Digitalization and Artificial Intelligence: A Comparative Study of Indices on Digital Competitiveness

Abstract

The digital economy, driven by innovative technologies and artificial intelligence (AI), is transforming economic systems and increasing the demand for accurate assessments of digital competitiveness. This study addresses the inconsistencies in country rankings derived from global digital indices and aims to determine whether these rankings differ due to methodological variations. It also examines whether the rankings correlate significantly across different evaluation frameworks. The research focuses on 29 European countries and analyzes rankings from four widely recognized indices: the World Digital Competitiveness Ranking (WDCR), Network Readiness Index (NRI), AI Readiness Index (AIRI), and Digital Quality of Life Index (DQLI). To assess the consistency and variability in rankings from 2019 to 2024, the study applies Friedman’s ANOVA and Kendall’s coefficient of concordance. The results demonstrate strong correlations at the level of country rankings, indicating a high degree of consistency, but also confirm statistically significant differences in rankings among the indices, which reflect the diversity of their conceptual foundations. Countries such as Finland, the Netherlands, and Denmark consistently achieve top rankings, indicating convergence, while more variability is observed in indices like the DQLI. These findings highlight the importance of rank-based, multidimensional assessments in evaluating digital competitiveness. They support the use of such assessments as policy tools for monitoring progress, identifying gaps, and promoting inclusive digital development.

1. Introduction

Competitiveness is a widely discussed topic across various research fields, and social sciences are no exception, particularly economics. Economists argue that competitiveness is one of the key factors influencing a country’s level of economic development [1].

According to the OECD [2], national competitiveness is defined as ‘the ability of companies, industries, regions, nations, or supranational regions to generate, while being exposed to international competition, relatively high factor income and factor employment levels on a sustainable basis’. The World Competitiveness Center [3] defines competitiveness as ‘the ability of countries, regions, and companies to manage their competencies to achieve long-term growth, generate jobs, and increase welfare’.

Historically, competitiveness has focused on cost efficiency, economies of scale, and product differentiation [4]. However, with the rise of the information age, the competitive landscape has shifted to emphasize knowledge-based factors such as intellectual capital, human resources, brand reputation, and innovation capabilities [5]. Digital transformation has further redefined the concept of competitiveness. To remain competitive, companies must integrate digital technologies into their core business operations [6].

Competing in today’s modern environment is challenging without the use of new digital technologies. Digital technologies are tools that create innovations and services that support and generate digital data [7]. ICTs enable the efficient and cohesive integration of processes, products, and services within an organization. ICTs are essential for a firm’s survival and growth [8].

The digital economy and innovative technologies drive economic and social transformation, enhancing global digital competitiveness. The digital competitiveness of a country can be assessed through a variety of established indexes that measure different facets of a nation’s readiness and capability to leverage digital technologies. These indexes include the World Digital Competitiveness Ranking (WDCR) [9], the Network Readiness Index (NRI) [10], the AI Readiness Index (AIRI) [11], and the Digital Quality of Life Index (DQLI) [12]. Countries that consistently rank at the top of these indexes tend to demonstrate superior competitiveness in terms of their digital infrastructure, innovation capacity, and ability to foster digital economies. These nations often lead in technological advancements, attracting businesses and talent while driving economic growth through digital transformation. However, a pertinent question arises: Are the rankings produced by these indices consistent, or do discrepancies exist between them? Furthermore, it is essential to explore whether a country’s digital competitiveness remains stable over time or undergoes significant changes as technological advancements, policy decisions, and global trends evolve. Understanding these dynamics is crucial for assessing the long-term sustainability of a nation’s position in the global digital economy.

Following the theoretical examination of issues in digitization and national competitiveness, this study explores how different indices assess countries’ digitalization levels and evaluates the consistency of the rankings they assign. It aims to determine whether these rankings differ based on the methodologies used and whether a statistically significant correlation exists between them, thereby examining the degree of agreement among different digital competitiveness indices.

The study is guided by the following research questions: (1) Do digital competitiveness indices produce significantly different country rankings due to methodological differences? (2) Is there a statistically significant correlation between country rankings across indices? (3) Do rankings remain consistent over time, or do they exhibit significant changes between 2019 and 2024? Accordingly, we formulate the key research hypotheses that guide our analysis:

Hypothesis H1. 

We assume there are statistically significant differences in the rankings of countries according to digital indices that use different assessment methodologies.

Hypothesis H2. 

We assume a statistically significant correlation between country rankings in different digital indices, indicating the reliability of these evaluations.

Hypothesis H3. 

We assume there are statistically significant differences in country rankings across the individual years 2019–2024 based on different digital competitiveness indices (WDCR, NRI, AIRI, DQLI).

2. Literature Review

The digitalization of economies has become a key factor in determining national competitiveness. Researchers and institutions increasingly emphasize the importance of digital infrastructure, innovation ecosystems, and AI readiness as critical drivers of economic and societal performance [9,10]. These components are also reflected in the indices analyzed in this study: WDCR, NRI, AIRI, and DQLI, which collectively provide a multidimensional perspective on digital competitiveness.

We selected these indices for analysis because they are among the most widely used measures for evaluating a country’s digital competitiveness. The data derived from the WDCR have become a crucial resource in a wide range of academic and industry studies, serving as a valuable benchmark for analyzing and comparing the digital readiness of countries worldwide, e.g., [13,14,15,16]. The data collected through the NRI have been utilized in numerous studies, serving as an essential tool for assessing a country’s digital infrastructure and its ability to leverage technology for economic and social development, e.g., [17,18,19,20]. The data from the AIRI have been incorporated into various studies to evaluate a country’s preparedness for adopting and integrating AI into its economic, industrial, and social systems, e.g., [21,22,23]. The data derived from the DQLI have been used in various studies to assess the overall well-being of citizens in relation to digital technologies, e.g., [23,24,25,26,27].

2.1. Digital Technologies and Competitiveness

Digitalization and the transformation it brings to businesses and societies have been widely analyzed in the academic literature, with a growing emphasis on its role in competitiveness. Bacca-Acosta et al. [28], for example, analyzed 17 Latin American and 28 European countries, confirming a positive relationship between digital technologies and business competitiveness.

The study by Ahmed et al. [29] reveals that digital transformation has a significant and positive impact on the market competitiveness of healthcare industries, encompassing hospitals, pharmacies, laboratories, pharmaceutical companies, and other related sectors.

Sui et al. [30] suggest that digital transformation positively impacts the competitiveness of manufacturing companies by enhancing total factor productivity, research and development intensity, and human capital—all of which contribute to improving their overall competitiveness.

The study by Danurdara [31] reveals a positive and significant impact of digital innovation on competitiveness. Additionally, both digital innovation and competitiveness positively influence hotel business performance.

Their results support the notion that countries with higher levels of digital adoption tend to exhibit greater competitive advantage, a premise closely linked to our analysis of digital competitiveness rankings.

Digitalization also plays a critical role in sustainability and environmental innovation. For example, Feng et al. [32], Xu et al. [33], and Xie et al. [34] found that digital transformation in enterprises enhances eco-innovation and sustainable performance.

E-government supports the delivery of public services, fostering sustainable and inclusive economic growth, social progress, and environmental protection, while also contributing to the efficient management of resources. In their analysis, Castro and Lopes [35] examined the effect of e-government on sustainable development using a logit model based on data from 103 countries between 2003 and 2018. The findings suggest that the development of e-government is a positive factor in a country’s pursuit of sustainable development.

The study by Xu et al. [36] reveals that digital transformation plays a crucial role in improving the environmental performance of enterprises. It does so by fostering green technology innovation, accelerating the accumulation of human capital, enhancing the disclosure of environmental information, and strengthening environmental governance.

These findings highlight how digital readiness may influence policy priorities across countries, offering indirect insight into why countries score differently in indices like the WDCR or AIRI, which emphasize innovation and future readiness.

Martincevic [37] confirmed a significant correlation between digital technology use and digital competitiveness levels across European nations. This finding directly supports the broader aim of this paper and substantiates the assumption behind Hypothesis H2, which posits that correlations between rankings can indicate reliability and consistency among indices.

2.2. Multi-Index Comparisons and Ranking Differences

Stankovic et al. [38] applied a multi-criteria decision-making approach to assess the digital competitiveness of EU countries and found that methodological differences across assessment frameworks lead to variations in rankings. This finding directly supports Hypothesis H1, which posits statistically significant differences in rankings that occur depending on the index used.

Similarly, Kő et al. [39] and Skvarciany and Jureviciene [40] examined how digital agility and competitiveness vary across countries and sectors, demonstrating that different evaluation tools may emphasize enterprise-level or national-level factors differently. These findings underscore the need to compare multiple indices and provide a solid theoretical foundation for our methodological approach.

Borowiecki et al. [41] and Balejová [42] noted that digital convergence varies widely across EU states, potentially affecting digital performance rankings. Their research further supports Hypothesis H1 by highlighting how structural and contextual factors influence the assessment of countries.

2.3. Ranking Reliability and Temporal Dynamics

Several studies have evaluated the consistency and evolution of digital competitiveness over time. For example, Soldić-Aleksić et al. [43] and Marinas et al. [44] used panel data from 2019 to 2022 to assess the stability of AI readiness rankings and employment outcomes. Their findings confirm that country performance in digital indices can vary across years, particularly in response to investment trends or policy reforms—providing evidence that directly supports Hypothesis H3, which posits significant ranking shifts between 2019 and 2024.

Rizvi et al. [45] found strong correlations among various innovation and digital indices (e.g., Global Innovation Index, Digital Adoption Index, Fintech Index), suggesting that reliable indices tend to converge in their assessments. This empirical evidence reinforces the rationale for Hypothesis H2 regarding the consistency of rankings across different digital assessment tools.

Other studies, such as Danik et al. [46] and Sagarik [47], analyzed index-based data in the context of military conflict and regional policy impacts, providing real-world validation for using composite indicators and statistical methods to evaluate ranking coherence.

2.4. The Diversity of Index Methodologies

Each of the four indices included in this study reflects different conceptual frameworks:

• The WDCR emphasizes innovation, human capital, and future readiness [9], focusing on knowledge-driven competitiveness.
• The NRI includes governance, inclusivity, and SDG alignment [10], making it particularly relevant for assessing societal impact.
• The AIRI evaluates AI strategies, ethics, and infrastructure [11], offering insight into national-level preparedness for advanced technologies.
• The DQLI focuses on internet affordability, quality, and user experience [12], offering a citizen-centered view of digital wellbeing.

Laitsou et al. [48] and Skare et al. [49] recommend using multiple indices to capture complementary dimensions of digital performance. Their work justifies the comparative approach adopted in this paper and strengthens the validity of using indices with different structures and emphases.

In summary, the literature shows that:

• Digital adoption drives competitiveness (supporting the use of digital indices).
• Index structures differ and affect rankings (supporting H1).
• Reliable indices strongly correlate (supporting H2).
• Country performance evolves over time (supporting H3).

These theoretical insights directly inform the hypotheses of this study and validate the statistical methods used to test ranking variability and consistency.

3. Materials and Methods

3.1. Data

The research sample includes 29 European countries (26 European Union countries, the United Kingdom, Norway, and Switzerland; data were not available for Malta). All evaluated countries were assigned a ranking based on the assessment of the selected indices. As part of the analysis, we determined whether the positions of these countries changed statistically significantly depending on the index used, or whether their rankings remained relatively stable regardless of the evaluation method.

Four indices—the WDCR, the NRI, the AIRI, and the DQLI—were selected to determine the correlations in evaluating countries’ digital competitiveness. Each index is characterized by different methodologies and structures.

3.1.1. The World Digital Competitiveness Ranking (WDCR)

The WDCR measures the capacity and readiness of 67 economies to adopt and explore digital technologies for economic and social transformation. This ranking is published annually by the IMD World Competitiveness Center and evaluates countries based on their ability to implement and sustain digital innovations [9]. The 2024 edition includes data from new entrants such as Ghana, Nigeria, and Puerto Rico, expanding the geographical scope of the analysis. The methodology of the WDCR defines digital competitiveness through three main factors: Knowledge, Technology, and Future Readiness. Each of these factors is divided into three sub-factors, resulting in a total of nine sub-factors. These nine sub-factors comprise 59 evaluation criteria (see

Table 1. Factors, sub-factors, and criteria of the WDCR, summarized by the authors based on [9].

Factors	Sub-Factors	Criteria
Knowledge	Talent	Educational assessment PISA-Math
		International experience
		Foreign highly skilled personnel
		Management of cities
		Digital/Technological skills
		Net flow of international students
	Training and education	Employee training
		Total public expenditure on education
		Higher education achievement
		Pupil–teacher ratio (tertiary education)
		Graduates in Sciences
		Women with degrees
		Computer science education index
	Scientific concentration	Total expenditure on R&D (%)
		Total R&D personnel per capita
		Female researchers
		R&D productivity by publication
		Scientific and technical employment
		High-tech patent grants
		Robots in Education and R&D
		AI articles
Technology	Regulatory framework	Starting a business
		Enforcing contracts
		Immigration laws
		Development and application of tech.
		Scientific research legislation
		Intellectual property rights
		AI policies passed into law
	Capital	IT and media stock market capitalization
		Funding for technological development
		Banking and financial services
		Country credit rating
		Venture capital
		Investment in Telecommunications
	Technological framework	Communications technology
		Mobile broadband subscribers
		Wireless broadband
		Internet users
		Internet bandwidth speed
		High-tech exports (%)
		Secure internet servers
Future readiness	Adaptive attitudes	E-Participation
		Internet retailing
		Tablet possession
		Smartphone possession
		Attitudes toward globalization
		Flexibility and adaptability
	Business agility	Opportunities and threats
		World robot distribution
		Agility of companies
		Use of big data and analytics
		Knowledge transfer
		Entrepreneurial fear of failure
	IT integration	E-Government
		Public–private partnerships
		Cyber security
		Software piracy
		Government cyber security capacity
		Privacy protection by law exists

Note: In the statistical analysis, we used the country rankings derived from each index (WDCR, NRI, AIRI, and DQLI), rather than the individual indicator values. Rankings from 2019 to 2024 were analyzed to evaluate consistency and differences between countries and indices.

), categorized as either hard data or soft data. Hard data are derived from international, regional, and national statistical sources and account for two-thirds of the final ranking. Soft data, representing executives’ perceptions collected through an international survey, contribute to the remaining one-third of the ranking. The use of both objective and subjective indicators ensures a balanced assessment of digital competitiveness.

An essential feature of the WDCR methodology is its emphasis on longitudinal assessment. The ranking not only captures a country’s current digital capabilities but also tracks its progress over time, allowing policymakers and businesses to monitor digital transformation trends. The 2024 edition highlights significant shifts in rankings, reflecting changes in regulatory policies, technological investments, and AI adoption across countries. For instance, the rise of the United Arab Emirates and Saudi Arabia in the rankings underscores the impact of proactive digital strategies in emerging economies. In contrast, Europe’s fragmented capital markets remain a challenge for sustaining digital competitiveness at the regional level.

Each sub-factor in the WDCR is weighted equally in the overall score (11.1%), ensuring that no single aspect disproportionately influences a country’s ranking. Notably, AI-related indicators have gained prominence in recent assessments, reflecting the growing impact of AI on digital transformation. This methodological adaptation underscores the WDCR’s responsiveness to emerging technological trends. The final score for each economy is computed using a composite index that integrates both executives’ perceptions and statistical data sources [9].

3.1.2. The Network Readiness Index (NRI)

The NRI is a comprehensive index that evaluates the digital readiness of countries, specifically their ability to leverage and implement technologies and digital innovations. The NRI is a composite index constructed with three levels. The primary level consists of four pillars that form the fundamental dimensions of network readiness: Technology, People, Governance, and Impact. Each of these fundamental pillars is divided into additional sub-pillars, which make up the second level (see

Table 2. Pillars and sub-pillars of the NRI, summarized by the authors based on [50].

NRI
Technology	People	Governance	Impact
Access	Individuals	Trust	Economy
Content	Businesses	Regulation	Quality of Life
Future Technologies	Governments	Inclusion	SDG Contribution

). The third level consists of 54 individual indicators distributed across the different sub-pillars and pillars of the primary and secondary levels. Compared to the 2023 edition, the NRI 2024 includes four new indicators, while four others have been removed or replaced to improve conceptual alignment. The weight of selected indicators has also been adjusted to enhance statistical coherence within specific sub-pillars. The NRI 2024 consists of 54 indicators: 34 quantitative (hard) data, 10 index/composite indicators, and 10 qualitative survey data. This index evaluates 133 economies, one less than in 2023 due to adjustments in country coverage [50].

Given the complexity of digital readiness, the NRI is designed to provide a detailed overview of various aspects of a country’s readiness. The index is created by aggregating indicators from the lowest level (indicators) to the final evaluation index. This process involves calculating the arithmetic mean without weights at each level, specifically: (i) aggregation of indicators within each sub-pillar, (ii) aggregation of sub-pillars within each pillar, and (iii) aggregation of pillars within the overall index [50].

Within the Technology pillar, the sub-pillar ‘Access’ evaluates the availability and affordability of technologies crucial for digital transformation. Indicators in this area include mobile broadband penetration, internet bandwidth speed, and the affordability of ICT services. The ‘Content’ sub-pillar focuses on scientific output, software development (measured through GitHub (https://github.com/) contributions), and the expansion of digital platforms. The ‘Future Technologies’ sub-pillar tracks the adoption of emerging technologies such as AI, big data, cloud computing, and robotics, along with corporate investments in these fields [50].

The People pillar examines digital literacy and workforce skills. The ‘Individuals’ sub-pillar assesses ICT skills in education, internet usage patterns, and digital participation. The ‘Businesses’ sub-pillar evaluates digital adoption in enterprises, cloud computing investments, and AI-related activities. The ‘Governments’ sub-pillar focuses on e-government capabilities, public sector digital transformation, and regulatory support for innovation [50].

The Governance pillar includes the ‘Trust in the Digital Environment’ sub-pillar, which evaluates cybersecurity readiness, online privacy regulations, and digital rights protection. The ‘Regulation’ sub-pillar examines policy frameworks for emerging technologies, data governance, and competition policies. The ‘Inclusion’ sub-pillar assesses e-participation levels and disparities in digital access [50].

The Impact pillar evaluates the economic and social effects of digital transformation. The ‘Economy’ sub-pillar includes high-tech exports, digital trade, and job creation in ICT sectors. The ‘Quality of Life’ sub-pillar measures life expectancy, digital health services, and well-being indicators. The ‘SDG Contribution’ sub-pillar tracks the alignment of digital policies with sustainable development goals, including universal health coverage and education accessibility [50].

These updates in the NRI 2024 framework enhance its reliability, alignment with global digital trends, and policy relevance, making it a valuable tool for assessing digital readiness across economies.

3.1.3. The AI Readiness Index (AIRI)

The AIRI examines how ready a given government is to implement AI in delivering public services to its citizens. The index includes 40 indicators (one more compared to 2023) across ten dimensions, comprising three pillars: Government, Technology Sector, and Data and Infrastructure (

Table 3. Pillars and dimensions of the AIRI, summarized by the authors based on [11].

AIRI
Government	Technology Sector	Data and Infrastructure
Vision	Human Capital	Data Representativeness
Governance and Ethics	Innovation Capacity	Data Availability
Digital Capacity	Maturity	Infrastructure
Adaptability

). This index ranks 193 countries. The Government AIRI aims to provide valuable insights into the conditions necessary for the effective and responsible integration of AI into public services [11].

The Government Pillar evaluates the variety and efficiency of AI strategies implemented worldwide. Although the number of new AI strategies has decreased, a notable share now comes from low- and lower-middle-income countries, reflecting their increasing recognition of AI’s potential. A government should have a strategic vision for how it develops and governs AI, supported by appropriate regulation and attention to ethical risks. It also needs strong internal digital capacity, including skills and practices that support adaptability to new technologies [11].

The Technology Sector Pillar highlights significant gaps in AI capabilities between high- and middle-income nations. High-income countries maintain a strong lead, but some middle-income nations, such as Malaysia, have made remarkable progress, securing positions among the top 50 globally. Public bodies rely on a strong supply of AI tools from the country’s technology sector, which must be mature enough to support the government. This sector should have high innovation capacity, backed by a business environment that fosters entrepreneurship and maintains strong levels of R&D spending. Equally important are good levels of human capital, which drive the development of advanced AI solutions and ensure the sector can adapt to the evolving needs of governments [11].

The Data and Infrastructure Pillar addresses persistent issues related to the digital divide. While generative AI offers opportunities for lower-income countries, limited data availability and weak infrastructure often result in dependence on external technologies, complicating efforts to meet local demands. AI tools require large amounts of high-quality data, which should also be representative of a country’s citizens to avoid bias and error. Furthermore, the potential of this data cannot be fully realized without the infrastructure needed to power AI tools and deliver them to citizens. Improving these areas is essential for providing fair and inclusive access to the benefits of AI [11].

3.1.4. The Digital Quality of Life Index (DQLI)

The DQLI provides insights into the factors influencing a country’s digital well-being. It evaluates 121 countries across five pillars: Internet Affordability, Internet Quality, Electronic Infrastructure, Electronic Security, and Electronic Government. These pillars encompass 14 indicators (see

Table 4. Pillars and indicators of the DQLI, summarized by the authors based on [12].

Index	Pillars	Indicators
DQLI	Internet Affordability	Work time it takes to afford the cheapest mobile internet (seconds) Work time it takes to afford the cheapest fixed internet (minutes)
	Internet Quality	Mobile speed (Mbps) Broadband speed (Mbps) Mobile internet stability (indexed value) Broadband internet stability (indexed value) Mobile internet speed growth (indexed value) Broadband internet speed growth (indexed value)
	Electronic Infrastructure	Individuals using the Internet (per 100 inhabitants) Network readiness
	Electronic Security	Cybersecurity (index) Data protection laws (indexed values)
	Electronic Government	Online Service Index (index) AI readiness (index)

) that collectively measure digital quality of life, as detailed in the 2024 DQL report [12]. Internet affordability assesses the amount of work time individuals need to allocate to afford reliable internet access. Lower affordability scores correlate with higher digital well-being, as elevated costs can restrict access to essential digital tools. Internet quality, characterized by connection speed and reliability, significantly impacts daily activities and workplace efficiency. Enhanced internet quality facilitates better communication and enriches user experiences. Electronic infrastructure assesses the readiness and robustness of a country’s digital framework, supporting daily internet usage across various sectors, including education, e-commerce, and financial services. A strong infrastructure ensures seamless engagement in these activities. Electronic security evaluates data protection laws and cybersecurity measures, examining the safety of online interactions and a nation’s readiness to manage cyber threats, thereby safeguarding user privacy and data integrity. E-government assesses the availability and quality of online government services. E-government efficiency is crucial for minimizing administrative hurdles and promoting transparency in public services, thereby enhancing their effectiveness and improving citizens’ quality of life [12].

In the 2024 DQLI, Germany leads the rankings, followed by Finland, France, the Netherlands, and Denmark. This reflects Europe’s strong performance in digital well-being, with nine out of the top ten countries hailing from the continent. The methodology of DQLI involves analyzing over 120 countries worldwide, focusing on the five core pillars mentioned above. Each pillar is assessed through specific indicators that collectively provide a holistic view of a nation’s digital quality of life. This comprehensive approach ensures that the DQLI accurately reflects the multifaceted nature of digital well-being in different countries [12].

3.2. Methods

Friedman’s ANOVA and Kendall’s coefficient of concordance (r_k) were used to verify the stated hypotheses. Friedman’s ANOVA is a non-parametric alternative to one-way ANOVA, suitable for analyzing differences. The prerequisite for this test is that the investigated variables are measured on an ordinal scale. Friedman’s ANOVA tests whether individual variables come from the same population or whether specific populations have identical medians. This method is used to test Hypotheses H1 and H3, which assume statistically significant differences in rankings according to various indices and over time (2019–2024), respectively.

Kendall’s coefficient of concordance is an alternative to Spearman’s rank correlation, which determines the correlation between two non-parametric variables. In contrast, Kendall’s coefficient expresses the existence of a relationship between several variables. It is used to test Hypothesis H2, which posits a statistically significant correlation (agreement) between rankings generated by different indices, indicating their internal consistency and reliability.

The application of Friedman’s ANOVA and Kendall’s coefficient of concordance is well-documented in empirical studies analyzing ranking consistency or evaluating changes across repeated measures or conditions. For instance, Cavaggioni et al. [51] used Friedman’s test to monitor seasonal changes in breathing patterns, trunk stabilization, and muscular power in Paralympic swimmers, while Kendall’s coefficient of concordance was employed to assess the level of agreement in repeated observations across three time points. Similarly, Martin et al. [52] applied both tests to evaluate food item preferences and interindividual consistency among rhesus macaques in reinforcement training. In both studies, Friedman’s ANOVA was used to detect statistically significant differences in ordinal data across multiple related samples, and Kendall’s coefficient of concordance was used to measure agreement or consistency across subjects. In a medical imaging context, He et al. [53] employed these tests to assess imaging quality and inter-rater agreement in magnetic resonance cholangiopancreatography protocols, demonstrating the broader applicability of these non-parametric methods in reliability and performance comparisons. These studies confirm the suitability of Friedman’s ANOVA and Kendall’s coefficient of concordance for comparing ranking-based data across different indices and time periods, as applied in our current study.

The choice of these methods was based on their suitability for ordinal data and their widespread application in comparative index analysis. Data for each index were collected for the period 2019–2024 and analyzed using Statistica 14 software.

4. Results

The results of the analysis, based on Friedman’s ANOVA and Kendall’s coefficient of concordance, provide insights into how country rankings differ over time and across digital competitiveness indices. This section presents statistical evidence that supports the formulated hypotheses. We examined the correlations in country rankings across the analyzed years (2019–2024) within the selected indices (WDCR, NRI, AIRI, DQLI) to assess whether countries’ positions in digital competitiveness changed over time. The test results, summarized in

Table 5. Friedman’s ANOVA and Kendall’s coefficient of concordance for country rankings across years and indices.

Variable	ANOVA χ2	p-Value	Kendall’s Coefficient of Concordance rk	Average Rank r
Ranks 2024	94.8483	0.0000	0.8469	0.7958
Ranks 2023	95.4345	0.0000	0.8521	0.8028
Ranks 2022	101.1793	0.0000	0.9034	0.8712
Ranks 2021	104.9241	0.0000	0.9368	0.9158
Ranks 2020	79.3287	0.0000	0.9444	0.9166
Ranks 2019	76.7540	0.0000	0.9137	0.8706
Ranks WDCR	161.0161	0.0000	0.9584	0.9501
Ranks NRI	165.0483	0.0000	0.9827	0.9789
Ranks AIRI	160.4736	0.0000	0.9552	0.9462
Ranks DQLI	99.9448	0.0000	0.8924	0.8565

, provide valuable insights into the consistency of rankings over time and across different indices.

The test results, summarized in Table 5, provide valuable insights into the consistency of country rankings within each year (2019–2024) and across each index (WDCR, NRI, AIRI, DQLI). Specifically, the first six rows (2019–2024) show the ranking positions of countries according to all four indices in the respective year, while the last four rows (WDCR, NRI, AIRI, DQLI) illustrate how countries were ranked within each index over the six-year period.

The second column reports the value of the Friedman test statistic (approximated by the χ² distribution), used to assess whether statistically significant differences exist in the rankings. This non-parametric test is suitable for ordinal data and is commonly applied when comparing more than two related groups. The associated p-values indicate whether the differences are statistically significant. High values of Kendall’s coefficient of concordance (r_k) in the fourth column suggest strong agreement among rankings within the compared groups.

Friedman’s ANOVA results reveal statistically significant differences between country rankings across the years analyzed (2019–2024) and according to the different evaluation indices used. For each year and index, the p-values are 0.0000, well below the significance threshold of 0.05. This indicates significant differences in the rankings of countries across the years, depending on the specific evaluation index employed. The ANOVA χ² values provide direct evidence that rankings among countries differ significantly over time, both for individual indices and across specific years based on the index used. These findings confirm the validity of Hypothesis H1, which posited that there are statistically significant differences in the rankings of countries according to digital indices that use different assessment methodologies. Additionally, the results support the validity of Hypothesis H3, which assumes that there are statistically significant differences in the rankings of countries from 2019 to 2024 based on various digital indices (WDCR, NRI, AIRI, DQLI).

In addition to the Friedman test, we applied Kendall’s coefficient of concordance (r_k) to assess the degree of agreement among country rankings. While Friedman’s ANOVA tests for statistically significant differences in rankings across groups, Kendall’s r_k quantifies the consistency of these rankings—both across different indices in a given year and across years within each index. Values of Kendall’s coefficient close to 1 indicate strong agreement in country rankings, whereas values near 0 suggest inconsistency. The results presented in Table 5 reveal high r_k values across all examined cases, indicating a substantial level of concordance in the rankings. This supports the robustness of the ranking results derived from these indices when used to compare countries’ digital competitiveness from both cross-sectional and longitudinal perspectives.

The values of the Kendall’s coefficient of concordance also suggest a high level of agreement in the assessment of countries according to individual indices (variables 2019–2024), as well as a high level of consistency within each index over time (variables WDCR, NRI, AIRI, DQLI). This strong correlation indicates that the rankings assigned by different indices are generally consistent, reinforcing the validity of the indices as reliable measures of digital competitiveness.

For the years analyzed (2019–2024), the Kendall coefficient values range from 0.8469 in 2024 to 0.9444 in 2020, indicating that despite differences in evaluation methodologies, the relative positions of countries remain stable over time. These results confirm that the country’s rankings exhibit significant consistency, with only minor deviations. The average values (r) for each year, ranging from 0.7958 in 2024 to 0.9166 in 2020, further support this conclusion, suggesting that the rankings are relatively stable. Additionally, the high Kendall’s coefficient of concordance values for the different indices (WDCR, NRI, AIRI, DQLI) suggest a strong correlation between the analyzed years, depending on the evaluation method used. The value of r_k for WDCR (0.9584) indicates that the ranking of countries in this index between 2019 and 2024 is relatively stable and unchanged. This consistency shows that the rankings of countries are similar across different indices, reinforcing the idea that the relative positions of countries in terms of digital competitiveness remain stable. These results confirm Hypothesis H2, which posited that there is a statistically significant correlation between the rankings of countries across different digital indices.

The results of this analysis provide compelling evidence of the reliability and consistency of country rankings derived from digital competitiveness indices. The statistically significant correlations observed in Kendall’s coefficient of concordance confirm that the country rankings remain stable across the different indices. Additionally, the high values of this coefficient further support the notion that these rankings exhibit general stability over time. The findings validate all the established hypotheses regarding significant differences in the rankings of countries and significant correlations in the evaluations across the individual indices, thereby reinforcing the robustness of the assessment methods employed in this study.

In addition to the statistical test results presented in Table 5, we provide descriptive statistics for each country (

Table 6. Descriptive statistics for 2024 data.

Country	Average Rank	Sum of Ranks	Mean	Sum	Median	Min	Max	Std. Dev.
Austria	10.50	42	10.50	42	11.0	6	14	3.4157
Belgium	12.50	50	13.25	53	12.0	10	19	4.0311
Bulgaria	24.00	96	24.75	99	26.0	18	29	4.7170
Croatia	26.75	107	28.50	114	29.0	23	33	4.1231
Cyprus	24.50	98	26.00	104	25.0	23	31	3.4641
Czech Republic	17.75	71	19.25	77	17.0	16	27	5.1881
Denmark	5.50	22	5.50	22	6.0	2	8	2.6458
Estonia	11.25	45	11.25	45	11.0	10	13	1.5000
Finland	3.50	14	3.50	14	3.5	1	6	2.3805
France	5.50	22	5.50	22	6.0	1	9	4.1231
Germany	5.75	23	5.75	23	5.0	1	12	4.6458
Greece	27.50	110	29.75	119	27.5	26	38	5.5603
Hungary	25.75	103	27.75	111	26.0	23	36	5.9090
Ireland	11.25	45	12.25	49	10.0	7	22	6.7020
Italy	17.50	70	19.00	76	18.5	13	26	5.7155
Latvia	21.75	87	23.25	93	23.0	19	28	3.7749
Lithuania	15.50	62	16.00	64	17.0	11	19	3.8297
Luxembourg	13.25	53	13.25	53	14.5	8	16	3.5940
Netherlands	3.50	14	3.50	14	3.5	3	4	0.5774
Norway	9.50	38	10.75	43	7.0	5	24	8.9209
Poland	19.25	77	20.00	80	20.0	20	20	0.0000
Portugal	18.25	73	19.75	79	18.0	14	29	6.4485
Romania	23.25	93	23.75	95	25.5	16	28	5.4391
Slovakia	25.25	101	27.25	109	26.0	22	35	5.5603
Slovenia	22.00	88	23.50	94	21.5	21	30	4.3589
Spain	13.75	55	13.75	55	15.0	7	18	4.7170
Sweden	6.00	24	6.00	24	5.0	2	12	4.5461
Switzerland	8.25	33	8.25	33	7.5	1	17	7.1822
United Kingdom	6.00	24	6.00	24	6.5	2	9	3.1623

Note: The values in the table are based on the rankings of countries across the four indices: WDCR, NRI, AIRI, and DQLI.

) and a box-and-whisker plot (Figure 1), which visually illustrate countries’ positions based on the assessments by individual indices in 2024 and their overall ranking in that year. Table 6 summarizes the descriptive statistics for the 2024 digital competitiveness rankings across the selected countries. It presents each country’s average rank, sum of ranks, median, minimum, maximum, and standard deviation.

The countries with the highest levels of digital competitiveness were the Nordic countries, such as Finland, Denmark, the Netherlands, and Sweden. The Nordic countries consistently rank among the top performers, thanks to their well-developed digital infrastructures, innovative ecosystems, and strong integration of AI. The top five countries were rounded out by the United Kingdom, primarily due to its strong performance in the AIRI. In contrast, more significant discrepancies in country rankings were observed in the DQLI, as seen in

Table 7. Spearman’s correlation coefficients. Note: The darker the green, the higher the correlation between the variables.

	WDCR 2024	NRI 2024	AIRI 2024	DQL 2024	WDCR 2023	NRI 2023	AIRI 2023	DQL 2023	WDCR 2022	NRI 2022	AIRI 2022	DQL 2022	WDCR 2021	NRI 2021	AIRI 2021	DQL 2021	WDCR 2020	NRI 2020	AIRI 2020	DQL 2020	WDCR 2019	NRI 2019	AIRI 2019	DQL 2019
WDCR 2024	1.0000	0.9158	0.8064	0.6798	0.9512	0.9074	0.8207	0.6970	0.9453	0.9133	0.8478	0.8148	0.9488	0.9163	0.8576	0.8700	0.9365	0.9207	0.8522	0.7384	0.9369	0.9015	0.7808	0.6875
NRI 2024		1.0000	0.9103	0.7507	0.9207	0.9793	0.9404	0.7369	0.9394	0.9675	0.9409	0.8665	0.9532	0.9675	0.9611	0.9000	0.9379	0.9576	0.9512	0.8090	0.9384	0.9488	0.9128	0.7402
AIRI 2024			1.0000	0.7118	0.8089	0.8936	0.9704	0.6813	0.8266	0.8773	0.9704	0.8429	0.8419	0.8892	0.9409	0.8419	0.8212	0.8626	0.9266	0.7805	0.8286	0.8626	0.9227	0.7326
DQL 2024				1.0000	0.6532	0.7424	0.7675	0.9153	0.7089	0.7064	0.7468	0.8074	0.7015	0.7335	0.7833	0.8163	0.7153	0.7113	0.7867	0.7115	0.7478	0.6695	0.7222	0.6390
WDCR 2023					1.0000	0.9222	0.8177	0.6606	0.9537	0.9350	0.8433	0.7813	0.9429	0.9414	0.8616	0.8305	0.9345	0.9374	0.8433	0.7433	0.9310	0.9236	0.7862	0.6232
NRI 2023						1.0000	0.9305	0.7419	0.9384	0.9872	0.9197	0.8463	0.9606	0.9857	0.9429	0.9054	0.9360	0.9798	0.9266	0.7783	0.9404	0.9724	0.8778	0.7265
AIRI 2023							1.0000	0.7438	0.8576	0.9069	0.9709	0.8704	0.8828	0.9103	0.9645	0.8946	0.8631	0.8990	0.9670	0.8041	0.8586	0.8956	0.9276	0.7436
DQL 2023								1.0000	0.7025	0.7030	0.6990	0.8094	0.7025	0.7296	0.7429	0.8576	0.6798	0.7108	0.7798	0.6902	0.7167	0.6670	0.6951	0.6219
WDCR 2022									1.0000	0.9414	0.8719	0.8355	0.9645	0.9517	0.8897	0.8631	0.9498	0.9493	0.8833	0.7964	0.9468	0.9340	0.8256	0.7067
NRI 2022										1.0000	0.8901	0.8414	0.9655	0.9931	0.9355	0.8926	0.9414	0.9946	0.9182	0.7871	0.9468	0.9872	0.8685	0.7231
AIRI 2022											1.0000	0.8468	0.8833	0.9020	0.9478	0.8576	0.8818	0.8818	0.9291	0.8352	0.8778	0.8734	0.9286	0.7600
DQL 2022												1.0000	0.8113	0.8567	0.8764	0.9330	0.7921	0.8458	0.9133	0.8396	0.8330	0.8133	0.8532	0.8202
WDCR 2021													1.0000	0.9640	0.9143	0.8729	0.9778	0.9734	0.9034	0.7876	0.9665	0.9680	0.8384	0.7026
NRI 2021														1.0000	0.9443	0.9049	0.9399	0.9916	0.9256	0.8117	0.9537	0.9813	0.8808	0.7497
AIRI 2021															1.0000	0.8941	0.8921	0.9345	0.9793	0.8238	0.9133	0.9197	0.9197	0.7258
DQL 2021																1.0000	0.8493	0.9005	0.9256	0.8101	0.8744	0.8754	0.8360	0.7969
WDCR 2020																	1.0000	0.9532	0.8759	0.8243	0.9655	0.9374	0.8276	0.6821
NRI 2020																		1.0000	0.9207	0.8128	0.9596	0.9901	0.8527	0.7443
AIRI 2020																			1.0000	0.8347	0.9034	0.9084	0.9281	0.7504
DQL 2020																				1.0000	0.8374	0.7789	0.8402	0.8318
WDCR 2019																					1.0000	0.9429	0.8335	0.7347
NRI 2019																						1.0000	0.8355	0.7463
AIRI 2019																							1.0000	0.7491
DQL 2019																								1.0000

, which represents Spearman’s correlation coefficient between the indices over the years. Although the correlation coefficient is high among all variables, the lowest correlation values are identified in the DQLI. According to this index, France and Germany ranked among the top countries.

Bulgaria, Greece, and Croatia exhibit the weakest levels of digitalization, reflecting gaps in digital skills, regulatory frameworks, and technology adoption. However, Romania, Cyprus, Hungary, Slovakia, and Slovenia frequently appear among the lowest-ranked countries in the DQLI.

A detailed descriptive statistical analysis (in Table 6) highlights the disparities in digital competitiveness among European countries.

The Box–Whisker plot in Figure 1 visually represents these rankings and illustrates the variability in country positions across the indices. The plot also highlights significant dispersion in rankings for certain countries, particularly in the DQLI.

To further explore the consistency of country rankings across the years and between the different indices, we calculated Spearman’s correlation coefficients based on the rankings derived from the indices, rather than the indices themselves. As shown in Table 7, the correlation coefficients are generally high across all rankings, indicating a strong relationship between them. However, DQLI consistently shows lower correlations compared to the other indices, particularly when compared with WDCR, NRI, and AIRI. This is evident in the consistently lower coefficients between DQLI and the other indices, such as 0.66 with WDCR in 2023, 0.74 with NRI in 2023, and 0.70 with AIRI in 2023.

These correlation results are crucial for understanding whether different indices assess digital competitiveness in similar ways. For example, comparing rankings such as NRI 2024 with DQLI 2019 helps identify whether countries’ digital performance is evaluated consistently over time and across different methodologies. Lower correlations with DQLI suggest that this index captures unique aspects of digitalization—such as internet affordability or cybersecurity—that may not fully align with the infrastructure and innovation focus of indices like NRI or WDCR. Such comparisons also reveal temporal dynamics and structural differences in how digital readiness is conceptualized and measured.

The DQLI displays the most variation in rankings, with countries like France and Germany performing relatively well, while others exhibit lower ranks. This suggests that the DQLI captures aspects of digitalization—such as internet affordability and cybersecurity—that may not be directly aligned with the economic and technological readiness components of other indices. These variations highlight the importance of considering multiple indices when evaluating digital competitiveness, as each captures distinct facets of a country’s digital landscape. For example, in 2023, France ranks higher in the DQLI compared to the other indices. In contrast, countries like Romania, Cyprus, Hungary, Slovakia, and Slovenia tend to have lower rankings in DQLI, further emphasizing the index’s unique focus.

The AIRI analysis revealed that countries with high scores in this index are closely aligned with the most digitally competitive nations, as measured by the WDCR and NRI. This correlation suggests that a country’s ability to implement AI technologies is strongly linked to its overall digital readiness and innovation potential.

5. Discussion

The results of this study provide strong evidence for the reliability and consistency of the rankings derived from the selected digital competitiveness indices. The analysis demonstrates that rankings based on the WDCR, NRI, AIRI, and DQLI exhibit a high degree of concordance, particularly visible in the values of Kendall’s coefficient across years and indices. These findings confirm the assumption that, despite methodological differences, the relative positions of countries in the digital competitiveness landscape remain remarkably stable, especially among top-ranked and bottom-ranked countries.

Our findings align with the broader understanding of how digital capabilities contribute to national competitiveness. For instance, Sui et al. [30] highlighted that digital transformation positively impacts the competitiveness of manufacturing enterprises, while AI-driven automation has emerged as a tool to enhance productivity, reduce costs, and strengthen economic resilience [54]. Although our study did not directly measure digital transformation processes, the convergence in country rankings across multiple indices supports the argument that digital infrastructure, AI readiness, and digital quality are integral components of competitiveness, as recognized by various global frameworks.

Similarly, the presence of countries such as Finland, the Netherlands, and Denmark among the top-ranked in all indices reflects their recognized strengths in digital infrastructure, innovation ecosystems, and strategic orientation toward digital technologies. These results align with those of Jenčová et al. [55], who identified these countries as digital leaders due to their innovative capacities and integration of advanced technologies. This is further supported by Zhu et al. [56], whose findings highlight the role of institutional support and well-developed digital ecosystems in achieving high competitiveness rankings.

In contrast, our results show that countries such as Romania, Greece, and Bulgaria consistently rank lower across all indices. This reflects persistent disparities in digital infrastructure and institutional support, aligning with previous research by Bacca-Acosta et al. [28], and highlights the digital divide both within and between European regions. The results demonstrate that weaker rankings are not confined to a single index but are reinforced across different frameworks, suggesting underlying structural challenges.

Our analysis also identifies areas of divergence. The DQLI, for instance, exhibits relatively lower correlation values with the other indices. This is consistent with its conceptual focus on affordability, cybersecurity, and digital well-being [12], in contrast to the WDCR and AIRI, which emphasize technological integration and economic readiness [9,11]. These findings demonstrate that each index captures distinct aspects of digital development. While high convergence in rankings suggests agreement on general patterns of competitiveness, divergence highlights the complementary nature of these indices. Therefore, this study confirms that a multidimensional approach is essential for a comprehensive assessment of digital competitiveness.

Furthermore, the high consistency in rankings among the leading countries underscores the robustness of the rankings derived from the evaluated indices. For instance, the Netherlands’ strong performance across multiple indices reflects its well-established digital ecosystem and innovation environment, as supported by Zhu et al. [56]. This consistency validates the use of these indices for benchmarking digital competitiveness and guiding policy decisions.

In contrast, the variability in rankings for countries with lower digital competitiveness emphasizes the need for a more detailed understanding of the impact of digital transformation. The differences in rankings between the DQLI and other indices suggest that while some countries may excel in specific areas of digital quality, they may still face challenges in broader digital readiness and technological advancement.

Overall, this study contributes to the ongoing debate on digital competitiveness by providing a comparative analysis of indices and validating their reliability. The consistent findings across most indices suggest a high level of agreement on countries’ relative digital competitiveness. However, the variations observed in specific indices highlight the importance of using a multidimensional approach to comprehensively evaluate and understand digital competitiveness.

Despite its contributions, this study has certain limitations that should be acknowledged. The analysis is based solely on publicly available data, which may not fully capture the complexity of national digitalization strategies or institutional dynamics. Additionally, the sample is restricted to European countries, limiting the global applicability of the findings. Future research could address these limitations by expanding the geographical scope of the analysis, incorporating sector-specific assessments, and employing more qualitative methods such as expert interviews or case studies. These qualitative approaches would allow for a deeper exploration of institutional practices and contextual factors that are often underrepresented in quantitative indices. While indices provide structured, comparable measures across countries, qualitative insights can reveal how digital readiness is implemented in practice and how it aligns with national priorities. Although the indices analyzed are compiled at the national level, sub-indices and disaggregated components offer useful insights into sectoral performance—especially when contextualized through qualitative evidence or national policy documentation. A more detailed focus on sectors such as education, healthcare, or public administration could further enrich the understanding of digital transformation and support targeted, evidence-based policymaking. In addition, future research could explore the periodic assessment of the reliability and stability of country rankings across indices. While our findings reveal a high degree of concordance, the observed year-to-year variability in some rankings suggests that index-based evaluations may be sensitive to methodological updates, indicator weightings, or external policy shifts. A systematic evaluation of these temporal dynamics could contribute to the refinement of digital assessment tools and support more informed policy responses.

The rapid advancement of AI and digital transformation calls for a comprehensive legal framework to regulate digital changes across borders, ensuring consistency in digital policies and fostering a harmonized approach to AI governance [57]. A growing body of research highlights the regulatory fragmentation in AI governance, emphasizing the need for standardized frameworks that ensure ethical AI deployment while balancing innovation and societal risks.

6. Conclusions

This paper explored how different indices assess countries’ digitalization levels and evaluated the reliability of the rankings derived from them by comparing country rankings. It aimed to determine whether these rankings differ due to methodological variations and whether a statistically significant correlation exists between them, thus confirming the consistency and accuracy of their rank-based evaluations.

Our findings indicate a significant correlation among the rankings derived from the selected indices, such as the WDCR, NRI, AIRI, and DQLI. These results demonstrate the high reliability of these rankings as tools for comparing digital competitiveness. Finland, the Netherlands, and Denmark emerged as leaders in digitalization across all indices, while Romania, Greece, and Bulgaria consistently ranked lower. The ability to identify countries at both the top and bottom of the rankings provides valuable insights into global progress in digitalization and offers an opportunity for other nations to adopt best practices from the top performers.

The originality of this study lies in its comprehensive evaluation of multiple indices, revealing both convergences and divergences in country rankings. These findings reinforce the importance of accurate and multidimensional measurement frameworks for assessing digital competitiveness, as different indices emphasize distinct aspects of digital transformation. The study’s conclusions can serve as a foundation for policymakers to design targeted strategies that foster digital inclusion, improve regulatory frameworks, and accelerate technological adoption in lagging economies.

Policymakers and stakeholders can use these findings to design strategies aimed at improving digital readiness and promoting inclusive growth, ultimately reducing global digital inequalities. The findings highlight the need for continuous investment in digital infrastructure, AI adoption, and inclusive digital policies to ensure sustainable economic growth and competitiveness in the digital era. Additionally, the results of this study emphasize the importance of a multidimensional assessment of digital competitiveness. The findings confirm the need for the systematic monitoring of countries’ readiness for AI and digital transformation. Furthermore, the proposed methodology can also be applied to evaluate AI policies and strategic digital initiatives.