Navigating the Convergence of Global Competitiveness and Sustainable Development: A Multi-Level Analysis

Abstract

Sustainable Development (SD) involves multiple factors and different perspectives that are critical to individuals, organizations, regions, countries, and the world. Even though relationships among Global Competitiveness, the Macroeconomic Environment, and SD dimensions, encompassing 17 Sustainable Development Goals (SDGs), are critical, for national and global policymaking, many of these relationships have not been studied systematically nor at different aggregation levels. Therefore, this study examines the relationships among the Global Competitiveness pillars (e.g., Institutions, Infrastructure, Education, Technology, Business Sophistication, and Innovation) and the Macroeconomic Environment and the SD dimensions (Social, Economic, and Environmental) using various models. Additionally, this study examined three country clusters (very-competitive, competitive, and less competitive). The PLS-SEM analysis used 11-year data from 128 countries. The results showed variability in the relationships studied as well as differences between country clusters and countries. The findings may be applicable for policymakers in reflecting and acting on the specificity of national SD, determining SD priorities for the future, as well as reflecting and acting on integrating national SD into global SD.

1. Introduction

Benchmarks to improve the Earth’s life-support systems while safeguarding continuous social progress levels have been established by the United Nations Sustainable Development Goals (SDGs), a set of 17 global goals designed to be a ’blueprint to achieve a better and more sustainable future for all’. However, progress toward the SDGs has been unsatisfactory [1,2,3]. One of the key requirements of Sustainable Development (SD) is to be globally competitive, since competitiveness is positively and strongly correlated with the Economic, Social, and Environmental dimensions of SD. Global Competitiveness (GC) is associated with dynamic development strategies and structural transformations according to global trends [4,5]. However, it must be in line with the necessities of the Economic, Social, and Environmental dimensions of SD.

The focus on competitiveness has shifted from exclusively economic-driven indicators to knowledge transfer, quality of life, Technology, and environment-driven indicators [6]. Thus, the Global Competitiveness Index, a widely recognized and comprehensive measure of competitiveness, is one of the most comprehensive indices that measures competitiveness, with a high accuracy level since it considers multiple pillars with 144 indicators. Such a comprehensive set of indicators is vital to study Sustainable Development’s three key dimensions’ (Economic, Social, and Environmental) relationships [7]. In addition, it is also necessary to understand how the Global Competitiveness pillars relate to each other and how they shape the Macroeconomic Environment, which, in turn, affects the SD dimensions. These studies may help policymakers to reconfigure and readjust their current and future policies, since working with policies efficiently and effectively would positively affect countries’ Macroeconomic Environments and GC levels [8,9]. Likewise, to be able to generate higher levels of economic growth, countries need to develop unique products and be involved in highly trending industries by cultivating their Innovation and Business Sophistication through better Institutions, Education, and Infrastructure, which would allow them to have a competitive economy [10,11].

Many studies have examined different sustainability facets, among others, the complexity of Environmental and Social factors [12]; the resilience of sustainability [13]; the economics of sustainability [14]; critical systems to advance sustainability [15]; the public administration aspects of sustainability [16]; the management of sustainable transitions [17]; the importance of agricultural intensification for SD [18]; goals, targets, and tradeoffs in SD [18,19]; the isolation of SDGs from each other [20] and the alignment of SDGs [21]. Similarly, numerous studies mainly examine the interactions of the Macroeconomic Environment components (e.g., government budget balance, pay, productivity, and inflation) with only one or with other factors [22,23]. However, to the best of our knowledge, no study has considered examining the interactions between the Macroeconomic Environment’s GC antecedents and the Macroeconomic Environment’s relations with different configurations of the SD dimensions at the global, country, and country cluster levels.

Most hitherto research on GC and the Macroeconomic Environment used selected competitiveness antecedents, limited countries, and short time frames. For instance, prior research has looked at the effects of Education [24], Institutions [25], Infrastructure [26], Business Sophistication [27], Technology [28], and Innovation [29] on national development. Consequently, it is necessary to study the multiplicity of interactions among the antecedents of GC, the Macroeconomic Environment, and SD dimensions.

This study examines the relationship between GC pillars and the Macroeconomic Environment and their effect on SD dimensions (Economic, Social, and Environmental) at the global and country cluster levels.

This research contributed to the literature in three ways. Firstly, this study examined the interactions among the GC pillars. The results of the pillars’ relationships helps to prioritize research and policy (e.g., which factor interactions have a larger effect, and/or identifying competitiveness factors that have adverse effects). Secondly, in contrast to previous research that explored an individual pillar’s relationships with the Macroeconomic Environment [24,25], we further studied the interactions between all GC pillars (Institutions, Infrastructure, Education, Technology, Business Sophistication, and Innovation) and the Macroeconomic Environment. Thirdly, this study created cluster countries based on countries’ competitiveness levels, employing data from 128 countries. Segregating the findings by country clusters would enable policymakers of the related country clusters (very competitive, competitive, and less competitive) to determine specific configurations of GC pillars, SD dimensions, and associated SDGs, balancing competitiveness and sustainability.

2. Literature Review

Our model set (see Figure 1, Figure 2, Figure A1, Figure A2, Figure A3, Figure A4 and Figure A5) draws from endogenous economic growth theory [30], Global Competitiveness theory [31], resource-based theory [32], dynamic capabilities theory [33], evolution theory [34], ecological economics theory [35], and complexity theory [36]. The underlying mechanisms of these theories are provided in the Discussion section.

This study used the models shown in Figure 1, Figure 2, Figure A1, Figure A2, Figure A3, Figure A4 and Figure A5. Figure 1, Figure A1, Figure A2, Figure A3, Figure A4 and Figure A5 depict the interactions among the GC pillars as well as the interactions among the Microenvironment Development and SD dimensions (Economic, Social, and Environmental) with six different configurations. Figure 2, in addition to previous models (Models 1–6), is used to measure the GC pillars’ direct and indirect relationships not only with the Macroeconomic Environment but also with the SD dimensions (Economic, Social, and Environmental).

A review of the models’ constructed relationships follows.

2.1. Global Competitiveness (GC)

In this study, we define Global Competitiveness (GC) as a set of policies, factors, and Institutions that measure countries’ productivity levels [37]. According to the neo-classical approach, Gross Domestic Product and its growth rate are critical indicators of a country’s competitiveness [38,39]. However, it is only one of the many GC indicators. Therefore, it is necessary to operationalize GC more comprehensively in the context of this study.

GC incorporates the macro and microeconomic fundamentals of competitiveness, encompassing the set of Institutions and circumstances that produce a country’s productivity [40,41,42]. GC is one of the components of socioeconomic development encompassing Technology, Innovation, and Business Sophistication, which in turn foster productivity [43,44]. The relationship between GC and the Macroeconomic Environment is crucial, as the effectiveness of production, which determines long-term growth and economic prosperity, is a key aspect of GC [37].

GC is assessed by a series of indicators reflected in the GC Index, established by the World Economic Forum (WEF) [45]. The index reflects how a country’s Institutions, Infrastructure, Education, Technology, and Innovation affect its overall competitiveness. The following briefing reviews each of the GC components and provides research evidence supporting, individually, the models’ relationships.

2.2. Institutions

Institutions and their role in the Macroeconomic Environment have been widely discussed [46,47]. Institutions’ role in the level of SD is essential. Efficiently functioning Institutions could lessen the cost of macroeconomic growth [48,49]. Likewise, governments’ guidelines for monitoring carbon dioxide (CO₂) emissions using well-functioning institutions would improve the country’s SD levels [50]. Unfortunately, studies examining interactions between Institutions and SD dimensions have only received increased attention in the last few years.

Institutions are an essential determinant of the Macroeconomic Environment regardless of a country’s economic development level [51,52]. Strong Institutions support establishing the rule of law, resisting corruption, enhancing the supervision of nations’ finances, and reducing the level of military interference in politics. Moreover, Institutions are affiliated with strategies recognized and legitimized by the country’s set of cultural and official organizations that advance the country’s socioeconomic levels [53,54]. Sequentially, improved socioeconomic conditions would, directly and indirectly, improve countries’ SD levels [55]. Ref. [56] noted that efficient and effective Institutions could decrease transaction costs, improving the country’s financial performance. Institutional reforms are particularly important for developing countries, since these countries have limited financial resources, and some rely primarily on foreign direct investment inflows to progress [51].

In contrast, weak Institutions may generate environmental issues. Therefore, Institutions must be studied more comprehensively [57]. Similarly, refs. [58,59] found that weak Institutions have an adverse permanent effect on the country’s Macroeconomic Environment. Nonfunctional Institutions could negatively impact almost every aspect of a country’s development, such as Infrastructure, Education, Technology, Business Sophistication, Innovation, Macroeconomic Environment, and SD [60]. Consequently, improving the quality of Institutions would catalyze pollution control [61,62] and improve countries’ socio-ecological conditions.

Past research has studied Institutions’ relationships with several development components, including, corruption [63,64], tourism [65], energy consumption [66,67], forestry and agriculture [68], natural resources [69,70], human capital [71,72], population density [73], renewable and non- renewable energy [74,75], electricity consumption [76,77], financial development [78,79], globalization [80], trade openness [81], urbanization [82], energy production [83] and imported Technology [84,85]. Despite such vast research, further research is needed about Institutions’ impact on each of the other GC pillars (e.g., Infrastructure, Technology, Education, Business Sophistication, and Innovation), how those other GC pillars influence Institutions, as well as how these relationships impact the Macroeconomic Environment and the SD dimensions.

2.3. Infrastructure

Development research frequently uses distinct econometric techniques in studies limited to one or few countries [86,87]. These studies show that Infrastructure may play an important role in national development. For instance, access to communication, transportation, energy, and financial services encourages and improves the development of businesses and the economy [88]. For example, ref. [89] found that Infrastructure influences economic development positively in 36 countries. Likewise, ref. [90] found a strong interaction between investment in Infrastructure and the economic development of developing economies. Similarly, ref. [91] found a positive association between Infrastructure and economic development in 22 OECD economies. Furthermore, refs. [92,93] also found that Infrastructure is positively related to economic development. In addition, [94] found that Infrastructure development substantially affects both productivity and economic growth in Norway, Ireland, Australia, Denmark, New Zealand, and Canada. In addition, ref. [95] in the USA, ref. [96] in Turkey, and ref. [97] in Romania, and ref. [98] in India, found a positive association between Infrastructure and economic development. These studies show that Infrastructure is a crucial construct that contributes to economic development.

Different studies show that the interaction between Infrastructure and economic growth may be a function of political regimes, specific country conditions, and institutional quality [99,100]. Infrastructure studies examining the effect of Infrastructure on countries’ economic development and their Macroeconomic Environment in the 2000s found varied findings. For example, ref. [101] in India found a non-significant relationship between Infrastructure and economic growth.

Infrastructure research has focused on different indicators. For example, these studies include, among others, ref. [102] on residential electricity, ref. [103] on the transport sector, [104] on energy consumption, ref. [57] on pollution emissions, ref. [105] on diesel consumption and the number of vehicles, refs. [80,106,107] on fuel energy consumption by transport. Thus, different Infrastructure components may have differential effects on the Macroeconomic Environment and SD. Past studies, mainly carried out at the micro level, are fragmented. Consequently, micro-, meso-, and macro-level combinations are required. This research must examine the diverse relationships among Infrastructure and other GC pillars (Technology, Business Sophistication, and Innovation), the Macroeconomic Environment, and the three SD dimensions.

2.4. Education

Education has been given a primary position during the development strategies of economies and plays a key role in achieving growth [108] since it helps countries to shape competitive human capital. Education is indispensable and dominant in the progression of macroeconomic growth by cultivating earnings [109], permitting people to excel [110], diminishing poverty [111], increasing flexibility in the environment [112], ensuring health, and raising competitiveness in economies [113]. It shapes individuals with the knowledge and skills to create, develop, and apply new technologies as well as with enhanced decision-making [114].

Furthermore, Education has a positive impact on countries’ Macroeconomic Environments and SD dimensions by generating high-quality human capital, which leads to technological advancements, Technology transfers, and higher levels of Business Sophistication and Innovation [115,116]. R&D activities also develop domestic technologies, increasing productivity, profitability, and, consequently, strong economic [114]. Ref. [117] highlighted that human capital buildup can ensure macroeconomic growth for protracted periods. An educated population is vital for a creative, innovative, and dynamic economy. Likewise, social responsibility can be upgraded through Education by improving respect for national values and laws and by creating a better sense of responsibility and better behaviors, since educated people are less likely to be tangled in crimes toward citizens and instead contribute positively to the country’s welfare [30,118]. Similarly, Education helps to form strong citizens. Thus, Education plays a key role in determining Economic, Social, and Environmental growth [108,119].

Education positively influences Technology, Business Sophistication, Innovation, the Macroeconomic Environment, and SD dimensions.

2.5. Technology

Technology may provide new opportunities for enhancing current operations, the conception of new businesses, and the growth of productivity in all economic activities [120,121]. Likewise, Technology is indispensable for effectively transitioning societies to sustainability [122,123].

Contemporary recognized theories, such as neo-Schumpeterian [124,125] and neo-classical [126] growth theories, suggest positive interactions among Technology, Technology readiness, Technology diffusion, and the Macroeconomic Environment. Technology produces added value at the firm and sectoral levels, thereby improving productivity and macroeconomic growth at the country level [127,128].

Refs. [129,130] found positive relationships between Technology, Technology diffusion, and Macroeconomic Environment improvement. Furthermore, ref. [131] studied the causal relationships among Technology (mainly information communication Technology), monetary development, Infrastructure, and macroeconomic growth using data from 21 Asian countries. They found that Technology infrastructure and monetary development positively correlate with macroeconomic growth. Similarly, ref. [127] found a positive relationship between Technology investment and macroeconomic growth. The author used panel data covering Islamic Cooperation (OIC) countries from 1990 to 2014. Furthermore, ref. [132] obtained similar findings using panel data covering G-20 countries from 2001 to 2012.

Nevertheless, some studies have found mixed results regarding the relationship between Technology and macroeconomic growth. For instance, ref. [133] conducted a study utilizing OPEC countries in 2002–2015 and found a weak relationship between Technology investment and macroeconomic growth. Similarly to [133] results, refs. [127,132] also found a weak relationship between Technology investment and macroeconomic growth. Likewise, refs. [134,135] found inconclusive results concerning the interactions among Technology diffusion, Technology investment, and Macroeconomic Environment growth. Analogously, Pohjola [136] found a non-significant relationship between Technology and the Macroeconomic Environment. It is important to point out that technological advancements open developing markets to developed markets. Hence, developed countries’ domination in the international markets may also exploit the competitive disadvantage of developing economies and deter their competitiveness and macroeconomic growth. Digital technologies have changed the means and sophistication of these asymmetrical relationships.

Most hitherto research does not comprehensively differentiate countries’ assets, starting development points, policies, and contexts. Thus, considering Technology’s interrelationships with Institutions, Infrastructure, Education, Innovation, Business Sophistication, the Macroeconomic Environment, and SD dimensions may improve the understanding of SD.

2.6. Business Sophistication

Creating value by properly using resources is a critical business skill for effectively competing in national and global markets [137]. Likewise, a cultured business environment would attract more investments and endorse entrepreneurship [138]. In turn, Business Sophistication would positively contribute to the Macroeconomic Environment [139], economic growth [139] and, possibly, SD [140].

Business Sophistication improves efficiency in the production of goods and services [139] and consequently improves nations’ competitiveness. The efficiency of business networks, quality of operations, and the strategies used increase the country’s R&D levels and productivity [141,142]. This is clearly shown in countries at a high development level where productivity development apparatuses have been exploited to a substantial level. The business network excellence of a country entails the effectiveness and efficiency of its domestic suppliers and their productive relationships [143,144]. When manufacturers and suppliers are interrelated in specific sectors and or provinces, their effectiveness in business Innovation improves, thereby creating natural barriers for newcomers, and improving profitability and economic growth [145]. Countries advance their GC by forming brands and value chains and manufacturing unique and sophisticated products and services [146,147]. Therefore, Business Sophistication promotes local suppliers’ quality and quantity, which may be reflected in the better effectiveness of the production of goods and services [148], as well as the relationships between countries’ business networks and the firms’ strategies through sound-strategized resource distribution, operational policies, Institutions, and Infrastructure [149,150]. In turn, spillovers of specific companies’ effective strategies (marketing, distribution, branding, innovative production processes, and the production of unique products) contribute nationwide to sophisticated business processes [150,151].

Thus, Business Sophistication may enhance countries’ Macroeconomic Environment and SD dimensions [150,152].

2.7. Innovation

Innovation is essential to enhance a country’s long-term living standards. Creating and producing unique products is vital to improving the private and public sectors. In turn, enhancing Innovation through well-functioning Institutions and Infrastructure would lead countries to longer-term economic growth as well as to improved SD levels [145,148]. However, achieving reasonable economic growth rates will not guarantee continuously rising national competitiveness unless firms’ and country-level production are accompanied by unique products and services [153]. Therefore, firms and countries should constantly strive to enhance technological Innovation for their longer-term competitiveness. It is also important to note that R&D is one of the foremost components of Innovation [154,155]. However, the contributions of R&D may not be mirrored in the economic system. For instance, ref. [150] found that not all Innovations are significant enough to improve the Macroeconomic Environment. Organizational structures and business processes are required to secure the total capacity of Innovation processes. Innovative Technology is also important in attaining structural change that is favorable to macroeconomic growth [124]. Similarly, researchers have found that economic growth negatively impacts pollution when innovative Technology is not adequately utilized [156]. Also, Innovation’s longer-term effect on the Macroeconomic Environment is vital in achieving higher SD [157,158]. Innovative Technology has become a crucial factor in obtaining efficient energy production. Hence, moving from fossil fuel energy production to sustainable energy sources imposes newer, environmentally friendly technologies [159,160]. For example, studies suggest that the use of innovative Technology plays a vital role in reducing environmental pollution [161,162].

Examining Technology’s interactions with Institutions, Infrastructure, Education, Business Sophistication levels, the Macroeconomic Environment, and SD dimensions may improve the understanding, realism, and practical relevance of development studies.

2.8. Macroeconomic Environment

A desirable Macroeconomic Environment is a country’s competence in producing services and goods under reasonable market conditions and creating adequate income for its citizens. A supportive Macroeconomic Environment is important for shaping and strengthening the relationships between the Economic, Environmental, and Social dimensions of SD by sourcing the essential conditions for businesses to prosper, for economic development to take place, and for developing toward SD.

Countries’ competitiveness is directly and indirectly associated with the Macroeconomic Environment [163,164]. Prior research has shown that policies affect the Macroeconomic Environment [165,166]. Similarly, it has been shown that Institutions, directly and indirectly, impact the Macroeconomic Environment through their economic and social policies [12,167].

The GC Index’s 27 out of 103 indicators (e.g., gross national savings, general government debt, inflation, legal rights, employment levels, soundness of banks, and living standards) are components of the Macroeconomic Environment [165]. Reaching desirable development levels requires continuously supporting the Macroeconomic Environment. Thus, relationships among the GC pillars (e.g., Institutions, Infrastructure, Education, Technology, Business Sophistication, and Innovation) play a key role in enhancing the Macroeconomic Environment [163].

2.9. Sustainable Development (SD)

Global environmental degradation has become one of the major concerns for academics, the public, and governments, because environmental changes have endangered the Earth’s livability conditions [168,169]. Therefore, the alarm for human well-being and the world’s environment has been frequently debated since the second half of the twentieth century, integrating and balancing the Economic, Social, and Environmental dimensions [170,171].

2.9.1. Economic Dimension

The future impacts of the economy on the environment are among the foremost indicators of whether societies can attain SD. The Economic dimension (comprising SDGs 8, 9, 10, and 11) considers the importance of the delivery and allocation of limited resources. Knowledge, Technology, and unceasingly changing values affect the economy and impact current and future SD [172,173]. In the long term, economic growth could create a balance between the world’s animals, natural plants, and human life.

2.9.2. Social Dimension

The Social dimension of SD (e.g., including SDGs 1, 3, 4, 5, 16, 17) encompasses human values and institutions. Social cohesion and justice among generations, countries, and states are essential to progress toward Sustainable Development [174,175]. The current generation must also be mindful of future generations’ needs and well-being.

2.9.3. Environmental Dimension

Environmental issues, biodiversity loss, deforestation, and climate change may partly result from globalization [176,177]. Both Social and Economic factors impact the environment. Since sustainability challenges are entangled [84], it is crucial to investigate the influence among SD’s Social, Economic, and Environmental dimensions [178] and explore how these three-way relationships could be balanced to achieve SD (Figures 2–7).

Attaining SD is still on the focal agenda of all countries. SD mainly focuses on the importance of human needs, increasing social equity and organizational capacities, and how human and technical competence can be reformed for sustainability [179,180].

As discussed above, relationships among the GC pillars (Institutions, Infrastructure, Education, Technology, Business Sophistication, and Innovation) and the Macroeconomic Environment directly and indirectly impact the UN 17 SDGs.

2.10. Hypothesis Development

One of the core issues regarding the Economic dimensions and their relationships with the Environmental and Social facets of SD is that most countries’ economic development is not only slower and lower than desired but that each country’s economic development process differs [181,182]. A country’s competitiveness is crucial for its overall environmental performance [183,184]. Thus, it is important to study, globally and in country clusters, the GC pillars and their relationships among themselves and, therefore, with the SD dimensions (Economic, Social, and Environmental). This complicated approach may provide new understandings and insights about the diversity of SD relationships, thereby potentially enhancing, adjusting, and reviewing each country cluster’s SDGs. Based on the previous literature synthesis, we pose the following:

Hypothesis 1:

The global and country clusters’ Global Competitiveness pillars have variant relationships with their Macroeconomic Environments and the three Sustainable Development dimensions.

There should be a balance between the Social, Economic, and Environmental dimensions. The relationships among these dimensions alter over time and from country to country, as well as from country cluster to country cluster. To partly consider such versatility, we created six models (see Models 1.1–1.6) and three country clusters based on competitiveness level. On such a basis, we propose the following:

Hypothesis 2:

The relationships among the Macroeconomic Environment and the three main Sustainable Development dimensions differ by country clusters’ Global Competitiveness levels.

3. Methodology

3.1. Measure Selection and Data Sources

This study examines the relationships among the 6 GC pillars (Institutions, Infrastructure, Education, Technology, Business Sophistication, and Innovation), the Macroeconomic Environment, and the 17 SDGs (clustered into three dimensions). These variables are assessed with 76 indicators (see Appendix A 

Table A1. Variables and indicators.

	INSTITUTIONS (18 Indicators)
INST1	Property rights, 1–7 (best)
INST2	Intellectual property protection, 1–7 (best)
INST3	Diversion of public funds, 1–7 (best)
INST4	Public trust in politicians, 1–7 (best)
INST5	Judicial independence, 1–7 (best)
INST6	Favoritism in decisions of government officials, 1–7 (best)
INST7	Wastefulness of government spending, 1–7 (best)
INST8	Burden of government regulation, 1–7 (best)
INST9	Transparency of government policymaking, 1–7 (best)
INST10	Business costs of terrorism, 1–7 (best)
INST11	Business costs of crime and violence, 1–7 (best)
INST12	Organized crime, 1–7 (best)
INST13	Reliability of police services, 1–7 (best)
INST14	Ethical behavior of firms, 1–7 (best)
INST15	Strength of auditing and reporting standards, 1–7 (best)
INST16	Efficacy of corporate boards, 1–7 (best)
INST17	Protection of minority shareholders’ interests, 1–7 (best)
INST18	Strength of investor protection, 0–10 (best)
	INFRASTRUCTURE (5 indicators)
INFRA1	Quality of overall infrastructure, 1–7 (best)
INFRA2	Quality of roads, 1–7 (best)
INFRA3	Quality of port infrastructure, 1–7 (best)
INFRA4	Quality of air transport infrastructure, 1–7 (best)
INFRA6	Quality of electricity supply, 1–7 (best)
	EDUCATION (9 indicators)
HEALTHEDU1	Business impact of malaria, 1–7 (best)
HEALTHEDU3	Business impact of tuberculosis, 1–7 (best)
HEALTHEDU5	Business impact of HIV/AIDS, 1–7 (best)
HIGHEDU3	Quality of the education system, 1–7 (best)
HIGHEDU4	Quality of math and science education, 1–7 (best)
HIGHEDU5	Quality of management schools, 1–7 (best)
HIGHEDU6	Internet access in schools, 1–7 (best)
TRAIN1	Availability of research and training services, 1–7 (best)
TRAIN2	Extent of staff training, 1–7 (best)
	TECHNOLOGY (3 indicators)
TECH1	Availability of latest technologies, 1–7 (best)
TECH2	Firm-level technology absorption, 1–7 (best)
TECH3	FDI and technology transfer, 1–7 (best)
	INNOVATION (6 indicators)
INNOVA1	Capacity for innovation, 1–7 (best)
INNOVA2	Quality of scientific research institutions, 1–7 (best)
INNOVA3	Company spending on R&D, 1–7 (best)
INNOVA4	University–industry collaboration in R&D, 1–7 (best)
INNOVA5	Gov’t procurement of advanced tech products, 1–7 (best)
INNOVA6	Availability of scientists and engineers, 1–7 (best)
	BUSINESS SOPHISTICATION (8 indicators)
BUSSOPHIS1	Local supplier quantity, 1–7 (best)
BUSSOPHIS2	Local supplier quality, 1–7 (best)
BUSSOPHIS3	State of cluster development, 1–7 (best)
BUSSOPHIS4	Nature of competitive advantage, 1–7 (best)
BUSSOPHIS5	Production process sophistication, 1–7 (best)
BUSSOPHIS6	Control of international distribution, 1–7 (best)
BUSSOPHIS7	Extent of marketing, 1–7 (best)
BUSSOPHIS8	Value chain breadth, 1–7 (best)
	MACROECONOMIC ENVIRONMENT (27 indicators)
MACROENV1	Government budget balance, % GDP
MACROENV2	Gross national savings, % GDP
MACROENV3	Inflation, annual % change
MACROENV4	General government debt, % GDP
DOMCOMP1	Extent of market dominance, 1–7 (best)
DOMCOMP2	Effectiveness of anti-monopoly policy, 1–7 (best)
DOMCOMP3	No. of procedures to start a business
DOMCOMP6	Agricultural policy costs, 1–7 (best)
FORECOMP1	Prevalence of trade barriers, 1–7 (best)
FORECOMP2	Prevalence of foreign ownership, 1–7 (best)
FORECOMP3	Business impact of rules on FDI, 1–7 (best)
FORECOMP4	Burden of customs procedures, 1–7 (best)
QUALDEM1	Degree of customer orientation, 1–7 (best)
QUALDEM2	Buyer sophistication, 1–7 (best)
FLEXI1	Cooperation in labor–employer relations, 1–7 (best)
FLEXI2	Hiring and firing practices, 1–7 (best)
FLEXI3	Flexibility of wage determination, 1–7 (best)
TALENT1	Pay and productivity, 1–7 (best)
TALENT2	Reliance on professional management, 1–7 (best)
EFFICNY1	Financing through local equity market, 1–7 (best)
EFFICNY2	Ease of access to loans, 1–7 (best)
EFFICNY3	Venture capital availability, 1–7 (best)
TRUST1	The soundness of banks, 1–7 (best)
TRUST2	Regulation of securities exchanges, 1–7 (best)
TRUST3	Legal rights index, 0–10 (best)
DOMMARKET3	Domestic market size index, 1–7 (best)
FORMARKET1	Foreign market size index, 1–7 (best)
	SUSTAINABLE DEVELOPMENT GOALS
SDG1	Sustainable Development Score 1 No poverty
SDG2	Sustainable Development Score 2 Zero hunger
SDG3	Sustainable Development Score 3 Good health and well-being
SDG4	Sustainable Development Score 4 Quality education
SDG5	Sustainable Development Score 5 Gender equality
SDG6	Sustainable Development Score 6 Clean water and sanitation
SDG7	Sustainable Development Score 7 Affordable and clean energy
SDG8	Sustainable Development Score 8 Decent work and economic growth
SDG9	Sustainable Development Score 9 Sustainable development: Infrastructure, industrialization, and innovation
SDG10	Sustainable Development Score 10 Reduced inequalities
SDG11	Sustainable Development Score 11 Sustainable cities and communities
SDG12	Sustainable Development Score 12 Responsible consumption and production
SDG13	Sustainable Development Score 13 Climate change action
SDG14	Sustainable Development Score 14 Conserve seas, oceans, and marine life
SDG15	Sustainable Development Score 15 Life below water and life on land
SDG16	Sustainable Development Score 16 Peace, justice, and strong institutions
SDG17	Sustainable Development Score 17 The power of partnership

). Data is obtained from the World Economic Forum 2018 database [37].

This study’s data covers the 17 UN SDGs (see Appendix A 

Table A5. Sustainable Development dimensions and related SDG.

Environmental Dimension
Sustainable Development Goal 2	Zero hunger
Sustainable Development Goal 6	Clean water and sanitation
Sustainable Development Goal 7	Affordable and clean energy
Sustainable Development Goal 12	Responsible consumption and production
Sustainable Development Goal 13	Climate change action
Sustainable Development Goal 14	Life below water
Sustainable Development Goal 15	Life on land
Social Dimension
Sustainable Development Goal 1	Poverty
Sustainable Development Goal 3	Good health and well-being
Sustainable Development Goal 4	Quality education
Sustainable Development Goal 5	Gender equality
Sustainable Development Goal 16	Peace, justice, and strong institutions
Sustainable Development Goal 17	Partnership for the goals
Economic Dimension
Sustainable Development Goal 8	Decent work and economic growth
Sustainable Development Goal 9	Build resilient infrastructure, promote and foster innovation
Sustainable Development Goal 10	Reduced inequalities
Sustainable Development Goal 11	Sustainable cities and community

) for 128 countries from 2007 to 2017 (for some countries, the most updated data for some of the critical indicators is from 2017). The clustering of the SDGs into three dimensions allows for the examination of every possible relationship among the GC pillars, the three SD dimensions, and the Macroeconomic Environment. We draw from [185,186,187] methodologies for the clustering process. The diversity of relationships in Models 1.1, 1.2, 1.3, 1.4. 1.5, and 1.6 and Model 2 seek to partly account for the multi-directionality of the effects, which, undoubtedly, characterize socio-ecological systems.

3.2. Country, Country Cluster, and Outlier Determination

Given our focus on GC and SD, GC index scores were used to determine the country clusters. This study utilized each country’s World Economic Forum 2018 [37] index scores and calculated each country’s weighted average GC index score for 11 years. Based on the distribution of the scores, the countries were assigned to three clusters (very competitive, competitive, and less competitive) (see

Table 1. Country clusters.

Very Competitive Countries	Competitive Countries		Less Competitive Countries
Australia	Argentina	New Zealand	Albania	Malta
Canada	Belgium	Oman	Algeria	Mauritania
China	Bolivia	Pakistan	Algeria	Mauritius
Czech Republic	Bosnia and	Panama	Armenia	Moldova
Denmark	Herzegovina	Paraguay	Azerbaijan	Mongolia
Egypt	Botswana	Peru	Bahrain	Montenegro
Estonia	Brazil	Philippines	Bangladesh	Morocco
Finland	Brunei Darussalam	Poland	Benin	Mozambique
France	Bulgaria	Portugal	Bhutan	Namibia
Germany	Colombia	Qatar	Burkina Faso	Nepal
Iceland	Cyprus	Romania	Burundi	Nicaragua
Indonesia	Dominican Republic	Russian	Cambodia	Nigeria
Ireland	El Salvador	Federation	Cameroon	Serbia
Israel	Greece	Saudi Arabia	Chad	Timor-Leste
Italy	Honduras	Senegal	Chile	Trinidad and Tobago
Jamaica	Hungary	Singapore	Costa Rica	Tunisia
Japan	India	Slovak Republic	Côte d’Ivoire	Uganda
Korea, Rep.	Iran, Islamic Rep.	Slovenia	Croatia	U.A Emirates
Kuwait	Jordan	South Africa	Ecuador	Uruguay
Latvia	Kenya	Spain	Georgia	Zambia
Netherlands	Kyrgyz Republic	Sri Lanka	Ghana	Zimbabwe
Norway	Lesotho	Ukraine	Guatemala	Tajikistan
Sweden	Lithuania	Venezuela	Kazakhstan
Switzerland	Luxembourg		Madagascar
Thailand	Malaysia		Malawi
United Kingdom	Viet Nam		Tanzania
United States	Mexico		Mali

).

Furthermore, we employed multilevel analysis to test our conceptual models at both the global and country cluster levels, with the goal of identifying outlier countries based on model-derived scores. Specifically, we conducted group analysis for each country cluster using Structural Equation Modeling (SEM) via WarpPLS 8.0. This software generated individual latent variable scores for each country based on the constructs (Economic, Social, and Environmental dimensions) in our conceptual model (Model 2).

These construct-level scores represented the degree to which each country aligned with the underlying theoretical framework and were exported to Microsoft Excel for further analysis. To visually examine the distribution of these scores across all countries, we created a box-and-whisker plot (boxplot). This allowed us to identify statistical outliers—countries whose scores fell outside the typical range.

In the boxplot analysis, countries with scores beyond the whisker limits—defined as falling outside 1.5 times the interquartile range (IQR) from the first (Q1) and third (Q3) quartiles—were classified as outliers. In our case, the minimum and maximum whisker thresholds were calculated to be −1.68 and +1.68, respectively. Therefore, countries with construct (Economic, Social, and Environmental dimension) scores below −1.68 or above 1.68 were flagged as outliers, indicating unusually low or high alignment with the model constructs relative to the global sample.

Using data from 128 countries and 3 country clusters, we examined the influences among the GC pillars, the Macroeconomic Environment, and the SD dimensions at the global and country cluster levels separately. The countries and country clusters appear in Table 1.

3.3. Statistical Method

Given the multidimensionality of socio-ecological systems, research rarely examines the many factors involved in their Economic, Social, and Environmental dimensions together. Most analysts use highly simplified models assuming linearity, stability, invariance, independence, and additivity. Furthermore, most simplified models are not ontologically isomorphic to real world socio-ecological systems. Thus, we analyzed the data using PLS-SEM. PLS-SEM analyzes the multiplicity of relationships by combining factor analysis and regression in a set of simultaneous equations [188].

3.4. Data Analysis

The 10 constructs studied are assessed with 76 indicators (see Appendix A Table A1). The items load highly (higher than 0.5) and significantly (p < 0.05) in their corresponding latent variables. Thus, Models (1.1–1.6 and 2) have convergent validity (see Appendix A 

Table A2. Construct correlations and square roots of AVEs for Models 1.1–1.6 and 2.

	INST	INFRA	EDU	TECH	INNO	BUSSO	MACR	SOCIAL	ECONO	ENVIRON
INST	0.840
INFRA	0.837	0.918
EDU	0.808	0.837	0.898
TECH	0.805	0.856	0.828	0.918
INNO	0.807	0.791	0.861	0.824	0.925
BUSSO	0.788	0.855	0.876	0.872	0.815	0.898
MACR	0.835	0.824	0.837	0.888	0.847	0.891	0.893
SOCIAL	0.487	0.613	0.706	0.539	0.528	0.610	0.530	0.928
ECONO	0.580	0.674	0.775	0.610	0.661	0.685	0.597	0.864	0.924
ENVIRON	0.538	0.656	0.757	0.612	0.598	0.681	0.593	0.906	0.910	0.920

Notes: Square roots of AVEs in the diagonal; INST = Institutions; INFRA = Infrastructure; EDU = Education; TECH = Technology; INNO = Innovation; BUSSO = Business Sophistication; MACR = Macroeconomic Environment; SOCIAL = Social Dimension; ECONO = Economic Dimension; ENVIRON = Environmental Dimension.

,

Table A3. Reliability measures.

	INST	INFRA	EDU	TECH	INNO	BUSSO	MACR	SOCIAL	ECONO	ENVIRON
Composite reliability	0.973	0.964	0.943	0.941	0.955	0.971	0.944	0.916	0.876	0.804
Cronbach’s alpha	0.969	0.953	0.931	0.905	0.942	0.965	0.932	0.885	0.807	0.827
Dijkstra’s PLSc reliability	0.981	0.954	0.95	0.93	0.948	0.969	0.966	0.925	0.845	0.853

Notes: INST = Institutions; MACR = Macroeconomic Environment; INFRA = Infrastructure; ENVIRON = Environmental Dimension; EDU = Education; SOCIAL = Social Dimension; TECH = Technology; ECONO = Economic Dimension; INNO = Innovation; BUSSO = Business Sophistication.

and

Table A4. Model fit indices for Models 1.1–1.6 and 2.

	Model 1.1	Model 1.2	Model 1.3	Model 1.4	Model 1.5	Model 1.6	Model 2
Average path coefficient (APC)	0.336 p < 0.001	0.337 p < 0.001	0.338 p < 0.001	0.338 p < 0.001	0.336 p < 0.001	0.370 p < 0.001	0.368 p < 0.001
Average R-squared (ARS)	0.759 p < 0.001	0.754 p < 0.001	0.762 p < 0.001	0.762 p < 0.001	0.756 p < 0.001	0.762 p < 0.001	0.760 p < 0.001
Average full collinearity VIF (AFVIF)	4.642	4.629	4.789	4.788	4.767	4.779	4.767
Average block VIF (AVIF)	5.412	5.412	5.412	5.412	5.412	5.412	5.382
Tenenhaus Goodness of Fit (GoF)	0.718	0.715	0.719	0.719	0.716	0.719	0.719

).

The square roots of AVEs (average variances extracted) with the correlations among the constructs are determined. Table A2 (see Appendix A) shows the discriminant validity values for Models (1.1–1.6) and 2. These results suggest that the constructs researched have acceptable discriminant validity. Composite, alpha, and Dijkstra’s reliabilities are shown in Table A3 (see Appendix A), which are deemed acceptable with values higher than 0.7 [189,190].

Model assessment in terms of average path coefficient (APC), average adjusted R-squared (ARS), average full collinearity VIF (AFVIF), average block VIF, and Tenenhaus Goodness of Fit (GoF) model fit indexes are shown in Table A4 (see Appendix A). These indicators for Models 1.1–1.6 and Model 2 have acceptable values. Hence, our structural models are reasonable.

4. Results

4.1. Interactions Among GC Pillars and Their Relationships with the Macroeconomic Environment (Global and Country Cluster Level)

Table 2. Interactions among Global Competitiveness pillars.

	Global	Very Competitive Countries	Competitive Countries	Less Competitive Countries
Relationships	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes
Institutions → Infrastructure	0.837 *** 0.787 to 0.886 N/A	0.799 *** 0.704 to 0.895 N/A	0.833 *** 0.756 to 0.911 N/A	0.775 *** 0.697 to 0.853 N/A
Institutions → Education	0.360 *** 0.309 to 0.412 0.808/0.653 ^^^	0.525 *** 0.426 to 0.625 0.859/0.737 ^^^	0.349 *** 0.268 to 0.431 0.786/0.617 ^^^	0.327 *** 0.245 to 0.409 0.679/0.461 ^^^
Institutions → Technology	0.186 *** 0.134 to 0.237 0.805/0.648 ^^^	0.145 ** 0.040 to 0.251 0.788/0.620 ^^^	0.162 *** 0.079 to 0.246 0.774/0.600 ^^^	0.236 *** 0.153 to 0.319 0.743/0.552 ^^^
Institutions → Innovation	0.238 *** 0.186 to 0.290 0.807/0.651 ^^^	0.200 *** 0.095 to 0.305 0.760/0.578 ^^^	0.178 *** 0.095 to 0.262 0.812/0.659 ^^^	0.246 *** 0.163 to 0.329 0.663/0.439 ^^^
Institutions → Business Sophistication	0.010 0.063 to 0.043 0.788/0.621 ^^^	0.344 *** 0.447 to 0.242 0.624/0.390 ^^^	0.123 ** 0.039 to 0.207 0.827/0.684 ^^^	0.142 *** 0.058 to 0.226 0.747/0.558 ^^^
Institutions → Macroeconomic Environment	0.488 *** 0.438 to 0.539 0.885/0.783 ^^^	0.552 *** 0.453 to 0.651 0.864/0.746 ^^^	0.605 *** 0.526 to 0.684 0.893/0.797 ^^^	0.411 *** 0.330 to 0.492 0.847/0.717 ^^^
Infrastructure → Education	0.535 *** 0.485 to 0.586 N/A	0.417 *** 0.316 to 0.518 N/A	0.524 *** 0.443 to 0.604 N/A	0.454 *** 0.373 to 0.535 N/A
Infrastructure → Technology	0.445 *** 0.394 to 0.496 0.609/0.521 ^^^	0.104 * 0.002 to 0.211 0.376/0.287 ^^	0.617 *** 0.538 to 0.696 0.682/0.583 ^^^	0.399 *** 0.318 to 0.481 0.532/0.419 ^^^
Infrastructure → Innovation	−0.187 *** −0.239 to −0.135 0.386/0.305 ^^	0.045 −0.062 to 0.152 0.528/0.422 ^^^	−0.138 *** −0.222 to −0.055 0.406/0.325 ^^	−0.336 *** −0.418 to −0.254 0.044/0.023 ^
Infrastructure → Business Sophistication	0.219 *** 0.167 to 0.271 0.653/0.559 ^^^	0.399 *** 0.297 to 0.501 0.712/0.538 ^^^	0.249 *** 0.167 to 0.332 0.632/0.558 ^^^	−0.046 −0.130 to 0.039 0.400/0.298 ^^
Infrastructure → Macroeconomic Environment	−0.198 *** −0.250 to −0.146 0.278/0.229 ^^	−0.221 *** −0.325 to −0.116 0.112/0.082 ^	−0.215 *** −0.298 to −0.132 0.214/0.173 ^^	−0.150 *** −0.233 to −0.066 0.183/0.134 ^
Education → Technology	0.306 *** 0.254 to 0.357 N/A	0.651 *** 0.553 to 0.749 N/A	0.124 ** 0.040 to 0.208 N/A	0.292 *** 0.209 to 0.374 N/A
Education → Innovation	0.198 *** 0.146 to 0.249 0.562/0.484 ^^^	−0.004 −0.112 to 0.104 0.520/0.435 ^^^	0.360 *** 0.279 to 0.442 0.564/0.491 ^^^	0.120 ** 0.036 to 0.204 0.541/0.382 ^^^
Education → Business Sophistication	0.408 *** 0.357 to 0.459 0.516/0.452 ^^^	0.462 *** 0.362 to 0.563 0.671/0.521 ^^^	0.322 *** 0.240 to 0.404 0.361/0.310 ^^	0.399 *** 0.318 to 0.480 0.544/0.450 ^^^
Education → Macroeconomic Environment	0.008 −0.045 to 0.060 0.296/0.248 ^^	0.070 0.037 to 0.177 0.437/0.370 ^^^	−0.121 ** −0.205 to −0.037 0.039/0.029 ^	−0.032 −0.117 to 0.053 0.306/0.229 ^^
Technology → Innovation	0.027 −0.026 to 0.080 N/A	0.224*** 0.120 to 0.329 N/A	0.002 −0.083 to 0.087 N/A	0.050 −0.035 to 0.135 N/A
Technology → Business Sophistication	0.355 *** 0.304 to 0.406 N/A	0.320 *** 0.217 to 0.423 N/A	0.313 *** 0.231 to 0.395 0.313/0.271 ^^	0.497 *** 0.416 to 0.577 0.497/0.427 ^^^
Technology → Macroeconomic Environment	0.323 *** 0.272 to 0.375 0.466/0.413 ^^^	0.263 *** 0.159 to 0.366 0.378/0.319 ^^	0.194 *** 0.110 to 0.277 0.377/0.314 ^^	0.324 *** 0.241 to 0.406 0.545/0.474 ^^^
Business Sophistication → Innovation	0.690 *** 0.640 to 0.740 N/A	0.563 *** 0.464 to 0.602 N/A	0.561 *** 0.481 to 0.641 N/A	0.748 *** 0.670 to 0.827 N/A
Business Sophistication → Macroeconomic Environment	0.484 *** 0.433 to 0.534 0.410/0.365 ^^^	0.185 *** 0.080 to 0.290 0.263/0.194 ^^	0.705 *** 0.627 to 0.784 0.586/0.522 ^^^	0.438 *** 0.357 to 0.519 0.445/0.398 ^^^
Innovation → Macroeconomic Environment	−0.107 *** −0.159 to −0.054 N/A	0.139 ** 0.034 to 0.245 N/A	−0.212 *** −0.295 to −0.128 N/A	0.010 −0.075 to 0.095 N/A

Notes: Significance Level: * p < 0.05, ** p < 0.01, *** p < 0.001; Effect Sizes ^ 0.02 < e < 0.15 = Low Effect Size, ^^ 0.15 < e < 0.35 = Moderate Effect Size, ^^^ e > 0.35 = Strong Effect Size; N/A: Not Applicable—the relationship does not exist in the model.

shows the results of the interactions among GC pillars with the Macroeconomic Environments at the global and country cluster levels.

Institutions’ direct and indirect relationships with Infrastructure, Education, Technology, Innovation, and the Macroeconomic Environment are positive at the global level. However, the direct relationship between institutions and Business Sophistication is non-significant. Nevertheless, the effect size of Institutions’ relationships with Business Sophistication is positive and strong, with the mediation of Infrastructure, Education, and Technology. All country clusters show similar findings.

Infrastructure’s direct and indirect relationships with Education, Technology, and Business Sophistication are positive at the global level. Infrastructure’s direct relationships with Innovation and the Macroeconomic Environment are negative. However, the effect size of Infrastructure’s relationship with Innovation and the Macroeconomic Environment becomes positive and strong with the mediation of Education, Technology, and Business Sophistication.

All the country cluster results are identical to the global-level results, except for Infrastructure’s relationship with Business Sophistication in the less competitive country cluster, which is negative, even though the relationship’s effect size becomes positive and strong with the mediation of Education and Technology.

Education’s direct and indirect relationships with Technology, Innovation, and Business Sophistication are positive at the global level. Education’s direct relationship with the Macroeconomic Environment is non-significant. However, the effect size of this relationship becomes positive and moderate with the mediation of Technology, Business Sophistication, and Innovation. Education’s direct relationship with Innovation in the very competitive country cluster and the Macroeconomic Environment in the competitive and less competitive country clusters is negative. However, all these relationships’ effect sizes are positive and moderate or strong.

Technology’s direct and indirect relationships with Business Sophistication and the Macroeconomic Environment are positive at the global level. Technology’s direct relationship with Innovation is non-significant. The results of Technology’s relationships with Business Sophistication and the Macroeconomic Environment in all country clusters are the same as those at the global level. However, in the very competitive country cluster, Technology’s relationship with Innovation is stronger compared to the global, competitive, and less competitive country clusters.

Business Sophistication’s direct and indirect relationships with Innovation and the Macroeconomic Environment are positive globally and in all country clusters.

Innovation’s direct relationship with the Macroeconomic Environment is negative at the global level. Technology’s relationship with the Macroeconomic Environment is positive in the very competitive country cluster, negative in the competitive country cluster, and non-significant in the less competitive country cluster.

4.2. Relationships Between Macroeconomic Environment and SD Dimensions (Global and Country Cluster Level) (Models 1.1–1.6)

Table 3. Results for Models (1.1–1.6).

		Model 1.1	Model 1.2	Model 1.3	Model 1.4	Model 1.5	Model 1.6
Sustainable Development Dimensions’ Interactions	Competitive Levels	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes
Macroeconomic Environment → Environmental Dimension	Global	0.066 ** 0.013 to 0.118 0.243/0.351 ^^^	0.066 ** 0.013 to 0.118 0.243/0.351 ^^^	0.076 ** 0.024 to 0.128 0.593/0.351 ^^^	0.593 *** 0.214 to 0.776 N/A	0.156 *** 0.104 to 0.208 N/A	0.593 *** 0.542 to 0.643 N/A
	Very Competitive	−0.051 −0.062 to −0.003 0.571/0.326 ^^	−0.051 −0.062 to −0.003 0.571/0.326 ^^	0.043 0.024 to 0.150 0.571/0.326 ^^	0.571 *** 0.472 to 0.670 N/A	0.020 0.012 to 0.128 0.571/0.326 ^^	0.571 *** 0.472 to 0.670 N/A
	Competitive	0.024 0.012 to 0.108 0.347/0.121 ^	0.024 0.012 to 0.108 0.347/0.121 ^	0.037 0.012 to 0.121 0.347/0.121 ^	0.348 *** 0.265 to 0.429 N/A	0.061 0.042 to 0.146 0.347/0.121 ^	0.348 *** 0.265 to 0.429 N/A
	Less Competitive	−0.111 ** −0.196 to −0.027 −0.390/0.152 ^^	−0.111 ** −0.196 to −0.027 −0.390/0.152 ^^	−0.116 ** −0.200 to −0.032 −0.390/0.152 ^^	−0.390 *** −0.471 to −0.308 N/A	−0.151 *** −0.234 to −0.067 −0.390/0.152 ^^	−0.382 *** −0.471 to −0.308 N/A
Macroeconomic Environment → Social Dimension	Global	0.530 *** 0.480 to 0.581 N/A	0.530 *** 0.480 to 0.581 N/A	−0.032 −0.085 to 0.021 0.530/0.281 ^^	−0.032 −0.085 to 0.021 0.530/0.281 ^^	0.530 *** 0.480 to 0.581 N/A	−0.011 −0.064 to 0.042 0.281 ^^
	Very Competitive	0.647 *** 0.254 to 0.885 N/A	0.647 *** 0.254 to 0.885 N/A	0.171 *** 0.066 to 0.276 0.647/0.419 ^^^	0.171 *** 0.066 to 0.276 0.647/0.419 ^^^	0.647 *** 0.549 to 0.745 N/A	0.229 *** 0.125 to 0.334 0.647/0.419 ^^^
	Competitive	0.336 *** 0.254 to 0.418 N/A	0.336 *** 0.254 to 0.418 N/A	0.003 0.000 to 0.089 0.336/0.113 ^	0.003 0.001 to 0.089 0.336/0.113 ^	0.336 *** 0.254 to 0.418 N/A	0.037 * 0.028 to 0.122 0.336/0.136 ^
	Less Competitive	0.289 *** 0.207 to 0.372 N/A	0.289 *** 0.207 to 0.372 N/A	−0.060 −0.144 to −0.030 0.289/0.084 ^	−0.060 −0.144 to −0.056 0.289/0.084 ^	0.289 *** 0.207 to 0.372 N/A	−0.059 −0.120 to −0.042 0.289/0.084 ^
Macroeconomic Environment → Economic Dimension	Global	0.194 *** 0.142 to 0.246 0.245/0.357 ^^^	0.194 *** 0.142 to 0.246 0.245/0.357 ^^^	0.597 *** 0.547 to 0.648 N/A	0.089 *** 0.037 to 0.141 0.597/0.357 ^^^	0.091 *** 0.039 to 0.144 0.597/0.357 ^^^	0.091 *** 0.039 to 0.144 0.357 ^^^
	Very Competitive	0.147 ** 0.017 to 0.223 0.630/0.397 ^^^	0.147 ** 0.017 to 0.223 0.630/0.397 ^^^	0.630 *** 0.532 to 0.729 N/A	0.748 *** 0.651 to 0.844 N/A	0.136 ** 0.030 to 0.241 0.630/0.397 ^^^	0.136 ** 0.030 to 0.241 0.630/0.397 ^^^
	Competitive	0.102 *** 0.017 to 0.186 0.374/0.140 ^	0.102 *** 0.017 to 0.186 0.374/0.140 ^	0.374 *** 0.292 to 0.455 N/A	0.814 *** 0.736 to 0.891 N/A	0.075 * 0.025 to 0.159 0.374/0.140 ^	0.075 * 0.038 to 0.159 0.374/0.140 ^
	Less Competitive	0.101 ** 0.016 to 0.185 0.335/0.112 ^	0.101 ** 0.016 to 0.185 0.335/0.112 ^	0.335 *** 0.253 to 0.417 N/A	0.002 0.000 to 0.008 0.335/0.112 ^	0.025 0.012 to 0.110 0.335/0.112 ^	0.023 0.018 to 0.110 0.335/0.112 ^
Social Dimension → Economic Dimension	Global		0.761 *** 0.711 to 0.811 N/A			0.219 *** 0.167 to 0.271 0.761/0.657 ^^^	0.219 *** 0.167 to 0.271 N/A
	Very Competitive		0.748 *** 0.732 to 0.887 N/A			0.296 *** 0.193 to 0.399 0.748/0.630 ^^^	0.296 *** 0.193 to 0.399 N/A
	Competitive		0.810 *** 0.732 to 0.887 N/A			0.434 *** 0.353 to 0.515 0.810/0.683 ^^^	0.434 *** 0.353 to 0.515 N/A
	Less Competitive		0.811 *** 0.733 to 0.888 N/A			0.385 *** 0.313 to 0.476 0.811/0.681 ^^^	0.385 *** 0.313 to 0.476 N/A
Social Dimension → Environmental Dimension	Global	0.469 *** 0.418 to 0.520 N/A	0.469 *** 0.418 to 0. 0.761/0.747 ^^^			0.824 *** 0.774 to 0.873 N/A
	Very Competitive	0.487 *** 0.474 to 0.633 N/A	0.487 *** 0.474 to 0.633 0.850/0.734 ^^^			0.850 *** 0.755 to 0.945 N/A
	Competitive	0.554 *** 0.474 to 0.633 N/A	0.554 *** 0.474 to 0.633 0.853/0.683 ^^^			0.853 *** 0.776 to 0.930 N/A
	Less Competitive	−0.511 *** −0.591 to −0.430 N/A	−0.511 *** −0.591 to −0.430 −0.827/0.720 ^^^			−0.828 *** −0.904 to −0.750 N/A
Economic Dimension → Social Dimension	Global	0.851 *** 0.801 to 0.900 N/A		0.235 *** 0.184 to 0.287 0.851/0.735 ^^^	0.235 *** 0.124 to 0.289 N/A
	Very Competitive	0.721 *** 0.452 to 0.852 N/A		0.288 *** 0.185 to 0.391 0.721/0.607 ^^^	0.288 *** 0.185 to 0.391 N/A
	Competitive	0.835 *** 0.758 to 0.912 N/A		0.369 *** 0.287 to 0.451 0.835/0.705 ^^^	0.369 *** 0.287 to 0.451 N/A
	Less Competitive	0.837 0.759 to 0.914 N/A		0.354 *** 0.272 to 0.436 0.837/0.702 ^^^	0.354 *** 0.272 to 0.436 N/A
Economic Dimension → Environmental Dimension	Global	0.466 *** 0.415 to 0.517 0.865/0.788 ^^^	0.466 *** 0.415 to 0.517 N/A	0.865 *** 0.816 to 0.914 N/A
	Very Competitive	0.486 *** 0.328 to 0.685 0.837/0.723 ^^^	0.489 *** 0.288 to 0.529 N/A	0.837 *** 0.742 to 0.932 N/A
	Competitive	0.370 *** 0.288 to 0.451 0.832/0.703 ^^^	0.370 *** 0.288 to 0.451 N/A	0.832 *** 0.755 to 0.909 N/A
	Less Competitive	−0.390 −0.472 to −0.309 −0.817/0.700 ^^^	−0.390 *** −0.472 to −0.309 N/A	−0.817 *** −0.895 to −0.740 N/A
Environmental Dimension → Social Dimension	Global			0.711 *** 0.661 to 0.761 N/A	0.711 *** 0.184 to 0.809 0.913/0.828 ^^^		0.913 *** 0.864 to 0.962 N/A
	Very Competitive			0.517 *** 0.417 to 0.617 N/A	0.517 *** 0.417 to 0.617 0.732/0.632 ^^^		0.732 *** 0.636 to 0.829 N/A
	Competitive			0.560 *** 0.480 to 0.640 N/A	0.560 *** 0.480 to 0.640 0.861/0.752 ^^^		0.861 *** 0.784 to 0.938 N/A
	Less Competitive			−0.591 *** −0.670 to −0.511 N/A	−0.591 *** −0.670 to −0.511 −0.893/0.778 ^^^		−0.893 *** −0.970 to −0.817 N/A
Environmental Dimension → Economic Dimension	Global				0.858 *** 0.729 to 0.895 N/A	0.658 *** 0.607 to 0.708 N/A	0.658 *** 0.607 to 0.962 0.781 ^^^
	Very Competitive				0.748 *** 0.651 to 0.844 N/A	0.531 *** 0.431 to 0.631 N/A	0.531 *** 0.431 to 0.631 0.748/0.646 ^^^
	Competitive				0.814 *** 0.736 to 0.891 N/A	0.440 *** 0.359 to 0.521 N/A	0.440 *** 0.359 to 0.521 0.814/0.688 ^^^
	Less Competitive				−0.857 *** −0.933 to −0.778 N/A	−0.503 *** −0.583 to −0.423 N/A	−0.513 *** −0.583 to −0.423 −0.856/0.733 ^^^

Notes: Significance Level: * p < 0.05, ** p < 0.01, *** p < 0.001; Effect Sizes ^ 0.02 < e < 0.15 = Low Effect Size, ^^ 0.15 < e < 0.35 = Moderate Effect Size, ^^^ e > 0.35 = Strong Effect Size; Background colored relationships do not exist.

shows the relationships between the Macroeconomic Environment and SD dimensions (global and country cluster level) (see Model 1.1 above and Figures for Models 1.2, 1.3, 1.4, 1.5, and 1.6, which appear in Appendix A).

Model 1.1 Results. The Macroeconomic Environment’s relationships with the Environmental, Social, and Economic dimensions are positive at the global, very competitive, competitive, and less competitive country cluster levels. The Economic dimension’s relationships with the Social and Environmental dimensions are positive at the global, very competitive, competitive, and less competitive country cluster levels. However, the Economic dimension has a negative and strong relationship with the Environmental dimension at the less competitive country cluster level. The Social dimension’s relationship with the Environmental dimension is positive at the global, very competitive, and competitive country cluster levels. However, the Social dimension has a negative and strong relationship with the Environmental dimension at the less competitive country cluster level.

Model 1.2 Results. The Macroeconomic Environment’s relationship with the Environmental dimension is positive at the global and competitive country cluster levels. Such a relationship is negative in the very competitive and less competitive country clusters. The Macroeconomic Environment’s relationships with the Social and Economic dimensions are positive at the global, very competitive, competitive, and less competitive country cluster levels. The Social dimension’s relationships with the Economic and Environmental dimensions are positive at the global, very competitive, competitive, and less competitive country cluster levels. However, the Social dimension has a negative and strong relationship with the Environmental dimension at the less competitive country cluster level.

The Economic dimension’s relationship with the Environmental dimension is positive at the global, very competitive, and competitive country cluster levels. However, the Economic dimension has a negative relationship with the Environmental dimension at the less competitive country cluster level.

Model 1.3 Results. The Macroeconomic Environment’s relationship with the Environmental dimension is positive at the global, very competitive, and competitive country cluster levels. It is negative at the less competitive country cluster level. Since these results are also similar for Models 1.4, 1.5, and 1.6., they are not repeated below. The Macroeconomic Environment’s relationship with the Social dimension is positive at the very competitive and competitive country cluster levels. This relationship is negative at the global and less competitive country cluster levels. The Economic dimension’s relationships with the Environmental and Social dimensions are positive at the global, very competitive, competitive, and less competitive country cluster levels. However, the Economic dimension has a negative relationship with the Environmental dimension at the less competitive country cluster level. The Environmental dimension’s relationship with the Social dimension is positive at the global, very competitive, and competitive country cluster levels. However, the Environmental dimension has a negative relationship with the Social dimension at the less competitive country cluster level.

Model 1.4 Results. The Macroeconomic Environment’s relationship with the Social dimension is positive at the very competitive and competitive country cluster levels, whereas this relationship is negative at the global and less competitive country cluster levels. The Macroeconomic Environment’s relationship with the Economic dimension is positive at the global, very competitive, competitive, and less competitive country cluster levels. The Environmental dimension’s relationships with the Economic and Social dimensions are positive at the global, very competitive, and competitive country clusters. Such relationships are negative at the less competitive country cluster level. The Economic dimension’s relationship with the Social dimension is positive at the global, very competitive, competitive, and less competitive country cluster levels.

Model 1.5 Results. The Macroeconomic Environment’s relationships with the Social and Economic dimensions are positive at the global, very competitive, competitive, and less competitive country cluster levels. The Social dimension’s relationship with the Economic dimension is positive at the global, very competitive, competitive, and less competitive country cluster levels. The Social dimension’s relationship with the Environmental dimension is positive at the global, very competitive, and competitive country cluster levels. Such a relationship is negative at the less competitive country cluster level. The Environmental dimension’s relationship with the Economic dimension is positive at the global, very competitive, and competitive country cluster levels. Such a relationship is negative at the less competitive country cluster level.

Model 1.6 Results. The Macroeconomic Environment’s relationships with the Social and Economic dimensions are positive at the very competitive and the competitive country cluster levels. Such relationships are negative at the global and less competitive country cluster levels. The Macroeconomic Environment’s relationships with the Economic dimension are positive at the global, very competitive, competitive, and less competitive country cluster levels. The Environmental dimension’s relationships with the economic and Social dimensions are positive at the global, very competitive, and competitive country cluster levels. Such relationships are negative at the less competitive country cluster level. The Social dimension’s relationships with the Economic dimension are positive at the global, very competitive, competitive, and less competitive country cluster levels.

4.3. Sustainable Development Dimensions and Country Outliers (Global) (Model 2)

In addition to Models 1.1–1.6,

Table 4. Model 2 results.

	Global	Very Competitive Countries	Competitive Countries	Less Competitive Countries
Relationships	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes	Path Coefficients Confidence Intervals Total Effects/Effect Sizes
Institutions → Environmental Dimension	−0.040 −0.093 to 0.012 0.538/0.289 ^^	−0.067 −0.174 to 0.040 0.552/0.305 ^^	−0.167 *** −0.250 to −0.083 0.259/0.067 ^	−0.060 −0.145 to 0.024 −0.438/0.192 ^^
Institutions → Social Dimension	0.120 *** 0.068 to 0.180 0.487/0.237 ^^	−0.387 *** −0.489 to −0.285 0.593/0.352 ^^^	0.355 *** 0.273 to 0.437 0.267/0.071 ^	0.043 −0.042 to 0.128 0.377/0.142 ^
Institutions → Economic Dimension	0.128 *** 0.076 to 0.180 0.580/0.336 ^^	−0.060 −0.167 to 0.047 0.660/0.435 ^^^	0.647 *** 0.563 to 0.726 0.344/0.118 ^	0.315 *** 0.233 to 0.397 0.442/0.195 ^^
Infrastructure → Environmental Dimension	−0.057 −0.109 to −0.005 0.687/0.451 ^^^	0.038 −0.070 to 0.1445 0.517/0.325 ^^	−0.166 *** −0.250 to −0.083 0.749/0.333 ^^	−0.247 *** 0.164 to 0.330 −0.416/0.211 ^^
Infrastructure → Social Dimension	0.087 *** 0.034 to 0.139 0.0.684/0.419 ^^^	0.189 *** 0.084 to 0.293 0.576/0.393 ^^^	0.239 *** 0.156 to 0.322 0.829/0.395 ^^^	−0.060 −0.145 to 0.025 0.451/0.213 ^^
Infrastructure → Economic Dimension	0.238 *** 0.187 to 0.290 0.0.630/0.424 ^^^	0.083 −0.023 to 0.190 0.453/0.313 ^^	0.379 *** 0.298 to 0.461 0.672/0.331 ^^	0.310 *** 0.228 to 0.392 0.490/0.264 ^^
Education → Environmental Dimension	0.093 *** 0.040 to 0.145 0.813/0.615 ^^^	0.115 * 0.009 to 0.221 0.896/0.655 ^^^	0.230 *** 0.147 to 0.313 0.953/0.591 ^^^	−0.093 * −0.177 to −0.008 −0.490/0.293 ^^
Education → Social Dimension	0.289 *** 0.22237 to 0.340 0.0.776/0.548 ^^^	0.323 *** 0.220 to 0.425 0.951/0.745 ^^^	0.396 *** 0.314 to 0.477 0.857/0.513 ^^^	0.146 *** 0.062 to 0.230 0.410/0.215 ^^
Education → Economic Dimension	0.796 *** 0.746 to 0.845 0.786/0.609 ^^^	0.927 *** 0.833 to 0.956 0.866/0.700 ^^^	0.869 *** 0.899 to 0.995 0.910/0.591 ^^^	0.529 *** 0.449 to 0.609 0.442/0.261 ^^
Technology → Environmental Dimension	0.079 ** 0.026 to 0.131 −0.052/0.032 ^	−0.218 *** −0.322 to −0.113 −0.141/0.083 ^	0.047 0.038 to 0.132 0.051/0.0019	−0.231 *** −0.314 to −0.148 0.084/0.038 ^
Technology → Social Dimension	−0.058 ** −0.111 to −0.006 −0.183/0.099 ^	0.207 *** 0.005 to 0.218 0.165/0.117 ^	−0.148 *** −0.232 to −0.064 −0.219/0.078 ^	−0.001 −0.087 to 0.084 −0.324/0.134 ^
Technology → Economic Dimension	0.131 *** 0.079 to 0.183 0.052/0.097 ^	0.033 −0.074 to 0.141 −0.044/0.001	0.223 *** 0.140 to 0.306 0.193/0.089 ^	0.084 −0.168 to 0.001 −0.043/0.014
Business Sophistication → Environmental Dimension	0.211 *** 0.159 to 0.263 0.112/0.077 ^	0.075 −0.032 to 0.182 −0.001/0.001	0.405 *** 0.324 to 0.486 0.228/0.111 ^	−0.215 *** −0.298 to −0.132 0.077/0.038 ^
Business Sophistication → Social Dimension	0.179 *** 0.137 to 0.241 0.008/0.005	0.112 * 0.005 to 0.218 0.054/0.034 ^	0.289 *** 0.206 to 0.371 0.0556/0.026 ^	0.131 ** 0.047 to 0.215 −0.116/0.012
Business Sophistication → Economic Dimension	0.124 *** 0.072 to 0.176 0.075/0.052 ^	0.038 −0.070 to 0.145 −0.045/0.018	0.210 *** 0.127 to 0.293 0.095/0.046 ^	0.310 *** 0.228 to 0.393 −0.026/0.011
Innovation → Environmental Dimension	−0.204 *** −0.256 to −0.153 −0.330/0.197 ^^	−0.091 −0.197 to 0.016 −0.309/0.173 ^^	−0.277 *** −0.360 to −0195 −0.594/0.232 ^^	0.326 *** 0.244 to 0.409 0.468/0.125 ^
Innovation → Social Dimension	−0.394 *** −0.445 to −0.343 −0.363/0.192 ^^	−0.365 *** 0.467 to −0.263 −0.309/0.237 ^^	-−0.417 *** −0.498 to −0.335 −0.565/0.212 ^^	−0.338 *** −0.420 to −0.256 −0.341/0.071 ^
Innovation → Economic Dimension	0.032 −0.0020 to 0.085 0.051/0.034 ^	−0.074 −0.180 to 0.033 −0.090/0.001	0.122 ** 0.038 to 0.206 0.188/0.089 ^	0.003 −0.082 to 0.088 −0.026/0.002
Macroeconomic Environment → Environmental Dimension	0.006 0.001 to 0.118 0.100/0.059 ^	0.087 −0.019 to 0.194 0.086/0.049 ^	−0.014 −0.099 to 0.071 0.246/0.085 ^	−0.026 −0.111 to 0.059 0.569/0.222 ^^
Macroeconomic Environment → Social Dimension	0.081 *** 0.031 to 0.095 0.053/0.028 ^	0.252 *** 0.148 to 0.356 0.161/0.104 ^	0.109 ** 0.024 to 0.093 0.317/0.106 ^	0.059 −0.025 to 0.144 −0.006/0.002
Macroeconomic Environment → Economic Dimension	0.173 *** 0.047 to 0.248 0.173/0.104 ^	0.158 ** 0.052 to 0.263 0.158/0.099 ^	0.313 *** 0.231 to 0.395 0.313/0.117 ^	0.760 *** 0.682 to 0.838 0.760/0.255
Social Dimension → Environmental Dimension	0.411 *** 0.347 to 0.548 0.411/0.372 ^^^	0.477 *** 0.377 to 0.578 0.477/0.412 ^^^	0.435 *** 0.354 to 0.516 0.4335/0.380 ^^^	−0.409 *** −0.490 to −0.327 −0.409/0.356 ^^^
Economic Dimension → Social Dimension	0.770 *** 0.547 to 0.812 0.770/0.665 ^^^	0.575 *** 0.476 to 0.674 0.575/0.484 ^^^	0.390 *** 0.309 to 0.472 0.679/0.574 ^^^	0.809 *** 0.732 to 0.887 0.809/0.679 ^^^
Economic Dimension → Environmental Dimension	0.485 *** 0.347 to 0.648 0.801/0.729 ^^^	0.493 *** 0.393 to 0.594 0.768/0.663 ^^^	0.665 *** 0.587 to 0.744 0.66/0.562 ^^^	−0.485 −0.656 to −0.404 −0.815/0.698 ^^^

Notes: Significance Level: * p < 0.05, ** p < 0.01, *** p < 0.001; Effect Sizes ^ 0.02 < e < 0.15 = Low Effect Size, ^^ 0.15 < e < 0.35 = Moderate Effect Size, ^^^ e > 0.35 = Strong Effect Size.

shows the results of the direct and indirect relationships between the GC pillars, the Macroeconomic Environment, and the SD dimensions.

On a global scale, the non-significant direct relationships of Institution and Infrastructure with the Environmental dimension, and Innovation’s non-significant direct relationships with the Economic dimension, undergo a significant transformation when the other GC pillars are introduced as mediators.

At the very competitive country cluster level, Institutions’, Infrastructure’s, and Microeconomic Environment’s non-significant direct relationships with the Environmental dimension and Infrastructure’s non-significant direct relationships with the Economic dimension become significant when the other GC pillars are added as mediators. In addition, Institution’s negative relationship with the Social dimension also becomes positive.

At the competitive country cluster level, Institutions’ and Infrastructure’s negative and Microeconomic Environment’s non-significant direct relationships with the Environmental dimension become significant when the other GC pillars are added into the relationships as mediators.

At the less competitive country cluster level, Technology’s and Business Sophistication’s negative and Institutions’ and Macroeconomic Environment’s non-significant direct relationships with the Environmental dimension become significant when the other GC pillars are added into the relationships as mediators, in addition to Institution’s and Infrastructure’s non-significant relationships with the Social dimension.

When we include both direct and indirect relationships, Institutions’, Infrastructure’s, and Education’s relationships with the Environmental, Social, and Economic dimensions at every country cluster level become positively and/or negatively stronger. However, in less competitive countries, Technology’s positive relationship with the Social dimension becomes significantly negative. In addition, Innovation’s non-significant relationship with the Social dimension becomes negatively stronger. Macroeconomic Environment’s negative relationship with the Environmental dimension in less competitive countries becomes positively stronger. In addition, Macroeconomic Environment’s non-significant relationship with the Social dimension in competitive countries becomes significant.

Figure 3, Figure 4 and Figure 5 show the country outliers (see Section 3.2) at the global level.

There are four country outliers regarding the Environmental dimension at the global level: the Philippines (+1.69), Timor-Leste (+1.71), Pakistan (−1.71), and Ghana (−1.69). The rest of the countries’ scores are between −1.68 and +1.68.

There are four country outliers regarding the Social dimension at the global level: Hong Kong (+1.69), Timor-Leste (+1.71), Pakistan (−1.71), and Madagascar (−1.69). The rest of the countries’ scores are between −1.68 and +1.68.

There are two country outliers regarding the Economic dimensions at the global level: Norway (+1.71) and Croatia (−1.71). The rest of the countries’ scores are between −1.68 and +1.68.

Figure 3. Environmental dimension scores (global).

Figure 3. Environmental dimension scores (global).

Figure 4. Social dimension scores (global).

Figure 4. Social dimension scores (global).

Figure 5. Economic dimension scores (global).

Figure 5. Economic dimension scores (global).

4.3.1. SD Dimensions and Country Outliers (Very Competitive) (Model 2)

There are no country outliers regarding the Environmental, Social, and Economic dimensions at the very competitive country cluster level. All countries’ scores are between −1.68 and +1.68.

4.3.2. SD Dimensions and Country Outliers (Competitive) (Model 2)

Figure 6, Figure 7 and Figure 8 show the country outliers at the competitive country cluster level.

There are two country outliers regarding the Environmental dimensions at the competitive country cluster level: New Zealand (+1.69) and Lesotho (−1.69). The rest of the countries’ scores are between −1.68 and +1.68.

There are two country outliers regarding the Social dimension at the competitive country cluster level: New Zealand (+1.69) and Lesotho (−1.69). The rest of the countries’ scores are between −1.68 and +1.68.

There are two country outliers regarding the Economic dimensions at the competitive country cluster level: Belgium (+1.69) and Lesotho (−1.69). The rest of the countries’ scores are between −1.68 and +1.68.

Figure 6. Environmental dimension scores (competitive countries).

Figure 6. Environmental dimension scores (competitive countries).

Figure 7. Social dimension scores (competitive countries).

Figure 7. Social dimension scores (competitive countries).

Figure 8. Economic dimension scores (competitive countries).

Figure 8. Economic dimension scores (competitive countries).

4.3.3. SD Dimensions and Country Outliers (Less Competitive) (Model 2)

Figure 9, Figure 10 and Figure 11 show the country outliers at the less competitive country cluster level.

There is one country outlier regarding the Environmental dimension at the less competitive country cluster level: Bahrain (−1.69). The rest of the countries’ scores are between −1.68 and +1.68.

There are two country outliers regarding the Social dimension at the less competitive country cluster level: Chile (+1.69) and Chad (−1.69). The rest of the countries’ scores are between −1.68 and +1.68.

There are two country outliers regarding the Economic dimensions at the less competitive country cluster level: Malta (+1.69) and Mauritania (−1.69). The rest of the countries’ scores are between −1.68 and +1.68.

Figure 9. Environmental dimension scores (less competitive countries).

Figure 9. Environmental dimension scores (less competitive countries).

Figure 10. Social dimension scores (less competitive countries).

Figure 10. Social dimension scores (less competitive countries).

Figure 11. Economic dimensions scores (less competitive countries).

Figure 11. Economic dimensions scores (less competitive countries).

5. Discussion

The purpose of this study is to examine the interrelationships among the GC pillars, and their relationships with the three sustainability dimensions.

The results of this research, whether at the global or country cluster level, suggest that the SD dimensions per country cluster need to be reconfigured and readjusted, since some of the relationships among the GC pillars, the Macroeconomic Environment, and SD dimensions vary significantly.

GC pillars’ direct relationships among themselves are primarily positive. However, some of the pillars’ direct interactions are, in some cases, either non-significant or negative. However, as a whole (including direct and indirect relationships), all the GC pillars have positive weak, medium, or strong interactions with the Macroeconomic Environment. The mediation effects highlight the need to systemically examine GC pillars’ relationships. Furthermore, these findings suggest the need to verify whether the results from piecemeal research remain valid when looked at more realistically, that is, as we usually observe socio-ecological systems. It means that at the global and country cluster levels, governments need to systematically ensure that they advance their focus on the GC pillars and create an architecture where the relationships among their GC pillars maximize positive synergetic effects so that higher GC and Macroeconomic Environment levels, together with desirable influences on the SD dimensions, are obtained.

The Macroeconomic Environment at the global, very competitive, and competitive country cluster levels has positive and medium or strong interactions with all SD dimensions. Similarly, it positively relates to the Social and Economic dimensions at the very competitive and competitive country cluster levels. Nevertheless, at the less competitive country cluster level, the Macroeconomic Environment has a negative and weak interaction with the Environmental dimension. Hence, the higher the GC level, the stronger the synergies with the SD dimensions. Similarly, whereas the Environmental dimension’s relationships with both the Economic and the Social SD dimensions are positive at the global, very competitive, and competitive cluster levels, such relationships are negative and strong in the less competitive cluster level. These findings may be explained because, in most situations, the higher the poverty level, the worse the environmental quality, the higher the dependence on environmental resources, the higher the environmental degradation, and the more social and economic inequalities are exacerbated. Thus, at higher competitiveness levels, the likelihood of positive synergies is higher, whereas at the less competitive levels, the likelihood of negative spirals increases.

The complicatedness of socio-ecological systems may be poorly studied by highly simplified models. Findings from our Models 1.1, 1.2, 1.3, 1.4, 1.4, 1.5, and 1.6 and Model 2 clearly show that direct and indirect effects may change as a function of model configuration. Hence, it is necessary to evaluate the results from highly simplified models by testing them within more comprehensive models involving a large number of relationships. The latter approximate more accurately to the pragmatic conditions and realities of stakeholders and are bound to be more meaningful and useful.

There are a few country outliers at the global, competitive, and less competitive country cluster levels. There are no country outliers at the very competitive country cluster level.

According to Environmental dimension scores, country outliers with the highest Environmental dimension scores in their clusters are the Philippines and Timor-Leste at the global level, New Zealand at the competitive country cluster level, and none at the less competitive country cluster level. When we study their commonalities, we see they all have higher SDG15 (life below water and life on land) and SDG14 (conserve seas, oceans, and marine life) SDG index scores. Likewise, country outliers with the lowest Environmental dimension scores in their clusters are Pakistan and Ghana at the global level, Lesotho at the competitive country cluster level, and Bahrain at the less competitive country cluster level. When we study their commonalities, we see they all have lower SDG15 (life below water and life on land) and SDG14 (conserve seas, oceans, and marine life) SDG index scores. The United Nations, at the global level, may also need to make some additional arrangements to preserve seas, oceans, marine life, and life on land. The above is essential because oceans are primarily outside the specific property and control of countries, mostly to humanity’s good.

According to the Social dimension scores, country outliers with the highest Social dimension scores in their clusters are China and Timor-Leste at the global level, New Zealand at the competitive country cluster level, and Chile at the less competitive country cluster level. When we study their commonalities, they all have higher levels of SDG3 (good health and well-being), SDG4 (quality Education), and SDG16 (peace, justice, and strong Institutions), in contrast to the country outliers with the lowest Social dimension scores at the global level, Pakistan and Madagascar; at the competitive country cluster level, Lesotho; and at the less competitive country cluster level, Chad.

According to the Economic dimension scores, the country outliers with the highest Economic dimension scores are Norway at the global level, Belgium at the competitive country cluster level, and Malta at the less competitive country cluster level. When we study their similarities, they all have higher levels of SDG8 (decent work and economic growth) and SDG11 (sustainable cities and communities), in contrast to the country outliers with the lowest Social dimension scores at the global level, Croatia; at the competitive country cluster level, Lesotho; and at the less competitive country cluster level, Mauritania.

Overall, our findings support Hypotheses 1 and 2.

The complicatedness and complexity of socio-ecological systems offer opportunities for several theories to contribute to their understanding. Some of these theories include the endogenous economic growth theory, the resource-based theory, Global Competitiveness theory, evolution theory, dynamic capabilities theory, ecological economics theory, and complexity theory.

Endogenous economic growth theory [30] provides a foundation for understanding some of the main factors, including Innovation, Technology, Infrastructure, Education, and Institutions, contributing to long-term economic development. This theory is particularly helpful in explaining the set of GC pillars.

Our findings are in line with the resource-based view theory [32], because advanced resources, some of which are rare, inelastic, and likely to remain in the long-run, may continuously produce Innovations and efficiency improvements, which are required to obtain competitive advantages in increasingly uncertain environments. This type of resource is more abundant in highly competitive countries. The higher capacity to leverage resources, including appropriate incentives and regulations, gives these countries the possibility of strategically integrating Innovation, productivity, and sustainability. In a similar fashion, Global Competitiveness theory [31] explains how national attributes, including determinants of competitiveness like those included in the GC components studied, interact with Sustainable Development objectives.

Similarly, our research results are aligned with evolution theory [34] because the relationships examined in our models may be viewed as a system reflecting the past change, selection, and reproduction of GC factors and SD dimensions involving path dependency, co-evolution, and adaptation to new conditions.

Likewise, our findings may be explained by dynamic capabilities theory [33] given that the most competitive countries have historically shown a higher ability to sense, seize, and reconfigure opportunities to obtain a better balance between social, economic, and environmental changes.

Furthermore, ecological economics [35] also helps us understand the mechanisms underlying our findings. Countries with a higher competitiveness level are better able to emphasize policies examining the interconnectedness between environmental and economic systems, looking at trade-offs and addressing imbalances. Conversely, less competitive countries are more dependent, poor, and vulnerable than more competitive countries. These conditions worsen environmental degradation which in turn exacerbates social and economic inequalities.

Most theories, including the above, take a specific perspective which in most cases is bound to be insufficient to cater for the dynamics linking the multidimensionality of manifold phenomena continuously co-evolving at multiple scales of time, space, and organizational complexity which will more closely approximate the nature of socio-ecological systems. In this regard, complexity theory [36] may be more promising. However, due to resource constraints and methodological and practical difficulties, “deep” operationalizations of it are still rare but do offer very interesting future possibilities for significantly increasing our understanding of socio-ecological systems.

5.1. Theoretical Implications

GC pillars are valuable in determining countries’ Macroeconomic Environments [44]. Most empirical studies examine only specific GC pillars’ relationships with the Macroeconomic Environment [26,27]. Thus, studying the main GC pillars and exploring their interactions among themselves, the Macroeconomic Environment, and the SD dimensions generated comprehensive insights into these relationships. Specifically, this study advances the understanding of each GC pillar’s role in the Macroeconomic Environment and SD dimensions. This research also shows that the influence of each of the GC pillars on the Macroeconomic Environment is improved when the rest of the GC pillars are included, suggesting synergetic effects.

By analyzing the effects of GC pillars on the SD dimensions, considering the Macroeconomic Environment as a mediator, this study contributes to the literature on SD. Similarly, this study shows that Macroeconomic Environments have numerous effects on the SD dimensions at the country cluster level, highlighting the centrality of the Economic dimension for furthering socio-ecological development. Likewise, this study shows that the enhancement of a country’s Social, Economic, and Environmental dimensions requires a systematic perspective among GC pillars (e.g., Institutions, Infrastructure, Education, Technology, and Innovation) and such dimensions, which is not achievable if just one or a few of the GC pillars are considered and improved.

Given the comprehensiveness of the constructs studied and the scope of the 76 indicators used in the analysis, multiple configurations may produce similar results, thereby offering, potentially, diverse development paths. Additionally, national development may be circumscribed by regional and global systems. These constraints are likely to have stronger effects in less developed nations.

These results suggest the need, and challenge, to jointly interpret, among others, institutional theory, resource-based views, evolution theory, dynamic capabilities theory, neo-classical economics theory, and ecological economics theory to better understand the multiple relationships between GC, the Macroeconomic Environment, and sustainability. A large and diverse theory set may help to examine the similarities and differences among multiple perspectives.

5.2. Practical Implications

This study has several practical implications. First, decision-makers should emphasize and build specific country balances among GC pillars, since they are highly interrelated and, as an overall system, have significant effects on the Macroeconomic Environment and SD dimensions.

Second, this study examined the relationships between the Macroeconomic Environment and SD dimensions, which may enhance the decision-maker’s country-cluster- and country-specific understanding of their socio-ecological systems, helping to better allocate particular resources to improve SD.

Decision-makers should sideline piecemeal approaches to socio-ecological development, and focus on the GC pillars, the Macroeconomic Environment, and the three SD dimensions simultaneously, since they are highly interconnected.

This study found several country outliers. These findings may also provide some insights into the outlier countries’ policymakers as they advance their SD programs.

Finally, while we know quite a lot about relationships in socio-ecological systems, and much remains to be known, the Environmental, Social, and Economic deficits require urgent action. In acting, model information may be valuable but not sufficient.

6. Limitations and Future Research

This study had several limitations. First, it only analyzed the interactions among the GC pillars, the Macroeconomic Environment, and the SD dimensions. Therefore, future studies may include more comprehensive SD antecedents, mediators, moderators, and outcomes. For instance, future research may focus on the antecedents of SDGs at the firm, regional, country, and supranational development levels. Potential future research possibilities may include, among others, the role of cultures, political regimes, and diverse education systems on SDGs at the country level and may study the role of leadership styles, organization types, industry types, and firm size on the SD dimensions. Second, most of this research’s development referred to the country cluster level. Thus, there is a need for more exhaustive research at the country and regional levels. Third, the data analyzed spans 11 years. Future studies may analyze a more extended period.

The results of this research were circumscribed to a particular perspective and data. SD’s complicatedness suggests the need to explore a large multiplicity of approaches for future research.

7. Conclusions

GC pillars have different interactions and effects, as do the Macroeconomic Environment components and SD dimensions at the global and country cluster levels. We also found a wide range of variability in the interactions among the Macroeconomic Environment and SD dimensions by country and by country cluster levels. Finally, in our country-level analysis, we found that SDGs 3 (good health and well-being), 4 (quality education), 8 (decent work and economic growth), 11 (sustainable cities and community), 14 (life below water), 15 (life on land), and 16 (peace, justice and strong institutions) are where surpluses and particularly deficits manifest the most for a country’s Environmental, Social, and Economic dimensions, rather than the rest of the SDGs.

This study expands our understanding of the GC pillars and their effects on the Macroeconomic Environment and the three main SD dimensions, encompassing 17 SDGs, at multiple levels (global and country cluster). It also utilizes multiple model configurations (Models 1.1–1.6 and 2). The main findings offered in this study deliver valuable foundations for researchers in the SD field to further investigate and improve this stream of research.