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Journal of
Social and Administrative Sciences
www.kspjournals.org
Volume 4 December 2017 Issue 4
The Fishbone diagram to identify, systematize and analyze the
sources of general purpose technologies
By
Mario COCCIAa†
Abstract. This study suggests the fishbone diagram for technological analysis. Fishbone
diagram (also called Ishikawa diagrams or cause-and-effect diagrams) is a graphical
technique to show the several causes of a specific event or phenomenon. In particular, a
fishbone diagram (the shape is similar to a fish skeleton) is a common tool used for a cause
and effect analysis to identify a complex interplay of causes for a specific problem or event.
The fishbone diagram can be a comprehensive theoretical framework to represent and
analyze the sources of innovation. Fishbone diagram is applied here as a novel graphical
representation to identify, explore and analyse whenever possible, the potential root causes
of the source and evolution of General Purpose Technologies (GPTs). Overall, then,
fishbone diagram seems to be an appropriate and general technique of graphical
representation to explore and categorize, clearly and simply, the potential root causes of the
evolution of technological innovations for an appropriate management of technology.
Keywords. Fishbone diagram, General purpose technology; Source of technical change,
Management of technology, Technology evolution, Evolution of technology.
JEL. B40, O31, O33.
1. Introduction
echnological progress has a great weight in supporting patterns of economic
growth over the long run (Helpman, 1998; Coccia, 2005b; 2007; 2009a;
2010a; 2010b; Ruttan, 2001; Rosenberg, 1982). A main element of the
technical progress is the path-breaking innovations, which make prior technical
knowledge obsolete and sustain industrial change (Sahal, 1981; Colombo
et al.,
2015). A path-breaking innovation is the General Purpose Technology (GPT),
which is one of the contributing factors of the long-run technological and economic
change in society (Bresnahan, 2010). The GPTs are enabling technologies for a
pervasive use in many sectors to foster new products and processes (Helpman,
1998). The GPTs generate changes of techno-economic paradigm (‚Technological
Revolutions‛), which affect almost every branch of the economy (Freeman &
Soete, 1987: 56-57) and support the ‚secular process of growth‛ (Bresnahan &
Trajtenberg, 1995: 83;
cf.
Helpman, 1998; Lipsey
et al.,
1998). Ruttan (2006)
argues that GPT is basic to sustain productivity and economic growth of nations
over time.
The driving forces of GPTs are different from those that support other
innovations of less intensity (Helpman, 1998; Ruttan, 1997; Lipsey
et al.,
1998,
Coccia, 2005, 2005a; 2010, 2014, 2014a; 2015; Schultz & Joutz, 2010). Scholars
have described several approaches to explain the source of technical change and
technological evolution (
cf.
Wright, 1997; Hall & Rosenberg, 2010; Helpman,
1998:. 2; Coccia, 2015; Wang
et al.,
2016; Li, 2015; Robinson
et al.,
2007; von
aa† Arizona State University, Interdisciplinary Science and Technology Building 1 (ISBT1) 550 E.
Orange Street, Tempe- AZ 85287-4804 USA.
. + 85287-4804
. mario.coccia@cnr.it
T
Journal of Social and Administrative Sciences
JSAS, 4(4), M. Coccia, p.291-303.
292
Hippel, 1988), however, an appropriate visualization technique for identifying and
analyzing the potential root causes of general purpose technologies (GPTs) is
hardly known. In particular, a problem is to represent in a comprehensive
theoretical framework the several drivers of General Purpose Technologies (GPTs)
that support the technological evolution for technological and economic change in
society over the long run (
cf.
Ruttan, 1997; 2006).
The study here confronts this scientific problem by using a graphical
representation with the fishbone diagram, which seems to be an appropriate
visualization technique for categorizing and analyzing the complex determinants of
the technological evolution of GPTs over time. The main aim of this study is
therefore to provide a novel graphical representation to explore whenever possible,
the potential root causes of the source and evolution of general purpose
technologies (GPTs) that explain the economic change in society.
2. Conceptual grounding
General Purpose Technologies (GPTs) are revolutionary changes from current
technological trajectories (Bresnahan, 2010:763-791). These path-breaking
innovations are mainly of transformative nature and generate a ‚destructive
creation‛ (Calvano, 2007), which makes prior products and knowledge obsolete
(
cf.
Colombo
et al.,
2015). Lipsey and colleagues (1998:43) define the General
Purpose Technology: ‚a technology that initially has much scope for improvement
and eventually comes to be widely used, to have many users, and to have many
Hicksian and technological complementarities‛. GPTs are enabling technologies
that exert a pervasive impact across firms, industries and that permeate the overall
structure of the economy (Coccia, 2005, 2010a). The diffusion of GPTs is by
several ripples of effects that remove barriers and generate significant techno-
economic change in society with new communications and transportation
technology. Coccia (2005) classifies the GPTs, in the scale of innovation intensity
with the highest degree of socio-economic impact. In particular, Coccia (2005, pp.
123-124) claims, referring to revolutionary innovations, such as GPTs, that:
The means of human communication are radically changed and a new means
of communication, which heavily affects all the economic subjects and
objects, is born, forcing all those who use it to change their habits. A new
technoeconomic paradigm is born… The propulsive capacity for
development offered by seventh-degree innovation is so high that it hauls the
entire economy. Thanks to the new methods of communication, there is also
greater territorial, social, and human integration. Another characteristic of
seventh-degree innovations is the ease of their spread. The mobility of
people, goods, capital, and information increases and the time taken to travel
and communicate is reduced.
Bresnahan & Trajtenberg (1995: 86-87) show that GPTs have a treelike
structure with basic new technology located at the top of the tree and all derived
technologies, for several industries, radiating out towards every branch of the
economy. In fact, the General Purpose Technologies generate clusters of new
technology in several industries because they are general mechanisms and/or
components and/or infrastructure for the architecture of various families of
products/processes that are made quite differently. The different applications of
new GPTs are driven by firms to maximize the profit and/or to exploit the position
of a (temporary) monopoly in different sectors and industries over time (Coccia,
2015).
In general, GPTs are characterized by pervasiveness, inherent potential for
technical improvements, and ‘innovational complementarities’, giving rise to
increasing returns-to-scale, such as the steam engine, the electric motor, and
semiconductors (Bresnahan & Trajtenberg, 1995: 83, original emphasis) 1.
1
cf.
also Lipsey
et al.,
2005; Bresnahan, 2010; Ristuccia & Solomou, 2014; Goldfarb, 2005.
Journal of Social and Administrative Sciences
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293
Jovanovic & Rousseau (2005: 1185) show that the distinguishing characteristics of
a general purpose technology are: (1)
Pervasiveness
: ‚The GPT should spread to
most sectors‛. It has an impact on technical change and productivity growth across
a large number of industries; (2)
Improvement
: ‚The GPT should get better over
time and, hence, should keep lowering the costs of its users‛. It should lead to
sustained productivity growth and cost reductions in several industries; (3)
Innovation spawning
: ‚The GPT should make it easier to invent and produce new
products and processes‛ (
cf.,
Bresnahan & Trajtenberg, 1995). Lipsey
et al.,
(1998:
38) describe other main characteristics of GPTs, such as: the scope for
improvement, wide variety and range of uses during its evolution and strong
complementarities with existing or potential new technologies. Another main
feature of GPTs is a long-run period between their initial emergence as invention
and final commercial introduction in new products (Lipsey
et al.,
1998; 2005).
Rosegger (1980: 198) showed that the estimated time interval between invention
and major innovation is about 50 years:
e.g.
electric motor is about 58 years,
electric arc lights 50 years, telegraph about 44 years, synthetic resins 52 years, etc.
Overall, then, GPTs are a complex technology that induce and affect other
technological innovations/products and/or construct a long-run platform in
communications and energy systems for corporate, industrial, economic and social
change over time (Coccia, 2015). Electricity power, information and
communications technology are regarded as the prototypic General Purpose
Technologies (Jovanovic & Rousseau, 2005).
3. Study design and methodology
Firstly
, to develop a theoretical framework for the technological analysis and
representation of the evolution of GPTs over the long run,this study describes
complex drivers of GPTs with a general overview of the socio-economic literature.
Secondly
, this study systematizes the
plexus
(interwoven combination) of drivers
of GPTs by using a fishbone diagram, which can provide an appropriate visual
representation of determinants underlying source and evolution of GPTs. Fishbone
diagrams (also called Ishikawa diagrams or cause-and-effect diagrams) is a
graphical technique to show the several causes of a specific event or phenomenon
(fig. 1). In particular, a fishbone diagram (the shape is similar to a fish skeleton) is
a common tool used for a cause and effect analysis to identify a complex interplay
of causes for a specific problem or event. This causal diagram was created by
Ishikawa (1990) in the research field of management.
Figure 1.
A Fishbone Diagram
Journal of Social and Administrative Sciences
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294
As a matter of fact, this Cause and Effect Analysis was originally developed as
a quality control tool, such as product design and quality defect prevention, to
identify potential factors causing an overall effect. Each cause is a source of
variation of the phenomena understudy. Causes are usually grouped into major
categories to identify the overall sources of variation that lead to a main effect (Fig.
1). In general, the Fishbone diagram can be used as an appropriate visual
representation of phenomena that involve the investigation of multiple cause-and-
effect factors and how they inter-relate (
cf.
Ayverdia
et al.,
2014; Buyukdamgaci,
2003; Ishii & Lee, 1996). Ramakrishna & Brightman (1986) compared their own
Fact-Net-Model with Fishbone Diagram, and Kepner and Tregoe Method to show
perceived differences. Overall, then, it seems that fishbone diagram can be an
appropriate tool to represent the inter-related drivers of complex technologies, such
as GPTs.
4. A general description of the plexus of determinants
generating major innovations
The source and evolution of major innovations (
e.g.
GPTs) depend on complex
drivers. Economic literature shows several determinants of GPTs (
cf.
Ruttan, 2006;
Bresnahan & Trajtenberg, 1995; Coccia, 2010; 2014; 2014a; 2015; De Marchi,
2016; Scientometrics, 1984). Some of them are discussed as follows.
4.1. Relevant problem
GPTs are naturally directed to solve critical problems to achieve competitive
advantages of leading nations (Coccia, 2015) or organizations in certain
environments (Atuahene-Gima & Wei, 2011). Usher (1954) explained the
evolution of new technology by using the theoretical framework of the Gestalt
psychology. Usher’s theory of cumulative synthesis is based on four concepts (
see
Basalla, 1988: 23): 1) Perception of the problem: an incomplete pattern in need of
resolution is recognized; 2) Setting stage: assimilation of data related to the
problem; 3) Act of insight: a mental act finds a solution to the problem; 4) Critical
revision: overall exploration and revision of the problem and improvements by
means of new acts of insight. This theory focuses on acts of insight that are basic to
solve problems and generate vital innovations. The main implications of Usher’s
theory are the psychological aspects of invention and the evolution of new
technology with a vital cumulative change (Basalla, 1988: 24). Coccia (2016) also
shows, through an inductive study in medicine, that consequential problems
support the evolution of several radical innovations, such as new and path-breaking
technological trajectories of target therapy in oncology (
cf.,
Coccia & Wang,
2015).
4.2. Geographical factors and temperate climate
Technological innovation is a vital human activity that interacts with
geographic factors and natural environment. Geographical characteristics of certain
areas support concentration and location of innovative activities and are also
determinants of vital technological innovations (Krugman, 1991). The new
geography of innovation analyses several spatial factors relating to the origin and
diffusion of technological innovation,
e.g.,
spatial proximity and agglomeration
(Rosenberg, 1992; Smithers & Blay-Palmer, 2001; Howells & Bessant, 2012). In
particular, new economic geography argues that ‚
all
production depends on and is
grounded in the natural environment‛ (Hudson, 2001: 300). Feldman & Kogler
(2010) claim that the natural advantages of resource endowments and climate in
certain places can induce innovationand economic growth (
cf.,
Moseley
et al.,
2014). Lichtenberg (1960) shows that geographical factors, rather than proximity to
raw materials or markets, influence the production of knowledge and the
cumulative nature of several innovations. Audretsch & Feldman (1996) confirm
that the agglomeration of innovative activities and firms is related to advantages in
Journal of Social and Administrative Sciences
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295
the natural environment, such as available resources and other factors of the
physical geography. In general, the concentration of human and natural resources is
in specific geographical places, such as major cities, long known to be society’s
predominant engines of innovation and growth (Bettencourt
et al.
, 2007). The
climate is also a main geographical factor that affects natural resources, natural
environment and human activities, such as the technical change. Long ago,
Montesquieu (1947[1748]) argued that the climate shapes human attitude, culture
and knowledge in society. Recent economic literature shows that warm temperate
climates have an appropriate natural environment for humans that, by an
evolutionary process of adaptation and learning, create complex societies, efficient
institutions and communications systems. This socio-economic platform supports,
in temperate latitude, the efficient use of human capital and assets that induce
inventions, innovations and their diffusion over time and space (Coccia, 2015a).
4.3. Cultural and religious factors
Barro & McCleary (2003: 760) argue that: ‚successful explanations of
economic performance must go beyond narrow measures of economic variables to
encompass political and social forces‛.In fact, modern literature is also analyzing
social forces of economic development such as the culture (
e.g.
Guiso
et al.,
2006:
23; Maridal, 2013). Weber (1956) discussed how the Protestant religious culture
has affected the economic attitude of people and the entrepreneurship of capitalistic
systems. Current socio-economic research also analyses the religion and culture as
basic drivers of economic growth and innovation (
cf.
Barro & McCleary, 2003;
2005; Guiso
et al.
, 2006; Spolaore & Wacziarg, 2013; Coccia, 2014). Guiso
et al.
(2003) show the interplay between intensity of religious beliefs and people’s
attitudes that are conducive to economic growth (
e.g.,
cooperation, trust, thriftiness,
government, institutions, women’s propensity to work, legal rules, and fairness of
the market). In particular, Guiso
et al.
(2003: 225): ‚find that on average, religious
beliefs are… conducive to higher per capita income and growth… Christian
religions are more positively associated with attitudes conducive to economic
growth‛ (
cf.
Bettendorf & Dijkgraaf, 2010). Religion shapes people’s attitude of
mind, education, culture and institutions of countries and likely is also a main
socio-cultural determinant of the patterns of technological innovation (Coccia,
2014). A study displays that, on average, societies with a predominance of the
Protestant, Jewish and Eastern religions have technological performance higher
than societies with other predominant religious cultures. These results may be due
to fruitful relation between religion and higher education institutions of countries
that support high human capital. In addition, a higher religious fractionalization in
advanced society,
ceteris paribus
, has a positive effect on technological outputs.
This appears to be particularly true among richer and more democratic countries,
which are mainly located in the European and North-American geo-economic areas
(Coccia, 2014). However, these findings are tentative and there is need for much
more detailed research into the relations between religion, culture and innovation
patterns.
4.4. Population and demography
Population growth plays a main role for patterns of technological innovation.
Kuznets (1960) claims that: ‚high population spurs technological change because it
increases the number of potential inventors‛ (as quoted by Kremer, 1993). In
particular, Kuznets (1960: 328) states: ‚Population growth… produces an
absolutely larger number of geniuses, talented men, and generally gifted
contributors to new knowledge whose native ability would be permitted to mature
to effective levels when they join the labor force‛.Moreover, Kuznets (1960) and
Simon (1977) argue that high populations have a higher probability to create
potential inventors because larger populations have proportionally more individuals
with new ideas. In fact, Jones states that: ‚‘More people means more Isaac
Newtons and therefore more ideas’‛ (as quoted by Strulik, 2005: 130). Moreover,
Journal of Social and Administrative Sciences
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296
many inventions and innovations are demand-driven by larger population, and an
active demographic change and high population can play a vital role for supporting
patterns of technological innovation in advanced national systems of innovation
(Boserup, 1981: 5; Coccia, 2014a). Some studies also show that an optimal level of
technological performance in advanced nations is due to positive growth rates of
population but lower than 1% (percentage of annual population growth rates,
Coccia, 2014a). This result confirms the study by Strulik (2005: 129) that: ‚long-
run growth is compatible with a stable population‛.
4.5. Major wars and environmental threats
Ruttan (2006) argues that the war may be one of contributing factors that
generates GPTs. In general, the high mobilization of scientific, technical, and
financial resources during major conflicts might support GPTs. In particular, a
major war, or threat of a major war, may be a vital condition to induce political and
economic institutions of great powers to commit the huge resources necessary to
generate and/or sustain the development of new path-breaking technologies
directed to provide a competitive advantage in aversive environments (Ruttan,
2006). Hence, Ruttan (2006: 184) argues that a
war
and/or
a threat of a majorwar
can support the development of strategic GPTs that subsequently generate clusters
of commercial innovations for the economic progress in society.
4.6. Purpose of global leadership
Coccia (2015) shows that the source of strategic GPTs is,
de facto
, due to
purposeful systems (
e.g.
leading countries), with high economic potential and
purposeful institutions having the purpose of achieving/sustaining a global
leadership that can engender GPTs to cope with consequential environmental
threats and/or to take advantage of important environmental opportunities. Coccia’s
(2015) theory generalizes the Ruttan’s approach, developing the theoretical
framework of global leadership-driven innovation: GPTs are originated by the
purpose of the global leadership of great powers, rather than wars
per se
.
In short, this theory by Coccia (2015) stresses the thesis that the source of GPTs
is due to the purpose of global leadership of great powers which generates a main
impetus for solving relevant and strategic problems during military and political
tensions, such as during the struggle to prove scientific and technological
superiority, and military strength in space between U.S. and Soviet Union in the
1960s. This struggle for global leadership has generated major advances in ICTs
and satellite technology, which are main GPTs in society. Another main example is
given by U.S. Navy's Mobile User Objective System, a current GPT to support
U.S. global leadership and, as a consequence, human progress (Coccia, 2016a).
4.7. Democratization
Democracy can be seen as a set of practices and principles that institutionalize
and protect freedom (Modelski & Perry, 2002; Norris, 2008). Barro (1999: 160)
points out that ‚increases in various measures of the standard of living forecast a
gradual rise in democracy‛. Acemouglu
et al.,
(2008) analyze the relationship
between income per capita and democracy and argue that political and economic
development paths are interwoven. Coccia (2010) shows that democratization is an
antecedent process (cause) to technological and economic change by historical and
statistical analyses. In particular, democratization seems to be a main driving force
for technological change: most free countries, measured with liberal, participatory,
and constitutional democracy indices, have a level of technological outputs higher
than less free and more autocratic countries. As a matter of fact, it seems that
‚democracy richness‛ generates a higher circulation of information and appropriate
higher education systems that, in advanced countries, support high human capital
for fruitful patterns of technological innovation with fruitful effects for the
wellbeing and wealth over the long run (Coccia, 2010).
Journal of Social and Administrative Sciences
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297
4.8. Research policy and national system of innovation
Governments in advanced economies devote much policy attention to
enhancing investment in R&D to support the technical progress. In fact, R&D
plays a key role for supporting both technical innovation and economic growth of
modern economies, and includes expenditures by the industry, government, higher
education and private non-profit sectors (
cf.
Jones & Williams, 1998: 1133
ff
;
Coccia, 2012).
Griffith
et al.,
(2004) display that R&D has a direct effect on the growth of the
Total Factor Productivity (TFP) across several OECD countries. Instead,
Mamuneas & Nadiri (1996, p.57) claim that: ‘The optimal mix of… [incremental
R&D tax credit and immediate deductibility provision of R&D expenditures] is an
important element for sustaining a balanced growth in output and productivity in
the manufacturing sector’’. Zachariadis (2004) investigates the relationship
between TFP and R&D investment and finds a positive relation between these
variables (
cf.
Goel
et al.
, 2008). Instead, Coccia (2012) shows that when R&D
spending of business enterprise sector exceeds R&D spending of government
sector, the labor productivity and GDP tend to growth,
ceteris paribus
. Moreover, a
range of R&D investment as percentage of GDP between 2.3 per cent and 2.6 per
cent seems to maximize the long-run impact on productivity growth of advanced
countries (Coccia, 2009). This finding is the key to explain the political economy
of R&D for sustained productivity, accumulation of scientific and technical
knowledge, as well as of technology improvements that are becoming more and
more necessary to modern economic growth of nations over time.
5. A comprehensive theoretical framework to represent the
drivers of GPTs: the Fishbone diagram
This study suggests a comprehensive theoretical framework to represent and
analyze the drivers of GPTs that explain the social and economic change over time.
In particular, an appropriate visual representation of the complex drivers of major
innovations can be the fishbone diagram. Figure 2 shows this comprehensive
theoretical framework (Fishbone diagram) to explain the source and evolution of
GPTs over time.
Figure 2.
Determinants of the source and evolution of GPTs in advanced nations
represented with the fishbone diagram.
Note: GPT = General Purpose Technology
In particular, the fishbone diagram in Figure 2 shows that the source of GPTs is
due to a complex interplay of causes represented at left, which support the
Journal of Social and Administrative Sciences
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298
evolution of GPTs (hexagon at right). Firstly, the presence of relevant problems in
temperate climate for advanced countries with socioeconomic potential is the first
stage for laying the foundations for a GPT. This condition is a necessary, but not a
sufficient factor because the GPTs need specific socioeconomic and cultural
background represented by high level of democratization and specific predominant
religions, such as Protestant religion that can fruitful affect the higher education
system and culture of human resources in society. However, an appropriate
socioeconomic background is an important base for the source of major
innovations but GPTs thrive mainly when great powers have to achieve and/or
support the purpose of global leadership to cope with consequential environmental
threats and/or take advantage of important opportunities (
e.g.,
during major
conflicts/threats and/or struggle to prove scientific superiority and military
strength). These factors are supported by an efficient and strong national system of
innovation that invests high economic and human resources to solve relevant
problems by creating new technology and, as a consequence, strategic competitive
advantages for sustaining patterns of economic growth. In this context, high growth
rates of population also play a vital role to support the evolution of leading
societies and long-term development of GPT and major technologies.
The sequential and complex factors, represented in Figure 2, are basic for the
source of GPTs that support long-run human development in society.
A final and important implication of this theoretical framework is that some of
the features and determinants that cause GPTs seem to be enduring and invariant
properties of human societies, rather than accidental shocks/events (
cf. also
Wright,
2005). Hence, GPTs seem to have regularity in their historical developmental paths
driven by specific environment in which great powers, with socioeconomic
potential, endeavour to achieve and/or sustain the purpose of global leadership.
6. Examples of fishbone diagrams in history of technology
The source of some GPTs visualized with the Fishbone diagram is represented
as follows.
Drivers of Steam Engine in England
Figure 3.
Determinants of the source of Steam engine from 1700s with the fishbone
diagram
IMPACT
in Society
Journal of Social and Administrative Sciences
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299
The sources of the GPT of Information and Communications Technologies
(ICTs) in the U.S. A.
Figure 4.
Determinants of the source of ICTs from 1950s represented with fishbone
diagram
7. Conclusions
History of technology shows that GPTs create strategic platforms for several
products/processes such as in communications and transportation technology for
lung-run human development (Singer
et al.,
1956). In general, GPTs are driven by
a large number of factors and it is important a simple visual representation for
explaining their source and evolution over time. What can be learned from
fishbone diagram designed here to represent the determinants of GPTs?
A main finding of this study is that the fishbone diagram offers an appropriate
theoretical framework for a visual representation and technological analysis of
complex factors of major innovations over time. This tool shows clearly and
simply the sequential and inter-related determinants of the source and evolution of
GPTs over time and space.
In particular,
(1) The conceptual framework here shows a visual representation of complex
and inter-related factors driving GPTs with a cause-effect approach over the long
run;
(2) The visual representation here is able to show similar drivers of several
GPTs and to detect regularity of sources over time and space;
(3) The visual representation here is able to explain
how
and
why
GPTs thrive
in specific geo-economic areas and time period.
The theoretical framework of this study satisfies main concepts of the
philosophy of science, such as
consilience, simplicity and analogy
(Thagard, 1988,
Chp. 5). In particular,
This conceptual framework seems to be consilient, since it explains a greater
number of similar drivers for different GPTs in the history of technology.
The simple elements of the study here are well known in economic and
managerial literature. The idea that GPTs is associated to different factors is not
new, however, the idea that a fishbone diagram can provide an appropriate visual
representation of sequential and inter-related drivers of GPTs has not been used in
current literature to display and explain the complex source of major innovations.
Journal of Social and Administrative Sciences
JSAS, 4(4), M. Coccia, p.291-303.
300
The characteristic of
analogy
of results is well-established by using the
Fishbone diagram for representing and explaining the source of different major
technologies at micro- and macro-level of analysis. In short, the fishbone diagram
seems to be a general tool for technological analyses of sources of GPTs and other
new technologies.
The findings of this study also show that some determinants of new technology
can be contest-dependent, whereas other ones can be invariant factors for the origin
of GPTs over time and space. Future research on these topics, to reinforce this
study, should (1) focus on additional and intervening factors affecting the source of
GPTs; (2) measure the evolution of GPTs and derived technological trajectories by
using phylogenetic approaches.
Overall, then, the study here seems to establish a general comprehensive
theoretical framework for an appropriate visual representation and technological
analysis (the fishbone diagram) of the complex drivers of major innovations over
time (
e.g.
, GPTs). However, we know that other things are often not equal over
time and place in the history of technology and therefore results here are tentative.
In fact, Wright (1997: 1562) properly claims that: ‚In the world of technological
change, bounded rationality is the rule‛. More fine-grained studies will be useful in
future, ones that can more easily examine other complex predictors of emerging
GPTs.
Journal of Social and Administrative Sciences
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References
Acemoglu, D., Johnson, S., Robinson, J.A., & Yared, P. (2008). Income and democracy,
American
Economic Review
, 98(3), 808-842. doi. 10.1257/aer.98.3.808
Atuahene-Gima, K., & Wei, Y. (2011). The vital role of problem-solving competence in new product
success,
Journal of Product Innovation Management
, 28(1), 81-98. doi. 10.1111/j.1540-
5885.2010.00782.x
Audretsch, D.B., & Feldman, M.P. (1996). R&D spillovers and the geography of innovation and
production,
The American Economic Review
, 86(3), 630-640.
Ayverdia, L., Nakiboğlu, C., & Aydın, S.Ö.Z. (2014). Usage of Graphic Organizers in Science and
Technology Lessons‛,
Procedia - Social and Behavioral Sciences
, 116, 4264-4269. doi.
10.1016/j.sbspro.2014.01.929
Barro, R.J. (1999). Determinants of democracy,
Journal of Political Economy
, 107(6), 158-183. doi.
10.1086/250107
Barro, R.J., & McCleary, R. (2003). Religion and economic growth across countries,
American
Sociological Review
, 68(5), 760-781. doi. 10.2307/1519761
Barro, R.J., & McCleary, R. (2005). Which countries have state religions,
Quarterly Journal of
Economics
, 120(4), 1331-1370. doi. 10.1162/003355305775097515
Basalla G. (1988)
The History of Technology
, Cambridge University Press, Cambridge.
Bettencourt, L.M.A, Lobo, J., Helbing, D., Kühnert, C., & West, G.B. (2007). Growth, innovation,
scaling, and the pace of life in cities,
PNAS
, 104(17), 7301-7306. doi. 10.1073/pnas.0610172104
Bettendorf, L., & Dijkgraaf, E. (2010). Religion and income: Heterogeneity between countries,
Journal of Economic Behavior & Organization
, 74(1-2), 12-29. doi. 10.1016/j.jebo.2010.02.003
Boserup, E. (1981)
Population and Technological Change: A Study of Long-term Trends,
University
of Chicago Press, Chicago.
Bresnahan, T. (2010). General purpose technologies,
in
B.H. Hall & N. Rosenberg (eds.)
Handbook
of the Economics of Innovation,
Ch. 18, Vol. 2, Elsevier.
Bresnahan, T.F., & Trajtenberg M. (1995). General purpose technologies: ‘engines of growth’?,
Journal of Econometrics, Annals of Econometrics
, 65(1), 83-108. doi. 10.1016/0304-
4076(94)01598-T
Buyukdamgaci, G. (2003) Process of organizational problem definition: how to evaluate and how to
improve,
Omega,
31(4), 327-338.
Calvano, E. (2007). Destructive creation, Stockholm School of Economics.
Working Paper Series in
Economics and Finance,
No.653. [Retrieved from].
Coccia, M. (2005). Measuring intensity of technological change: The seismic approach,
Technological Forecasting and Social Change
, 72(2), 117–144. doi.
10.1016/j.techfore.2004.01.004
Coccia, M. (2005a). Technometrics: Origins, historical evolution and new direction,
Technological
Forecasting & Social Change
, 72(8), 944-979. doi. 10.1016/j.techfore.2005.05.011
Coccia, M. (2005b). Countrymetrics: valutazione della performance economica e tecnologica dei
paesi e posizionamento dell’Italia,
Rivista Internazionale di Scienze Sociali
, CXIII(3), 377-412.
Coccia, M. (2007). A new taxonomy of country performance and risk based on economic and
technological indicators,
Journal of Applied Economics,
10(1), 29-42.
Coccia, M. (2009). What is the optimal rate of R&D investment to maximize productivity growth?,
Technological Forecasting & Social Change,
76(3), 433-446. doi. 10.1016/j.techfore.2008.02.008
Coccia, M. (2009a). Measuring the impact of sustainable technological innovation,
International
Journal of Technology Intelligence and Planning,
5(3), 276-288. doi. 10.1504/IJTIP.2009.026749
Coccia, M. (2010). Democratization is the driving force for technological and economic change,
Technological Forecasting & Social Change
, 77(2), 248-264. doi. 10.1016/j.techfore.2009.06.007
Coccia, M. (2010a). The asymmetric path of economic long waves,
Technological Forecasting &
Social Change,
77(5), 730-738. doi. 10.1016/j.techfore.2010.02.003
Coccia, M. (2010b). Foresight of technological determinants and primary energy resources of future
economic long waves,
International Journal of Foresight and Innovation Policy,
6(4), 225–232.
doi. 10.1504/IJFIP.2010.037468
Coccia, M. (2012). Political economy of R&D to support the modern competitiveness of nations and
determinants of economic optimization and inertia,
Technovation,
32(6), 370–379. doi.
10.1016/j.technovation.2012.03.005
Coccia, M. (2014). Socio-cultural origins of the patterns of technological innovation: What is the
likely interaction among religious culture, religious plurality and innovation? Towards a theory of
socio-cultural drivers of the patterns of technological innovation,
Technology in Society
, 36(1),
13-25. doi. 10.23760/2421-7158.2017.004
Coccia, M. (2014a). Driving forces of technological change: The relation between population growth
and technological innovation-Analysis of the optimal interaction across countries,
Technological
Forecasting & Social Change,
82(2), 52-65. doi. 10.1016/j.techfore.2013.06.001
Coccia, M. (2015). General sources of general purpose technologies in complex societies: Theory of
global leadership-driven innovation, warfare and human development,
Technology in
Society
, 42, 199-226. doi. 10.1016/j.techsoc.2015.05.008
Coccia, M. (2015a). Patterns of innovative outputs across climate zones: the geography of innovation,
Prometheus. Critical Studies in Innovation
, 33, 165-186. doi. 10.1080/08109028.2015.1095979
Journal of Social and Administrative Sciences
JSAS, 4(4), M. Coccia, p.291-303.
302
Coccia, M. (2016). Problem-driven innovations in drug discovery: co-evolution of radical innovation
with the evolution of problems,
Health Policy and Technology
, 5(2), 143-155. doi.
10.1016/j.hlpt.2016.02.003
Coccia, M., & Wang, L. (2015). Path-breaking directions of nanotechnology-based chemotherapy and
molecular cancer therapy,
Technological Forecasting and Social Change,
94, 155–169. doi.
10.1016/j.techfore.2014.09.007
Colombo, M.G., Franzoni, C., & Veugelers, R. (2015). Going radical: producing and transfering
disruptive innovation,
The Journal of Technology Transfer
, 4(4), 663-669. doi. 10.1007/s10961-
014-9361-z
De Marchi, M. (2016). A taxonomy of S&T indicators,
Scientometrics
, 106(3), 1265–1268. doi.
10.1007/s11192-015-1823-z
Feldman, M.P., & Kogler, D. F. (2010). Stylized Facts in the Geography of Innovation,
in
B. Hall &
N. Rosenberg (Eds),
Handbook of Economics of Technical Change
, 1, (pp. 381-410), Elsevier.
Freeman, C., & Soete, L. (1987).
Technical change and full employment,
Basil Blackwell, Oxford,
UK.
Goel, R.J., Payne, J.E., Ram, R. (2008). R&D expenditures and U.S. economic growth: A
disaggregated approach,
Journal of Policy Modeling
, 30(2), 237–250. doi.
10.1016/j.jpolmod.2007.04.008
Goldfarb, B. (2005). Diffusion of general purpose technologies: understanding patterns in the
electrification of US Manufacturing 1880–1930,
Industrial and Corporate Change
14, 745–773.
doi. 10.1093/icc/dth068
Griffith, R., Redding, S., & Van Reenen, J. (2004). Mapping the two Faces of R&D: Productivity
growth in a Panel of OECD Industries,
Review of Economics and Statistics,
86(4), 883–895. doi.
10.1162/0034653043125194
Guiso, L., Sapienza, P., & Zingales, L. (2003). People’s opium? Religion and economic attitudes,
Journal of Monetary Economics
, 50(1), 225-282. doi. 10.1016/S0304-3932(02)00202-7
Guiso, L., Sapienza, P., & Zingales, L. (2006). Does culture affect economic outcomes?,
The Journal
of Economic Perspectives
, 20(2), 23-48. doi. 10.1257/jep.20.2.23
Hall, B.H., & Rosenberg, N. (2010).
Handbook of the Economics of Innovation,
Vol.1; 2, Elsevier.
Helpman, E. (1998).
General Purpose Technologies and Economic Growth
. MIT Press, Cambridge,
MA.
Howells, J., & Bessant,J. (2012). Innovation and economic geography: A review and analysis,
Journal
of Economic Geography
, 12(5), 929-942. doi. 10.1093/jeg/lbs029
Hudson, R. (2001)
Producing Places
, Guildford, NY.
Ishii, K., & Lee, B. (1996). Reverse fishbone diagram: a tool in aid of design for product retirement,
Proceedings ASME design engineering technical conferences and computers in engineering
conference
, 96-DETC/DFM-1272.
Ishikawa, K. (1990)
Introduction to Quality Control
, Taylor & Francis.
Jones, C.I., & Williams, J.C. (1998). Measuring the social return to R&D,
The Quarterly Journal of
Economics
, 113(4), 1119–1135. doi. 10.1162/003355398555856
Jovanovic, B., & Rousseau, P.L. (2005). General purpose technologies,
in
P. Aghion & S.N. Durlauf
(Eds),
Handbook of Economic Growth
, Volume 1B. Elsevier.
Kremer, M. (1993). Population growth and technological change: one million B.C. to 1990,
Quarterly
Journal of Economics,
108(3), 681-716. doi. 10.2307/2118405
Krugman, P. (1991).
Geography and Trade
, MIT Press, Cambridge.
Kuznets, S. (1960). Population change and aggregate output,
in
Demographic and Economic Change
in Developed Countries,
Special Conference Series No.11 Universities-National Bureau for
Economic Research (Princeton University Press: Princeton), pp. 324-340. Reprinted in
Economic
Growth and Structure: Selected Essays.
New York: W.W. Norton & Co., 1965.
Li, M. (2015). A novel three-dimension perspective to explore technology evolution,
Scientometrics
,
105(3), 1679-1697. doi. 10.1007/s11192-015-1591-9
Lichtenberg, R.M. (I960)
One-tenth of a Nation: National Fortes in the Economic Growth of the New
York Region,
Harvard University Press, Cambridge, MA.
Lipsey, R.G., Bekar, C.T., & Carlaw, K.I. (1998). What requires explanation?,
in
Helpman E. (ed.)
General Purpose Technologies and Long-Term Economic Growth,
(pp.15-54), MIT Press,
Cambridge, MA.
Lipsey, R.G., Carlaw, K.I., & Bekar, C.T. (2005).
Economic Transformations: General Purpose
Technologies and Long Term Economic Growth
, Oxford University Press, Oxford.
Mamuneas, T.P., & Nadiri, M.I. (1996). Public R&D policies and cost behavior of the US
manufacturing industries,
Journal of Public Policy,
63(1), 57–81. doi. 10.1016/S0047-
2727(96)01588-5
Maridal, J.H. (2013). Cultural impact on national economic growth,
The Journal of Socio-Economics,
47, 136-146. doi. 10.1016/j.socec.2012.08.002
Modelski, G., & Perry, G. III, (2002). Democratization in long perspective revisited,
Technological
Forecasting and Social Change
, 69(4), 359-376. doi. 10.1016/S0040-1625(01)00152-4
Montesquieu, C. (1947).
The Spirit of Laws,
Harper and Row, New York.
Moseley, W.G., Perramond, E., Hapke, H.M., & Loris, P. (2014).
An Introduction to Human-
Environment Geography
, Wiley.
Norris, P. (2008).
Driving Democracy: Do Power-Sharing Regimes Work?
, Cambridge University
Press, Cambridge (UK).
Journal of Social and Administrative Sciences
JSAS, 4(4), M. Coccia, p.291-303.
303
Ramahrishna, H.V., & Brightman, H.J. (1986). The fact-net model: a problem diagnosis procedure,
Interfaces,
16(6), 86–94. doi. 10.1287/inte.16.6.86
Ristuccia, C.A., & Solomou, S. (2014). Can general purpose technology theory explain economic
growth? Electrical power as a case study,
European Review of Economic History
, 18(3), 227-247.
doi. 10.1093/ereh/heu008
Robinson, D.K.R., Ruivenkamp, M., & Rip, A. (2007). Tracking the evolution of new and emerging
S&T via statement-linkages: Vision assessment in molecular machines,
Scientometrics
, 70(3),
831–858. doi. 10.1007/s11192-007-0314-2
Rosegger, G. (1980).
The Economics of Production and Innovation,
Pergamon Press, NY.
Rosenberg, N. (1982)
Inside the Black Box: Technology and Economics,
Cambridge University
Press.
Rosenberg, N.J. (1992). Adaptation of agriculture to climate change,
Climatic Change
, 21(4), 385-
405. doi. 10.1007/BF00141378
Ruttan, V.W. (1997). Induced innovation, evolutionary theory and path dependence: sources of
technical change,
Economic Journal,
107(444), 1520–1529. doi. 10.1111/j.1468-
0297.1997.tb00063.x
Ruttan, V.W. (2001).
Technology, Growth and Development, An Induced Innovation Perspective,
Oxford University Press, New York.
Ruttan, V.W. (2006).
Is War Necessary For Economic Growth? Military Procurement and
Technology Development,
Oxford University Press, New York.
Sahal, D. (1981).
Patterns of Technological Innovation
, Addison-Wesley Publishing Company, Inc.,
Reading, Massachusetts.
Schultz, L.I., & Joutz, F.L. (2010). Methods for identifying emerging General Purpose
Technologies: a case study of nanotechnologies,
Scientometrics
, 85(1), 155–170. doi.
10.1007/s11192-010-0244-2
Scientometrics, (1984). Indicators of measurement of impact of science and technology on socio-
economic development objectives,
Scientometrics
, 6(6), 449-463.
Simon, J.L. (1977)
The Economics of Population Growth
, Princeton University Press: Princeton.
Singer C., Holmyard E.J., Hall A.R., & Williams T.I. (1956).
A History of Technology
, Vol. I and II,
Clarendon Press, Oxford University Press.
Smithers, J., & Blay-Palmer, A. (2001). Technology innovation as a strategy for climate adaptation in
agriculture,
Applied Geography
, 21(2), 175–197. doi. 10.1016/S0143-6228(01)00004-2
Spolaore, E., & Wacziarg, R. (2013). How deep are the roots of economic development?,
Journal of
Economic Literature,
51(2), 1-45. doi. 10.1257/jel.51.2.325
Strulik, H. (2005). The role of human capital and population growth in R&D-based models of
economic growth,
Review of International Economics,
13(1), 129-145. doi. 10.1111/j.1467-
9396.2005.00495.x
Thagard, P. (1988).
Computational Philosophy of Science,
The MIT Press, Cambridge, MA (USA).
Usher, A.P. (1954)
A history of mechanical inventions,
Harvard University Press, Cambridge.
von Hippel E. (1988).
The Sources of Innovation
, Oxford University Press.
Wang, C.-C., Sung H.-Y., & Huang M.-H. (2016). Technological evolution seen from the USPC
reclassifications,
Scientometrics
, 107(2), 537-553. doi. 10.1007/s11192-016-1851-3
Weber, M. (1956).
The Protestant Ethnic and the Spirit of Capitalism,
Unwin, London.
Wright, G. (1997). Towards a more historical approach to technological change,
The Economic
Journal
, 107(444), 1560-1566. doi. 10.1111/j.1468-0297.1997.tb00066.x
Zachariadis, M., (2004). R&D-induced growth in the OECD?,
Review of Development Economics
,
8(3), 423-439. doi. 10.1111/j.1467-9361.2004.00243.x
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