Methodology for Calculating the European Innovation Scoreboard—Proposition for Modification
Abstract
:1. Introduction
2. Innovation and Its Measurement—Conceptual Background
2.1. Innovations—Theoretical Approach
2.2. Innovation Measurement Methods
- The first includes methods examining the innovativeness of a sector or company. In this area, we can distinguish:
- Subject methods, consisting primarily in measuring the number and nature of innovations that actually exist. This method collects special statistical data from both the technical press and reports created by enterprises;
- Object methods, which use specially constructed questionnaires to examine enterprises that have introduced innovations;
- Statistical and mathematical methods, which use both descriptive statistics and statistical inference tools, as well as more advanced techniques, to assess the level of innovation;
- Organizational innovation measurement tools, for which there has been a significant dispersion with the aim of systematizing the tools used to assess the level of innovation in enterprises. Innovativeness, understood as a feature of an innovative enterprise, does not have a synthetic measure and the problem is not systematized. The most commonly used measures are number and type of introduced innovations, number of patent applications, R&D expenditure, the value of sales of new products per employee, and number of new products. Numerous researchers have indicated the relationships between different dimensions, for example:
- ∘
- number of new products introduced, in relation to competition [60];
- ∘
- ∘
- financial performance as a measure of innovativeness [63]; and
- ∘
- measures of the activity of innovative companies, taking into account features of innovative activity (innovative potential, innovative processes, effects of innovative processes), quantitative measures, and their descriptive characteristics [64] (p. 185).
However, the study of relationships between measures has been shown to possess various difficulties. First of all, we are assuming linear relationships, which might not be the case. Secondly, there may be significant relationships between various independent and dependent variables [65]. - The Community Innovation Survey (CIS) is a study of the innovative activity of enterprises, in order to provide information on the innovation of sectors by type of enterprise, different types of innovation, and different aspects of innovation development, such as objectives, sources of information, public financing, and expenditure [66].
- The second group of methods is comprised of tools for researching innovation in economies. It includes, among others:
- Statistical and mathematical methods, using both descriptive statistics and statistical inference, as well as more advanced techniques in the field of data mining. These include distribution series and histograms, statistics of a random variable, estimation and testing of statistical hypotheses, analysis of interdependence of features (independence tests, correlation), econometric models, neural networks, rules, and decision trees [67];
- The Global Innovation Index (GII) uses two groups of indicators: “creating innovation” and “innovative activity”. The index primarily examines five main areas, which have been recognized as the most important domains, thanks to which the innovation of a given country can be determined. These areas include institutions, human resources and research, and infrastructure, as well as market and enterprise development. The sphere of implementation of this index focuses on researching scientific and creative results [68];
- The Global Competitiveness Index (GCI), developed by the World Economic Forum. The structure of this index is based on 12 pillars. Based on these, the economies of individual countries can be classified into three stages of development, in which the economy is driven by basic requirements (factor-driven economies), factors improving efficiency (efficiency-driven economies), or innovativeness (innovation-driven economies). Economies with the highest level of development are driven by innovations and other conditions of the business environment. Thus, for countries aspiring to the classification of their economy at the highest level of competitiveness, it is crucial to acknowledge the weight of innovativeness [69,70].
- The Summary Innovation Index (SII) is used to evaluate the innovativeness of European Union countries and is calculated on the basis of 25 partial indicators. This report distinguishes three main areas influencing innovation: elements necessary for the occurrence of innovation, company operations, and results. They constitute the basis for distinguishing eight dimensions of innovation which, in turn, can be measured by means of the 25 partial indicators [71,72].
- The European Innovation Scoreboard (EIS) provides a comparative analysis of innovation performance in EU countries, other European countries, and their regional neighbors. It assesses relative strengths and weaknesses of national innovation systems and helps countries to identify the areas which they need to address [73].
- Framework condition: The human resources dimension includes three indicators and measures the availability of a high-skilled and educated workforce. Human resources captures new doctorate graduates, population aged 25–34 with completed tertiary education, and population aged 25–64 involved in education and training (Lifelong learning). Attractive research systems includes three indicators and measures the international competitiveness of the science base by focusing on international scientific co-publications, most cited publications, and foreign doctorate students. Innovation-friendly environment captures the environment in which enterprises operate and includes two indicators—broadband penetration among enterprises and opportunity-driven entrepreneurship—measuring the degree to which individuals pursue entrepreneurial activities as they see new opportunities, for example, resulting from innovation.
- Investments: This aspect captures investments made in both the public and business sectors and differentiates between two innovation dimensions: Finance and support includes two indicators and measures the availability of finance for innovation projects by venture capital expenditures, as well as the support given by governments for research and innovation activities, measured by R&D expenditures in universities and government research organizations. Firm investments includes three indicators of both R&D and non-R&D investments that firms make to generate innovations, as well as the efforts enterprises make to upgrade the ICT skills of their personnel.
- Innovation activities: Innovation activities capture different aspects of innovation in the business sector and differentiate between three dimensions. Innovators includes three indicators: measuring the share of firms that have introduced innovations onto the market or within their organizations covering both product and process innovators, marketing and organizational innovators, and SMEs that innovate in-house. Linkages also includes three indicators, measuring innovation capabilities by looking at collaboration efforts between innovating firms, research collaboration between the private and public sector, and the extent to which the private sector finances public R&D activities. Intellectual assets captures different forms of Intellectual Property Rights (IPR) generated in the innovation process, including PCT (The Patent Cooperation Treaty) patent applications, trademark applications, and design applications.
- Impacts: this captures the effects of firm innovation activities and differentiates between two innovation dimensions. Employment impacts measures the impact of innovation on employment and includes two indicators: employment in knowledge-intensive activities, and employment in fast-growing firms operating in innovative sectors. Sales impacts measures the economic impact of innovation and includes three indicators: measuring exports of medium and high-tech products, exports of knowledge-intensive services, and sales due to innovation activities.
- Innovation Leaders are all countries with a relative performance in 2019 above 125% of the EU average in 2019.
- Strong Innovators are all countries with a relative performance in 2019 between 95% and 125% of the EU average in 2019.
- Moderate Innovators are all countries with a relative performance in 2019 between 50% and 95% of the EU average in 2019.
- Modest Innovators are all countries with a relative performance in 2019 below 50% of the EU average in 2019.
3. Determinants Affecting the Summary Innovation Index—Research Results
- Missing data was estimated using the mice package.
- Stepwise regression was used to identify determinants influencing the Summary Innovation Index results using the mass package, and modelling by AIC in a Stepwise Algorithm was chosen.
- Countries were ranked using the linear ordering method and the Hellwig taxonomic measure using the linearOrdering package.
- Kendall’s coefficient of concordance, implemented in the irr package, was used to assess the compliance of the rankings created by the linear ordering method and the European Innovation Scoreboard.
- The countries were divided into groups, based on statistical criteria using the arithmetic mean and standard deviation of the Hellwig synthetic measure.
- The rand index, implemented in the fossil package, was used to assess the compliance of the cluster of countries into four performance groups, according to Hellwig’s synthetic measure, and the method implemented in the European Innovation Scoreboard.
4. Results
4.1. Identification of Determinants Influencing the Position on the European Innovation Scoreboard
- C.3—SMEs innovating in-house
- C.5—Public–private co-publications
- C.7—PCT patent applications
- B.5—Enterprises providing ICT training
- A.6—Foreign doctorate students
4.2. Using the Linear Ordering Method to Create a Country Ranking
- 1.
- Normalization of variables
- 2.
- Pattern co-ordinates
- 3.
- Distance of objects from the pattern
- 4.
- Value of an aggregate variable
>kendall(IRR_kendall_annexC, correct = FALSE) Kendall’s coefficient of concordance W Subjects = 37 Raters = 2 W = 0.991 Chisq(36) = 71.3 p-value = 0.000409 |
4.3. Division of Countries into Four Performance Groups
- Cluster 1: Innovation Leaders;
- Cluster 2: Strong Innovators;
- Cluster 3: Moderate Innovators; and
- Cluster 4: Modest Innovators.
- Cluster 1 (high level):,
- Cluster 2 (higher average level):,
- Cluster 3 (lower average level):, and
- Cluster 4 (low level):,
>rand.index(Cluster$Clusters_Hellwig, Cluster$Clusters_EIS) [1] 0.8498498 |
5. Conclusions
- The use of the stepwise analysis made it possible to select, from among the 27 factors taken into account when creating the European Innovation Scoreboard, those that are not determinants influencing the Summary Innovation Index. These factors were: SMEs innovating in-house, public–private co-publications, PCT patent applications, enterprises providing ICT training, and foreign doctorate students.
- The linear ordering method based on Hellwig’s synthetic measure can be used to create rankings based on measures of innovation.
- Limiting the number of measures of innovation to those that constitute determinants influencing the country’s position in the ranking only slightly changed the way that countries are ranked, as evidenced by the high level of Kendall’s coefficient of concordance (0.991).
- When dividing countries into performance groups using statistical criteria based on the mean and standard deviation, based on Hellwig’s synthetic measure, we obtained a similar division into four performance groups, as evidenced by the high value of the Rand index (0.8498498).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Biographical Note (s)
References
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Steps | Characteristics |
---|---|
Step 1: Setting reference years | For each indicator, a reference year is identified, for all countries, based on data availability for countries for which data availability is at least 75%. For most indicators, this reference year lags one or two years behind the year to which the EIS refers. |
Step 2: Imputing for missing values | Reference year data are then used for “2019”, and so on. If data for a year-in-between are not available, missing values are replaced with the value for the previous year. If data are not available at the beginning of the time-series, missing values are replaced with the next available year. The following examples clarify this step and show how ‘missing’ data are imputed. If data are missing for all years, no data is imputed (i.e., the indicator does not contribute to the Summary Innovation Index). |
Step 3: Identifying and replacing outliers | Positive outliers are identified as those country scores which are higher than the mean across all countries for all years plus twice the standard deviation. Negative outliers are identified as those country scores which are lower than the mean across all countries for all years minus twice the standard deviation. These outliers are replaced by the respective maximum and minimum values observed over all the years and all countries |
Step 4: Transforming data that have highly skewed distributions across countries | For those indicators where the degree of skewness across the full eight-year period is above one, the data are transformed using a square root transformation (i.e., using the square root of the indicator value, instead of the original value). |
Step 5: Determining Maximum and Minimum scores | The maximum score is the highest score found for the eight-year period within all countries (excluding positive outliers). Similarly, the minimum score is the lowest score found for the eight-year period within all countries (excluding negative outliers). |
Step 6: Calculating re-scaled scores | Re-scaled country scores (after correcting for outliers and a possible transformation of the data) for all years are calculated by first subtracting the minimum score and then dividing by the difference between the maximum and minimum score. The maximum re-scaled score is thus equal to 1, while the minimum re-scaled score is equal to 0. For positive and negative outliers, the re-scaled score is equal to 1 or 0, respectively. |
Step 7: Calculating composite innovation indexes | For each year, a composite Summary Innovation Index is calculated as the unweighted average of the rescaled scores for all indicators, where all indicators receive the same weight (which is 1/27 if data are available for all 27 indicators). |
Step 8: Calculating relative-to-EU performance scores | Performance scores, relative to the EU, are then calculated as the Summary Innovation Index (SII) of the respective country divided by the SII of the EU multiplied by 100. Relative performance scores are calculated for the full eight-year period, compared to the performance of the EU in 2012 and, for the latest year, compared to that of the EU in 2019. For the definition of the performance groups, only the performance scores relative to the EU in 2019 are used. |
Codes | Framework Conditions | Codes | Framework Conditions |
---|---|---|---|
A.1 | New doctorate graduates | C.1 | SMEs with product or process innovations |
A.2 | Population completed tertiary education | C.2 | SMEs with marketing or organizational innovations |
A.3 | Lifelong learning | C.3 | SMEs innovating in-house |
A.4 | International scientific co-publications | C.4 | Innovative SMEs collaborating with others |
A.5 | Scientific publications among top 10% most cited | C.5 | Public–private co-publications |
A.6 | Foreign doctorate students | C.6 | Private co-funding of public R&D expenditures |
A.7 | Broadband penetration | C.7 | PCT patent applications |
A.8 | Opportunity-driven entrepreneurship | C.8 | Trademark applications |
B.1 | R&D expenditure in the public sector | C.9 | Design applications |
B.2 | Venture capital investments | D.1 | Employment in knowledge-intensive activities |
B.3 | R&D expenditure in the business sector | D.2 | Employment in fast-growing firms belonging to innovative sectors |
B.4 | Non-R&D innovation expenditure | D.3 | Medium- & high-tech product exports |
B.5 | Enterprises providing ICT training | D.4 | Knowledge-intensive services exports |
D.5 | Sales of new-to-market and new-to-firm innovations |
Start: AIC = 62.29 SII ~ A.1 + A.2 + A.3 + A.4 + A.5 + A.6 + A.7 + A.8 + B.1 + B.2 + B.3 + B.4 + B.5 + C.1 + C.2 + C.3 + C.4 + C.5 + C.6 + C.7 + C.8 + C.9 + D.1 + D.2 + D.3 + D.4 + D.5 | |||
Df | Sum of Sq | RSS | AIC |
- C.3 | 0.006 | 43.864 | 60.296 |
- C.5 | 0.316 | 44.174 | 60.557 |
- C.7 | 0.917 | 44.775 | 61.057 |
- A.6 | 1.769 | 45.628 | 61.755 |
- B.5 | 2.003 | 45.861 | 61.944 |
<none> | 43.858 | 62.292 | |
- A.5 | 7.252 | 51.110 | 65.954 |
- C.9 | 8.466 | 52.324 | 66.822 |
- C.8 | 9.236 | 53.094 | 67.363 |
- D.1 | 10.524 | 54.382 | 68.249 |
- A.8 | 11.923 | 55.781 | 69.189 |
- A.2 | 11.956 | 55.815 | 69.211 |
- A.4 | 12.842 | 56.700 | 69.794 |
- A.3 | 12.932 | 56.791 | 69.853 |
- C.4 | 13.074 | 56.932 | 69.945 |
- B.1 | 13.799 | 57.657 | 70.413 |
- A.7 | 15.148 | 59.007 | 71.269 |
- D.5 | 16.891 | 60.749 | 72.346 |
- C.6 | 18.905 | 62.764 | 73.553 |
- D.4 | 22.482 | 66.340 | 75.603 |
- B.3 | 31.618 | 75.476 | 80.377 |
- A.1 | 32.857 | 76.716 | 80.980 |
- C.2 | 34.014 | 77.873 | 81.534 |
- C.1 | 37.269 | 81.127 | 83.049 |
- D.2 | 54.975 | 98.833 | 90.353 |
- B.4 | 55.262 | 99.121 | 90.460 |
- D.3 | 77.758 | 121.616 | 98.028 |
- B.2 | 112.629 | 156.487 | 107.356 |
Step: AIC = 60.3 SII ~ A.1 + A.2 + A.3 + A.4 + A.5 + A.6 + A.7 + A.8 + B.1 + B.2 + B.3 + B.4 + B.5 + C.1 + C.2 + C.4 + C.5 + C.6 + C.7 + C.8 + C.9 + D.1 + D.2 + D.3 + D.4 + D.5 | |||
Df | Sum of Sq | RSS | AIC |
- C.5 | 0.310 | 44.174 | 58.557 |
- C.7 | 0.912 | 44.776 | 59.058 |
- A.6 | 1.769 | 45.633 | 59.759 |
- B.5 | 2.001 | 45.865 | 59.947 |
<none> | 43.864 | 60.296 | |
+ C.3 | 0.006 | 43.858 | 62.292 |
- A.5 | 7.337 | 51.201 | 64.019 |
- C.9 | 8.900 | 52.764 | 65.131 |
- C.8 | 9.347 | 53.211 | 65.444 |
- D.1 | 10.602 | 54.466 | 66.306 |
- A.8 | 11.981 | 55.844 | 67.231 |
- A.2 | 12.784 | 56.647 | 67.759 |
- C.4 | 13.091 | 56.955 | 67.960 |
- A.4 | 13.122 | 56.986 | 67.980 |
- A.3 | 13.271 | 57.135 | 68.077 |
- B.1 | 13.906 | 57.770 | 68.485 |
- A.7 | 15.526 | 59.389 | 69.508 |
- D.5 | 17.023 | 60.887 | 70.430 |
- C.6 | 19.942 | 63.806 | 72.162 |
- D.4 | 24.141 | 68.005 | 74.520 |
- B.3 | 31.616 | 75.480 | 78.379 |
- C.2 | 34.474 | 78.338 | 79.754 |
- A.1 | 35.744 | 79.608 | 80.349 |
- D.2 | 58.536 | 102.400 | 89.665 |
- B.4 | 58.549 | 102.412 | 89.669 |
- C.1 | 64.458 | 108.322 | 91.745 |
- D.3 | 77.778 | 121.642 | 96.036 |
- B.2 | 121.869 | 165.732 | 107.480 |
Steps | AIC Value | Variables with the Lowest AIC Value |
---|---|---|
Step 1 | AIC = 62.29 | C.3—SMEs innovating in-house |
Step 2 | AIC = 60.3 | C.5—Public–private co-publications |
Step 3 | AIC = 58.56 | C.7—PCT patent applications |
Step 4 | AIC = 57.07 | B.5—Enterprises providing ICT training |
Step 5 | AIC = 56.03 | A.6—Foreign doctorate students |
Codes | Variables | AIC |
---|---|---|
A.5 | Scientific publications among top 10% most cited | 57.082 |
C.9 | Design applications | 58.558 |
A.2 | Population completed tertiary education | 61.229 |
C.4 | Innovative SMEs collaborating with others | 62.729 |
C.8 | Trademark applications | 63.336 |
D.1 | Employment in knowledge-intensive activities | 63.690 |
A.3 | Lifelong learning | 64.401 |
C.6 | Private co-funding of public R&D expenditures | 64.698 |
B.1 | R&D expenditure in the public sector | 65.572 |
A.8 | Opportunity-driven entrepreneurship | 66.114 |
D.5 | Sales of new-to-market and new-to-firm innovations | 71.032 |
A.7 | Broadband penetration | 72.632 |
C.2 | SMEs with marketing or organizational innovations | 73.629 |
D.4 | Knowledge-intensive services exports | 77.696 |
D.2 | Employment in fast-growing firms of innovative sectors | 83.796 |
A.1 | New doctorate graduates | 84.232 |
B.3 | R&D expenditure in the business sector | 87.247 |
B.4 | Non-R&D innovation expenditure | 90.102 |
A.4 | International scientific co-publications | 91.514 |
D.3 | Medium- & high-tech product exports | 93.459 |
C.1 | SMEs with product or process innovations | 94.639 |
B.2 | Venture capital investments | 107.347 |
Call: lm(formula = SII ~ A.1 + A.2 + A.3 + A.4 + A.5 + A.7 + A.8 + B.1 + B.2 + B.3 + B.4 + C.1 + C.2 + C.4 + C.6 + C.8 + C.9 + D.1 + D.2 + D.3 + D.4 + D.5, data = analiza_final_annexC) Residuals: Min 1Q Median 3Q Max −1.8815 −0.8863 −0.1275 0.9664 2.1918 Coefficients: | |||||
Estimate | Std. Error | t value | Pr(>|t|) | ||
(Intercept) | −1.497138 | 2.269953 | −0.660 | 0.520248 | |
A.1 | 0.052394 | 0.012132 | 4.319 | 0.000707 | *** |
A.2 | 0.017388 | 0.009248 | 1.880 | 0.081064 | |
A.3 | 0.023798 | 0.010533 | 2.259 | 0.040343 | * |
A.4 | 0.053959 | 0.010633 | 5.075 | 0.000169 | *** |
A.5 | 0.035840 | 0.027689 | 1.294 | 0.216491 | |
A.7 | 0.014820 | 0.004719 | 3.141 | 0.007224 | ** |
A.8 | 0.031457 | 0.012832 | 2.451 | 0.027964 | * |
B.1 | 0.057414 | 0.024009 | 2.391 | 0.031382 | * |
B.2 | 0.049089 | 0.007161 | 6.855 | 7.88 × 10−6 | *** |
B.3 | 0.079540 | 0.017185 | 4.629 | 0.000391 | *** |
B.4 | 0.035522 | 0.007211 | 4.926 | 0.000223 | *** |
C.1 | 0.103440 | 0.019124 | 5.409 | 9.21 × 10−5 | *** |
C.2 | 0.067355 | 0.020770 | 3.243 | 0.005896 | ** |
C.4 | 0.018243 | 0.008839 | 2.064 | 0.058065 | . |
C.6 | 0.049038 | 0.021385 | 2.293 | 0.037844 | * |
C.8 | 0.037790 | 0.017692 | 2.136 | 0.050832 | . |
C.9 | 0.029158 | 0.019171 | 1.521 | 0.150529 | |
D.1 | 0.031610 | 0.014518 | 2.177 | 0.047059 | * |
D.2 | 0.039411 | 0.009221 | 4.274 | 0.000771 | *** |
D.3 | 0.065950 | 0.012486 | 5.282 | 0.000116 | *** |
D.4 | 0.052524 | 0.014368 | 3.656 | 0.002595 | ** |
D.5 | 0.043760 | 0.014707 | 2.976 | 0.010026 | * |
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.833 on 14 degrees of freedom Multiple R-squared: 0.999, Adjusted R-squared: 0.9974 F-statistic: 628.9 on 22 and 14 DF, p-value: < 2.2 × 10−16 |
Country | Hellwig’s Synthetic Measure | Position in the Ranking by the Linear Ordering Method | Summary Innovation Index | Position in the European Innovation Scoreboard |
---|---|---|---|---|
Switzerland | 0.718 | 1 | 0.837 | 1 |
Sweden | 0.597 | 2 | 0.713 | 2 |
Finland | 0.577 | 3 | 0.709 | 3 |
Denmark | 0.530 | 4 | 0.682 | 4 |
Netherlands | 0.529 | 5 | 0.648 | 5 |
United Kingdom | 0.504 | 6 | 0.613 | 8 |
Germany | 0.502 | 7 | 0.608 | 10 |
Belgium | 0.490 | 8 | 0.615 | 7 |
Austria | 0.470 | 9 | 0.596 | 11 |
Luxembourg | 0.461 | 10 | 0.639 | 6 |
Israel | 0.453 | 11 | 0.563 | 14 |
Ireland | 0.426 | 12 | 0.568 | 13 |
France | 0.420 | 13 | 0.530 | 15 |
Norway | 0.419 | 14 | 0.611 | 9 |
Estonia | 0.401 | 15 | 0.502 | 16 |
Iceland | 0.384 | 16 | 0.579 | 12 |
Portugal | 0.364 | 17 | 0.490 | 17 |
Spain | 0.336 | 18 | 0.432 | 19 |
Slovenia | 0.335 | 19 | 0.431 | 20 |
Czechia | 0.316 | 20 | 0.427 | 21 |
Italy | 0.311 | 21 | 0.420 | 23 |
Lithuania | 0.302 | 22 | 0.404 | 24 |
Cyprus | 0.300 | 23 | 0.451 | 18 |
Greece | 0.275 | 24 | 0.389 | 25 |
Malta | 0.263 | 25 | 0.426 | 22 |
Latvia | 0.215 | 26 | 0.320 | 28 |
Slovakia | 0.214 | 27 | 0.338 | 26 |
Hungary | 0.212 | 28 | 0.337 | 27 |
Poland | 0.206 | 29 | 0.299 | 31 |
Serbia | 0.200 | 30 | 0.313 | 30 |
Croatia | 0.165 | 31 | 0.298 | 32 |
Turkey | 0.163 | 32 | 0.316 | 29 |
Bulgaria | 0.121 | 33 | 0.230 | 33 |
North Macedonia | 0.079 | 34 | 0.226 | 34 |
Ukraine | 0.065 | 35 | 0.167 | 36 |
Montenegro | 0.056 | 36 | 0.220 | 35 |
Romania | 0.029 | 37 | 0.160 | 37 |
Clustered by Hellwig’s Synthetic Measure | Clusters of European Innovation Scoreboard | |
---|---|---|
Cluster 1: Innovation Leaders | Switzerland, Sweden, Finland, Denmark, Netherlands, United Kingdom, Germany | Switzerland, Sweden, Finland, Denmark, Netherlands, Luxembourg |
Cluster 2: Strong Innovators | Luxembourg, Belgium, Norway, Austria, Iceland, Ireland, Israel, France, Estonia, Portugal, Spain | Belgium, United Kingdom, Norway, Germany, Austria, Iceland, Ireland, Israel, France, Estonia, Portugal |
Cluster 3: Moderate Innovators | Cyprus, Slovenia, Czechia, Malta, Italy, Lithuania, Greece, Slovakia, Hungary, Latvia, Serbia, Poland | Cyprus, Spain, Slovenia, Czechia, Malta, Italy, Lithuania, Greece, Slovakia, Hungary, Latvia, Turkey, Serbia, Poland, Croatia |
Cluster 4: Modest Innovators | Turkey, Croatia, Bulgaria, North Macedonia, Montenegro, Ukraine, Romania | Bulgaria, North Macedonia, Montenegro, Ukraine, Romania |
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Bielińska-Dusza, E.; Hamerska, M. Methodology for Calculating the European Innovation Scoreboard—Proposition for Modification. Sustainability 2021, 13, 2199. https://doi.org/10.3390/su13042199
Bielińska-Dusza E, Hamerska M. Methodology for Calculating the European Innovation Scoreboard—Proposition for Modification. Sustainability. 2021; 13(4):2199. https://doi.org/10.3390/su13042199
Chicago/Turabian StyleBielińska-Dusza, Edyta, and Monika Hamerska. 2021. "Methodology for Calculating the European Innovation Scoreboard—Proposition for Modification" Sustainability 13, no. 4: 2199. https://doi.org/10.3390/su13042199
APA StyleBielińska-Dusza, E., & Hamerska, M. (2021). Methodology for Calculating the European Innovation Scoreboard—Proposition for Modification. Sustainability, 13(4), 2199. https://doi.org/10.3390/su13042199