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The Economics and Econometrics of Global Innovation Index

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  • Professor Extraordinarius; College of Graduate Studies; University of South Africa; Pretoria (South Africa) Monarch University; Faculty of Economics and Finance; Zug; Switzerland

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Innovation is understood by different people differently and there is no reason to think otherwise until and unless innovation is objectified. Several measures have been developed to represent innovation in one or the other form. At times, due to dearth of data on innovation, proxies are used that may not represent innovation in its true sense. One of such measure, though unpopular but relevant, is global innovation index (GII). Indexes are used to quantify as well as measure the variability over a period of time in comparison to base values. As the approach of GII is new and promising, the present chapter will attempt to understand the economics of global innovation index for the available data. This will add to the understanding of innovation and may act as a strategy. On the other hand, econometrics as an emerging branch will also be used to identify certain simple hypothesis for the data of global innovation index. The chapter thus aims to delve deeper into the understanding of global innovation index.
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Chapter 66
DOI: 10.4018/978-1-5225-9273-0.ch066
ABSTRACT
Innovation is understood by different people differently and there is no reason to think otherwise until
and unless innovation is objectified. Several measures have been developed to represent innovation
in one or the other form. At times, due to dearth of data on innovation, proxies are used that may not
represent innovation in its true sense. One of such measure, though unpopular but relevant, is global
innovation index (GII). Indexes are used to quantify as well as measure the variability over a period of
time in comparison to base values. As the approach of GII is new and promising, the present chapter
will attempt to understand the economics of global innovation index for the available data. This will
add to the understanding of innovation and may act as a strategy. On the other hand, econometrics as
an emerging branch will also be used to identify certain simple hypothesis for the data of global inno-
vation index. The chapter thus aims to delve deeper into the understanding of global innovation index.
INTRODUCTION
The world is dynamic and the worldly is innovative. The statement may appear ambiguous to people in
general due to the fact that human beings talk about innovation with an obscure understanding. It ap-
pears that in the present era of globalisation and competition, innovation is required at each and every
level. People, business entities, governments, non-government organisations all raise a common voice of
promoting and practicing innovation. It is important to make it crystal clear that innovation is different
at the microeconomic level and different at the macroeconomic level. Researchers and policymakers
alike have attempted to solve the problem of subjectivity and halo effect while talking and discussing
The Economics and
Econometrics of Global
Innovation Index
Badar Alam Iqbal
Monarch University, Switzerland
Mohd Nayyer Rahman
Aligarh Muslim University, India
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The Economics and Econometrics of Global Innovation Index
about innovation. Several proxies, indicators have been developed for the same objective. However,
understanding of these indices is not common.
The mission and vision that came in with positivism, aims to objectify things even if they are too
subjective. While this point will remain debated in the post-positivism era though again in subjective
sense. One such objective measuring index of recent origin is Global Innovation Index (GII) developed
and co-published by Cornell University, INSEAD, and the World Intellectual Property Organization
(WIPO, an agency of the United Nations). The knowledge partners includes The Confederation of
Indian Industry (CII), PricewaterhouseCoopers (PwC) and Strategy and the National Confederation of
Industry (CNI) and Serviço Brasileiro de Apoio às Micro e Pequenas Empresas (SEBRAE). Not only
governments but independent organisations have started using the index in one or the other manner. The
movement in the country ratings is followed by media and officers and is widely reported. This is true
for both developed and developing countries (ET Bureau, 2016).
In the present study, an attempt is made to develop the understanding of Global Innovation Index
with respect to its economics and econometrics. An attempt is made to understand innovation when it
is objective rather than relying on subjective parameters based on perception, hindsight and emotions
regarding innovation. The different positions of the countries would be compared. Econometrics as an
emerging branch will also be used to identify certain simple hypothesis which may clarify the concept
of innovation. The chapter is divided into 6 sections. Section 2 deals with review of existing body of
literature while section 3 discusses the conceptual framework/ theoretical foundations. Section 4 ex-
clusively deals with economics of GII. On the other hand, section 5 elucidates on the econometrics of
innovation with respect to GII. The chapter concludes in section 6.
REVIEW OF LITERATURE
There is no dearth of studies conducted on innovation. There is a generally accepted notion that in-
novation leads to economic growth (Romer, 1990; Bilbao-Osorio and Rodríguez-Pose, 2004; Harris,
2011; Nunes et al., 2012). It includes from a micro perspective, macro perspective, from technological
viewpoint et cetera. Studies have attempted to objectively identify implications of innovation on different
micro and macro variables as well. However, few innovation indices has been popularized as well as ac-
cepted globally. The list of such indices includes International Innovation Index (produced jointly by the
Boston Consulting Group, the National Association of Manufacturers and The Manufacturing Institute),
Global Innovation Index (GII) and Bloomberg Innovation Index. Not much have been discussed about
the indices except quoting the rankings as a passer-by.
The new understanding of innovation is a bit different than the neo-classical understanding. Under
neo-classical economics, innovation was seen as an interaction between producers of innovation and
users of innovation. However, there is overwhelming evidence that the better understanding of innova-
tion develops when national economic structures, institutions (primary and auxiliary) and policies are
considered as well (Lundvall, 2016). In the recent years (particularly in the last decade), it has been
identified that there is a need for adequate statistical data. Researchers working on innovation face the
problem of data inputs to reach to verified conclusions and are not helped with aggregate data availabil-
ity. Thus, researchers have pushed for new indicators of innovation output (Bain & Kleinknecht, 2016).
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The Economics and Econometrics of Global Innovation Index
Studies have identified that a major technological innovation may have a negative short-term impact
on economic activity but will eventually have a positive impact on long term economic growth. This is
due to the time required for adjusting the benefits of technological innovation. In many cases, it has been
observed that the benefits of innovation comes up in the second phase of business cycle and not in the
first one (Aghion & Howitt, 2000). A review of 291 innovation related publications identified the frequent
usage of four-stage model in innovation. The linear process consists of stages such as – (1) obtaining,
(2) integrating, (3) commercialising external innovations combined with (4) interaction between entity
and collaborators. It was identified that less effort has been given to the competencies and culture that
pushes innovation (West & Bogers, 2014).
Quantification of innovation is not a recent phenomenon and new branches have added to the discipline
and understanding of innovation. For example, from within econometrics branch, spatial econometric
tools have been introduced in the field of innovation with inherent objective of quantification. Before
this, much of the work on linking innovation with econometrics has been credited to Anselin (1988)
and Paelinck and Klaassen (1979) (Autant-Bernard, 2012). There has been attempts made to quantify
innovation. One of such metrics developed is Potential Innovation Index (PII) focusing on innovation
management in SMEs. For development of the metrics six innovation practices were weighted such as
Creativity and Concept Generation, New Product development and Project Management. The conclusions
drawn from the sample was that all the SMEs of traditional sectors have a very low level of innovation.
Of the sample companies, 95% fell into the two lowest categories of innovation while 55% in the worst
category (Galvez, Camargo, Rodriguez, & Morel, 2013).
A study on the innovation differences between UK and Spain revealed that UK and Spanish firms
are at different levels of innovation. For the purpose, a two-step Heckman model was used based on
homogeneous information extracted from the European Community Innovation Survey. The research
was able to identify the role of location as an additional factor in expounding asymmetries between
technologically advanced and backward regions. Thus the outcome bolstered the argument that country
of origin is important (Mate-Sanchez-Val & Harris, 2014). This may be extended to the importance of
studying innovation at country level. Moreover, for this innovation indices applicable at global level must
be used as well as studied. In another recent research, the indicators for innovation were divided into two
categories, that is, innovation efforts indicators and indicators of innovation results. The indicators for
innovation efforts (IE) included human resources dedicated R&D, financial investment in R&D, type of
investment, organisational configuration favourable to innovation, physical structure destined to R&D&I,
organizational culture aimed at innovation, maturity in innovation processes and technology and innova-
tion management practices. On the other hand, the indicators for innovation results (IR) included number
of innovation projects, percentage of revenue obtained with new products and services, cost economy
with new products and services, selling of technology to others, number of patents required/ceded and
prizes received as a result of innovations (Saraceni, Martins De Resende, Serpe, & De Andrade, 2015).
However, the study focused too much on firms of Brazil rather than focusing on the country itself. Thus,
there is a need to study innovation at country level and global innovation index is befitting for this objec-
tive. Though there is no publication as such on GII yet is has been widely cited as and when required
by researchers. This gives GII a credibility and becomes in turn a plausible argument if not probable
argument for studying its nuances.
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The Economics and Econometrics of Global Innovation Index
THEORETICAL FOUNDATIONS PERTAINING TO GII
The launch of GII comes at a time when innumerable measures of innovation are prevalent but are limited
to few indicators. The simple definitions yet multitude ones creates the problem of continuous evaluation
and GII attempts to fill this problem gap. The conventional mode of identifying innovation inputs and
innovation outputs fails to focus on the innovation climate and innovation infrastructure. The GII use
the notion of innovation as stated in Oslo Manual (Mortensen & Bloch, 2005) in the following words:
An innovation is the implementation of a new or significantly improved product (good or service), a
new process, a new marketing method, or a new organizational method in business practices, workplace
organization, or external relations.
GII shifts its approach from focusing too much on the traditional variable of R&D irrespective of the
nature of obscurity. However, it should be included and considered that official objective data related
to innovation is largely scarce.
The argument by proponents is that still, no data is available for the variable “amount of innovative
activity” for any country (Cornell University, INSEAD, and WIPO, 2016). Figure 1 presents the pictorial
view of theoretical foundation related to GII. GII is based on the average of the innovation efficiency
ratio (ratio of the Output Sub-Index to the Input Sub- Index). The innovation efficiency ratio is further
calculated based on innovation input sub-index and innovation output sub-index. Thus, considering
both innovation inputs and innovation outputs as important indicators. The variable included under in-
novation inputs include institutions, human capital and research, infrastructure, market sophistication
and business sophistication. The logic behind innovation inputs is that these proxies’ help to capture the
potential drivers of innovation and without the support of these there can be no expectation for innova-
tion outputs. The sub-variables within institutions are themselves proxies such as political environment,
regulatory environment, and business environment. In order to maintain a symmetry, all sub-variables
contains three proxies. A comparison with traditional innovation metrics will show that a minute scale is
dedicated to R&D while traditionally it has been the sole and major contributor. R&D remains only one
proxy under the head human capital and research, the other two being education and tertiary education.
The plausible argument that infrastructure supports and pushes innovation motivates to consider ICTs,
general infrastructure, and ecological sustainability. The two other variables of paramount importance
are market sophistication and business sophistication, both used as indicators of market and business
innovation inputs. The market sophistication includes the credit availability, investment and “trade, com-
petition & market scale”. While on the other hand, business sophistication includes knowledge workers
and knowledge absorption. The innovation output sub-index (a proxy for innovation outputs) includes
knowledge and technology outputs and creative outputs. The former further considers knowledge cre-
ation, knowledge impact, and knowledge diffusion while the latter considers intangible assets, creative
goods and services and online creativity. All the variables whether in the index or in sub-index works
at the national level and not on the firm level.
The theoretical foundations highlight that all the variables used in the global innovation index are
macroeconomic but are the aggregation of micro variables. Thus, the variables of the index reflect both
the macroeconomic perspective as well as microeconomic perspective.
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The Economics and Econometrics of Global Innovation Index
ECONOMICS OF GLOBAL INNOVATION INDEX
This section is dedicated to understanding the economics of GII. By economics, it is meant that what
are the trends in the GII and on what economic variables is GII focusing. It is to differentiate between
economic and non-economic variables. The two categories of innovation inputs sub-index and innovation
outputs sub-index has a multitude of variables within themselves. However, only economic variables
need to be identified and discussed further. Table 1 shows the list of economic variables used in GII.
Table 1 highlights the variables directly associated with the economics of the nation. These variables
are in one or the other form reflective of one or the other form of a macroeconomic variable. For example,
most of the economic variable used are calculated with the help of GDP, FDI et cetera. This is the simple
usage of macroeconomic variables. Thus, global innovation index is reflective of the national position
due to the reason that it is using macroeconomic variables. Out of the total 82 variables, 22 are purely
reflective of the economic variables. In other words, 18% of the variables used are economic in nature.
Figure 1. Theoretical foundation of Global Innovation Index
Source: Reproduced by the authors from Cornell University, INSEAD, and WIPO (2016)
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The Economics and Econometrics of Global Innovation Index
The global innovation index scores are further used to identify anomalies and only those countries are
considered which have data available for all four years (2013, 2014, 2015, 2016). Table 2 shows the list
of top five scorers in the sample years.
From Table 2 it is crystal clear that the highest score has been 66.6 on a scale of 100 achieved by
Switzerland in 2013 and it has retained the first position on the global innovation index. Sweden and
UK have shared interchangeably the places second and third for the sample years. Netherlands has been
at fourth spot for 2013 and 2015 but at fifth place in 2014. However, in 2016 it is out from the top five
Table 1. Economic variables identified in GII
Variable/proxy Category (immediate) Sub-index category Innovation efficiency
index category
Expenditure on education, % GDP Education Human capital & research pillar Innovation inputs
Gov’t expend. on edu./pupil, secondary Education Human capital & research pillar Innovation inputs
Gross expenditure on R&D, % GDP Research and development
(R&D) Human capital & research pillar Innovation inputs
Global R&D firms, Avg. EXP. top 3, mn
$US
Research and development
(R&D) Human capital & research pillar Innovation inputs
Gross capital formation, % GDP General infrastructure Infrastructure pillar Innovation inputs
Domestic credit to private sector, % GDP Credit Market sophistication pillar Innovation inputs
Microfinance gross loans, % GDP. Credit Market sophistication pillar Innovation inputs
Market capitalization, % GDP Investment Market sophistication pillar Innovation inputs
Total value of stocks traded, % GDP Investment Market sophistication pillar Innovation inputs
Domestic market scale, bn PPP$ Trade, competition, and
market scale Market sophistication pillar Innovation inputs
GERD performed by business, % GDP Knowledge workers Business sophistication pillar Innovation inputs
Intellectual property payments, % total
trade Knowledge absorption Business sophistication pillar Innovation inputs
High-tech imports less re-imports, % tot.
trade Knowledge absorption Business sophistication pillar Innovation inputs
ICT services imports, % total trade Knowledge absorption Business sophistication pillar Innovation inputs
FDI net inflows, % GDP Knowledge absorption Business sophistication pillar Innovation inputs
Computer software spending, % GDP Knowledge impact Knowledge & technology
outputs pillar Innovation outputs
Intellectual property receipts, % total
trade Knowledge diffusion Knowledge & technology
outputs pillar Innovation outputs
High-tech exports less re-exports, % total
trade Knowledge diffusion Knowledge & technology
outputs pillar Innovation outputs
ICT services exports, % total trade Knowledge diffusion Knowledge & technology
outputs pillar Innovation outputs
FDI net outflows, % GDP Knowledge diffusion Knowledge & technology
outputs pillar Innovation outputs
Cultural & creative services exp., % total
trade Creative goods and services Creative outputs pillar Innovation outputs
Creative goods exports, % total trade Creative goods and services Creative outputs pillar Innovation outputs
Source: Prepared by the researcher from Cornell University, INSEAD, and WIPO (2016)
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The Economics and Econometrics of Global Innovation Index
positions. Finland has achieved the fourth position in 2014 and fifth in 2016. The USA has remained on
fifth for two years (2013, 2015) while at fourth in one year (2016). Figure 2 presents in a nutshell the
top innovators globally.
ECONOMETRICS OF GLOBAL INNOVATION INDEX
In this section, the global innovation index scores are analyzed for simple hypothesis testing in order to
reach to the depth of the scores. This will help in inferring from the GII scores. The results of the top
five performers for the sample period would be compared with the overall sample of 124 countries. For
the ease of analysis and for symmetry only those countries are considered that have GII scores available
for sample period (2013-2016). Even if a country has one score missing it is excluded from the sample.
This is to maintain the purity of the task and to bolster the analysis. Table 3 compares the descriptive of
top performers and sample 124 countries.
Table 2. Top 5 performer countries in GII
Ranks Country Score
2013 Country Score
2014 Country Score
2015 Country Score
2016
1 Switzerland 66.6 Switzerland 64.8 Switzerland 68.3 Switzerland 66.3
2 Sweden 61.4 UK 62.4 UK 62.4 Sweden 63.6
3 UK 61.2 Sweden 62.3 Sweden 62.4 UK 61.9
4 Netherlands 61.1 Finland 60.7 Netherlands 61.6 USA 61.4
5 USA 60.3 Netherlands 60.6 USA 60.1 Finland 59.9
Source: Prepared by the authors
Figure 2. Top performers in GII score
Source: Prepared by the authors from data in Table 2
*For a more accurate representation see the electronic version.
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The Economics and Econometrics of Global Innovation Index
From Table 3 it is clear that the mean values of top performers is quite high when compared with
the sample mean of 124 countries which is just 39.96. The average of the means of the top performers
is also very high when compared with the value of 39.96. The average standard deviation of the top
performers is also less than the sample standard deviation of 1.6. It shows that with respect to the scores
of GII less variation is found among the top performers.
For the sample countries, we need to check for equality of means across 124 countries. The equality
of means is studied as a single factor analysis of variance. The presumption for the test is that if the
subgroups (countries) have the same mean then the variability should be same. Mathematically, the
i
th
observation in subgroup
g
as x i
g, where i ng
= …1, , for groups g G= …1 2, , , . The F-statistic for the
equality of group means is computed as:
FSS G
SS N G
B
W
=
( )
( )
/
/
1
where
N
is the total number of observations. The F-statistic has an F-distribution with
G1
nu-
merator degrees of freedom and
N G
denominator degrees of freedom under the null hypothesis of
independent and identical normal distributed data, with equal means and variances in each subgroup.
Table 4 shows the output of equality tests with respect to mean.
The null hypothesis for the equality of means is “all series in the group have the same mean”. As the
probability value of Anova F-test (0.0000) and Wlech (1951) F-test (0.0000) is less than 0.05, the null
hypothesis is rejected. It means that all the series (countries) have different mean and this shows the
asymmetry in the available data. In the similar fashion, equality of variance is also tested. The objective
of variance equality test is to check the hypothesis that all the variances in subgroups are equal. The
alternative then is that at least one subgroup has a different variance. For this, three alternate methods
are employed. First, one is Barlett Test (Bartlett, 1954; Bartlett & Kendall, 1946) which compares the
log of the weighted average variance with the weighted sum of log of variances. The test is sensitive to
the departures for normality. The second variance test is Levene test (Levene, 1960) based on ANOVA
of the absolute difference from the mean.
G=1
is the numerator degrees of freedom and
N G
is
Table 3. Comparison of descriptive statistics
Descriptive Statistic Sample 124
countries
Top performers
Switzerland Sweden UK USA Netherlands Finland Average
Mean 37.96 66.50 62.43 61.97 60.47 60.40 60.02 61.97
Median 38.09 66.45 62.35 62.15 60.20 60.85 59.95 61.99
Maximum 39.61 68.30 63.60 62.40 61.40 61.60 60.70 63
Minimum 36.04 64.80 61.40 61.20 60.10 58.30 59.50 60.88
Std. Dev. 1.6 1.44 0.90 0.56 0.62 1.45 0.49 0.91
Skewness -0.06 0.12 0.28 -0.65 1.08 -0.89 0.49 0.07
Kurtosis 1.83 1.97 2.01 1.82 2.27 2.14 2.03 2.04
Source: Prepared by the authors
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The Economics and Econometrics of Global Innovation Index
the denominator degrees of freedom. The null hypothesis remains to be of equal variances in the sub-
groups. The third test is Brown-Forsythe test (Brown & Forsythe, 1974a; 1974b) also known as modified
Levene test (modified Levene) test. In this test, unlike absolute mean difference, absolute median dif-
ference is used. The null again remains the same. Table 5 shows the output of the variance equality tests.
With the help of Table 5 it can be inferred that according to Bartlett and Levene statistic, the null
of equality of variance is rejected. This is because in both the cases the probability value is less than
0.05 (0.0000; 0.0000). On the other hand, Brown-Forsythe test statistic accepts the null of equality of
variance. This is due to the reason that probability values is more than 0.05 (0.19). The result form this
is clear that the variance are not equal. Non-equality of variances and means denotes the asymmetry
between the countries with respect to global innovation index scores.
CONCLUSION
The study has been successful in identifying the economic variables relevant to global innovation index
and justifying that GII considers both microeconomic and macroeconomic perspective. The caveat found
in the review is filled with the discussion on GII. GII emerges to be both objective and comprehensive for
national economies. Eventually, the equality tests supports the view that innovation is not geographically
distributed in a symmetry rather in asymmetry. For hypothesis testing all countries were not considered
and they may be in future when data is available of all countries.
Table 4. Test for equality of means between series
Method df Value Probability
Anova F-test (123, 372) 77.45433 0.0000*
Welch F-test* (123, 125.904) 898.9576 0.0000*
*Test allows for unequal cell variances
Analysis of Variance
Source of Variation df Sum of Sq. Mean Sq.
Between 123 63782.16 518.5541
Within 372 2490.528 6.694966
Total 495 66272.69 133.8842
Source: Output generated by the authors through eviews 9.5
*indicates rejection of null hypothesis
Table 5. Test for equality of variances between series
Method df Value Probability
Bartlett 123 489.2877 0.0000*
Levene (123, 372) 5.374785 0.0000*
Brown-Forsythe (123, 372) 1.127997 0.1975
Source: Output generated by the authors through eviews 9.5
*indicates rejection of null hypothesis
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The Economics and Econometrics of Global Innovation Index
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... The final value of GII index is gained by the simple average of obtained sub-index scores. The In papers [10][11][12][13][14][15][16][17][18][19][20][21][22] values for the GII index for different years and countries have been analyzed. ...
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1: Introduction.- 2: The Scope of Spatial Econometrics.- 3: The Formal Expression of Spatial Effects.- 4: A Typology of Spatial Econometric Models.- 5: Spatial Stochastic Processes: Terminology and General Properties.- 6: The Maximum Likelihood Approach to Spatial Process Models.- 7: Alternative Approaches to Inference in Spatial Process Models.- 8: Spatial Dependence in Regression Error Terms.- 9: Spatial Heterogeneity.- 10: Models in Space and Time.- 11: Problem Areas in Estimation and Testing for Spatial Process Models.- 12: Operational Issues and Empirical Applications.- 13: Model Validation and Specification Tests in Spatial Econometric Models.- 14: Model Selection in Spatial Econometric Models.- 15: Conclusions.- References.
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This study considers the role of national differences, derived from structural characteristics in each country, and how they impact on companies’ innovation. To do this we include in a firm-level empirical model of innovation traditional factors impacting on innovation, and measure any differences in these determinants between two countries: the UK (comprising more advanced regions) and Spain (which belongs to the “follower” groups of countries in Europe). Using the European Community Innovation Surveys (CIS4), we select two samples comprising private manufacturing firms and estimate a two-step Heckman model to explain firms’ innovation. Our results suggest that Spanish firms are at a different stage, with Spain lagging behind the UK in terms of being able to benefit from R&D. Thus in Spain, we find that public support is more important in promoting innovation activities; whereas linkages with international markets are more important for companies in the UK. Based on our results, we would argue that in order to reduce the technological gap between these two countries regional policies to promote innovation in Spain should concentrate more on the promotion of market relationships between co-located firms; while a greater exposure to internationalisation would benefit both countries.
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This paper reviews research on open innovation that considers how and why firms commercialize external sources of innovations. It examines both the “outside-in” and “coupled” modes of open innovation. From an analysis of prior research on how firms leverage external sources of innovation, it suggests a four-phase model in which a linear process—(1) obtaining, (2) integrating, and (3) commercializing external innovations—is combined with (4) interaction between the firm and its collaborators. This model is used to classify papers taken from the top 25 innovation journals, complemented by highly cited work beyond those journals. A review of 291 open innovation-related publications from these sources shows that the majority of these articles indeed address elements of this inbound open innovation process model. Specifically, it finds that researchers have front-loaded their examination of the leveraging process, with an emphasis on obtaining innovations from external sources. However, there is a relative dearth of research related to integrating and commercializing these innovations.
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A list is given, for reference, of various approximate tests, based on the asymptotic approximations for likelihood ratios, but with adjusted multiplying factors.
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Four statistics which may be used to test the equality of population means are com-pared with respect to their robustness under heteroscedasticity, their power, and the overlap of their critical regions. The four are: the ANOVA F-statistic; a modified F which has the same numerator as the ANOVA but an altered denominator; and two similar statistics proposed by Welch and James which differ primarily in their approximations for their critical values.The critical values proposed by Welch are a better approximation for small sample sizes than that proposed by James. Both Welch's statistic and the modified F are robust under the inequality of variances. The choice between them depends upon the magnitude of the means and their standard errors. When the population variances are equal, the critical region of the modified F more closely approximates that of the ANOVA than does Welch's.
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Book description: Traditionally, economists have considered the accumulation of conventional inputs such as labor and capital to be the primary force behind economic growth. Now, however, many macroeconomists place technological progress at the center of the growth process. This shift is due to new theoretical developments that allow researchers to link microeconomic aspects of the innovation process with macroeconomic outcomes. Most economists have viewed technological progress as an incremental process. A few have focused on the role of drastic innovations—those that introduce a discontinuity. The contributors to this volume are concerned with the type of drastic innovation called general purpose technologies (GPTs). A GPT has the potential to affect the entire economic system and can lead to far-reaching changes in such social factors as working hours and constraints on family life. Examples of GPTs are the steam engine, electricity, and the computer. The study of GPTs is relatively new. A universal theoretical framework for dealing with GPTs does not yet exist. The essays in this book both further our understanding of GPT-driven economic growth and lay the foundation for further developments of the available frameworks.