ICT
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Industrial
spillover
effects
Table III.
Comparison of
backward and
forward linkage
effects of ICT industry
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IMDS
4.2 Spillover effect of the machinery·equipment industry
Levene test showed that backward linkage effect in the machinery·equipment industry is
2.076 and the significance probability is 0.093, meaning that the homogeneous variance
assumption is valid. Thus, Scheffe’s test was conducted. On the other hand, forward linkage
effect is 25.156 and the significance probability is 0, indicating that the homogeneous
variance assumption is invalid, and hence Tamhane’s T2 was conducted after Welch’s
ANOVA test (Figure 4 and Table IV ).
Backward linkage effects of the machinery·equipment industry are high in all five
countries in the order of China WJapan≑KoreaWthe USA WGermany. However, backward
linkage effect in Germany decreased year after year from 1.558 in 2001 to 1.0124 in 2014,
which was lower than that observed in the remaining four countries. On the other hand,
although backward linkage effect in the USA was lower than that in Korea and Japan, it
increased slightly from 1.1176 in 2000 to 1.1279 in 2014.
Differences in forward linkage effects among the five countries are statistically
significant, in the order of China WKoreaWGermany≑Japan≑the USA Unlike the ICT
industry, forward linkage effects of the machinery·equipment industry are mostly low (less
than 1), while China and Korea is the country that has high forward linkage effects (greater
than 1). The USA has the lowest forward linkage effect among the five countries. Germany
and Japan had similar forward linkage effect until 2009, but since then, Germany has started
to have a higher forward linkage effect than Japan year after year. Although forward
linkage effect in China was extremely high, it was gradually decreasing. On the other hand,
forward linkage effect in Korea showed upward trend.
4.3 Spillover effects of the ICT industry and machinery·equipment
See Table V.
5. Discussion and conclusion
5.1 Conclusions
See Table VI.
The forward linkage effect of the ICT industry is higher than the backward linkage effect
in all countries, indicating ICT’s high innovation effect on the supply side to boost the
growth of other industries. The reason is that rapid technology innovation does not only
happen within the ICT industry, but also stimulates other industries to accelerate emergence
and servicitization between industries and foster the development of new industries.
Demand goes on in other industries such as the servicitization of manufacturing industries,
and the trend is expected to continue in the future.
Korea and China, which are ICT manufacturing-oriented, have high backward linkage
effects. On the contrary, backward linkage effect in the USA continuously experienced a
downward trend along with the lowered ICT manufacturing proportion. Coefficients of
0.6
KOR
CHN
USA
GER
JPN
20
20
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
0.8
20
1
00
1
00
1.4
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
1.2
20
Figure 4.
Comparison of
backward and
forward linkage
effects of
machinery·equipment
Machinery Forward linkage effect
1.8
01
Machinery Backward linkage effect
1.4
KOR
CHN
USA
GER
JPN
1.0552
1.0558
1.0459
1.0555
1.0550
1.0532
1.0523
1.0355
1.0408
1.0367
1.0066
1.0145
1.0182
1.0127
1.0124
1.2816
1.2826
1.2745
1.2716
1.2798
1.2857
1.2994
1.3085
1.3160
1.3183
1.3095
1.3116
1.3199
1.3334
1.3408
1.1584
1.1783
1.1839
1.1782
1.1843
1.2147
1.2266
1.2215
1.1773
1.1672
1.1845
1.1770
1.1562
1.1512
1.1609
1.1176
1.1124
1.1312
1.1377
1.1204
1.1182
1.1226
1.1208
1.1258
1.1347
1.1400
1.1377
1.1569
1.1340
1.1279
F ¼ 324.063***
Backward linkage effect (C28)
CHN
USA
GER
KOR
China W Japan≑Korea Wthe USAWGermany
Notes: *p o0.05; **po 0.01; ***po 0.001
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Machinery in
five countries
Machinery
1.2015
1.2193
1.2387
1.2099
1.2077
1.1934
1.1900
1.1911
1.1801
1.1631
1.1593
1.1769
1.1795
1.1586
1.1429
JPN
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Machinery in
five countries
Machinery
0.9662
0.9355
0.9372
0.9726
0.9835
1.0231
1.0972
1.1595
1.1075
1.0725
1.2060
1.2164
1.1449
1.1295
1.1391
KOR
1.6452
1.6757
1.6777
1.6472
1.6165
1.4876
1.5637
1.6066
1.5665
1.6021
1.4595
1.4638
1.3322
1.3783
1.3537
0.8608
0.8522
0.8576
0.8527
0.8362
0.8380
0.8352
0.8246
0.8222
0.8526
0.8261
0.8359
0.8566
0.8619
0.8622
F ¼ 251.544***
0.9183
0.9212
0.9064
0.9334
0.9218
0.9136
0.9212
0.9045
0.9080
0.8784
0.8734
0.8906
0.8998
0.9013
0.9085
Forward linkage effect (C28)
CHN
USA
GER
China WKoreaW Germany≑Japan≑the USA
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0.9108
0.9069
0.8801
0.8797
0.9106
0.9282
0.9208
0.9291
0.9116
0.8516
0.8536
0.8751
0.8893
0.8821
0.8531
JPN
Industrial
spillover
effects
Table IV.
Comparison of
backward and
forward linkage
effects of the
machinery·equipment
industry
IMDS
Country
F-value
Result
Figure
KOR(ICT/Machinery) linkage effect
2.2
ICT(F) > ICT(B)#Machinery(B) > Machinery(F)
2
1.8
Korea
F=236.928***
Inducement effect in ICT industry is higher than
that in Machinery·Equipment Industry, but
forward linkage effect in ICT industry is
decreasing while that in Machinery·Equipment
Industry is increasing
1.6
1.4
1.2
1
0.8
0.6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
ICT(B)
ICT(F) > Machinery(F) > Machinery(B) > ICT(B)
China
F=122.293***
Forward linkage effect in ICT industry is the
highest while backward linkage effect is the
lowest. Different from other countries, forward
linkage effect in Machinery·Equipment Industry
was high
Machinery(B)
ICT(F)
Machinery(F)
CHN(ICT/Machinery) linkage effect
2.2
1.8
1.4
1
0.6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
ICT(B)
Machinery(B)
ICT(F)
Machinery(F)
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USA(ICT/Machinery) linkage effect
ICT(F) > Machinery(B) > ICT(B) > Machinery(F)
2.4
2.2
USA
F=508.542***
Forward linkage effect in ICT industry of the
USA was the highest in 2000 with the value of
2.2297. The effect dropped to 17,953 in 2014,
which was lower than the inducement effect of
Japan and China. Backward linkage effect in
ICT industry was also continuously decreasing
2
1.8
1.6
1.4
1.2
1
0.8
0.6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
ICT(B)
Machinery(B)
ICT(F)
Machinery(F)
GER(ICT/Machinery) linkage effect
2.4
2.2
ICT(F) > Machinery(B) > ICT(B) > Machinery(F)
2
1.8
Germany
F=1,319.904***
1.6
Forward linkage effect in ICT industry of
Germany was the lowest among the five
countries, but it is showing an upward trend
1.4
1.2
1
0.8
0.6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
ICT(B)
Machinery(B)
ICT(F)
Machinery(F)
JPN(ICT/Machinery) linkage effect
2.4
ICT(F) > Machinery(B) > ICT(B) > Machinery(F)
Table V.
Comparison of
backward and
forward linkage
effects between the
ICT industry and the
machinery·equipment
industry
2.2
2
Japan
F=3,010.270***
Forward linkage effect is higher than backward
linkage effect in ICT industry, while backward
linkage effect is higher than forward linkage
effect in Machinery·Equipment Industry with a
stable trend
1.8
1.6
1.4
1.2
1
0.8
0.6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
ICT(B)
Machinery(B)
ICT(F)
Machinery(F)
Notes: *p < 0.05; **p < 0.01; ***p < 0.001
Korea’s backward linkage effect have been gradually declining since 2009. The downturn
may be due to the decline in capital investments by domestic ICT manufacturers and the
increment in the proportion of overseas production, so strong policies are necessary for
motivating domestic investments. Since backward linkage effect in ICT industry of
China exceeded that of Korea and continuously increased since 2010, China needs to
increase production inducement on related industries through continuous investment in
order to boost all industries. Japan has the highest forward linkage effect, and the USA,
which is strong in ICT services, has a high forward linkage effect, as expected. The US
productivity growth in the mid-1990s was pulled by ICT service industries (Basu and
Fernald, 2006). However, the forward linkage effect declined from an overwhelming high
at 2.2297 in 2000 to 1.7953 in 2014. Political support seemed necessary for the ICT sector,
Hypothesis
Result
Contents
A1a Accepted Backward linkage effect: Korea≑ChinaWJapan Wthe
USA≑Germany
Backward linkage effect in Korea was the highest, but has been
on the downtrend since 2009
Coefficient of the USA reduced from 1.0652 in 2001 to 0.8893
in 2014
A1b Accepted Forward linkage effect: Japan ≑W the USA≑Korea ≑W China
≑W Germany Forward linkage effect in each country are all
higher than 1
Forward linkage effect in Japan remained high, while that in the
USA continued to decrease
Backward linkage effect of China exceeded that in Korea since 2010
A2 (machinery·equipment) A2a Accepted Backward linkage effect: ChinaWJapan≑Korea Wthe
USAW Germany
Backward linkage effect of China maintained that highest
since 2000
A2b Accepted Forward linkage effect: ChinaW KoreaWGermany≑Japan≑
the USA
Forward linkage effect of China is the highest, and forward
linkage effect of China and Korea are higher than 1
A3 (ICT/
A3a Accepted ICT in all five countries: backward linkage effect oforward
machinery·equipment)
linkage effect
A3b Accepted Machinery·equipment in four countries: forward linkage
effectobackward linkage effect
China: forward linkage effectWbackward linkage effect
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A1 (ICT)
which has been left to only private parties. As of 2000, forward linkage effect in Japan
(2.0471) was lower than that in the USA, but remained stable until 2014 (1.9337). Such
results may be due to the continuous efforts made in the service sector, including enacting
“Basic Act on the Formation of an Advanced Information and Telecommunications
Network Society” in 2000, establishing and implementing ICT national strategy “e-Japan”
in 2001, and investing $3.71bn (98.7 percent of American Standards) in telecommunication
service in 2014.
Machinery·equipment industry has higher backward linkage effect than forward in
four countries. However, forward linkage effect in China is even higher, while both
backward and forward linkage effect exceed 1, implying high inducement effect.
Furthermore, since both backward and forward linkage effect in the machinery equipment
industry of China are overwhelmingly higher than other countries, continuous investment
on achinery equipment industry will be a good investment direction for invigorating all
industries in China.
5.2 Implications and limitations
The IO analysis will play a big role in determining the important or leading industries in the
national economy based on each industry’s spillover effect, as well as in providing
significant standards for analysis to determine the priority of investing using limited
resources. As presented in Figure 5, the inducement effects in the ICT and
machinery·equipment industries can be divided into two groups clearly. Since both
backward and forward linkage effects of the ICT and machinery·equipment industries in
China and Korea exceed 1, active government investments are strongly recommended
because it will help invigorate all industry through its inducement effect on other industries.
Especially, the intentional convergence of the ICT and machinery·equipment industries will
Industrial
spillover
effects
Table VI.
Summary of results
IMDS
2.0
Industries that
depend on
the demand of
other industries
J(I)
C(I)
U(I)
Industries that
depend on
other industries on
the whole
K(I)
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Forward Linkage Effect
G(I)
Figure 5.
Industrial spillover
effects of the ICT
industry and
machinery·equipment
industry in Korea,
China, the USA,
Germany and Japan
C(M)
K(M)
1.0
G(M)
U(M)
J(M)
Industries that
depend on
the supply of
other industries
Industries that
have weak
connections with
other industries
0
1.0
2.0
Backward Linkage Effect
Notes: K(I), KOR ICT; C(I), CHN ICT; U(I), USA ICT; G(I), GER ICT;
J(I), JPN ICT; K(M), KOR machinery; C(M), CHN machinery;
U(M), USA machinery; G(M), GER machinery; J(M), JPN machinery
increase mutual synergy, and thus help China to become the “manufacturing powerhouse”
that it is pursuing, and it will enable response to the 4th Industrial Revolution through the
Smart Factory projects in Korea.
In the USA, the forward linkage effect of ICT service industry is high, whereas
the backward linkage effect of the ICT industry and the spillover effect of
machinery·equipment industry are relatively low. Thus, the current innovation strategy
in manufacturing industry to prepare for the 4th Industrial Revolution appears suitable,
which is a strategy based on a global platform that utilizes the strong cloud computing
power. The spillover effect of ICT industry is the lowest in Germany among the five
countries. Despite Germany having the greatest manufacturing capabilities in the world,
its enterprises may remain as subcontractors of US platform companies if the convergence
with the domestic ICT industry is delayed. Therefore, Germany must increase the pace of
construction of effective and smooth smart production systems by strengthening ICT
services, and seek strategies to increase the inter-industry spillover effect. The ICT service
industry and the machinery·equipment industry in Japan are in a stable state. Japan has
already had a good foundation for constructing the high-tech economic and social system
that merges the ICT industry with robotics. In contrast to Germany, which pursues
innovation in the entire manufacturing industry, Japan is concentrated in the robotics
sector. Thus, the scope of innovation may be limited, although the synergy effect is
expected to be high.
This study identifies the industrial structure that establishes a foundation of the
Industrial Revolution, and makes academic and practical contributions by trying for the
first time to derive each country’s strengths and weaknesses to provide implications on
policy formulation through quantitative comparison with developed countries. The IO
analysis provides the standard for analyzing a key industry based on the spillover effect of
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each industry. However, the production inducement effect computed using IO table failed to
consider direct effects such as R&D investment, which may lead to underestimation. On the
other hand, the effect may be overestimated since it is a static analysis that does not
consider changes in price. Therefore, since it is difficult to make conclusive evaluations on
relative size, people should be cautious toward the practical applications of the results and
additional research is needed to overcome this limitation.
In addition, it is difficult to understand all infrastructure industries for the 4th
Industrial Revolution in each country by only examining the spillover effects of the ICT
and machinery·equipment industries; thus, future studies may include biomedical
industries and divide the ICT industry into manufacturing and service to conduct a more
detailed research. Furthermore, to examine the argument that the backward linkage
effects of ICT in Japan and Korea are decreasing due to the overseas expansion of the two
countries’ ICT component enterprises, it is necessary to conduct further analysis that
considers imports. Finally, it will also be meaningful for future studies to examine the
economic influence of the convergence between the machinery·equipment and ICT
industries in each country.
References
Alemanno, A. (2017), “Consumers or citizens? How the 4th Industrial Revolution can help people
change law and policy”, Discussion Paper, Digital Society-Education, Inclusion, and
Jobs, Friends of Europe, Brussels, pp. 71-75.
Arnold, H. (2001), “The recent history of the machine tool industry and the effects of technological
change”, Institute for Innovation Research and Technology Management, University of Munich,
Munich, 2001-2014.
Arntz, M., Gregory, T., Lehmer, F., Matthes, B. and Zierahn, U. (2017), “Technology and jobs in the
fourth industrial revolution”, JEL, J21, J23, D24, O33.
Basu, S. and Fernald, J. (2006), “Information and communications technology as a general-purpose
technology: evidence from U.S industry data”, Working Paper No. 2006-29, San Francisco, CA.
Drath, R. and Horch, A. (2014), “Industry 4.0: hit or hype?”, Industry Forum: IEEE Industrial Electronics
Magazine, Vol. 8 No. 2, pp. 56-58.
Ernst, H. (1995), “Patenting strategies in the German mechanical engineering industry and their
relationship to company performance”, Technovation, Vol. 15 No. 4, pp. 225-240.
Fransman, M. (1986), “International competitiveness, technical change and the state: the machine tool
industry in Taiwan and Japan”, World Development, Vol. 14 No. 12, pp. 1375-1396.
Fransman, M. (2009), The New ICT Ecosystem: Implications for Policy and Regulation,
Cambridge University Press, New York, NY.
Garcia, M.A.S. and Vincente, M.R. (2014), “ICT technologies in Europe: a study of technological
diffusion and economic growth under network theory”, Telecommunication Policy, Vol. 38 No. 4,
pp. 360-370.
Gartner (2014), “Gartner market databook”, 4Q14 Update, available at: www.gartner.com/doc/2953117?
ref=mrktg-srch (accessed November 15, 2017).
Graetz, G. and Michaels, G. (2015), “Robots at work”, CEP Discussion Paper No. 1335, Centre for
Economic performance, pp. 2042-2695.
Hirschman, A.O. (1958), The Strategy of Economic Development, Yale University Press, New Haven,
CT, 58.
Jung, H.J., Youn, K.Y. and Yoon, C.H. (2013), “The role of ICT in Korea’s economic growth: productivity
changes across Industry since the 1990s”, Telecommunications Policy, Vol. 37 No. 4, pp. 292-310.
Kalafsky, R.V. and Macpherson, A.D. (2002), “The competitive characteristics of U.S manufacturers in
the machine tool industry”, Small Business Economics, Vol. 19 No. 4, pp. 355-369.
Industrial
spillover
effects
Downloaded by 1.251.17.59 At 09:27 10 October 2018 (PT)
IMDS
Kim, T.-E. (2014), “Japan’s ICT international competitiveness strengthening international development
initiative report”, Institute of Information and Telecommunication Policy, Vol. 26 No. 16,
pp. 20-34.
Kin, T.H. (2015), 2010 and 2013 Regional Inter-Industry Input Table, Korea Bank, Seoul.
Kwak, K.H. and Park, J.Y. (2009), “Analysis of the Korean machinery industry using Korea’s inputoutput analysis”, Journal of Industrial Economics and Business, Vol. 22 No. 1, pp. 179-199.
Lee, J., Bagheri, B. and Kao, H.-A. (2015), “A cyber-physical systems architecture for Industry 4.0-based
manufacturing systems”, Manufacturing Letters, Vol. 3, pp. 18-23.
Lee, J.K. (2017), “Characteristics and implications of ICT industry in Korea”, Hyundai Research Institute,
Vol. 675 No. 2, pp. 1-11.
Mattioli, E. and Lamonica, G.R. (2013), “The ICT role in the world economy: an input-output analysis”,
Journal of World Economic Research, Vol. 2 No. 2, pp. 20-25.
Monostori, L. (2014), “Cyber-physical production systems: roots, expectations and R&D challenges”,
Procedia CIRP, Vol. 17, pp. 9-13.
Mosterman, P.J. and Zander, J. (2016), “Industry 4.0 as a cyber-physical system study”, Software and
Systems Modeling, Vol. 15 No. 1, pp. 17-29.
OECD (2006), ICT Sector Definition, Presentation for 10th Meeting of the WPIIS OECD, DSTI/ICCP/IIS
(2006)/FINAL, Paris.
OECD (2009), Guide to Measuring the Information Society, OECD, Paris.
OECD (2017), The Next Production Revolution: Implications for Governments and Business, OECD
Publishing, Paris, available at: www.oecd-ilibrary.org/science-and-technology/the-nextproduction-revolution_9789264271036-en (accessed November 12, 2017).
Porter, M.E. and Heppelmann, J.E. (2014), “How smart, connected products are transforming
competition”, Harvard Business Review, HBR Reprint R1411C, November, pp. 1-23.
Rasmussen, P.N. (1957), “Studies in inter-sectoral relations”, The American Economic Review, Vol. 47
No. 3, pp. 432-435.
Richter, R. and Streb, J. (2011), “Catching-up and falling behind: knowledge spillover from American to
German machine toolmakers”, The Journal of Economic History, Vol. 71 No. 4, pp. 1006-1031.
Schwab, K. (2016), “The fourth industrial revolution: what it means, how to respond”, Davos 2016,
available at: www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-meansand-how-to-respond/ (accessed October 20, 2017).
Shinno, H., Hachiag, S., Yoshioka, H. and Marpaung, S. (2006), “Quantitative SWOT analysis on
global competitiveness of machine tool industry”, Journal of Engineering Design, Vol. 177 No. 3,
pp. 251-258.
Song, J.Y. and Kwak, K.H. (2012), “Machine industry ICT convergence and value added effect”, Journal
of Mechanical Science and Technology, Vol. 52 No. 11, pp. 37-42.
Statistics Korea (2017), “Korea’s machinery equipment industry”, available at: http://kosis.kr/
statisticsList/statisticsListIndex (accessed November 20, 2017).
Suganya, G. (2017), “A study on challenges before higher education in the emerging fourth industrial
revolution”, International Journal of Engineering Technology Science and Research, IJETSR,
Vol. 4 No. 10, pp. 1-3.
UN (2006), “International Standard of Industrial classification of All Economic Activities(ISIC):
Revision 4”, Statistical Papers Series M No. 4, Economic & Social Affairs.
Weiss, A., Huber, A., Minichberger, J. and lkeda, M. (2016), “First application of robot teaching in an
existing Industry 4.0 environment: does it really work?”, Societies, Vol. 6 No. 3, pp. 1-21.
Xing, W., Ye, X. and Kui, L. (2011), “Measuring convergence of China’s ICT industry: an input-output
analysis”, Telecommunications Policy, Vol. 35 No. 4, pp. 301-303.
Yang, C., Lee, S.-G. and Lee, J. (2013), “Entry barrier’s difference between ICT and non-ICT industries”,
Industiral Management & Data Systems, Vol. 113 No. 3, pp. 461-480.
Downloaded by 1.251.17.59 At 09:27 10 October 2018 (PT)
Further reading
Alberto, B.-M., Margarita, B. and Fernado, L.-L. (2013), “Perceived performance effects of ICT in
manufacturing SMEs”, Industrial Management & Data Systems, Vol. 113 No. 1, pp. 117-135.
Dietzenbacher, E., Stehrer, R., Timmer, M. and Vries, G. (2012), “Trade performance in internationally
fragmented production networks: concepts and measures”, WIOD Working Paper No. 11,
Groningen.
Korea Bank (2015), Inter-Industry Analysis Commentary, Korea Bank, Seoul.
Timmer, M.P., Los, B. and Vries, G.J. (2016), “An anatomy of the global trade slowdown based on the
WIOD 2016 release”, Groningen Growth and Development Centre, University of Groningen,
Groningen, No. GD-162.
Timmer, M.P., Temursho, U., Streicher, G., Stehrer, R., Rueda, J.M., Pindyuk, O., Los, B., Francois, J.F.,
Vries, G.J. and Arto, I. (2012), “The World Input-Output database (WIOD): contents, sources and
methods”, WIOD Working Paper No. 10, WIOD, Groningen.
Ya-Ching, L., Pin-Yu, C. and Hsien-Lee, T. (2011), “Corporate performance of ICT-enabled business
process re-engineering”, Industrial Management & Data Systems, Vol. 111 No. 5, pp. 735-754.
Corresponding author
Sang-Gun Lee can be contacted at: slee1028@sogang.ac.kr
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