research and development ranking by country

Research and development expenditure - Country rankings

Research and development expenditure, percent of gdp, 2021:.

Research & development spending as a share of GDP

What you should know about this indicator, how is this data described by its producer.

Gross domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development.

Limitations and exceptions: Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers.

R&D totals from SNA accounts may differ from these estimates, due in part to the different treatments of software R&D in the totals.

Statistical concept and methodology: The gross domestic expenditure on R&D indicator consists of the total expenditure (current and capital) on R&D by all resident companies, research institutes, university and government laboratories, etc. It excludes R&D expenditures financed by domestic firms but performed abroad.

The OECD's Frascati Manual defines research and experimental development as "creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications." R&D covers basic research, applied research, and experimental development.

(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view

(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.

(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.

The fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:

• Natural sciences;
• Engineering and technology;
• Medical and health sciences;
• Agricultural sciences;
• Social sciences;
• Humanities and the arts.

The data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors.

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Sources and processing, this data is based on the following sources, unesco institute for statistics – world development indicators.

The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

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Research and Development Statistics

OECD Research and Development Statistics (RDS) provide a wide range of recent data on the resources devoted to R&D in all OECD countries and selected non-member economies.

Select a language

Research and development statistics (rds) data.

RDS is based on the data reported to OECD and Eurostat in the framework of the joint OECD/Eurostat international data collection on resources devoted to R&D.

The OECD's R&D statistical series cover four main areas. R&D data are collected at national level through surveys and other sources following the recommendations of the OECD Frascati Manual, which is the internationally recognised standard in this area.

Gross domestic expenditure on research and experimental development (GERD)

These tables cover all R&D carried out on a national territory in the year concerned using various breakdowns. R&D expenditure data are expressed in million national currency, million current PPP USD, and million constant USD (2015 prices and PPPs).

• Gross domestic expenditure on R&D by sector of performance and source of funds
• Gross domestic expenditure on R&D by sector of performance and type of expenditure
• Gross domestic expenditure on R&D expenditure by sector of performance and type of R&D
• Gross domestic expenditure on R&D by sector of performance and field of R&D
• Gross domestic expenditure on R&D by sector of performance and socio-economic objective

R&D personnel

This set of tables covers resources devoted to R&D measured in labour terms, i.e. R&D personnel by sector of employment and various breakdowns. R&D personnel data are expressed in full time equivalents on R&D (FTE) and in head counts.

• R&D personnel by sector of employment and function
• R&D personnel by sector of employment and qualification
• R&D personnel by sector of employment and R&D field

Business enterprise R&D

Datasets on R&D expenditure and personnel are also provided for R&D carried out in the business enterprise sector with detailed data by industry or size class. Additionally R&D in the higher education and private non-profit sectors are also available with detailed data by field of research and development or type of costs.

• Business enterprise R&D expenditure by industry
• Business enterprise R&D expenditure by main activity (focussed) and source of funds
• Business enterprise R&D expenditure by main activity (focussed) and type of expenditure
• Business enterprise R&D expenditure by source and funds and number of person employed
• R&D personnel in the business entreprise sector by main activity

Government budgets on R&D

The R&D expenditure and personnel tables are based on surveys of the units carrying out the R&D and national estimates and provisional data have been included when available. More up-to-date information on government intentions or objectives when committing money to R&D can be derived from budgets. These data are shown in the  government budget allocations for R&D (GBARD) table, which includes the breakdown of government R&D budgets by socio-economic objective (SEO).

Historical R&D expenditure series

In addition to these tables updated once a year, a subset of (discontinued)  historical series  are available:

• Gross domestic expenditure on R-D by sector of performance and source of funds (1963-1980)
• R-D expenditure by sector of performance and type of R-D (1963-1980)
• R-D personnel by sector of employment and occupation (1963-1980)

These data were collected by the OECD following the publication of the OECD Frascati Manual in 1963 and its two subsequent revisions in 1970 and 1974. Due to discontinuities in the data, these historical series are presented separately from the OECD Research and Development Statistics publication. Researchers and the public in general with an interest in this subject are invited to investigate the features of these historical data. Independent efforts to attempt to construct consistent series and derive long-term historical indicators are encouraged for research purposes, subject to appropriate attribution and description of sources.

To complement these historical series, a few datasets reflecting the former data collection based on the 2002 version of the Frascati Manual are also available:

• Business entrerprise R&D expenditure by industry (ISIC rev. 3)
• Business entrerpsie R&D expenditure by industry and source of funds (ISIC rev. 3)
• Business entreprise R&D expenditure by industry and type of cost (ISIC rev. 3)
• Business entreprise R&D personnel by industry (ISIC rev. 3)
• Other national R&D expenditure on R&D by field of science and source of funds
• Other national R&D expenditure on R&D by field of science and type of costs

Related data

• Dataset Analytical Business Enterprise Research and Development The OECD's Analytical Business Enterprise Research and Development (ANBERD) database informs the international monitoring of R&D expenditures across industries.
• Dataset Main Science and Technology Indicators The OECD Main Science and Technology Indicators (MSTI) provide a set of indicators that draw principally on data on financial and human resources devoted to research and experimental development, complemented by patent data and international trade in R&D-intensive industries.
• Dataset Science, Technology and Innovation Scoreboard The STI.Scoreboard provides science and innovation policy makers, analysts and the public at large with a resource to retrieve, visualise and compare statistical indicators of science, technology and innovation (STI) systems across OECD countries and several other economies.

Please contact us directly by e-mail if you have any questions regarding this dataset.

Research And Development Expenditure (% Of GDP) By Country

  markets,   gdp,   labour,   prices,   money,   trade,   government,   business,   consumer,   housing,   taxes,   energy,   health,   climate.

Chart: The Global Leaders in R&D Spending, by Country and Company

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Chart: The Global Leaders in R&D Spending

The countries and firms that put the most into r&d.

The Chart of the Week is a weekly Visual Capitalist feature on Fridays.

Given a choice of being the disruptor or the disrupted, many would prefer to choose the former.

But it’s not as easy as just flipping a switch and subsequently reaping the benefits of a forward-looking vision, new product categories, and forthcoming patents. Instead, an organization has to proactively acquire this innovation and intellectual capital from somewhere .

For many established companies, there is no other choice but to buy innovative startups through M&A to get their innovation fix, though this typically comes at a marked up price. On the flipside, especially for companies in tech, auto, and pharmaceutical spaces, innovation likely has to be in the DNA of the company culture, managerial decisions, and any new talent hired.

Global Leaders in R&D Spending

For those companies that aim to innovate internally, money needs to go into the research and development (R&D) line of their income statement.

Today’s chart looks at the countries that put the most into R&D (in % of GDP terms, and absolute $ terms), as well as the companies that have spent the most on R&D in the last 12 months trailing.

It’s also worth noting that money put into R&D is like any other investment – the money can be used wisely or simply squandered into initiatives that don’t pay off. Despite this, R&D spending is a loose approximation for global innovation.

R&D by Country

Global R&D spending is heavily concentrated in the G20 countries. In total they account for 92% of all R&D spending, and 94% of all patents granted at the U.S. Patent and Trademark office.

Here is a breakdown of R&D spending by GDP of select G20 countries:

By total dollar spend, however, massive economies like the United States, China, and the EU take their rightful place.

R&D by Company

On a company basis, the top five R&D spenders today are mostly technology companies.

Interestingly, that wasn’t always the case – even as short as 10 years ago, the largest R&D spenders tended to be pharmaceutical companies like Pfizer, or automobile manufacturers like GM and Volkswagen.

A final note: Amazon doesn’t list R&D on its income statement directly, and instead classifies everything under a “Technology and Content” line which includes R&D. While it is likely Amazon is an R&D leader, there isn’t an exact number for it as of now.

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Research and development expenditure (% of GDP) - Country Ranking

Definition: Gross domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development.

Source: UNESCO Institute for Statistics (http://uis.unesco.org/)

See also: Thematic map , Time series comparison

More rankings: Africa | Asia | Central America & the Caribbean | Europe | Middle East | North America | Oceania | South America | World |

Development Relevance: Expenditure on research and development (R&D) is a key indicator of government and private sector efforts to obtain competitive advantage in science and technology.

Limitations and Exceptions: Estimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers. R&D totals from SNA accounts may differ from these estimates, due in part to the different treatments of software R&D in the totals.

Statistical Concept and Methodology: The gross domestic expenditure on R&D indicator consists of the total expenditure (current and capital) on R&D by all resident companies, research institutes, university and government laboratories, etc. It excludes R&D expenditures financed by domestic firms but performed abroad. The OECD's Frascati Manual defines research and experimental development as "creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications." R&D covers basic research, applied research, and experimental development. (1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view (2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective. (3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed. The fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are: 1. Natural sciences; 2. Engineering and technology; 3. Medical and health sciences; 4. Agricultural sciences; 5. Social sciences; 6. Humanities and the arts. The data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors.

Aggregation method: Weighted average

Periodicity: Annual

Note: This page was last updated on December 28, 2019

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Research and development expenditure (% of GDP)

All Countries and Economies

Country Most Recent Year Most Recent Value

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How much does your country invest in R&D?

Combien votre pays investit-t-il en r-d , ¿cuánto invierte su país en i+d.

Global spending on R&D has reached a record high of almost US$ 1.7 trillion. About 10 countries account for 80% of spending. As part of the Sustainable Development Goals (SDGs), countries have pledged to substantially increase public and private R&D spending as well as the number of researchers by 2030.

Explore the latest available data from the UNESCO Institute for Statistics (UIS), which is monitoring progress globally towards this key SDG target. To evaluate a country’s commitment to R&D, look at spending as a percentage of GDP. You can then see the actual amounts being spent in purchasing power parity dollars (PPP$).

Les dépenses mondiales de R-D ont atteint un record d’environ 1000, 7 milliards de dollars. Une dizaine de pays concentrent 80% des dépenses. Dans le cadre des Objectifs de développement durable (ODD), les pays se sont engagés à accroître considérablement les dépenses publiques et privées en R-D ainsi que le nombre de chercheurs d’ici 2030.

Explorez les dernières données disponibles de l’Institut de statistique de l’UNESCO (ISU), qui suit les progrès accomplis à l’échelle mondiale pour réaliser cette cible des ODD. Pour évaluer l’engagement d’un pays envers la R-D, regardez ses dépenses en pourcentage du PIB. Vous pouvez voir ensuite les montants réels dépensés en parités de pouvoir d’achat en dollars ($PPA).

El gasto mundial en I+D ha alcanzado la cifra récord de casi 1,7 billones de dólares estadounidenses. Unos 10 países representan el 80% del gasto. Como parte de los Objetivos de Desarrollo Sostenible (ODS), los países se han comprometido a aumentar considerablemente de aquí a 2030 el gasto público y privado en I+D y el número de investigadores.

Examine los últimos datos disponibles del Instituto de Estadística de la UNESCO (UIS), que supervisan el progreso mundial en la consecución de este ODS. Para evaluar el compromiso de un país en materia de I+D, busque el gasto como porcentaje del PIB. Ahí puede ver las cantidades que realmente se gastan, expresadas en dólares de paridad de poder adquisitivo ($PPA).

R&D SPENDING BY COUNTRY

Dépenses de r-d par pays, gasto en i+d por país.

The circles show the amounts countries are spending on R&D in PPP$. Countries farther to the right are spending relatively more in terms of their GDP. Those closer to the top have higher numbers of researchers per 1 million inhabitants.

Les cercles indiquent les montants dépensés par les pays en R-D en $PPA. Les pays les plus à droite dépensent relativement plus en termes de PIB et ceux plus en haut ont le plus grand nombre de chercheurs par million d’habitants.

Los círculos indican el importe del gasto en I+D por país, expresado en $PPA. Los países situados más a la derecha gastan relativamente más, en relación con su PIB. Los que están en la zona superior del gráfico tienen más investigadores por millón de habitantes.

Financial Resources

Ressources financières, recursos económicos, human resources, ressources humaines, recursos humanos.

What do the top 15 countries have in common? Strong spending by the business sector is an underlying factor for success.

Many countries try to stimulate greater investment in both the private and public sectors by setting national targets for R&D spending as a share of GDP. But notice how the rankings change when you switch from GDP to total spending in PPP$.

Quels sont les points communs des 15 premiers pays ? De fortes dépenses du secteur privé sont un facteur de réussite sous-jacent.

De nombreux pays tentent d’inciter les secteurs public et privé à accroître leurs investissements en fixant des objectifs nationaux pour les dépenses de R-D en proportion du PIB. Mais remarquez comment les classements changent quand vous passez du PIB aux dépenses totales en $PPA.

¿Qué tienen en común los 15 países mejor situados? El elevado gasto del sector empresarial es uno de los factores subyacentes del éxito.

Numerosos países tratan de fomentar la inversión, tanto en el sector privado como en el público, fijando objetivos nacionales de I+D como fracción del PIB. Pero comprueba cómo cambia la clasificación cuando se pasa del PIB al gasto total en términos de $PPA.

TOP 15 R&D SPENDERS

Les 15 pays qui dépensent le plus en r-d, los 15 que más gastan en i+d, share of r&d expenditure by the business sector.

The inner circles show the strength of the business sector, either in terms of total spending on R&D in PPP$ or as a share of GDP. Watch the ranking change as you toggle between the indicators.

Les cercles intérieurs indiquent la force du secteur privé en termes de dépenses totales de R-D, en $PPA ou en proportion du PIB. Regardez les classements changer quand vous passez d’un indicateur à l’autre.

Los círculos interiores indican la importancia del sector empresarial, ya sea en términos de gasto total en I+D expresado en $PPA o como fracción del PIB. Compruebe cómo cambia la clasificación al alternar los indicadores.

Media reports are rife with speculation as to when Asia, led by China, will overtake North America and Western Europe in terms of R&D spending. By looking at regional trends over time, we can track the rise of emerging players who have been steadily ramping up their investments and numbers of researchers.

Les médias abondent en spéculations pour savoir quand l’Asie, Chine en tête, dépensera plus que l’Amérique du Nord et l’Europe occidentale en R-D. Pour suivre l’émergence des nouveaux acteurs qui augmentent régulièrement leurs investissements en R-D et leur nombre de chercheurs, consultez les tendances régionales dans le temps.

En la prensa abunda la especulación sobre cuándo Asia, encabezada por China, superará a Norteamérica y Europa Occidental en términos de gasto en I+D. Al examinar las tendencias regionales a lo largo del tiempo, podemos seguir el crecimiento de los nuevos protagonistas que han venido aumentando sus inversiones y el número de sus investigadores.

REGIONAL TRENDS AND RANKINGS

Tendances régionales et classements, tendencias y clasificaciones regionales.

The circles show the share of the world’s researchers. See how the rankings change depending on your selection. For a relative measure of R&D investment, select % of GDP. Look at the current PPP$ to see how much regions are spending on R&D today. By switching to constant PPP$, you can track trends over time without inflation.

Les cercles indiquent la part de chercheurs dans le monde. Voyez comment les classements changent selon votre sélection. Pour une mesure relative de l’investissement en R-D, sélectionnez le pourcentage du PIB. Utilisez les $PPA courantes pour voir combien les régions dépensent en R-D aujourd’hui. Passez en $PPA constantes pour suivre les tendances dans le temps hors inflation.

Los círculos indican porcentajes de investigadores en relación con el total mundial. Compruebe cómo cambia la clasificación, según el círculo que escoja. Para un índice relativo de la inversión en I+D, seleccione el % del PIB. Examine el gasto actual en $PPA y vea cuánto gastan hoy en I+D las regiones. Si se desplaza a valores en dólares constantes ($PPA) podrá examinar las tendencias a lo largo del tiempo, excluida la inflación.

This product presents data from the UNESCO Institute for Statistics (UIS), which is the only agency to produce internationally-comparable indicators on R&D and innovation for countries at all stages of development. The data are collected through global surveys and partnerships with regional organizations.

Ce produit présente les données de l’Institut de statistique de l’UNESCO (ISU), qui est le seul organisme à produire des indicateurs comparables au plan international sur la R-D et l’innovation pour tous les pays à tous les niveaux de développement. Les données sont recueillies à travers des enquêtes mondiales et des partenariats avec des organisations régionales.

Este producto presenta datos del Instituto de Estadística de la UNESCO (UIS), que es el único organismo que genera indicadores de I+D e innovación comparables internacionalmente, para los países de todos los niveles de desarrollo. Los datos se compilan mediante encuestas internacionales e iniciativas conjuntas con organizaciones regionales.

All requests for commercial use and translation rights should be submitted to:  [email protected]

Toutes les demandes pour usage commercial et relatives aux droits de traduction doivent être adressées à :  [email protected]

Cualquier solicitud relativa al uso comercial o los derechos de traducción deberá dirigirse a:  [email protected]

Ready to explore more data? See the barriers facing  Women in Science  and consult our complete database on  R&D and innovation  for more than 200 countries and territories.

Prêt à explorer plus de données ? Découvrez les obstacles que rencontrent  les Femmes en sciences  sur notre base de données complète sur la  R-D et l’innovation  pour plus de 200 pays et territoires.

¿Está dispuesto a examinar más datos? Vea los obstáculos que afrontan  las Mujeres Científicas  y consulte nuestra base de datos sobre  I+D e innovación  para más de 200 países y territorios.

SCIENCE & ENGINEERING INDICATORS

Research and development: u.s. trends and international comparisons.

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Cross-National Comparisons of R&D Performance

Two key indicators of national R&D performance are gross domestic expenditures on R&D (GERD)—a measure of a country’s total R&D investment—and national R&D intensity (GERD-to-GDP ratio)—a measure of a country’s investment in R&D relative to its overall economic activity. Together, they paint a broad picture of the current distribution of global R&D activities and the changing global R&D landscape as countries build capabilities in science and technology to improve their national economy and society.

This section compares R&D performance in the United States with other major R&D-performing nations globally, including China, Japan, South Korea, France, Germany, India, and the United Kingdom as well as key regional and geopolitical groupings, such as the European Union (EU-27) and East-Southeast and South Asia. It also presents cross-national analyses of trends in the composition of R&D by sector and by R&D type.

The national R&D expenditures presented in this report are from the Organisation for Economic Co-operation and Development’s (OECD) Main Science and Technology Indicators and the United Nations Educational, Scientific and Cultural Organization’s (UNESCO) Institute for Statistics. The global R&D total is estimated by NCSES based on these sources and reflects R&D performance by 119 countries with reported annual R&D expenditures of $50 million or more. Main Science and Technology Indicators (September 2021 edition) and from R&D statistics for additional countries assembled by UNESCO’s Institute for Statistics (March 2021 release). Presently, no database on R&D spending is comprehensive and consistent for all nations performing R&D. The OECD and UNESCO databases together provide R&D performance statistics for 163 countries, although the data are not current or complete for all. NCSES’s estimate of total global R&D reflects 119 countries, with reported annual R&D expenditures at or above \$50 million annually, which accounts for most of the current global R&D." data-bs-content="NCSES’s estimates for total global R&D are based on OECD’s Main Science and Technology Indicators (September 2021 edition) and from R&D statistics for additional countries assembled by UNESCO’s Institute for Statistics (March 2021 release). Presently, no database on R&D spending is comprehensive and consistent for all nations performing R&D. The OECD and UNESCO databases together provide R&D performance statistics for 163 countries, although the data are not current or complete for all. NCSES’s estimate of total global R&D reflects 119 countries, with reported annual R&D expenditures at or above \$50 million annually, which accounts for most of the current global R&D." data-endnote-uuid="fdd3a1ee-d294-44c2-80ae-2e2eab5b201a">​ NCSES’s estimates for total global R&D are based on OECD’s Main Science and Technology Indicators (September 2021 edition) and from R&D statistics for additional countries assembled by UNESCO’s Institute for Statistics (March 2021 release). Presently, no database on R&D spending is comprehensive and consistent for all nations performing R&D. The OECD and UNESCO databases together provide R&D performance statistics for 163 countries, although the data are not current or complete for all. NCSES’s estimate of total global R&D reflects 119 countries, with reported annual R&D expenditures at or above $50 million annually, which accounts for most of the current global R&D. These countries account for most of the current global R&D.

R&D expenditures for all countries are reported in current U.S. dollars (not adjusted for inflation) using purchasing power parities (PPPs). PPPs convert different currencies to a common currency while adjusting for differences in price levels between economies. The use of PPPs thus enables direct comparisons of R&D expenditures across countries. (See the Technical Appendix for more details.)

The regional analysis focuses on the regions with the largest R&D expenditures: North America (United States, Canada, and Mexico), Europe (including the EU-27 member countries), and the portion of Asia that includes the regions of East-Southeast Asia (including China, Japan, South Korea, and Taiwan), and South Asia (including India and Pakistan). The groupings of countries into regions are from The World Factbook (CIA 2021) .

Patterns and Trends in Total National R&D

Country and regional patterns in total national r&d, 2019.

The estimated total for global R&D expenditures in 2019 is just over $2.4 trillion ( Figure RD-5 ). Global R&D performance is concentrated in the following geographic regions: East-Southeast and South Asia (combined R&D expenditures of $955.0 billion, or a 39% share of global R&D), North America ($706.1 billion, or 29%), and Europe ($529.6 billion, or 22%). All other regions combined account for 10% of global R&D performance.

Global R&D expenditures, by region: 2019

PPP = purchasing power parity.

Foreign currencies are converted to dollars through PPPs. Some country data are estimated. Countries are grouped according to the regions described by The World Factbook (CIA 2021).

National Center for Science and Engineering Statistics, estimates as of December 2021. Based on data from Organisation for Economic Co-operation and Development, M ain Science and Technology Indicators (September 2021 edition), and United Nations Educational, Scientific and Cultural Organization, Institute for Statistics, Science Technology and Innovation data set (March 2021 release).

Science and Engineering Indicators

R&D performance is even more concentrated when comparing individual countries. The United States and China lead R&D performance globally, jointly accounting for half of global R&D ( Figure RD-6 ). The United States performed $668.4 billion (28%) of global R&D in 2019. trends section. For consistency with international standards, U.S. GERD includes federal capital funding for federal intramural and nonprofit R&D (typically totaling just over \$1 billion annually) in addition to what is reported as U.S. total R&D." data-bs-content="U.S. GERD in this section differs slightly from the U.S. total R&D reported in the U.S. trends section. For consistency with international standards, U.S. GERD includes federal capital funding for federal intramural and nonprofit R&D (typically totaling just over \$1 billion annually) in addition to what is reported as U.S. total R&D." data-endnote-uuid="7947339f-3ff7-40b9-b0a7-46abb64245f0">​ U.S. GERD in this section differs slightly from the U.S. total R&D reported in the U.S. trends section. For consistency with international standards, U.S. GERD includes federal capital funding for federal intramural and nonprofit R&D (typically totaling just over $1 billion annually) in addition to what is reported as U.S. total R&D. China followed, with $525.7 billion (22%) of global R&D.

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GERD and R&D intensity for world's top 17 R&D-performing countries and economies: 2019 or most recent data year

GERD = gross domestic expenditure on R&D; PPP = purchasing power parity.

Top 17 R&D-performing countries or economies (based on annual GERD). Data for most countries are from 2019; data for India, Brazil, and Australia are 1 year or 2 years earlier. National R&D intensity is the ratio of gross domestic expenditures on R&D to gross domestic product.

National Center for Science and Engineering Statistics, National Patterns of R&D Resources (2019–20 edition); Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (September 2021 edition); United Nations Educational, Scientific and Cultural Organization, Institute for Statistics, Science Technology and Innovation data set (March 2021 release).

The next tier of top R&D performers includes Japan (7% of global R&D), Germany (6%), and South Korea (4%), each with R&D expenditures above $100 billion. Together with the United States and China, these countries accounted for two-thirds of global R&D in 2019.

France, India, and the United Kingdom make up the third tier of top R&D performers, each with R&D expenditures above $50 billion, or around 2%–3% of the global R&D total. The fourth tier includes Russia, Taiwan, Italy, and Brazil, each with R&D expenditures from $36 billion to $45 billion, or 1.5%–2.0% of the global R&D total. Canada, Spain, Turkey, the Netherlands, and Australia follow, with R&D expenditures between $22 billion and $30 billion, or about 1% of the global R&D total each.

These top 17 R&D-performing countries collectively performed 87% of the global R&D in 2019 ( Figure RD-6 ). Many other countries also perform R&D but do so at a comparatively much smaller scale ( Table RD-5 ).

International comparisons of gross domestic expenditures on R&D and R&D share of gross domestic product, by region, country, or economy: 2019 or most recent year

GDP = gross domestic product; GERD = gross domestic expenditure on R&D; G20 = Group of Twenty; OECD = Organisation for Economic Co-operation and Development; PPP = purchasing power parity.

a Data for U.S. GERD differ slightly from the U.S. total R&D data tabulated earlier in this report. For better consistency with international standards, U.S. GERD includes federal capital funding for federal intramural and nonprofit R&D, in addition to what is reported as U.S. total R&D.

b Data for the European Union (EU) include the 27 EU member countries.

Year of data is listed in parentheses. Foreign currencies are converted to dollars through PPPs. Countries in this table have an annual GERD of $500 million or more. Countries are grouped according to the regions described by The World Factbook (CIA 2021). Data for Israel are civilian R&D only. See sources below for GERD statistics on additional countries.

Trends in Total National R&D

Total global R&D expenditures continue to rise substantially as countries intensify their R&D efforts. Global R&D expenditures increased more than threefold from 2000 ($725.0 billion) to 2019 ($2.4 trillion) ( Figure RD-7 ). The annual increase in global total R&D averaged 6.9% over the 2000–10 period and 6.2% for 2010–19.

Global R&D expenditures, by region: 2000, 2010, and 2019

The global concentration of R&D performance continues to shift from North America and Europe to the East-Southeast and South Asia regions ( Figure RD-7 ). R&D performed in North America accounted for 40% of the global total R&D in 2000 but only 29% in 2019. Europe accounted for 27% of global R&D in 2000 but declined to 22% in 2019. In contrast, the East-Southeast and South Asia regions accounted for 25% of the global total R&D in 2000, and their global share rose to 39% in 2019.

China accounted for 29% ($492.8 billion) of the global increase in R&D since 2000 ( Figure RD-8 ). The United States accounted for 24% ($399.8 billion), and the EU-27 member countries accounted for 17% ($281.5 billion). The increases of several other major Asian R&D performers were also noticeable: South Korea and Japan jointly accounted for 9% of the increase ($158.3 billion).

Total R&D expenditures and contributions to the increase in worldwide R&D expenditures, by selected region, country, or economy: 2000 and 2019

EU = European Union; PPP = purchasing power parity.

Other East-Southeast and South Asia include Brunei, Cambodia, India, Indonesia, Malaysia, Mongolia, Myanmar, Nepal, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, and Vietnam.

The United States remains the leader among the world’s R&D-performing nations; its rate of increase in R&D expenditures has averaged 4.3% over the 2000–10 period and 5.6% in 2010–19 ( Figure RD-9 ; Table RD-6 ; Table SRD-1 ). R&D expenditures in China continue to increase at the world’s fastest pace. The rate of China’s increase in R&D performance has been remarkably high for many years, although it has slowed down in the last decade, averaging 10.6% annually over the 2010–19 period compared to 20.5% over the 2000–10 period.

Gross domestic expenditures on R&D, by selected region, country, or economy: 1990–2019

Data are for the top eight R&D-performing countries and the EU. Data are not available for all countries for all years. Data for U.S. gross domestic expenditure on R&D (GERD) differ slightly from the U.S. total R&D data tabulated earlier in this report. For better consistency with international standards, U.S. GERD includes federal capital funding for federal intramural and nonprofit R&D in addition to what is reported as U.S. total R&D. Data for Japan from 1996 onward may not be consistent with earlier data because of changes in methodology. Data for the EU include the 27 EU member countries. See also Table SRD-1 .

Comparative growth rates for gross domestic expenditures on R&D and gross domestic product, top R&D-performing countries: 2000–10 and 2010–19

GDP = gross domestic product; GERD = gross domestic expenditure on R&D; PPP = purchasing power parity.

a Data for U.S. GERD differ slightly from the U.S. total R&D data tabulated earlier in this report. For better consistency with international standards, U.S. GERD includes federal capital funding for federal intramural and nonprofit R&D in addition to what is reported as U.S. total R&D.

b Most recent data for India are 2018. The listed growth rates for India for both GERD and GDP are 2010–18.

Table shows the top eight R&D-performing countries in 2019. The growth rates are calculated as compound average annual rates. Year of data is listed in parentheses. By way of comparison, the National Center for Science and Engineering Statistics estimates that the average annual pace of growth of the global total of R&D was 6.9% for 2000–10 and 6.2% for 2010–19.

The latest data show a more pronounced gap in R&D expenditures between the United States and China than previously measured. Indicators 2020 reported China’s R&D expenditures for 2017 at $496.0 billion, or 90% of the U.S. level of $549.0 billion (NSB Indicators 2020 : Figure 4-7 ). In comparison, current data place China’s level of annual total R&D expenditures in 2017 at $420.8 billion, or 76% of the U.S. level of $555.1 billion. This reset in China’s R&D expenditure levels stems entirely from the conversion of China’s R&D data to U.S. PPP dollars following the latest release of PPP data by the International Comparison Program (ICP) at the World Bank. https://www.worldbank.org/en/programs/icp ." data-bs-content="The International Comparison Program (ICP) is a global statistical initiative established to produce, among other measures, PPPs and internationally comparable price level indexes. It is managed by the World Bank with the support of the United Nations Statistical Commission. It is the largest data collection initiative for global price data. More information on the program is available at https://www.worldbank.org/en/programs/icp ." data-endnote-uuid="b01ec8a6-9400-43c8-abcd-651056e91f80">​ The International Comparison Program (ICP) is a global statistical initiative established to produce, among other measures, PPPs and internationally comparable price level indexes. It is managed by the World Bank with the support of the United Nations Statistical Commission. It is the largest data collection initiative for global price data. More information on the program is available at https://www.worldbank.org/en/programs/icp . This release included benchmark PPP data for the new reference year 2017, revised PPP data for reference year 2011, and annual PPPs for non-benchmark years (2012–16). https://www.worldbank.org/en/programs/icp ." data-bs-content="The ICP 2017 results were released in May of 2020. As part of this release, the 2011 benchmark data were also revised to incorporate updated data on expenditures, regional PPPs, population, and market exchange rates. More information on the data, methodology, and ICP revision policy is available at https://www.worldbank.org/en/programs/icp ." data-endnote-uuid="b4e146d1-a789-45e5-99fb-7c6bfbf44a34">​ The ICP 2017 results were released in May of 2020. As part of this release, the 2011 benchmark data were also revised to incorporate updated data on expenditures, regional PPPs, population, and market exchange rates. More information on the data, methodology, and ICP revision policy is available at https://www.worldbank.org/en/programs/icp . According to OECD (2020), the revised PPPs for China imply a higher cost of performing R&D because China’s relative price of investment had been previously underestimated. In addition to the reset in China’s R&D expenditure levels, the U.S. R&D expenditure total for 2017 was also revised upward by several billions of dollars as a result of a number of revisions. Even so, China continues to move closer to the United States ( Figure RD-9 ). China’s total R&D expenditures in 2019 were 79% of the U.S. level.

Among other top R&D-performing Asian countries, the rise in R&D expenditures in South Korea has also been quite high, averaging 10.9% annually over 2000–10 and 7.8% for 2010–19 ( Figure RD-9 ; Table RD-6 ; Table SRD-1 ). India’s increase in R&D expenditures averaged 9.4% annually over 2000–10 and 4.4% for 2010–19. Japan’s corresponding increases of R&D have been considerably slower, at 3.6% and 2.4%, respectively.

Total R&D expenditures by the EU-27 nations have been increasing at an annual average rate of about 5.5% over both 2000–10 and 2010–19, with Germany at 4.9% and 6.1% and France at 4.3% and 4.1%, respectively ( Table SRD-1 ). Over the same periods, the R&D expenditures in the United Kingdom have been increasing at 4.1% and 4.7%, respectively.

Patterns and Trends in National R&D Intensity

Country patterns in national r&d intensity, 2019.

Despite ranking at the top of the R&D-performing countries by total R&D expenditures, the United States ranked ninth in national R&D intensity (the GERD-to-GDP ratio) among the economies tracked by OECD and UNESCO data ( Table RD-5 ). The United States is one of ten countries overall and one of five top R&D-performing countries with R&D intensities above 3.0% ( Figure RD-6 ; Table RD-5 ). Other top R&D-performing countries with R&D intensities above 3.0% are Japan (3.20%), Germany (3.19%), and South Korea (4.64%).

Israel and South Korea have the highest R&D intensities across all countries. Israel continues to hold the top spot, with an R&D intensity of 4.93% ( Table RD-5 ). Israel, however, ranks 19th by total R&D expenditures. South Korea (4.64%) is second and the only country among the largest R&D-performing countries with an R&D intensity above 4.50%. Taiwan comes in third (3.49%), followed by Sweden (3.39%). Other countries with comparatively high R&D intensity ratios are Austria (3.13%) and Switzerland (3.18%). The R&D intensities of the remaining top R&D performers are as follows: China at 2.23%, France at 2.20%, the United Kingdom at 1.76%, and India at 0.65%.

Trends in National R&D Intensity

R&D intensity increased across several of the top R&D-performing countries in 2019 ( Figure RD-10 ). U.S. R&D intensity has ranged from 2.5% to under 3.0% since 2000 and, for the first time, reached 3.13% in 2019 ( Figure RD-10 ; Table SRD-1 ). The U.S. rank in this indicator has changed over time, fluctuating between 8th and 11th (NSB Indicators 2012 , Indicators 2014 , Indicators 2016 , Indicators 2018 , Indicators 2020 ). Despite historically high U.S. R&D intensity levels, these rank changes are not surprising as other countries have been expanding their R&D efforts.

Gross domestic expenditures on R&D as a share of gross domestic product, by selected region, country, or economy: 1990–2019

EU = European Union.

Data are for the top eight R&D-performing countries and the EU. Data are not available for all countries for all years. Data for U.S. gross domestic expenditure on R&D (GERD) differ slightly from the U.S. total R&D data tabulated earlier in this report. For better consistency with international standards, U.S. GERD includes federal capital funding for federal intramural and nonprofit R&D in addition to what is reported as U.S. total R&D. Data for Japan in 1996 onward may not be consistent with earlier data because of changes in methodology. Data for the EU include the 27 EU member countries. See also Table SRD-1 .

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What Country Spends the Most on Research and Development?

Israel and South Korea are the world’s leading spenders on research and development (R&D) as a percentage of gross domestic product (GDP) . In pure dollar terms, however, the United States is consistently the largest spender on R&D. Let's take a look at which other countries are the highest spenders in R&D and how they invest their spending. It's important to note that there can be many criteria to evaluate R&D spend. In this article, we'll primarily look at R&D spending compared to a country's GDP.

Key Takeaways

• Israel and South Korea lead global R&D spending as a percentage of GDP, while the United States dominates in pure dollar terms.
• Israel's growth is fueled by programs like Yozma, attracting foreign investors and promoting venture capital funds.
• South Korea planned a 4.6% increase in 2022 R&D spending, emphasizing global tech leadership and allocating funds to numerous projects.
• The U.S. 2023 budget proposes a significant increase in federal R&D funding, focusing on science, technology, pandemic preparedness, and climate innovation.
• R&D (% of GDP): 5.56%

Israel has seen an extended period of expansion in terms of research and development. The Israeli government has introduced different programs over the last few decades to promote growth, and the business sector has also stepped up. One of the programs with the biggest impact on Israel's growth in R&D is Yozma , which is the Hebrew word for initiative . Yozma invested in venture capital funds and drew foreign investors by offering them insurance on risk.

• R&D (% of GDP): 4.93%

The Korea Presidential Advisory Council on Science and Technology approved a 4.6% increase in 2022 R&D spending, totaling 23.5 trillion won (roughly $20.7 billion). Emphasizing the need for global technology leadership, the council planned to invest in nearly 1,200 projects with 21.3 trillion won allocated to 892 ongoing projects and 1.9 trillion won for 290 new areas.

It may not have made this list based on dollars and GDP, but Switzerland has been recognized as the most innovative country by the World Intellectual Property Organization 13 years in a row, most recently in 2023.

• R&D (% of GDP): 3.46%

The 2024 budget proposed a substantial increase in federal R&D funding. Biden's proposal reached $210 billion and focused on leveraging the full potential of science and technology for the benefit of the American people.

The budget allocated:

• $48.6 billion in the National Institutes of Health
• $16.5 billion for research in climate science and clean energy initiatives
• $2.8 billion for the Cancer Moonshot
• $1.4 billion toward STEM education

While South Korea and Israel lead the spending charge on a percentage of GDP basis, the U.S. is first and China is second on a dollar basis.

• R&D (% of GDP): 3.43%

Belgium's government has consistently evolved as a global innovator, contributing a higher percentage of its GDP every year since 2005. The European Commission notes Belgium as a leader in innovation compared to the rest of the European Union (EU) with the country's strengths residing in finance, firm investments, product innovation, and export of innovative goods. The World Economic Forum (WEF) also notes Belgium as one of the top innovation spenders in Europe.

• R&D (% of GDP): 3.42%

In 2023, the Swedish government spent SEK 221.8 billion for R&D, reflecting an increase of SEK 5.9 billion in fixed prices compared to the previous year. The bump in spending was primarily attributed to the business enterprise sector, which accounted for about 75% of the R&D budget. The higher education and government sectors followed with the second and third highest spending in research and development.

What Country Has the Most Innovative Technology?

There are different ways to measure technological innovation. For example, the World Economic Forum notes 33 technology pioneer companies being in North America with 23 being in Europe.

What Initiatives Does the U.S. Government Take to Support and Promote R&D?

The U.S. government supports and promotes research and development across various industries through initiatives such as the National Institutes of Health (NIH), the National Science Foundation, and the Department of Energy (DOE). These agencies provide funding, grants, and collaborative programs to stimulate innovation in fields ranging from healthcare and biotechnology to energy and technology.

How Does the U.S. Government Facilitate Collaboration Between Industry, Academia, and Research Institutions?

The U.S. government actively fosters collaboration between industry, academia, and research institutions to advance R&D initiatives. Programs like the Small Business Innovation Research and Small Business Technology Transfer facilitate partnerships, while federal grants encourage joint projects that bridge the gap between theoretical research and practical applications.

There's different ways to measure the most innovative countries in the world. One potential measure is to see what country spends the most on research and development. In this article, we've looked at the countries that spend the most on R&D compared to that country's GDP. With this criteria, Israel, Korea, and Sweden top the list.

The World Bank. " Research and Development Expenditure (% of GDP) ."

Yozma. " Go Global From the Start ."

Entrepreneurship Research Center on G20 Economies. " Korea ."

World Intellectual Property Organization. " Global Innovation Index 2023 ."

The White House. " FACT SHEET: President Biden’s 2024 Budget Invests in American Science, Technology, and Innovation to Achieve Our Nation’s Greatest Aspirations ."

The World Bank. " Research and Development Expenditure (% of GDP) - Belgium ."

European Commission. " European Innovation Scoreboard 2023, Country Profile: Belgium ."

World Economic Forum. " These Are the Top 5 Most Innovative Countries in the European Union ."

Statistics Sweden. " Preliminary statistics for 2023 show increased R&D expenditure in Sweden ."

World Economic Forum. " Technology Pioneers 2022 ."

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20 countries that spend the most on research and development.

In this article, we will look into the 20 countries that spend the most on research and development. If you want to skip our detailed analysis you can go directly to the 5 Countries that Spend the Most on Research and Development .

Global R&D Outlook

According to the R&D report by AAAS, global investments in research and development have witnessed a significant surge over the past 20 years. The global R&D investments tripled from $672 billion in 1992 to more than $2.2 trillion by 2021. However, the aftermath of the pandemic hindered this growth, with a decline in annual increases to less than 2% in 2020 and 2021, compared to 3.6% in the last decade. Although there are reporting limitations from China to quantify its data, it is evident that it is the largest contributor to non-OECD investments. OECD countries are continuously increasing their R&D spending. In 2021, the R&D investments by OECD countries reached 2.71%. The business sector is the key driver of these investments, accounting for over 60% of R&D investments in 2020. The R&D intensity of the US surpassed 3% in 2019 after ranging between 2.5% to 3% for over two decades. South Korea and China also experienced substantial growth over the past decade, with their R&D spending surging from 2.1% to 4.6% and from 0.9% to 2.2%, respectively. Germany's research and development spending also reached 3.2% in 2019.

Global Tech Investments

The strong research and development portfolio of countries globally reflects directly on the pace of technological advancements in today's world. With the growing digital and tech adoption, the world is continuously advancing toward key technology trends such as AI, cloud computing, quantum science, and edge computing. The workforce in the tech sector is a major driver of the growth of the tech sector. Tech-related fields experienced a 15% rise between 2021 and 2022. Nearly 1 million job postings were publicized by applied AI and next-generation software development between 2018 and 2022.

On March 5, Reuters reported that China outlined plans to strengthen its self-sufficiency in the tech sector. The country aims to focus on emerging trends such as quantum computing and AI. This plan is a result of the current trade tensions with the US. China will introduce major science and technology programs and utilize its political systems to integrate capabilities and reduce dependency on foreign suppliers. Moreover, it plans to develop a robust pool of researchers and innovators to fuel domestic advancements across various tech domains.

Some of the most technologically advanced countries such as the US, China, and South Korea are also actively investing in AI. According to research from Bloomberg Intelligence, the global AI market is anticipated to reach $1.3 trillion by 2032, growing at a compound annual growth rate of 42%. PwC estimates that AI could lead to a staggering contribution of $15.7 trillion to the global economy by 2030. Additionally, AI is expected to fuel an increase of 26% in the GDP of local economies by 2030. The United States is leading the ongoing AI  race with the highest number of AI startups. For instance, Anthropic is a leading AI startup in the US, backed by Microsoft Corporation (NASDAQ: MSFT ) and Amazon.com, Inc. (NASDAQ: AMZN ). On March 4, Reuters reported that the Silicon Valley startup, Anthropic released its latest suite of AI models, Claude 3. The model claims to outshine competitors such as OpenAI's GPT-4 and Alphabet Inc.'s (NASDAQ: GOOG ) Gemini 1.0 Ultra. The Claude 3 Opus will be ideal when dealing with complex tasks such as financial analysis.

Companies Driving Innovation in the Tech Sector

Some of the top companies driving innovation in the tech sector include Amazon.com, Inc. (NASDAQ:AMZN), NVIDIA Corporation (NASDAQ: NVDA ), and Alphabet Inc. (NASDAQ:GOOG).

On January 19, the global tech leader, Amazon.com, Inc. (NASDAQ:AMZN) announced that AWS will be making a significant investment of 2.26 trillion yen into its cloud infrastructure in Japan by 2027. This investment will contribute to the rising demand for cloud services in the country and also add to Japan's digital transformation.  Moreover, the project will induce a positive economic impact, supporting jobs and fueling GDP.

NVIDIA Corporation (NASDAQ:NVDA) is a leading tech company, specializing in robotics, AI, HPC, and autonomous vehicle technology. On February 6, the company announced that it had allied with digital communications technology company, Cisco Systems, Inc. (NASDAQ: CSCO ) to provide robust and manageable AI infrastructure solutions to businesses. The partnership aims to manage the growing demand for secure and efficient tools to deploy AI at scale. NVIDIA Corporation (NASDAQ:NVDA) will leverage Cisco Systems, Inc.'s (NASDAQ:CSCO) networking capabilities to offer a diverse suite of solutions. It will also integrate its GPUs into Cisco servers, granting access to NVIDIA AI Enterprise software. Moreover, Cisco Systems, Inc.'s (NASDAQ:CSCO) AI-assisted management and monitoring tools will facilitate infrastructure operations.

On February 15, Alphabet Inc. (NASDAQ:GOOG) launched the next generation of its large language model, Gemini 1.5 with significant improvements compared to its predecessors. The new model will deliver improved performance and will have a novel MoE architecture enhancing its efficiency. The Gemini 1.5 will be capable of processing up to 1 million tokens. Alphabet Inc. (NASDAQ:GOOG) has initially released the language model with a standard context window of 128,000 tokens, with restricted access to 1 million tokens through an early access program.

Now, let's have a look at the 20 countries that spend the most on research and development.

Methodology

To compile our list of the 20 countries that spend the most on research and development, we utilized the R&D Expenditure as a percentage of GDP data from the World Bank. However, this data does not tell us about the overall economic size of a country. So, we calculated the absolute spending in USD for each country. We used the percentage share of R&D and the GDP data of the country to calculate the absolute spending, utilizing the latest available year for R&D spending data. Our list ranks the 20 countries that spend the most on research and development in ascending order of their absolute R&D spending.

Note: We have not included Israel in our list despite the highest R&D intensity due to the ongoing war in Gaza hindering economic growth and advancements in the region.

20. Finland

R&D Expenditure as a Percentage of GDP (2021): 2.98% GDP (2021): $296 billion Estimated Absolute R&D Spending: $8.86 billion

Finland is ranked among the countries that spend the most on research and development. In 2021, Finland spent 2.98% of its GDP, amounting to a total of $8.86 billion in spending.

R&D Expenditure as a Percentage of GDP (2021): 1.93%

GDP (2021): $503 billion

Estimated Absolute R&D Spending: $9.75 billion

Norway ranks 19th on our list. In 2021, it spent 1.93% of its GDP on research and development. The total absolute R&D expenditure of the country was $9.75 billion in 2021.

R&D Expenditure as a Percentage of GDP (2021): 1.43%

GDP (2021): $681 billion

Estimated Absolute R&D Spending: $9.78 billion

Poland is one of the highest spenders on R&D in the world. In 2021, the country reported an R&D expenditure of 1.43% of its GDP or $9.78 billion.

17. Denmark

R&D Expenditure as a Percentage of GDP (2021): 2.81%

GDP (2021): $406 billion

Estimated Absolute R&D Spending: $11.41 billion

Denmark is one of the most technologically advanced and innovative countries. In 2021, it spent a staggering $11.41 billion on research and development which was 2.81% of its total GDP.

R&D Expenditure as a Percentage of GDP (2021): 1.40%

GDP (2021): $820 billion

Estimated Absolute R&D Spending: $11.50 billion

Turkey is one of the most developed countries. Its innovation and technological development is evident by its high R&D expenditure. The country spent 1.40% of its GDP on research and development in 2021.

15. Austria

R&D Expenditure as a Percentage of GDP (2021): 3.25%

GDP (2021): $479 billion

Estimated Absolute R&D Spending: $15.61 billion

Austria is one of the most innovative economies in Europe. In 2021, the country spent $15.61 billion on research and development, accounting for 3.25% of its total GDP. It is one of the top spenders on research and development in the world.

R&D Expenditure as a Percentage of GDP (2021): 3.41%

GDP (2021): $592 billion

Estimated Absolute R&D Spending: $20.22 billion

Sweden is ranked among the countries that spend the most on research and development. In 2021, the country had an R&D intensity of 3.41%. It spent $20.22 billion on research and development in 2021.

13. Belgium

R&D Expenditure as a Percentage of GDP (2021): 3.42%

GDP (2021): $601 billion

Estimated Absolute R&D Spending: $20.60 billion

Belgium is one of the most innovative European countries. The country spent 3.42% of its GDP or $20.60 billion on research and development in 2021. It is ranked 13th on our list.

GDP (2021): $1.45 trillion

Estimated Absolute R&D Spending: $20.66 billion

Spain is one of the best countries in the world in terms of technological advancement and quality of life. The country spent 1.43% of its GDP on research and development in 2021. It is ranked 12th on our list.

11. Netherlands

R&D Expenditure as a Percentage of GDP (2021): 2.30%

GDP (2021): $1.03 trillion

Estimated Absolute R&D Spending: $23.77 billion

The Netherlands is ranked 11th among the countries that spend the most on research and development. In 2021, it spent 2.30% of its GDP or $23.77 billion on R&D.

10. Switzerland

R&D Expenditure as a Percentage of GDP (2021): 3.36%

GDP (2021): $813 billion

Estimated Absolute R&D Spending: $27.32 billion

Switzerland is one of the most innovative and technologically advanced countries in the world. In 2021, the country spent a staggering 3.36% of its GDP on research and development.

R&D Expenditure as a Percentage of GDP (2021): 1.45%

GDP (2021): $2.16 trillion

Estimated Absolute R&D Spending: $31.33 billion

Italy ranks 9th among the countries that spend the most on research and development. The country reported an R&D spending of 1.45% or $31.33 billion in 2021.

R&D Expenditure as a Percentage of GDP (2021): 1.69%

GDP (2021): $2.01 trillion

Estimated Absolute R&D Spending: $34.07 billion

Canada is one of the top spenders on research and development in the world. In 2021, the country spent $34.07 billion on R&D, accounting for 1.69% of its GDP.

R&D Expenditure as a Percentage of GDP (2021): 2.2%

GDP (2021): $2.96 trillion

Estimated Absolute R&D Spending: $65.67 billion

France is one of the most innovative economies in Europe. In 2021, the country reported an R&D spending of 2.2% as a percentage of its GDP. It is ranked 7th on our list.

6. South Korea

R&D Expenditure as a Percentage of GDP (2021): 4.93%

GDP (2021): $1.82 trillion

Estimated Absolute R&D Spending: $89.65 billion

South Korea is ranked 6th among the countries that spend the most on research and development. In 2021, South Korea spent $89.65 billion on R&D, accounting for 4.93% of its GDP.

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Statistical assessment of digital transformation in European Union countries under sustainable development goal 9

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• Published: 06 September 2024

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• Barbara Fura   ORCID: orcid.org/0000-0002-9601-6634 1 ,
• Aneta Karasek 2 &
• Beata Hysa 3  

The pivotal role of digital transformation (DT) in contemporary socio-economic development cannot be overstated. This crucial aspect is highlighted in the Agenda 2030, specifically in goal 9 among the 17 objectives. This article presents the results of a study assessing the level of DT in industry, innovation, and infrastructure in the 27 European Union (EU) countries in 2015 and 2020. Central to this study is the proposition of an aggregated Digital Transformation Assessment Indicator (DTAI), serving as a metric to gauge the progression of EU member states. Utilizing this indicator, the article assesses the advancement status of EU countries and orchestrates a comparative ranking of their achievements in fulfilling Sustainable Development Goal (SDG) 9 between 2015 and 2020. Moreover, a classification of countries into analogous groups based on this criterion for both periods is provided. The DTAI is prepared following the methodology of the linear ordering of objects—countries of the EU 27. The zero unitarization method (ZUM) is used as the main ordering method. To compare the results obtained, the DTAI value and classifications of countries in 2015, and 2020, are also presented using Hellwig’s pattern development method. The findings of this investigation underscore the variances existing among the EU 27 nations concerning the implementation of SDG 9. Furthermore, notable fluctuations in ranking positions are also observed. The research outcomes underscore significant challenges in DT implementation, particularly within Central, Eastern, and Southern European nations. The utilized research methodology bears substantial implications for the effective realization of the 2030 Agenda and its corresponding SDGs, both at the individual nation-state level and within the broader framework of the EU.

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1 Introduction

Sustainability is a complex concept and includes economic, environmental, and social aspects (Grzebyk et al. 2023 ). The 17 issues (objectives) for assessing the implementation of the concept of sustainable development (SD) are also complex (Pakkan et al. 2023 ). Filipiak et al. ( 2023 ) note that although SD is recognized as an essential element affecting the economy, not all European Union (EU) countries surveyed consider this factor. Therefore, states, mainly EU member states, evaluate the situation.

Both individual countries and Small and Medium Enterprises (SMEs) are looking for opportunities to gain a competitive advantage, which is why they are attempting to leverage the possibilities afforded by digital transformation (DT). DT is observed in all areas of socio-economic life in both highly developed and less developed countries. This applies in particular to the 27 EU countries. Thanks to DT, SD is possible based on modern solutions implemented in various industries, economic sectors and society.

Due to the complexity of the DT process, it isn’t easy to estimate it, especially concerning the economic and societal challenges (Kolupaieva and Tiesheva 2023 ). The literature on digital business transformation is classified into three clusters based on technological, business and social impact (Kraus et al. 2021 ). Furthermore, digitisation is presented to accelerate a sustainability transition (George and Schillebeeckx 2022 ). Consequently, the subjects of public debate are DT and sustainability (Brenner and Hartl 2021 ; Del Río Castro et al. 2021 ). Moreover, intensifying the DT to ensure sustainability is of interest to intergovernmental bodies, countries and enterprises (Ionascu et al. 2022 ).

Research has examined the significance of digital technologies in achieving economic and sustainability objectives (Nambisan et al. 2019 ; Guandalini 2022 ). This research has highlighted the crucial role of digital technologies, particularly in advanced European countries, in propelling economic growth and sustainability (Bocean and Vărzaru 2023 ). Moreover, it is worth emphazising that DT and sustainability represent paradigmatic economic, social, and ecological changes (Caputo et al. 2021 ). New digital technologies exert increasingly noticeable effects, particularly in developed European countries (Tannady et al. 2023 ). On the other hand, DT supports achieving Sustainable Development Goals (SDGs) through inclusive data collection and analysis by computational techniques to reveal patterns and trends in the environment, human behaviours and experiences that help policymakers monitor progress, establish the proper programs of development and dynamic improvement (ElMassah and Mohieldin 2020 ).

The development of the theory of SD has been undertaken by many researchers (Rosário and Dias 2022 ; Pakkan et al. 2023 ), and the perception of DT in the economy and society as one of its distinguishing features has increased in importance in recent years. The role of DT is noticed by both researchers (Vial 2019 ; Briggs 2020 ; Zaoui and Souissi 2020 ), practitioners (Sabatini, Cucculelli and Gregori, 2022), and decision-makers (Grabowska 2019 ; Nawrocki and Jonek-Kowalska 2022 ; Yang et al. 2023 ). The ‘2030 Digital Compass’ identifies targets for digitisation focusing on data, technology, and infrastructure (European Commission 2021 ).

Digital technologies play a crucial role in social and economic life and are essential to the SD of society and the economy. This is reflected in the SDGs, where goal 9 references “three important aspects of sustainable development: infrastructure, industrialization and innovation”.

The essence of the transformation has been recognized at the EU level, where a plan for realising the EU’s DT by 2030 has been proposed in the form of the document ‘Road to the Digital Decade’. The plan aims to establish a governance framework that will allow Member States to work together to achieve agreed goals. The Digital Decade policy programme, with concrete targets and objectives for 2030, guides Europe’s DT: skills, DT of businesses, secure and sustainable digital infrastructures and digitalization of public services. Similarly, the European Digital Compass indicates the digital goals that should be achieved by 2030, including an increase in digital skills, which 56% of adults currently have, with the aim of 80% (Eurostat database, https://ec.europa.eu/eurostat/data/database ). This is particularly important as many new employment opportunities are emerging through DT.

Therefore, it is essential to monitor the degree of DT, which makes it possible to observe the level of advancement in implementing solutions and identify good practices implemented in individual countries. Therefore, it is reasonable to develop a metric that would synthetically enable the measurement of DT. As we can see, there are different perspectives on DT, but from the literature review, it seems relevant to take into account the other actors and sectors influencing DT (Varakamin et al. 2017 ; Tangi et al. 2020 ). In particular, it seems essential to develop one indicator, which would measure the level of advancement of EU 27 countries in achieving goal 9, which calls for building resilient and sustainable infrastructure and promoting inclusive and sustainable industrialisation. The development of such an indicator would make it possible to compare EU countries in terms of their progress towards SDG 9.

Although you can find in the literature publications attempting to assess the state of progress of countries in individual areas of goal 9, i.e. industry (Zawada 2008 ), innovation (Andrijauskiene et al. 2023 ), and infrastructure (Koszela et al., 2020), there is no research on aggregated indicators that would assess the achievement of these three areas together under goal 9 of the SDGs.

It is worth emphasizing that the different areas of DT interact with each other, hence the need for an aggregated measure. While single measures provide some insight into the phenomenon under study, synthetic measures are superior to them. The advantage of synthetic measures over single indicators lies mainly in the ability to capture information from different areas of the phenomenon under study using a synthetic indicator. This is particularly important when measuring complex phenomena. The synthetic measure would facilitate comparison between countries in implementing SDG 9. Therefore, the paper’s main aim is to determine the level of DT of EU 27 countries as part of the implementation of SDG 9 using an aggregated measure in the years 2015–2020. The Digital Transformation Assessment Indicator (DTAI) is constructed using the selected methods of statistical multidimensional analysis, i.e. the zero unitarization method (ZUM) and Hellwig’s method. These methods make it possible to order the objects under study due to the level of phenomena that a single measure cannot measure. The selected methods synthesize information from individual indicators and assign a single aggregate measure to the phenomenon under study.

This approach provides an opportunity for international comparisons in terms of the level of implementation of DT under SDG 9. It also enables the classification of countries into groups similar in terms of DT and the identification of countries that are the best/weakest in this respect.

Despite many studies on the DT and implementation of SDG 9 (Nambisan et al. 2019 ; Guandalini 2022 ; Rosário and Dias 2022 ; Pakkan et al. 2023 ), there is still a research gap in the lack of an indicator to assess the 9 influencing areas of DT highlighted in SDG 9 (Varakamin et al. 2017 ; Luken et al. 2022 ; Kynčlová et al. 2020 ). Our new approach allows an in-depth analysis of different aspects of DT, which have been combined into one aggregated measure. This has made it possible to create a comprehensive measure containing information from each DT area, facilitating a better assessment of individual countries. Such research allows for preparing practical activities in different aspects of DT to accelerate it. Given the objective of the article, the following results are expected:

Identification of the EU countries demonstrating the most outstanding leadership in achieving SDG 9 among the 27 member countries.

Identification of the EU countries’ weakest performance in achieving SDG 9 among the 27 member countries.

Identification of the EU countries that have demonstrated the most significant progress in achieving SGD 9 in 2020, compared to 2015.

Identification of the countries with the most significant declines in their progress towards achieving SDG 9 in 2020, compared to 2015.

Development of a ranking of the EU 27 countries in terms of their achievement of SDG 9 in 2015 and 2020.

The article is structured as follows. An introduction is followed by a literature review on the implementation of SDGs, with a focus on goal 9 in EU countries. The section on data and method presents the diagnostic variables and their characteristics, as well as the basis for further statistical analysis. The results and discussion section presents the research findings and corresponding discussions. The paper concludes with a section on the limitations of the study.

2 Literature review

The literature review indicates different definitions of DT depending on the perspective adopted by researchers. It is defined as “doing things in a new, digital way and closely connected with the digital revolution” (Olczyk and Kuc-Czarnecka 2022 ). In another perspective, it is described as “a new development model that calls for redefining relationships between companies, their stakeholders, and clients and reviewing previous approaches to offering services and products as companies undergo multidimensional transformation” (Zaoui and Souissi 2020 ).

G. Vial ( 2019 ) identifies as many as 23 unique definitions of DT presented in various academic papers. This enabled him to create his definition of DT, which he calls “a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies” (Vial 2019 ). Thus, we can see that DT is analyzed from multiple perspectives and its definitions concern various aspects of socio-economic life. For this study, we have chosen the definition of DT formulated by G. Vial, which appears to be the most accurate. This definition emphasizes the transformation of information from messages of the age of algorithms to automated processes that convert existing information into practical knowledge (Hilbert 2020 ).

The literature review provides information on different aspects of DT that affect SD. The impact of DT on EU countries based on the digitalization of society and economy is caused by digital skills and technological development (Malkowska et al., 2021). Other research shows that the impact is highlighted by both e-government and big data (El-Massah and Mohieldin, 2020). DT and enterprise innovation all positively impact the SD of enterprises (Su and Wu 2024 ).

Balbi ( 2023 ) notes that digitalization significantly impacts communication, representing a revolution and a radical change for those who experience it. Digitisation has shifted from 19 and 20th-century mechanical and analogue electronic expertise to digital electronics, indicating that DT is advancing through innovation. Furthermore, DT fosters business actions that support the transition to sustainability (Chatzistamoulou 2023 ). These changes are visible in the industry. To implement digital technology, an infrastructure is required to enable information sharing (Tangi et al. 2020 ; Manny et al. 2021 ). Due to the large data sets and the need to synchronize tasks in many areas, DT is a highly demanding activity.

The EU member states aim to make Europe the digital leader by 2030 (European Commission 2021 ). They are implementing a digital policy that empowers citizens and businesses by setting standards and tasks to achieve this. The European Digital Compass outlines the digital goals that must be achieved by 2030. The European Commission has been monitoring the progress of individual countries on digitalization since 2014 through the Digital Economy and Society Index (DESI) (Digital Economy and Society Index, 2021). In addition, from 2023 onwards, the Digital Decade policy programme measures digital skills, digital infrastructure, and digitalization of business and public services to assess the implementation of the actions taken.

The practical implementation of SDGs requires the development of new technologies, digitalization, and intelligent automated production processes. The success of the implementation of goal 9—“Industry, innovation and infrastructure”—depends to a large extent on the DT that is carried out (Ringel et al. 2016 ). Depending on the level of development of information and communication technologies (ICT), the economies of individual countries effectively develop their innovation potential (Wu et al. 2018 ; Khajuria et al. 2022 ; Rosário and Dias 2022 ; Yang et al. 2023 ). Furthermore, digital technology has long been identified as a crucial driver of economic growth and development, as well as a critical determinant of sustainable urban development (Graziano 2021 ; Sabatini, Cucculelli and Gregori, 2022; Yang et al. 2023 ). Digital technologies such as artificial intelligence, cloud computing, and the Internet of Things (IoT) can support sustainable production by improving organizational efficiency, planning processes, experimenting with new business models, and creating innovative ecosystems. (Wu et al. 2018 ; Bonamigo and Frech 2020 ). Moreover, the contemporary innovation-intensive economy requires companies to have the capacity to repeat the development of potentially radical innovations at every stage of their existence to create sustainable long-term value (Cornell et al. 2020 ).

However, the extent of ICT usage varies across countries and is influenced by a range of cultural, economic, technological, and social factors (Ringel et al. 2016 ; Asongu et al. 2018 ; Sabatini, Cucculelli and Gregori, 2022; Yang et al. 2023 ). Moreover, the global innovation landscape is changing during the pandemic, as well as recovery and geopolitical upheaval, as observed in the Global Innovation Index (GII) Report (WIPO 2023 ). Studies (Androniceanu et al., 2019; Bilozubenko et al., 2020; Chakravorty, Chaturvedi, 2017; Milosevic et al., 2018) show differences in countries’ digital progress. Moreover, it is observed that the level of digitalization in European countries is differentiated (Kolupaieva and Tiesheva 2023 ). The research shows that localization allows governments to effectively tailor SD strategies locally, which can be boosted with DT (ElMassah and Mohieldin 2020 ). It is observed that the progress and impact of DT might be different than in the global context. Research shows that countries with a high level of DT have also adopted sustainability principles and recorded high economic growth rates per capita (Bocean and Vărzaru 2023 ).

There are many studies in the literature on this topic regarding measurement methods, selection of indicators, and factors influencing the implementation of SDG 9 and the progress of its implementation (Varakamin et al. 2017 ). For example, Varakamin et al. ( 2017 ) use the AHP method to evaluate SDG 9 indicators in Thailand, which in turn is used to measure the progress and performance of SDG 9 in 20 Sub-Saharan African (SSA) countries, while Luken et al. ( 2022 ) use two indicators, SDG 9 progress and SDG 9 performance (Luken et al. 2022 ).

Kynčlová et al. ( 2020 ) base their criteria selection on the global framework of indicators for the Agenda 2030 goals adopted by the United Nations General Assembly (Kynčlová et al. 2020 ). The results of their analysis show that industrialised economies outperform other countries, with the top five in the 2016 ranking being Ireland, Germany, the Republic of Korea, Switzerland and Japan (Kynčlová et al. 2020 ). Ulbych (2020) takes the same indicators into account and compares the degree to which goal 9 is met in the Czech Republic and Poland countries. Both economies are transit countries with a high share of manufacturing in GDP, so a developing competitive and sustainable industrial base is crucial. It is also essential to modernise production processes and increase the share of value added by medium- and high-tech industries in total value added.

Other indicators and a different methodology to assess the level of EU countries in building stable infrastructure, promoting sustainable industrialisation and supporting innovation are adopted by Brodny and Tutak ( 2023 ). The basis of the proprietary methodology they have developed is a multi-criteria decision-making approach. They use TOPSIS, WASPAS, and EDAS methods to determine the index, entropy and CRITIC methods to determine the weight of the indicators adopted. In addition, the use of Spearman’s and Kendall’s nonparametric tau tests allows the analysis of the relationship between the SDG 9 indicator and the fundamental economic, environmental and energy parameters, as well as the digitalization of the countries studied (Brodny and Tutak 2023 ).

Therefore, it can be seen that many researchers have attempted to assess the level of achievement of SDG 9 using different methods and indicators (Nambisan et al. 2019 ; Guandalini 2022 ; Rosário and Dias 2022 ; Pakkan et al. 2023 ). However, a critical literature review has shown that the presented methods and indicators measure DT imperfectly. Therefore, efforts have been made to develop a new measure. The literature review provides evidence that the areas of innovation (Gajdzik et al. 2024 ; Li et al. 2024 ; Xiao et al. 2024 ), industry (Chatzistamoulou,  2023 ; Sołtysik- Piorunkiewicz and Zdonek  2021 ), and infrastructure (Hodson et al. 2024 ; Li et al. 2024 ) are relevant to DT. Therefore, areas within these sectors that are relevant for the measurement of DT have been identified.

2.1 The importance of innovation in the DT

The implementation of DT takes place through undertaken innovation activities. Essential aspects for assessing the level of innovation are, e.g., Gross Domestic Expenditure on R&D by sector (GERD), R&D personnel by sector and patent applications to the European Patent Office by country of residence of the applicant/inventor. Their role in the development of DT is outlined below.

2.1.1 Gross domestic expenditure on R&D by sector (GERD)

Information technology (IT) plays a significant role in shaping gross national research and development expenditure. IT infrastructure, such as high-performance computing, data analysis and simulation tools, helps scientists conduct complex experiments, analyze vast amounts of data, and accelerate the pace of research (Sołtysik-Piorunkiewicz and Zdonek 2021 ; Nuseir et al. 2022 ). By facilitating efficient and effective R&D processes, IT contributes to the growth of R&D (Wu et al. 2018 ). Additionally, IT enables collaboration between scientists, both locally and globally. R&D activities generate large amounts of data that require advanced management and analysis. IT infrastructure, including databases, data mining algorithms, and machine learning, enables scientists to extract insights from data, identify trends, and make informed decisions (Tannady et al. 2023 ). Effective data management and analysis contribute to more efficient and productive R&D processes, leading to increased GERD. Advanced communication and collaboration technologies such as videoconferencing, cloud platforms, and virtual research environments facilitate knowledge sharing, collaborative projects, and interdisciplinary research (Michna and Kmieciak 2020 ; Yaqub and Al-Sabban 2023 ). IT-enabled collaboration increases GERD by leveraging diverse expertise and pooling resources for R&D initiatives (Bonamigo and Frech 2020 ; Michna and Kmieciak 2020 ; Nawrocki and Jonek-Kowalska 2022 ).

Information technology facilitates the transfer of research results into commercial applications. IT infrastructures such as technology transfer platforms, intellectual property management systems and online marketplaces connect researchers with potential industrial partners and investors. By supporting technology transfer and commercialisation, IT encourages private sector investment in R&D, thereby increasing GERD (Sabatini et al. 2022 ; Yaqub and Al-Sabban 2023 ).

2.1.2 R&D personnel by sector

IT across various sectors of the economy (businesses, government institutions, academia, and non-profit organizations) enables more efficient management of data and information related to R&D. Customer Relationship Management (CRM) systems can aid in analysing and tracking customer needs and provide feedback for the research process. Moreover, analytical tools and artificial intelligence support data analysis, business processes, and better decision-making (Roba and Maric 2023 ). An example is the application of Big Data Analytics in the Business Enterprise Sector (BES), which facilitates identifying trends, customer preferences, and new development opportunities (Taleb et al. 2020 ; Sołtysik-Piorunkiewicz and Zdonek 2021 ; Nuseir et al. 2022 ).

Government agencies, on the other hand, can utilize advanced IT tools for data collection, analysis, and management, enabling evidence-based decision-making. Examples of such tools and initiatives include e-health systems, smart cities, e-administration, and digital services for citizens (Corte et al. 2017 ; Graziano 2021 ; Jonek-Kowalska 2022 ).

In higher education, introducing modern e-learning tools and platforms enables remote research, access to scientific resources, and collaboration and knowledge exchange among researchers from different institutions. Furthermore, IT technologies support scientific research, data analysis, simulations, and modelling, accelerating research processes and enabling innovative discoveries.

To a limited extent, even in the Private Non-Profit Organizations (PNP) sector, IT technologies play a significant role as they support the work of these organizations. IT tools and systems aid in data management, project monitoring, result analysis, and reporting (Mayer and Fischer 2023 ). Examples include CRM systems, which help non-profit organizations build and maintain long-term relationships with donors and track donations and outcomes. IT technologies enable non-profit organizations to acquire knowledge and access scientific resources through e-learning tools and collaboration platforms (Berardi and Rea 2010 ; Miković et al. 2020 ).

The impact of ICT on the size of R&D personnel in various sectors can be complex. On one hand, IT technologies may contribute to increasing the efficiency and productivity of research processes, leading to an increased demand for R&D personnel. On the other hand, advancements in automation, machine learning, and artificial intelligence may affect the automation of specific tasks, potentially reducing the demand for R&D personnel in some areas.

2.1.3 Patent applications to the European Patent Office by applicant’s/inventor’s country of residence

Patents are essential indicators of innovation and technological progress as they represent the creation and protection of new inventions and technologies (Higham et al. 2021 ). Technological innovation is crucial for developing sustainable infrastructure, promoting industrialisation and supporting innovation in the context of SDG 9. Patents represent progress in various areas, including renewable energy, clean technologies, waste management, transport systems, and digital solutions. These areas are crucial components of the SDGs (Javeed et al. 2022 ; Liu et al. 2023 ).

Additionally, patent-protected inventions can create new market opportunities and industries, leading to job creation and economic development. Patents also provide inventors and companies with intellectual property rights, enabling them to commercialise their inventions and attract investment for further R&D. This financial support and economic growth contribute to the successful implementation of SDG 9.

2.2 The importance of infrastructure in the DT

Infrastructure is another crucial aspect to consider when measuring the impact of DT. Developed and modern infrastructure can improve residents’ living standards and contribute to countries’ economic development.

2.2.1 Share of buses and trains in inland passenger transport

Digital technology has a significant impact on enhancing sustainable transportation in cities. This includes the involvement of buses and trains in urban passenger transport and the participation of railways and inland waterway transport in inland freight transport. Primarily, digital technology increases efficiency in these areas by enabling better planning, scheduling, and management of bus and rail services (Corte et al. 2017 ; Jararweh et al. 2020 ; Graziano 2021 ).

Real-time data analysis and optimization algorithms can be utilized to optimize routes, reduce congestion, and improve overall performance (Molinillo et al. 2019 ; Walentek 2021 ). This leads to better utilization of existing resources and encourages the use of public transportation. Additionally, digital solutions such as mobile applications, online ticketing systems, and passenger information systems provide convenience and enhance overall passenger experiences. Passengers can access real-time information about schedules, delays, and seat availability, making public transportation more accessible and user-friendly (Anthopoulos 2015 ; Li et al. 2017 ; Hysa et al. 2021 ; Yadav et al. 2021 ).

Digital platforms and applications facilitate the implementation of demand-responsive services, where public transport routes and schedules can be dynamically adjusted based on passenger demand. This helps provide more flexible and personalized transport options, encouraging people to choose public transportation over private vehicles, which is crucial for SD and environmental protection.

2.2.2 Share rail and inland waterways in inland freight transport

Digital technology enables the integration of different supply chain elements, including rail and inland waterways. Through data analytics, sensors and automation, logistics processes can be optimized, leading to improved efficiency, reduced costs and better use of resources (Tannady et al. 2023 ). Additionally, digital tools like GPS tracking, RFID tags and IoT devices can track and monitor cargo shipments over time (Lin et al. 2023 ; Wang 2023 ). This improves cargo visibility throughout the transport process, enabling better security, reduced theft and better supply chain management.

Digital platforms facilitate the seamless integration of different modes of transport, such as rail and inland waterways, with other parts of the logistics network.

2.3 The importance of industry in the DT

The other indicators selected to measure DT do not only belong to the industrial sector but also significantly impact the economic level of companies and the implementation of SDGs. Thanks to them, observing the effects of the implemented DT activities is possible. Among them are industrial air emission intensity, tertiary educational attainment by gender, gross value added in the environmental goods and services sector, and high-speed internet coverage by type of area.

2.3.1 Air emission intensity from industry

The intensity of air emissions from industry is closely linked to the development of digital technology. Digital technology enables the implementation of advanced monitoring and control systems in industry. Through sensors, data analysis, and automation, industries can continuously monitor their operations and identify sources of air emissions (Chang 2015 ; Pirc et al. 2016 ; Corte et al. 2017 ). Real-time data collection and analysis help detect inefficiencies, optimize processes, and minimize pollution emissions.

Digital control systems can also regulate and adjust production processes to ensure compliance with environmental standards, reducing the intensity of air emissions. By integrating digital systems with industrial machinery and devices, companies can optimize energy consumption, reduce waste, and limit emissions. For example, smart grids can more effectively balance energy supply and demand, minimizing fossil fuel-based energy sources and reducing air emissions (Myeong and Shahzad 2021 ).

By leveraging digital technology, the industry can improve its environmental efficiency, reduce air emissions intensity, and contribute to the achievement of SDG 9. The development and adoption of digital solutions increase monitoring and control capabilities, optimize energy efficiency, promote industrial automation, support sustainable supply chains, and facilitate collaboration for SD.

2.3.2 Tertiary educational attainment by sex

Tertiary education is vital in human capital development, equipping individuals with the knowledge, skills, and experience to address complex environmental challenges. Higher education institutions create a skilled workforce capable of driving innovation, designing sustainable infrastructure, and implementing technological solutions that support SDG 9 (Podgórska and Zdonek 2022 ).

Furthermore, research conducted at universities and research institutes can lead to breakthroughs in renewable energy, clean technologies, waste management, and transportation efficiency, thereby supporting SDG 9. Higher education fosters an environment that promotes critical thinking, creativity, and problem-solving skills necessary for driving innovation and finding sustainable solutions (Michna and Kmieciak 2020 ). Higher education fosters a learning environment that cultivates a deep understanding of the principles of SD, enabling graduates to make practical contributions to the implementation of SDG 9 across various sectors (Michna and Kaźmierczak 2020 ; Miković et al. 2020 ).

2.3.3 Gross value added in the environmental goods and services sector

Digital technology enables innovation and productivity improvement in the environmental goods and services sector. Companies can optimize their operations, streamline processes, and develop innovative solutions by integrating digital tools such as data analytics, the IoT, and automation. These advancements contribute to increased productivity, cost reduction, and overall performance improvement, ultimately leading to higher gross value added in the sector (Gössling 2021 ; Rosário and Dias 2022 ; Yang et al. 2023 ).

Moreover, digital technology facilitates developing and implementing smart solutions and services in the environmental protection sector. For example, digital systems enable better monitoring and control of energy production, predictive maintenance, and network optimization in renewable energy generation. In waste management, digital platforms can streamline waste collection, recycling processes, and resource recovery (Asongu et al. 2018 ; Jararweh et al. 2020 ; Ulbrych 2020 ; Luken et al. 2022 ). These smart solutions and services contribute to SD and generate economic value, thereby increasing gross value added in the environmental goods and services sector.

Digital technology opens up new markets and business opportunities in the environmental protection sector. Adopting digital tools enables the creation of innovative products, services, and business models that address environmental challenges. This market expansion and growing demand for sustainable solutions lead to sector growth and increased gross value added (Grabowska 2019 ; Sabatini et al. 2022 ; Liu al., 2023).

2.3.4 High-speed internet coverage, by type of area

High-speed internet access accelerates DT across various sectors, including infrastructure, manufacturing, transportation, and energy. It enables the integration of digital technologies such as the IoT, cloud computing, and data analytics, which optimize operations, improve efficiency, and promote sustainable practices (Bratulescu et al. 2023 ).

DT, supported by fast internet, enhances industrial productivity, resource management, and environmental sustainability. Fast internet also enables effective e-governance and delivery of digital public services. It streamlines administrative processes, citizen engagement, and access to government information. These digital advances contribute to the efficiency and effectiveness of public administration in the context of SDG 9. Access to fast internet fosters innovation and entrepreneurship by providing a platform for collaboration, knowledge sharing, and entrepreneurial activities (Wang 2023 ; Wang et al. 2023 ). Fast internet access facilitates the development of digital startups, promotes technological innovation, and enables individuals to create and scale sustainable businesses. This contributes to economic growth, job creation, and the overall success of SDG 9.

Within the EU, several countries have highly developed and extensive fast internet infrastructure. Countries such as Denmark (Saunavaara et al. 2022 ), Sweden, Finland (Helms Jørgensen et al. 2019 ), the Netherlands (Tangi et al. 2020 ), and Luxembourg (Hunady et al. 2022 ) prioritize investments in digital technologies and have robust networks supporting high-speed internet connections.

In summary, we can observe that DT encompasses many dimensions of SD, with these dimensions intersecting each other. DT is a multi-faceted and multi-threaded phenomenon, so measuring it requires a multidimensional approach. Previous literature research has indicated that no single indicator encompasses this complex phenomenon. The multidimensional approach used in this article allowed for considering 9 areas of DT. Each area contributed theoretically to constructing the proposed measure (Fig.  1 ).

Source own elaboration

The theoretical framework for constructing the synthetic measure DTAI.

Due to the identified research gap, a literature review of the individual aspects comprising SDG 9 innovation, industry, and infrastructure was conducted. This allowed for constructing a theoretical multidimensional model preceding the construction of an empirical model.

3 Data and Method

We conducted a comparative analysis of EU 27 member states based on their level of advancement of SDG 9 using variables identified by Eurostat (Eurostat database, https://ec.europa.eu/eurostat/data/database ). Table 1 presents the variables with the names and symbols of indicators relevant to the EU and UN (United Nations).

Among the examined variables are eight stimulants (X1–X5, X7–X9) and one destimulant (X6). Stimulants are variables whose growing values positively affect the studied phenomenon. Destimulants are variables whose growing values have a negative impact (Pociecha and Zając 1989 ).

The assessment used data from the launch of the seventeen SDGs from 2015 to 2020, which was the time when the most recent comparable statistical data on the issue under consideration was available. We performed the initial evaluation of the SDG 9 accomplishment using a set of descriptive measures (mean, maximum and minimum, CV and CA) for individual indicators (variables). Individual indicators allowed us to describe the countries’ industry, innovation and infrastructure situation in detail. However, the conclusions based on this evaluation presented only a particular image of the SDG 9 implementation in the different EU member countries. That was why a multi-feature evaluation of the EU member countries was performed in the following stage of the studies, taking all indicators.

The essence of multidimensional comparative analysis (MCA) is the determination of a synthetic (aggregate) measure, which is a function of multiple variables (Grzebyk and Stec 2023 ). Among the numerous methods in this study, we selected the ZUM (Kukuła 2000 ) and Hellwig’s development pattern method (Hellwig 1968 ). Hellwig’s method for linear ordering of objects initiated the development of MCA in Polish subject literature. This method was also promoted in the world literature in 1972 through the UNESCO research project on the human resources indicators for less developed countries (Hellwig, 1972). Hellwig’s method is based on distance from the pattern object, also used in the well-known and popular TOPSIS method (Jefmański et al. 2021 ). However, MUZ consists of comparing multiple objects using selected criteria. Different quantities can express these criteria. The purpose of the ZUM is to normalize the criteria under consideration.

The MCAs find their application in empirical studies of complex phenomena. Inherently connected with the concept of complex phenomenon are the concepts of diagnostic variables X and synthetic (aggregate) variables Q . In addition, mention should be made of the output set W of describing variables, the set Y of describing variables reduced in the selection process, and the set Z of transformed (normalized) variables.

There are many requirements for diagnostic variables in the subject literature, and here are the most important ones (Kukuła 2020): (a) diagnostic variables must play a significant role in describing the phenomenon under study, (b) diagnostic variables must be available, (c) diagnostic variables, if possible, should be measured on strength scales (interval or quotient), (d) selected variables should be weakly correlated among themselves so that they do not duplicate the information carried by other variables, (e) variables from set X should be strongly correlated with variables from set Y , i.e. reduced variables.

Diagnostic variables tend to have different units and ranges of variation, making it impossible to compare them directly. Thus, wishing to bring the diagnostic features to comparability, they must transform their values. The transformation of diagnostic variables that brings their values to a state of comparability is called normalization. Among the normalization procedures, four groups of methods can be distinguished: rank, standardization, unitarization, and quotient transformation (Kukuła 2000 ).

The next step in constructing the synthetic variable is to weigh the normalized diagnostic features. The weighting of variables generates numerous discussions and disputes regarding the need to differentiate their importance and the methods used. In most empirical work in which a synthetic variable is constructed, an assumption is made about the equal weights of all selected diagnostic variables (e.g. Grzebyk and Stec 2023 ; Fura et al. 2017 ; Kukuła and Bogocz 2014 ; Skica et al. 2020 ). In any case, equal weighting does not mean "no weights" but implicitly implies equal weights (OECD 2008 ). This is predominantly the result of the lack of information about the circumstances affecting diagnostic features’ differential importance and role (Kukuła 2000 ).

The last thing to do when constructing a synthetic variable is to use the right formula to aggregate the normalized features. Aggregation formulas can be divided into “with a pattern” and “without a pattern”. In the non-pattern aggregation, there is an averaging, adding, or multiplication of diagnostic variables normalized, taking into account the weighting system. In pattern aggregation, there is an aggregate comparison of the normalized features of a given object with those of the pattern object. This comparison is made using one of the distance measures (Kukuła 2000 ).

In this paper, to build the synthetic measure, we used the ZUM, which belongs to non-pattern methods, and Hellwig’s method, which represents the group of pattern methods. The methodologies used in the methods mentioned above are presented below.

The level of a complex phenomenon is considered r objects: O 1 , O 2 ,…, O r . N diagnostic variables describe each of the objects. The gathered information about diagnostic variables form a two-dimensional matrix of the following form:

where \(x_{ij}\) represents the value of the variable \(X_{j}\) in the object \(W_{i}\) . Each object is characterized by a vector of diagnostic variables:

Diagnostic variables selected to measure complex phenomena should effectively discriminate the classified objects, so we determine their variability using the coefficient of variation at the first stage of their selection. From the set of potential diagnostic features, we remove those whose coefficient of variation is determined from the equation:

where \(v_{j}\) —coefficient of variation of the \(j\) -the variable, \(S_{j}\) —standard deviation of the \(j\) -th variable and \(\left| {\overline{x}_{j} } \right|\) —the absolute value of the mean of the \(j\) -th variable, does not exceed 10% (Sobczyk 1983 ).

Zero unitarization method (ZUM).

According to the ZUM, there is a constant reference point, which is the range of the normalized variable:

Normalization of the feature \(X_{j} ,\) which is a stimulant \((X_{j} \in S)\) is performed as follows:

where \(z_{ij} \in \left[ {0,1} \right]\) . Furthermore:

Normalization of feature \(X_{j, }\) which is a destimulant \((X_{j} \in D)\) is performed as follows:

where \(z_{ij} \in \left[ {0,1} \right]\) . Also in this case the normalized variables are from the range [0,1]. In addition:

It is worth noting that the diagnostic variables, both stimulants and destimulants, are subjected to a linear transformation according to the ZUM.

Normalized diagnostic variables form the following matrix:

Thus, the matrix ( X ) with dimensions ( \(r \times n\) ) crosses through the matrix Z with the same dimensions. Each object is described by the vector of normalized features:

To obtain an assessment characterizing a given object all normalized variables should be summed up for each object:

The assessment of the variable which characterizes the i -th object is called a synthetic variable \(Q_{i}\) :

The synthetic variable obtained through the formula ( 12 ) assumes values in the range [0,1].

The closer the measure’s value is to 1, the better the situation of the assessed object in terms of the examined phenomenon.

Hellwig’s pattern development method.

The values of the variables \(X_{j}\) are standardized in the studied set of objects according to the following formula:

\(z_{ij}\) —the normalized value of the j -th variable for the i -th object,

\(x_{ij}\) —the value of the j -th variable for the i -th object,

\(\overline{x}_{j}\) —mean of the j -th variable,

\(S_{j}\) —standard deviation of the j -th variable.

The coordinates of the development pattern are established using the following relations:

The following formula:

is used to calculate Euclidean distances of objects from the pattern, obtaining a sequence of distance values \(\left( {D_{10} , D_{20} , \ldots , D_{n0} } \right)\) .

From the above Euclidean distances, the arithmetic mean is determined according to the formula:

and the standard deviation of these distances is calculated from the formula:

Then the measure of development is defined by the formula:

The measure \(Q_{i}\) generally takes values in the interval [0, 1]. If there are objects that are significant outliers, then the normalized values of Hellwig’s aggregate measure may go beyond this interval. The closer the values of the measure \(Q_{i}\) are to unity, the closer the evaluated object is to the pattern, i.e. it ranks higher in the ranking of objects.

To check for the robustness of the DTAI, we correlated it with GII. This index was chosen because of the availability of data and the fact that it provides a comprehensive assessment of a country’s innovation performance.

According to the presented methods, the next step of the statistical analysis is classifying objects into similar groups. Classifying objects takes place as follows:

where: \(\overline{Q}_{i}\) is the mean value of the synthetic measure, \(S_{i}\) is the standard deviation of the synthetic measure.

At the final stage of the analysis to distinguish homogeneous groups, we also used one of the multivariate analysis methods, i.e., cluster analysis. It allowed the combining of multidimensional objects into groups (clusters) that meet the condition of internal homogeneity and external heterogeneity. In the cluster analysis, we used the Euclidean distance to measure the distance between objects. Ward’s technique was used to calculate the distance between clusters (Ward 1963 ). To represent the results of hierarchical grouping, we applied a binary tree called a dendrogram, which presents the agglomeration process.

Ward’s method differs from the two previously employed methods. The previously used methods belong to the group of linear ordering methods, while Ward’s method is one of the agglomerative clustering methods. We use linear ordering methods to determine the hierarchy of objects, that is, to order them from the object standing highest in the hierarchy to the object standing lowest in it. Ward’s method, on the other hand, allows us to determine the similarity of objects without establishing their hierarchy.

4 Results and Discussion

4.1 selection of variables.

The first step in selecting diagnostic variables (X1–X9) was to assess their variability (Table  2 ). For this purpose, the coefficient of variation (CV) was used.

The variability of the variables under study took on values ranging from 0.21 for variable X7 to 1.27 for variable X3 in 2015. High variability was characterized by the diagnostic variables for 2020. The lowest value of the coefficient of variation was recorded for variable X7 (0.11) and the highest for variable X3—1.16. Following the criterion of sufficient variability, no variable was removed from the diagnostic variables (Table  2 ).

In the second stage, the variables’ correlation level was assessed (Table  3 ).

A strong correlation was observed for the variables X1, X2 and X2, X3. This was valid for both 2015 and 2020 (Table  3 ). Despite the high correlation of the mentioned variables due to their substantive importance for the analyzed phenomenon, we decided not to remove them. Therefore, in the second stage of variable selection, as in the first stage, none were removed from the diagnostic variables. Thus, the adopted variable selection procedure led to the adopting variables X1–X9 as the final set of diagnostic variables.

4.2 Descriptive statistics

We present the variables and their descriptive statistics (Table  4 ). Variable X1, a stimulant, recorded Sweden’s highest value in 2015 and 2020. This indicates that in both periods, gross domestic expenditure on R&D by sector was highest among the EU 27 countries in this country. The lowest level of this variable in 2015 was observed in Cyprus, and in 2020, it was in Romania. Member states exhibited average variability in this variable, with most countries achieving values below the mean. This was valid for both 2015 and 2020. R&D personnel by sector (X2) was highest in Denmark in 2015 and 2020. These results suggest that economically, more developed countries allocate more funds to R&D than countries that cannot afford increased expenditure (Brodny and Tutak 2023 ). The lowest values of this variable were recorded in Cyprus (2015) and Romania (2020). Member states of the EU exhibited relatively average variability in this variable. The variable’s values were distributed below (2015) and above the mean (2020). Variable X3—patent applications attained the highest value in Luxembourg in 2015 and 2020, while the lowest was recorded in Romania in both years. EU countries showed a high degree of variation in the number of patents filed in 2015 and 2020. The coefficient of variation values were comparable in both time periods.

The number of patents filed in most EU countries was significantly below the mean. It is commonly believed that the number of patents is a measure of innovativeness; however, as highlighted by Higham et al. ( 2021 ), quality rather than quantity may be more critical (Higham et al. 2021 ). The highest level of variable X4—share of buses and trains in inland passenger transport was in Hungary in 2015 and 2020. This variable recorded its lowest value in Portugal in 2015 and Lithuania in 2020. The studied EU countries exhibited relatively low diversity in this variable, as indicated by a coefficient of variation of 23% in 2015 and 27% in 2020. Most EU countries recorded values of variable X4 slightly below the mean.

Share of rail and inland waterways in inland freight transport—X5 achieved its highest values in 2015 in Latvia and Lithuania in 2020. This variable recorded its lowest values in both comparable time periods in Ireland. EU countries showed high variability in terms of variable X5 in both 2015 and 2020. Most EU countries recorded values of this variable below the mean. This indicates the need for further increase in funding for digital technologies in these areas as it will significantly improve the management of bus and rail services (Corte et al. 2017 ; Graziano 2021 ). Algorithms for real-time data analyzis and optimization can be utilized to optimize routes and reduce congestion (Molinillo et al. 2019 ; Walentek 2021 ).

Unlike the other variables, the following variable had a destimulating nature, meaning that the lowest possible values were expected from the perspective of the phenomenon under study. The highest values of this variable (X6)—air emission intensity from industry was recorded in Portugal in 2015 and 2020, while the lowest was in Denmark. The diversity of EU countries was high in both comparable time periods, although it decreased in 2020 compared to 2015. Most EU countries achieved values of variable X6 below the mean in 2015 and 2020. Another variable analyzed was variable X7—tertiary educational attainment by sex. The highest value was recorded in Lithuania in 2015, and the lowest in Italy. In 2020, Luxembourg and Romania had the highest and lowest values, respectively. The values of this variable were the least differentiated among all the variables studied in both time periods. In 2015, most EU countries were below the mean, while in 2020, the values of variable X7 hovered around the mean.

In the case of variable X8, Finland, in both years, had the highest values. The lowest values of this variable were observed for Ireland (in 2015) and Hungary (in 2020). Analyzing the area of higher education, it can be assumed that digital technologies also significantly contribute to the success of SDG 9, developing human capital, stimulating R&D, influencing policy development, building potential, and facilitating cooperation. Countries with solid higher education systems like Luxembourg are better prepared to meet SDG challenges.

The last of the analyzed variables was variable X9—High-speed internet coverage, by type of area. It reached its highest value in 2015 in Latvia and its lowest in Cyprus. In 2020, the highest and lowest values were recorded for Malta and Greece, respectively. Variable X9 exhibited average variability, and most countries recorded values below the mean. This result may be surprising as highly developed countries such as Denmark, Finland, or Luxembourg have robust networks supporting high-speed internet connections and prioritize investments in digital technologies (Helms Jørgensen et al. 2019 ; Saunavaara et al. 2022 ).

4.3 Ranking of EU 27 countries

The ranking of EU countries was based on the values of DTAI calculated for the EU 27 countries for 2015–2020. DTAI values for the years 2015–2020 were obtained using the ZUM. For the extreme years 2015 and 2020, the values of the measure were also calculated using Hellwig’s method (Table  5 ).

In addition, using the correlation coefficient, the convergence of the results obtained by the two linear ordering methods was assessed (Table  5 ). This convergence, determined by the correlation coefficient, was 0.8918 for 2015 and was even higher at 0.9725 for 2020. The synthetic measure values obtained for 2015 and 2020 were also positively correlated with the GII values. The highest correlation with GII was for ZUM results 2015 (0.8263).

Based on the DTAI values calculated by the ZUM, the ranking of countries for 2015–2020 was presented (Table  6 ).

The best results in terms of achieving SDG 9 throughout the entire study period of 2015–2020 were observed in Sweden (Table  6 ). Comparing these results to a study by Kynčlová et al. ( 2020 ), it can be noted that Sweden is the leading country in rankings regarding achieving goal 9. However, in the study by Brodny and Tutak ( 2023 ), another Scandinavian country, Denmark, was the leader in the ranking, while Sweden was seventh.

The second position throughout the period 2015–2019 was noted in Denmark. In 2020, Denmark was third in the ranking. High in the ranking was another Scandinavian country, Finland (3rd position in 2015–2017, 4th position in 2018–2019 and second position in 2020). Highly developed countries such as Luxembourg, Austria, and the Netherlands occupied subsequent ranking positions during the study period. These results indicate that economically developed countries perform better in achieving SDGs. This may be because economically developed countries have significant R&D investments (Kynčlová et al. 2020 ; Pakkan et al. 2023 ), which translates into innovation, infrastructure development, and industrial growth.

Among the new member states, Slovenia ranked high. Among the countries of the former Eastern Bloc, Lithuania ranked the highest. Latvia, Slovakia, and the Czech Republic held lower-ranking positions. The lowest-ranking positions were held by the least-developed countries in the EU, Romania, and Bulgaria (Bocean and Vărzaru 2023 ). Poland, a more developed country, held positions comparable to those mentioned countries during the study period. Southern European countries such as Greece, Croatia, and Portugal performed poorly during the study period.

The most remarkable progress (in 2020 compared to 2015) in achieving goal 9 was noted in Malta (+ 9 positions) and Ireland (+ 7 positions). The most significant decrease in ranking positions (in 2020 compared to 2015) was observed in Latvia and Hungary (-4 positions). This result differs from the ranking proposed by Brodny and Tutak ( 2023 ), where Cyprus showed the most significant progress (+ 8), and the Czech Republic showed the most significant decline (-6). Bulgaria, Croatia, Greece, the Netherlands, Poland, Portugal, and Sweden did not change their ranking positions in 2020 compared to 2015. The remaining countries experienced slight changes in ranking positions (in 2020 compared to 2015).

4.4 Classification of EU 27 countries

Based on the rankings of EU countries presented in Table  6 , classifications of countries were prepared in 2015 and 2020 regarding the implementation of SDG 9. EU countries were divided into four relatively similar groups to achieve this goal using the ZUM and Hellwig’s method (Table  7 ).

Below, we present the characteristics of the distinguished groups first, according to the ZUM:

Group 1: The first group consisted of countries with a high level of achievement of goal 9 (high level). In 2015, this group comprised 6 highly developed Western countries: Sweden, Denmark, Finland, Luxembourg, Austria and the Netherlands. In 2020, the composition of group one did not differ. These results differ from Bocean and Vărzaru ( 2023 ) and the Digital Economy and Society Index (DESI, 2021) due to the inclusion of France in this group.

Group 2: The second group consisted of countries with a medium–high level of achievement of SDG 9. In 2015, this group comprised 8 countries, which decreased to 6 countries in 2020. This result is different from the ranking by Kynčlová et al. ( 2020 ), where Ireland was the leader. In 2015, both old and new member states were included in this group, while in 2020, it consisted of two new member states (Lithuania and Slovenia).

Group 3: The third group consisted of countries with a medium–low level of achievement of goal 9. This group consisted of both old and new member countries. In 2015, this group comprised 8 countries, which increased to 11 countries in 2020.

Group 4: The fourth group consisted of countries with the lowest level of achievement of goal 9 (low level). In 2015, there were 5 countries in this group, including Southern and Central-Eastern countries such as Bulgaria. This result is comparable to Kynčlová et al. ( 2020 ) and Brodny and Tutak ( 2023 ). In 2020, Malta and Bulgaria moved to the third group, and Cyprus joined the fourth group.

The classification of the EU 27 countries obtained using Hellwig’s method gave similar results. The composition of group one obtained by Hellwig’s method was similar to the classification obtained by the ZUM, with the difference that in 2015, Luxembourg was classified into group two. The second group obtained by Hellwig’s method included, apart from Luxembourg, highly economically developed countries such as Germany, France, and Belgium, as well as new member countries and Spain (for 2015). In 2020, the number of countries in group two decreased from 10 to 4. Group two in 2020 consisted of the highly developed Belgium, France, Germany and Slovenia. Apart from the island Malta, the third group obtained by the mentioned method consisted of new member countries (2015). In 2020, the composition of the third group expanded to include five more countries. The third group was joined by Ireland, Spain and Italy, among others. In group four in 2015 were 5 countries, including highly developed Ireland. In 2020, Ireland moved to the beginning of group three, and Croatia moved to the end. In 2020, Romania dropped to group four.

Figures  2 a and 2b provide classifications of the EU-27 countries according to the ZUM for 2015 and 2020, respectively. Figures  3 a and 3b show classifications of the EU 27 countries according to Hellwig’s method for 2015 and 2020, respectively.

a The classification of the EU 27 countries in 2015, according to the ZUM. b The classification of the EU 27 countries in 2020, according to the ZUM.

a The classification of the EU 27 countries in 2015, according to Hellwig’s method. b The classification of the EU 27 countries in 2020 according to Hellwig’s method.

As mentioned in the data and method section, the research was complemented by cluster analysis using Ward’s algorithm, where the Euclidean distance between their centroids and data scale normalization (bringing the values of each variable into the range [0, 1]) were used (Fig.  4 a and Fig.  4 b).

Source authors’ design using R version 4.3.0

a Cluster analysis results for 2015. b Cluster analysis results for 2020.

The results of the analysis are countries divided into three clusters, A, B, and C, contrasting the previous methods, where countries were divided into four groups. A comparison of the outcomes for 2015 and 2020 is presented in Table  8 .

Cluster A: Countries in Cluster A exhibited a high level of achievement concerning SDG 9. In 2015, this cluster comprised all countries previously classified as group 1 and some from group 2. Generally, these were highly developed Western countries, consistent with previous analyses (Table  7 ). The composition of Cluster A remained unchanged in 2020. These findings align with the results of Bocean and Vărzaru ( 2023 ) and the DESI.

Cluster B: Countries in Cluster B showed a medium–low level of achievement regarding goal 9. According to previous analyses (Table  7 ), these countries belonged to group 3. However, by 2020, all countries in Cluster B had declined to Cluster C.

Cluster C: Cluster C included countries with a low level of implementation of goal 9. In prior analyses (Table  7 ), these countries were part of groups 3 and 4. The composition of Cluster C in 2020 remained consistent, with no recorded improvements.

5 Conclusions

The implementation of SD necessitates action across social, economic, and environmental domains, with activities under the 17 SDGs requiring monitoring to assess progress. Due to its complexity, DT significantly influences SD but demands a multidimensional approach for accurate measurement. Addressing a research gap, this article presents a theoretical framework for constructing the aggregated measure to evaluate DT statistically.

The paper’s main aim was to determine the level of DT of EU 27 countries as part of the implementation of SDG 9 in the years 2015–2020. The aim was achieved by constructing the aggregated DTAI, which enables a comprehensive DT assessment. DTAI was built using two methods of linear ordering of objects: the ZUM and Hellwig’s method. Both methods yielded comparable results, as indicated by the high values of the correlation coefficient. In addition, the high positive correlation with GII indicated that the measures obtained are robust and adequately designed. Based on the DTAI values, we created rankings of countries for achieving goal 9, appointing leaders and losers in SDG 9 adoption. Sweden was the most outstanding leader in achieving SDG 9 among the 27 EU member countries throughout the entire study period of 2015–2020. At the same time, the weakest performance in achieving SDG 9 among the 27 member countries was observed in Portugal. Malta and Ireland made the most significant progress towards achieving SDG 9 in 2020 compared to 2015. The most significant decrease in ranking positions (in 2020 compared to 2015) was observed in Latvia and Hungary.

In addition to ranking countries, our research allowed us to classify countries into relatively similar groups to achieve goal 9. This classification was made using the ZUM, Hellwig’s method, and cluster analysis with Ward’s technique. The first two methods yielded relatively similar classification results. The countries with the highest level of implementation of goal 9 included the Scandinavian countries and highly developed Austria and the Netherlands (in 2015 and 2020). The countries with the lowest levels of SGD 9 implementation were mainly Romania, Greece, Malta, Cyprus and Portugal. Using cluster analysis, we obtained three groups of countries that were relatively similar in achieving goal 9. However, this method did not allow us to rank the countries, only to classify them.

Using aggregated measures and statistical analysis, the rankings and classifications of countries conducted revealed challenges in accomplishing goal 9, mainly in Central, Eastern, and Southern European countries, with Scandinavian and Western European nations leading.

Based on the presented results, theoretical and managerial implications were possibly formulated. The paper extends the domains of SD and DT theory. Innovative in the article is the proposal of a new measurement method, which, through a synthetic indicator encompassing information from multiple variables, allows for a relatively simple assessment of DT over time and space. Among the managerial implications, the proposed multi-criteria evaluations enable a comparison between EU countries using only one synthetic measure. Managers can identify best practices in specific countries and take corrective actions accordingly. Moreover, the research procedure has profound implications for the effective implementation of the 2030 Agenda by individual countries and the EU. Additionally, the proposed indicator can be used to measure the advancement of DT in non-EU countries.

From a practical perspective, the results underscore the need for EU policymakers to devise targeted strategies to address the DT gaps identified among member states. The DTAI can be a diagnostic tool for policymakers to monitor progress and identify areas needing intervention. Countries lagging in DT implementation, particularly in Central, Eastern, and Southern Europe, can benefit from tailored support programs, digital infrastructure, and education investment. It will ultimately contribute to ensuring the SD of the countries that make up the EU. Furthermore, the insights gained from this study can inform the allocation of EU funding and resources to maximize impact, ensuring that all member states can achieve the objectives of SDG 9. The practical application of this research extends to designing effective policies that promote inclusive DT, fostering innovation, and improving infrastructure across the EU.

Limitations include data availability constraints, linear methodology, and the focus on EU countries, suggesting avenues for future research, such as qualitative studies on determinants influencing DT and classification group identification. The proposed approach offers insights crucial for policymakers and decision-makers in driving SD agendas.

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Conceptualization—Barbara Fura, Aneta Karasek, and Beata Hysa; literature review—Aneta Karasek, and Beata Hysa; methodology—Barbara Fura, Aneta Karasek, and Beata Hysa; formal analysis—Barbara Fura; writing—Barbara Fura, Aneta Karasek, and Beata Hysa; discussion and conclusions—Beata Hysa, and Aneta Karasek. The authors have read and agreed to the published version of the manuscript.

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Fura, B., Karasek, A. & Hysa, B. Statistical assessment of digital transformation in European Union countries under sustainable development goal 9. Qual Quant (2024). https://doi.org/10.1007/s11135-024-01972-0

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Automotive, Aerospace & Defense

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Chemicals, Energy & Industrial Manufacturing

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