Sustainable Economic Dynamics in Europe: Confirming the Role of Structural Intellectual Capital Using PCA, Panel ARDL, PSTR and SEM-PLS Models

Abstract

This study examines the influence of social capital, intellectual capital, resource rents, and investment capital on the economic performance of the 18 member states of the European Union from 2005 to 2022. Principal component analysis and factor analysis are employed to construct composite measures of social and intellectual capital. The empirical model integrates static panel estimations with Monte Carlo simulations and Panel Smooth Transition Regression (PSTR) to examine nonlinear and regime-dependent growth functions. Investment capital exerts a greater influence on growth when intellectual capital is above a certain threshold, but social capital and resource rents exhibit diverse effects across various regimes; this is consistent with semi-endogenous growth models. In regimes with low intellectual capital, resource rents adversely influence growth, consistent with the resource curse concept; however, this effect diminishes as intellectual capital rises. Finally, partial least squares structural equation modeling indicates that social capital, investment capital, and resource rents influence economic growth, with this effect mediated by intellectual capital. The findings underscore the necessity for the European Union to cultivate and enhance knowledge-based assets while reducing reliance on resource rents to achieve more resilient and sustainable economic development.

1. Introduction

Intellectual capital (IC) has gradually become central to explaining growth, competitiveness, and also sustainability, especially now that many economies rely much more on knowledge and other intangible assets than on physical inputs. Traditional growth models that focus mainly on labor and physical capital do not really capture what is happening. IC combines several kinds of intangible resources—human abilities, organizational routines, various relational structures, and a general capacity to innovate. One of the early attempts to organize this idea was the Skandia Navigator, which separated IC into human, structural, and relational capital (Skandia, 1994; Edvinsson & Malone, 1997; Edvinsson et al., 2005). Later, a national IC system was proposed based on human, market, process, and renewal capital (Bontis, 2004). Research conducted in Poland confirmed that areas with more intense IC operated in a more efficient manner (Nitkiewicz et al., 2014). This lent more evidence to the argument that knowledge and networks have economic value.

At firm level, IC shows up in how organizations innovate, how information moves internally, and how processes improve over time (Edvinsson, 2013; Morris et al., 2017). Business investments that strengthen human capital usually improve employees’ judgment and capabilities overall. At the same time, relational networks offer businesses partnerships and information access that they would otherwise lack. At the national level, IC links to broader development paths and can affect productivity, civic engagement, and environmental outcomes. Research showed how integrating resources into IC can improve productivity and sustainability development (Kianto et al., 2014). This has added value to areas that are usually improved by traditional growth activities. Some literature also ties IC to progress on the United Nations’ sustainability development goals’ (UN SDGs), especially in topics related to climate resilience and energy systems (Suciu & Năsulea, 2019; Secundo et al., 2020; Birtchnell et al., 2017).

Social Capital (SC) operates with IC but has some slight differences in focus, which are mainly trust, cooperation, and the depth of the social networks. Micro settings describe the effect of customer networks and relationships on the SC while also describing the negative sides of over-reliance on these for innovation (Chaudhary et al., 2023; Bosma et al., 2004). Organizations can produce better results as a whole by balancing the development of human, social, and relational capital, and not by overstressing one of them (Barajas-Gonzalez et al., 2024). Similarly, human capital investments signal firm value to stakeholders, reinforcing IC’s central role in organizational and economic performance (Morris et al., 2017).

There is also a line of research linking IC with sustainability. IC, along with renewable energy and trade diversification, reduced material footprints in BRICST economies (Sun et al., 2023). Economic complexity and knowledge decreased the level of CO₂ emissions in the MENA regions (Saud et al., 2023). Comparable results are present in other works (Yao et al., 2020; Wang et al., 2024; Rahman et al., 2021; Danish & Hassan, 2023; Tran et al., 2022; Ståhle et al., 2015). Nonetheless, trade-offs exist; while IC can reduce CO₂ emissions in Pakistan, it may simultaneously increase the ecological footprint, illustrating the complex dynamics between IC and sustainability (Zhang et al., 2021).

Innovation literature also places Intellectual Capital (IC) in a central position. Considering Ukraine and Kondratiev cycles, patents were found to be a determinant of innovation; however, this did not lead to an increase in GDP (Sazonets et al., 2021). This contradicts findings that IC does yield economic growth albeit the impact differing in scale and character across countries as well as choices of measurement (Lee et al., 2017; Marcin, 2013). Educational-based indicators do not capture the full extent of IC and wider parameters should be employed (Stevanović et al., 2018).

The literature on social capital (SC) and intellectual capital (IC) is still mostly lacking despite substantial advancements. Many studies continue to use narrow indicators, such as years of education or patent applications, that fail to capture the more complex structure of intellectual capital, which includes market, process, and renewal capital, as well as social capital (Bontis, 2004; Edvinsson et al., 2005; Marcin, 2013; Kianto et al., 2014; Asiaei & Jusoh, 2015; Lee et al., 2017; Pedro et al., 2018). Although some studies suggest that IC and human capital improves performance or sustainability (Cao et al., 2024; Sun et al., 2023; Saud et al., 2023), others reveal trade-offs and inconsistent findings (Zhang et al., 2021; Yao et al., 2020; Danish & Hassan, 2023). Although certain studies indicate that IC enhances productivity or sustainability (Sun et al., 2023; Saud et al., 2023), others demonstrate trade-offs and variable outcomes (Zhang et al., 2021; Yao et al., 2020; Danish & Hassan, 2023).

Moreover, an excessive focus on social capital in the absence of sufficient balanced investment in human capital may diminish returns and hinder innovation and productivity (Bosma et al., 2004; Morris et al., 2017; Chaudhary et al., 2023; Barajas-Gonzalez et al., 2024).

Numerous research fields have shown that specific structural components influence the relationship between human capital accumulation and subsequent economic growth. According to Castelló-Climent (2010), the impact of human capital growth is determined by levels of inequality and economic development. He regards these characteristics as conditioning thresholds. Similarly, Benhabib and Spiegel (2005) discuss state-dependent methods by which human capital promotes technical innovation and adaptability in emerging countries. Christopoulos et al. (2024) carried out a study using human capital as the threshold variable. He provides evidence that the impact of human capital accumulation differs across lower and higher human capital regimes. Human capital research remains highly fragmented, with many studies undertaken in isolation. It is frequently unclear where threshold dynamics in intellectual and social capital exist. The gap is particularly evident in macroeconomic estimates of European economies (Kuzkin et al., 2019; Roze, 2021).

This study contributes to endogenous and neo-classical growth theories by jointly examining IC and SC in a European macro-level framework while accounting for their dynamic interaction, nonlinear and threshold effects in knowledge-driven growth, and adjustment under cross-country interdependence.

2. Literature Review

2.1. Theoretical Background

Intellectual capital research follows endogenous growth theory rather than the neoclassical Solow model (Solow, 1956) and focuses on human capital, knowledge, innovation, and institutional quality as key drivers of long-term growth, through learning and accumulation (Lucas, 1988), productivity effects (Barro, 1991; Benhabib & Spiegel, 1994), and improvements in total factor productivity (Ståhle et al., 2015).

Lucas (1988) was among the first to formalize endogenous growth theory, emphasizing human capital accumulation and learning spillovers as the primary drivers of long-run economic growth, with knowledge largely embodied in individuals rather than modeled as a separate productive factor. Marchese and Privileggi (2018) extend this framework by explicitly incorporating disembodied knowledge as an independent stock in the production function, showing that ideas and innovation raise productivity and output per capita and can be represented empirically through a linear specification, thereby supporting the use of an intellectual capital index as a measurable proxy for knowledge accumulation. Building on this literature, Jones (2022) develops a semi-endogenous growth model that removes scale effects, demonstrating that while knowledge accumulation increases income levels, it does not generate permanent growth-rate effects, with long-run growth driven instead by innovation dynamics and research efficiency rather than by the size of the economy itself. Bofota et al. (2016) advance the more recent literature by including social capital in an endogenous-growth model, showing that trust, norms, and networks increase the productivity of human and physical capital and social capital is a growth factor as it fosters innovative, cooperative, and efficient institutions. These studies place human, knowledge, and social capital at the center of endogenous growth of the economy by positing each of these to be a factor that is used in the production process and determines the economy’s long-run capacity and growth.

However, as per the Resource Curse theory, the presence of abundant natural resources can weaken growth. Sachs and Warner (1999, 2001); Leamer et al. (1999) and Stijns (2005) document how resource-rich economies tend to experience a slower pace of technological progress and have poorer institutions, as their dependence on oil and gas revenues stifles the accumulation of human and physical capital. Thus, though both tangible and intangible capitals together support economic performance, too much natural resource capital can hinder the knowledge-based systems that foster endogenous, innovation-led growth.

In their 2019 publication, Marchese et al. contribute a great deal theoretically, suggesting that knowledge can grow through recombinant processes that exploit patent tax–subsidy mechanisms to affect the diffusion of ideas and influence transitional growth. They model knowledge spillovers to illustrate their notion that productivity dynamics will be impacted. Although their ideas focus on productivity as a driver of growth, they do not consider the more micro dimensions of intellectual and social capital which operate in various countries. Using their ideas as a stepping stone, this paper attempts to do the first the empirical extension of constructing IC and SC indices through PCA, and examining their long run relationship to GDP while controlling for resource rents.

2.2. Intellectual Capital Index

The relationship between intellectual capital (IC) and productivity is fundamental to growth theory. Gashe et al. (2024) examined 29 countries (1990–2020) utilizing a composite IC index encompassing human, market, process, and renewal capital, and identified a strong positive correlation with total factor productivity (TFP). They emphasize both technological and social innovation as essential to reducing productivity disparities. Stevanović et al. (2018), with a focus on Southeast Europe, utilized proxies including R&D expenditure, researchers per million inhabitants, high-tech exports, mobile subscriptions, patents, and tertiary enrollment. IC demonstrated a positive impact on growth; however, tertiary enrollment was not statistically significant, prompting the authors to recommend alternative indicators such as literacy rates, graduate output, or the quality of higher education.

Ståhle et al. (2015) built upon prior efforts to associate TFP with intangible assets via the ELSS model, which redefined TFP as an efficiency measure influenced by human, process, market, and renewal capital, rather than as a residual derived from the Cobb–Douglas function. By incorporating innovation, international networks, and knowledge exchanges, the model emphasized how intellectual capital enhances national prosperity beyond tangible resources. Nevertheless, it also revealed asymmetries: renewal and market capital frequently contributed to GDP growth, whereas human and process capital yielded weakened or inconsistent impacts, highlighting the distinct role of intellectual capital and the necessity for context-specific policy measures.

Numerous additional indices have endeavored to quantify the contribution of IC to economic growth. Phusavat et al. (2011) established an intellectual capital (IC) index in Thailand and identified a positive correlation with GDP, whereas Radenovic et al. (2021) investigated IC in European Union countries employing proxies such as educational expenditure, high-tech exports, mobile subscriptions, and research personnel in R&D. Their findings were mixed: although technological exports and the number of scholars involved in R&D were positively associated with growth, education and R&D expenditure exhibited unexpectedly negative impacts. Tran et al. (2022) expanded the discourse by associating IC components with the magnitude of the shadow economy, demonstrating that fragile institutional environments hinder the IC–growth relationship. These findings indicate that measurement inconsistencies and contextual particularities continue to pose significant challenges in the development of reliable empirical generalizations.

IC is associated with productivity; however, studies frequently rely on limited proxies such as education, R&D, researchers, high-tech exports, or mobile device usage. These measures encompass only a portion of IC, resulting in varied outcomes influenced by institutions and governance. A comprehensive framework is required, emphasizing human, process, relational, and social capital to more effectively evaluate their contributions to sustained long-term growth. The discourse regarding intellectual capital (IC) and economic development centers on identifying which IC components—human, process, market, renewal, or creative capital—exert the most significant influence on growth and whether their impacts are uniform across different regions. Research conducted in lower-middle-income countries, especially within Arab nations and African states (Bontis, 2004; Bentour & Fund, 2020; Ngepah et al., 2021), presents varied outcomes, with human and market capital frequently exerting the most significant influence, whereas process and renewal capital tend to have more indirect effects. Conversely, studies conducted in industrialized nations, particularly in Western Europe, Russia, and other advanced countries (Popkova et al., 2015), emphasize creative and technological capabilities as primary drivers.

Differences in IC measurement and economic indicators further complicate comparisons, rendering it ambiguous which IC components unconditionally influence growth versus those effects that are context-dependent.

Research on human capital and economic development underscores its significance through education, health, and demographic factors. Putra (2021) examined ASEAN countries through the lens of labor force, school enrollment, and life expectancy, concluding that both the labor force and life expectancy have a positive and statistically significant impact on growth. Ehrlich and Pei (2020) analyzed Asia-Pacific economies by incorporating fertility rates, investments in human capital, growth rates of human and physical capital, knowledge spillovers, and skilled migration. They demonstrated that technological disruptions, such as increased life expectancy, can lead to sustained economic growth when investments in human capital exceed a certain threshold. Their framework highlights intra-family learning and inter-group spillovers, whereby high-skill groups transfer knowledge to lower-skill groups, thereby reducing inequality and fostering long-term growth (Ehrlich & Kim, 2007).

Parallel work emphasizes the significance of both cognitive and non-cognitive abilities. Balart et al. (2018) demonstrate that persistence and motivation influence standardized test results, indicating that variations in economic development across nations cannot be solely ascribed to measured cognitive abilities, as was emphasized by Hanushek and Woessmann (2012), separate interventions are required to foster both cognitive and non-cognitive skills. These findings indicate that the accumulation of human capital relies not solely on formal education and inherited familial endowments but also on wider processes of skill development. Across these perspectives, human capital is identified as both a catalyst for endogenous growth and a fundamental element of national intellectual capital, with its influence conveyed through technology, education, social spillovers, and variation in skills.

Within this framework, human capital (HC) is conventionally regarded as the foundation of intellectual capital; however, its connection to economic outcomes is complex and varies according to context. Szafran and Curie-Skłodowska (2015) emphasized the multidimensional nature of human capital by integrating education, health, occupational mobility, and income into analyses of economic performance. Health indicators, including life expectancy, were identified as notably significant, suggesting that human capital encompasses more than merely formal education proxies. Rossi (2020, 2022) similarly demonstrated that cognitive skills, educational quality, extracurricular learning, and health indicators have a more significant impact on cross-country growth than formal educational attainment alone. These findings emphasize the significance of demographic and life-cycle factors, including immigration, age distributions, and workforce engagement, in influencing healthcare accumulation and its wider economic implications. Lagakos et al. (2018) offer further insights that emphasize the role of migration and lifecycle human capital accumulation in these economic dynamics.

Labor market analyses offer further insights into the manner in which human capital influences economic results. Baily et al. (2021) examined the returns to education and work experience in the United States, Germany, and Japan utilizing the Mincer model, and concluded that gender wage disparities are predominantly attributable to differing returns to education rather than supply–demand imbalances. Bowlus et al. (2022) employed Solow residual and total factor productivity (TFP) decomposition techniques in Canada, concluding that human capital per hour constitutes a substantial contributor to GDP growth, especially via mechanisms not encompassed by conventional metrics. Dasci Sonmez and Cemaloglu (2021) expanded this analysis to encompass 31 countries employing panel VAR models, revealing bidirectional causality between health, education, and GDP in developing economies. In contrast, advanced economies exhibited unidirectional relationships predominantly between human capital and innovation. These studies collectively highlight that human capital influences growth both directly, by increasing productivity, and indirectly, by affecting labor market dynamics and innovation capacity.

In addition to its impact on productivity and economic performance, human capital also assumes a vital role in tackling wider developmental and distributional issues. Olopade et al. (2019) observed that public expenditure on health and education alleviates poverty throughout OPEC nations, whereas Ahmed et al. (2020) showed that investments in human capital help address structural disparities within labor markets. Rahim et al. (2021) emphasized the ability of human capital to mitigate the “resource curse” in N-11 countries, where natural resource rents hinder growth, but strategic educational investments serve to counteract these negative impacts.

These findings indicate that human capital not only enhances productivity but also plays a vital role in achieving broader societal objectives, such as social mobility, labor market inclusion, and sustained economic resilience. Yu and Liu (2021) and Knapińska and Siński (2022) emphasize the intricate relationships among human capital, economic advancement, and local well-being, with particular attention to demographic factors such as population growth and migration. Their research conducted in Poland and Ustka analyzed both quantitative variables—such as population size, proportion of working-age individuals, employment within larger firms, and professional engagement—and qualitative indicators, including educational attainment, school enrollment, and participation in cultural activities. Nevertheless, the absence of Gini index data constrains the understanding of inequality. Results indicate that the quality of human capital is fundamental to economic growth; however, additional research is necessary to examine environmental factors and the impacts of the post-COVID-19 period. Furthermore, proxies such as government education expenditure and enrollment in the INIC may oversimplify the role of human capital in development.

Market and relational capital constitute a fundamental component of intellectual capital, emphasizing the institutional, social, and network frameworks that underpin economic performance. While human capital highlights individual skills and knowledge, market and relational capital encompass the broader context in which these skills function, including institutional quality, network strength, and social trust. According to Bontis (2004), market capital indicates a nation’s ability to harness intellectual resources and attract foreign investment, as demonstrated by trade openness, external relations, and the development of financial markets. Empirical evidence further corroborates this: Lin et al. (2023) demonstrate that market and process capital significantly impact foreign direct investment (FDI) in developing countries, where institutional quality and networks are frequently less developed. Conversely, human and renewal capital exert a greater influence in developed economies, suggesting that the effect of IC components is contingent upon a country’s level of development and institutional maturity. Developing countries with fragile institutions and limited relational capital face challenges in converting foreign direct investment into productivity enhancements, whereas nations with robust networks and market institutions are better equipped to channel investments toward technological advancement and innovation.

Kuzkin et al. (2019) examined intellectual capital and growth in post-socialist Central and Eastern Europe, categorizing intellectual capital into human (teachers, schools, libraries per 1000), market (IP transactions, technology transfers, students studying abroad), structural (internet providers per capita), and innovation capital (R&D expenditure, FDI in innovation, high-tech employees, venture firms, USPTO patents, employee training). Based on UN sustainable development statistics and GDP regression analyses, they identified innovation capital as crucial for middle-income countries, whereas education was most significant in lower-middle-income economies; the diffusion of ICT and structural capital were essential across all categories. Roze (2021), concentrating on Russian regions, highlighted human capital (education, advanced technologies) as the primary catalyst of IC, whereas relational capital (trust, networks, collaboration) differed across regions, influencing knowledge transfer and innovation. These findings underscore a tension: whether innovation capital, as observed in Central Europe, or human and relational capital, as exemplified in Russia, function as the main drivers of development, contingent upon institutional and regional contexts.

In their 2024 study, Barajas-Gonzalez et al. (2024) emphasize the importance of human capital (HC), structural and social capital (SC), and renewal capital (RC) for organizational performance, demonstrating that moderate investment in these areas improves results, whereas excessive investment may diminish advantages. Nevertheless, these findings prompt more comprehensive concerns regarding the comparative significance of market and relational capital, especially within the context of macroeconomic considerations.

Although human capital is frequently highlighted, market and relational capital—comprising institutional quality, networks, social trust, and market connectivity—continue to be insufficiently examined within European IC research.

As Hauser et al. (2007) and Muringani et al. (2021) indicate, bonding networks can enhance local trust but may restrict broader economic participation, whereas bridging networks facilitate interregional knowledge exchange and international cooperation, thereby potentially fostering innovation and economic growth. This indicates that the structural and relational aspects of IC are not solely supportive of HC but can also function autonomously to influence economic performance. However, their limited examination in empirical research raises the question of how variations in trade openness, foreign investment, and social capital among European nations affect the productivity improvements associated with IC. Addressing this is essential for determining whether policy should focus exclusively on enhancing human and technological capital or also strategically invest in networks, market access, and relational infrastructure to optimize economic outcomes.

Process and renewal capital are essential elements of a nation’s intellectual capital, comprising the ability for innovation, technological integration, and the development of forward-looking knowledge and competencies. These elements encompass not only the current body of knowledge and organizational expertise but also the capacity of economies to consistently adapt, learn, and reallocate resources in response to changing technological and market dynamics.

In 2004, Dr. Bontis developed the National Intellectual Capital Index (NICI), which assesses four components: human capital (literacy rates, tertiary educational institutions, teacher qualifications, tertiary students and graduates, male and female grade 1 intake), process capital (telephones, computers, Internet access, mobile phones, radio, television, newspapers), market capital (high-tech exports, patents, hosted meetings), and renewal capital (book and periodical imports, R&D expenditure, R&D personnel, funding for tertiary education). Utilizing a structural equation model (SEM), he determines that human capital influences market, process, and renewal capital; process capital supports renewal capital; and renewal capital, in turn, enhances market capital.

Sazonets et al. (2021) investigated the influence of innovation on the development of both global and national economies within the context of Kondratiev cycles. Utilizing indicators such as patent applications, intellectual property, gross domestic product, and innovation activity, they identified a worldwide increase in patent filings, projecting growth from 3.4 million in 2020 to nearly 4.8 million by 2030—more than 1.5 times greater than in 2018. This trend exemplifies the modernization stage of the ongoing Kondratiev wave, propelled by the cyber revolution. However, the study identified a contrast in Ukraine, where GDP continues to be closely linked to dependence on raw materials despite increasing innovation indicators, highlighting the necessity of transitioning from state-monopoly capitalism to a more inclusive model of “human capitalism”. On a global scale, although patent growth exhibits a positive correlation with GDP, the deceleration of worldwide GDP growth amid rising application numbers indicates diminishing returns, aligning with the cyclical rise-peak-decline pattern characteristic of Kondratiev waves. The authors emphasize that future development will rely less on the volume of innovations and more on qualitative diversification into new technological fields.

Ståhle et al. (2015) recognized renewal capital as a fundamental catalyst for national innovation and productivity. They assessed it using concrete indicators including business R&D investment, expenditures on fundamental research, R&D expenditure as a percentage of GDP, R&D per capita, patent applications, scientific publications, intellectual property protection, and collaboration between universities and industry. These variables reflect a nation’s ability to produce and utilize new knowledge. Their research indicates that countries with greater renewal capital are more proficient at converting technological and scientific innovations into concrete productivity improvements, emphasizing the vital importance of innovation-oriented investments in fostering sustained economic growth.

Szafran and Curie-Skłodowska (2015) further expanded the measurement framework for structural capital by including indicators such as export capacity, environmental standards, patent application activity, R&D expenditure, and the number of researchers. Li et al. (2012) associated renewal capital with the capacity of multinational corporations to harness local capabilities within emergent economies, employing proxies such as cross-border R&D, licensing, and subsidiary innovations. Their analysis of Chinese companies indicated that host-country technological advantages, investment experience, GDP growth, per capita income, and the presence of overseas Chinese firms positively influence investment, whereas geographical, cultural, and institutional distances tend to diminish it. Later work used proxy variables to describe technology adoption and innovation capacity, such as cross-country diffusion measures for technologies including telephones, railways, electricity, and ICT goods, capturing how widely and intensively they were adopted (Comin & Hobijn, 2004, 2010). Patent counts, scientific publications, and broader indicators of technological capabilities and knowledge linkages were also commonly employed (Furman et al., 2002; Archibugi & Coco, 2005), as well as absorptive capacity influenced by institutional quality (Cassiman & Veugelers, 2006).

Structural factors are also significant: corruption, infrastructure, and economic openness influence foreign direct investment inflows (Saini & Singhania, 2018). In the European Union, Radenovic et al. (2021) discovered that R&D expenditure alone does not ensure growth; its success hinges on innovation systems, patent protection, industrial clusters, and the quality of institutions. Together, these studies demonstrate that renewal capital is oriented toward the future but depends on supporting human and institutional frameworks.

European research frequently neglects process and renewal capital, emphasizing education while disregarding R&D absorption, technological diffusion, and innovation capacity. Proxies such as patent-to-R&D ratios, technology adoption rates, high-tech exports, and researchers per capita more accurately reflect disparities, indicating disparate distribution of renewal capital across regions.

Recent research emphasizes the complex influence of social capital (SC) on economic development. Hauser et al. (2007) and Muringani et al. (2021) examined 120 European regions employing an index of bridging and bonding capital, utilizing proxies such as neighborhood engagement and group affiliations. Bonding capital may adversely influence GDP, whereas bridging capital exerts a positive effect; higher education alleviates the negative consequences of bonding capital, indicating intricate interactions with human capital. Ruiz et al. (2011) contend that GDP neglects intangible assets and suggest the adoption of the National Index of Knowledge Capital (NIKC), which encompasses human capital (skills, knowledge), structural capital (processes, research and development, social and environmental factors), and non-explicit capital (tacit knowledge, organizational culture).

Xue et al. (2025) conducted a meta-analysis of 993 estimates derived from 81 studies, classifying social capital into cognitive (trust), structural (association membership), and other categories, with GDP growth or income serving as the outcomes. They observed modest to moderate positive effects, with a marginally greater impact on cognitive capital. These findings demonstrate that SC’s impact is context-dependent, varies according to type and proxies, and interacts with human capital, underscoring the importance of precise measurement in research and policy.

Environmental and social outcomes are infrequently studied, despite the presence of EU sustainability initiatives such as the Green Deal. Methodological diversity—spanning from composite IC indices to individual proxies such as patents or education—diminishes comparability and results in inconsistent findings. Overall, Europe lacks an integrated, multidimensional framework that captures the interactions between IC, socio-capital (SC), productivity, innovation, and sustainability, highlighting a critical gap for both research and policy guidance. Bellucci et al. (2021) and Abdallah et al. (2025) extend this perspective by advocating for an investigation of IC within wider social contexts, highlighting that IC management must be considered within societal and cultural frameworks. This indicates an increasing acknowledgment that IC and SC are not distinct domains but interconnected constructs: relational and market capital intersect directly with SC, influencing institutional trust, innovation processes, and knowledge dissemination.

Despite these progressions, research remains fragmented. Most studies on intellectual capital overlook social capital variables, while research on social capital often neglects intellectual capital. This division hinders a comprehensive understanding of how intangible assets and social structures work together to foster sustainable development. In Europe, where the differences between bonding and bridging social dynamics differ across regions, this disparity is particularly significant. Research done in other areas supports the reason for this.

Saud et al. (2023) demonstrate that economic sophistication across 17 countries in the MENA region diminishes CO₂ emissions and reduces the ecological footprint, thereby reaffirming the benefits of sustainable knowledge diversification. Tran et al. (2022) also demonstrate that the national shadow economy has a positive correlation with the national IC and is developed through the enhancement of the three forms of capital (human, structural, and relational), which in turn is likely to reinforce the governance system and contribute to economic decline.

Jie and Lan (2024) examine the interplay between human capital and natural resources in fostering economic growth and advancing the other objectives of sustainable development. They employed sophisticated econometric growth models to demonstrate that the stock of human capital, in the form of education, unequivocally promotes growth, whereas a country’s natural resource rents may either stimulate or hinder growth, depending on the direction of its administrative policies. Their model incorporates both favorable and unfavorable indicators of growth, namely carbon emissions and trade, respectively, along with other controlled variables such as the inflation rate and capital formation. Overall, Jie and Lan (2024) argue that in order for a nation to attain significant economic development from its natural resources, it must prioritize the investment in its citizens’ education to a greater degree. An escalating number of sources validate the association between Intellectual Capital (IC) and environmental sustainability.

Based on the Environmental Kuznets Curve (EKC) theory, Wang et al. (2024) employed dynamic Generalized Method of Moments (GMM) models and identified the U-shaped relationship between GDP and CO₂ emissions. Their findings corroborate that, while economic growth contributes to increased emissions, the human capital stock, international trade, and the adoption of renewable energy sources serve to mitigate the adverse environmental impacts. Therefore, human capital serves both as a beneficial resource and as a moderating factor within the environment. Therefore, investing in human capital through education and the transfer of knowledge will foster economic development while simultaneously advancing environmental sustainability.

Xie (2025) investigates how the synergistic impact of educational and health human capital influences the advancement of green total factor productivity (GTFP) and the overall progression of a sustainable green economy. In the study, GTFP is determined utilizing the inputs of a standard production function—namely, labor, capital, and energy—together with desirable outputs such as GDP and undesirable outputs including CO₂ emissions, industrial wastewater, sulfates, and solid refuse. Human capital is quantified using indicators such as total years of education, enrollment and achievement levels, life expectancy, and health expenditure. Xie also examines forms of civil capital that promote GTFP, such as green technological innovation (e.g., green patents), research and development investment, and workforce quality. The analysis accounts for variables including industrial structure, urbanization, energy efficiency, trade openness, environmental policies, and GDP per capita. The findings indicate that civil capital contributes to enhancing green productivity through both direct and indirect mechanisms, including the promotion of innovation and the improvement of labor quality.

Ahmed et al. (2020) employ a similar perspective in the context of G7 nations. They discover that although human capital, education levels, and population size influence a country’s ecological footprint, these factors are secondary to the environmental pressures resulting from accelerated urbanization and economic growth. Their findings, together with other research in this field, endorse the perspective that economic growth can be separated from environmental degradation when nations implement human-capital-focused policies that enhance environmental consciousness and develop expertise in green technologies. This conclusion is consistent with Rahim et al. (2021), who demonstrate that human capital alleviates the adverse impacts of natural resource rents across N-11 countries. Their research illustrates that strategic investment in human capital can mitigate the conventional resource curse and foster sustainable development.

Collectively, these studies emphasize how human capital influences intellectual capital and facilitates sustained growth—not merely by enhancing efficiency but also by promoting environmentally responsible innovation and decision-making.

Liu et al. (2024) examine the relationship between carbon emissions, green growth, and the advancement of China’s digital economy. They assess the digital economy through indicators such as digital infrastructure, internet utilization, and digital financial services, which are commonly employed in regional Chinese research. To evaluate environmental sustainability, they utilize emissions data and GTFP, which is defined as the ratio of undesirable outputs (CO₂, SO₂, industrial refuse, wastewater) to desirable output (real GDP). Energy efficiency is defined by the quantity of energy utilized in relation to the useful output produced. Their model accounts for GDP per capita, industrial structure, urbanization, technological innovation, population density, energy consumption, latitude, and trade openness. The findings indicate that the digital economy markedly enhances ecological performance and green productivity, especially in provinces with more robust technological and economic bases.

Despite the available empirical evidence, considerable uncertainty remains. For example, most studies investigate intellectual capital (IC) and social capital (SC) separately, overlooking the ways in which they are correlated and their impact. Human and renewal resources are essential for enhancing the dissemination of information and for the adoption and development of technologies and sustainable practices. However, certain IC-driven policies lack balance across energy efficiency, regulatory frameworks, and resource management. These policies will exert increased pressure on the environment. In European IC research, human capital clearly dominates, with a notable deficiency in market, relational, process, and renewal capital.

Most studies emphasize education, neglecting critical factors such as R&D absorption, innovation, technological diffusion capacity, and advanced indicators of renewal capital, including patent-to-R&D ratios, high-tech exports, researcher numbers, and technology transmission. Initiatives undertaken by the European Union, such as the Green Deal, do not incorporate social and environmental variables. A significant degree of overlap among the measures has resulted in varying outcomes. This is due to the utilization of composite IC indices in conjunction with singular proxies and other methodologies. Overall, Europe lacks a cohesive and comprehensive perspective on IC, SC, and their connections to productivity, innovation, and sustainability. This underscores a gap between prospective research and policy.

3. Methodology

3.1. Data Selection

The methodology is grounded in the standard endogenous-growth production function and neoclassical growth models, where output is generated through investment capital (K), human capital (H), and knowledge spillovers (A):

Y_it = A_itK_it^αH_it^β,

With growth driven internally by the accumulation of skills and knowledge (Lucas, 1988). For empirical estimation, this structure is transformed into a log-linear model that replaces the unobservable knowledge stock with our constructed Intellectual Capital (IC) index and incorporates Social Capital (SC) as an additional productivity-enhancing factor: This study draws on 2005–2022 data for 18 EU countries from the World Bank, WVS, EVS, and EuroStat, focusing on 2022 as the latest year with comparable cross-country information and available data. IC is computed as a weighted combination of its four core components, SC is derived through PCA, and resource rents are added as control variables the model follows exploratory factor analysis (EFA). Investment capital is measured by fixed physical capital formation as percentage of GDP.

Ln(REALGDP) = C + β₁ ln(IC) + β₂ ln(SC) + β₃ ln(K) + β₄ (RR) + μi

(1)

μi accounts for unobserved influences, while the equation reformulates GDP per capita in log form

LnGDP = ƒ (IC, SC, K, RR)

3.2. Principal Component Analysis (PCA)

This study constructs the intellectual capital index using PCA in R-Studio, which reduces variables into uncorrelated components (Smith, 2002). PC₁ explains the most variance, with subsequent PCs capturing less (Vyas & Kumaranayake, 2006). Components with eigenvalues above 1 are retained, and their weighted scores (summing to 1) are combined across countries and years to form the index. The IC index was constructed using data-driven PCA weights computed in R, where the program derives the eigenvector loadings from the first principal component, allowing the data—not subjective judgement—to determine each indicator’s weight across countries and years.

PC₁ = a₁₁x₁ + a₁₂x₁₂ + …+ a_1nx_n
PC_m = a_m1x₁ + a_m2x₂ + …+ a_mnx_n
IC = (σi²)·(PC₁)+(σi²)·(PC₂) + (σi²)·(PC₃)
IC = (σi²)·(HC) + (σi²)·(PC)+(σi²)·(MC) + (σi²)·(RC)

(2)

Here, σi² represents the variance explained by each principal component.

3.3. Panel Autoregressive Distributed Lag (Ardl) Model

ARDL model as it is widely used in panel time series analysis as flexible alternatives to multivariate models (Verma, 2007). This study uses the ARDL Simulations model, following Saud et al. (2023) and others. Due to cross-sectional heterogeneity, a panel approach was applied. Stationarity was tested with Levin et al. (2002) and Im et al. (2003), showing both I(0) and I(1) variables. The best ARDL model was selected, and panel co-integration confirmed via Kao Fisher tests (Kao et al., 1999). The study first explored the long-run relationship between endogenous variables with GDP growth as the response variable, to confirm long-term link.

Δln(GDP_i,t) = ϕ [ln(GDP_i,t−1) − β₁ ln(IC_i,t−1) − β₂ ln(SC_i,t−1) − β₃ ln(K_i,t−1) − β₄ (RR_i,t−1)]
+ ∑_jγ_j ΔX_i,t−j + μ_i + ε_it

(3)

X = {ln(GDP), ln(IC), ln(SC), ln(K), (RR)}

(4)

Real GDP per capita represents economic growth, IC and SC represent intellectual and social capital indexes, and RR represents resource rents added as a control variable following resource curse theory (constant 2015 GDP per capita) for Europe (2005–2022).

3.4. Empirical Results and Discussion

This section presents results from descriptive statistics, Pearson correlation for variable relationships. Due to panel co-integration and serial correlation, Panel ARDL estimation was applied.

3.4.1. Factor Analysis

The intellectual capital index was calculated by computing proportion variance, selecting variables via Maximum Likelihood, Varimax rotation, and deriving Eigenvalues from the correlation matrix (Jolliffe, 2002). In accordance with Bontis (2004), Principal Component Analysis with Varimax rotation was performed, retaining the initial four components—Human, Renewal, Process, and Market Capital—which collectively explained 65.19% of the total variance. Human capital includes education, culture, health, and demographic characteristics, reflecting workforce quality, labor market participation, and population dynamics, in accordance with findings linking human capital to economic performance and demographic structure (e.g., Knapińska & Siński, 2022). Renewal capital denotes the ability to innovate via research, development, and scientific contributions. Process capital refers to information and communication technology (ICT) infrastructure and efficiency, while market capital indicates trade integration and investment attractiveness. The proxies maintained inside each component are grouped according to their highest factor loadings, as shown in Table 1.

The rotated Varimax matrix aligns with the theoretical model, with component 4 mainly representing the four IC indicators. Selected variables based on Varimax loadings are listed in the table below.

Table 1. Varimax Factor Loading.

Variable Name	MR1	MR4	MR2	MR3	Source
Gross Enrollment Ratio, Tertiary (%)	0.283	−0.409		0.469	World Bank
Gross Enrollment Ratio, Secondary (%)	0.737			0.141	World Bank
Gross Enrollment Ratio, Primary (%)	0.472	0.218	0.115	0.258	World Bank
Government Expenditure on Education (% of GDP)	0.636		−0.154	−0.102	World Bank
Current Health Expenditure (% of GDP)	0.675	−0.191	0.452	−0.175	World Bank
Life Expectancy at Birth (years)	0.716	−0.318	0.223	0.170	World Bank
Gross Domestic Expenditure on R&D (% of GDP)	0.814		0.289	−0.128	World Bank
Researchers in R&D (per million people)	0.871			−0.156	World Bank
Alcohol Consumption (liters per capita)	−0.147			−0.708	World Bank
Physicians (per million people)	0.321	−0.567	0.181		World Bank
Hospital Beds (per 1000 people)	−0.360		0.342	−0.515	World Bank
Income Share of Top 10%	−0.366	0.150	0.199	0.454	World Bank
Creative Goods Exports (% of total exports)	−0.107	0.220	0.318	0.601	EuroStat
Individuals Using the Internet (%)	0.680	−0.143		−0.242	World Bank
Trade (% of GDP)		−0.491	−0.468		World Bank
Net Outward Foreign Direct Investment (% of GDP)		−0.171			World Bank
Book Imports (per capita)	0.111	−0.238	−0.464	−0.359	Eurostat
Scientific and Technical Journal Articles (per million people)	0.185		0.935		Eurostat
Pupil-Teacher Ratio, Primary	−0.181	0.746			World Bank
Pupil-Teacher Ratio, Secondary		0.881	0.252	0.345	World Bank
Pupil-Teacher Ratio, Tertiary	−0.264			0.763	World Bank
Pupil-Teacher Ratio, upper secondary	0.172	0.777	0.233	0.237	World Bank
Infant Mortality Rate (per 1000 live births)	−0.578	0.570		0.339	World Bank
Patent Applications by Residents (per million people)	0.116		0.862	−0.164	World Bank
High-Technology Exports (% of total exports)	0.101	0.946			World Bank
Age Dependency Ratio (%)	0.324	−0.423	0.377	−0.229	World Bank
% of Primary Teachers with Required Qualifications	−0.181	0.746			World Bank
% of Secondary Teachers with Required Qualifications		0.881	0.252	0.345	World Bank
% of Tertiary Teachers with Required Qualifications	−0.264			0.763	World Bank

Source: R Studio. Version: 4.2.1.

Table 1. Varimax Factor Loading.

Variable Name	MR1	MR4	MR2	MR3	Source
Gross Enrollment Ratio, Tertiary (%)	0.283	−0.409		0.469	World Bank
Gross Enrollment Ratio, Secondary (%)	0.737			0.141	World Bank
Gross Enrollment Ratio, Primary (%)	0.472	0.218	0.115	0.258	World Bank
Government Expenditure on Education (% of GDP)	0.636		−0.154	−0.102	World Bank
Current Health Expenditure (% of GDP)	0.675	−0.191	0.452	−0.175	World Bank
Life Expectancy at Birth (years)	0.716	−0.318	0.223	0.170	World Bank
Gross Domestic Expenditure on R&D (% of GDP)	0.814		0.289	−0.128	World Bank
Researchers in R&D (per million people)	0.871			−0.156	World Bank
Alcohol Consumption (liters per capita)	−0.147			−0.708	World Bank
Physicians (per million people)	0.321	−0.567	0.181		World Bank
Hospital Beds (per 1000 people)	−0.360		0.342	−0.515	World Bank
Income Share of Top 10%	−0.366	0.150	0.199	0.454	World Bank
Creative Goods Exports (% of total exports)	−0.107	0.220	0.318	0.601	EuroStat
Individuals Using the Internet (%)	0.680	−0.143		−0.242	World Bank
Trade (% of GDP)		−0.491	−0.468		World Bank
Net Outward Foreign Direct Investment (% of GDP)		−0.171			World Bank
Book Imports (per capita)	0.111	−0.238	−0.464	−0.359	Eurostat
Scientific and Technical Journal Articles (per million people)	0.185		0.935		Eurostat
Pupil-Teacher Ratio, Primary	−0.181	0.746			World Bank
Pupil-Teacher Ratio, Secondary		0.881	0.252	0.345	World Bank
Pupil-Teacher Ratio, Tertiary	−0.264			0.763	World Bank
Pupil-Teacher Ratio, upper secondary	0.172	0.777	0.233	0.237	World Bank
Infant Mortality Rate (per 1000 live births)	−0.578	0.570		0.339	World Bank
Patent Applications by Residents (per million people)	0.116		0.862	−0.164	World Bank
High-Technology Exports (% of total exports)	0.101	0.946			World Bank
Age Dependency Ratio (%)	0.324	−0.423	0.377	−0.229	World Bank
% of Primary Teachers with Required Qualifications	−0.181	0.746			World Bank
% of Secondary Teachers with Required Qualifications		0.881	0.252	0.345	World Bank
% of Tertiary Teachers with Required Qualifications	−0.264			0.763	World Bank

Source: R Studio. Version: 4.2.1.

Variables are included in factor analysis if their loadings are ≥0.4, indicating a moderate to strong relationship with the factor. Loadings <0.4 are weak and often excluded (Guadagnoli & Velicer, 1988). Accordingly, the IC components are defined based on prior research, while the proxies are grouped and weighted according to their factor loadings obtained from PCA in R as shown in Table 1 and Appendix A.

The next step was to calculate indices using the selected variables (PC₁, PC₂, …, PC_m) for all countries, variables, and years. The socio-capital index, based on Hauser et al. (2007) and Muringani et al. (2021), used PCA and included bridging, and bonding variables. It was computed as a weighted average of these indicators, and data were extracted from WVS and EVS,

Table 2. Social Capital Operational Variables.

Social Capital

○ Bonding Social Capital: Proxy: Measured through individuals’ participation in political parties, local political action groups, and trade unions. ○ Bridging Social Capital: Calculated through the share of the population in different types of voluntary associations, such as cultural, religious, and human rights organizations.

Source: (EVS/WVS).

below. Due to the sparseness and infrequent collection of EVS/WVS indicators, a linear interpolation approach was used to maintain dataset continuity. This ensured that the dataset remained complete and that the SC index was created with the same level of rigor for the methodology as for the other dimensions of the IC. Thus, PCA was performed.

3.4.2. Model Identification

The PCA-derived indexes were regressed on Ln(GDP), controlling for resource rent (RR), following unit root and co-integration tests to select the best panel analysis method. Stationarity was tested with results in

Table 3. Unit Root Test.

Test	Level p-Value	First-Difference p-Value
Levin, Lin & Chu	0.136	0.000 ***

Source: R Studio. Version: 4.2.1. Notes: *** p < 0.01

.

The table presents the results of the unit root test by Levin et al. (2002). At the level, all variables (Ln(GDP), IC, SC, and RR) have p-values greater than 0.05, indicating non-stationarity. However, at the first difference, all variables have p-values of 0.000, showing stationarity. This suggests that the data become stationary after first differencing.

3.4.3. Panel Estimation

The estimates of pooled OLS, fixed effects, and random effects are detailed in

Table 4. Panel Pooled, Fixed and Random Effects, and Hausman test.

Variable	Pooled Estimate	Pooled p Value	FE Estimate	FE p Value	RE Estimate	RE p Value
Intercept	2.758	0.000 ***	—	—	0.61	0.000 ***
Ln(IC)	0.274	0.000 ***	0.168	0.000 ***	0.171	0.000 ***
Ln(SC)	0.247	0.000 ***	0.007	0.173	0.011	0.063 *
RR	−0.023	0.000 ***	−0.004	0.16	−0.005	0.111
Ln(K)	0.026	0.000 ***	0.155	0.000 ***	0.138	0.000 ***

Source: R Studio. Version: 4.2.1. Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

and begin with the pooled model. This model shows that the impact of intellectual capital (β = 0.274, p < 0.001), social capital (β = 0.247, p < 0.001) and physical capital (β = 0.026, p < 0.001) on economy growth is positive, while the resource rents negatively affect growth (β = −0.023, p < 0.001). However, the fixed effects, which control for the unobserved country-specific heterogeneity, show that of the four, only intellectual capital (β = 0.168, p < 0.001) and physical capital (β = 0.155, p < 0.001) remain strong, while social capital (β = 0.007, p = 0.173) and resource rents (β = −0.004, p = 0.160) become statistically insignificant. Hausman’s test done for random effects on the data is also strongly rejected (χ² = 69.775, df = 4, p < 0.001), leading to the conclusion that the fixed effects estimator is the most suitable. However, the static panel results still suffer from the endogeneity problem generated from the combination of reverse causality, growth and the knowledge based aspects.

Additionally, Pesaran CD tests in

Table 5. Hausman and cross dependence test.

Hausman Test	Chi-Square	df	p-Value
Hausman	69.775	4	0.000 *
Cross-sectional dependence test:	z-statistic	p-value	Conclusion
Pesaran CD	5.811	0.000 ***	Cross-sectional dependence present

R Studio. Version: 4.2.1. Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

(z = 5.811, p < 0.001) confirm cross-sectional dependence, suggesting that European nations experience identical shocks and spillover effects. This explains the necessity for further examinations, including panel cointegration, panel ARDL, and diagnostic tests, to identify and analyze the short- and long-term relationships and to derive coherent and valid conclusions.

The static panel estimates would still suffer from endogeneity on account of common shocks, reverse causality and dynamic feedback. This explains the presence of cross-sectional dependence and justifies the use of panel cointegration, panel ARDL, and PSTR models.

The Kao residual cointegration test results in

Table 6. Panel Co-integration Test.

Test Statistic	Value	p-Value
ADF (t-statistic)	−3.025	0.001 ***

R Studio. Version: 4.2.1. Notes: *** p < 0.01.

, rejects the null hypothesis of no cointegration at the 1% significance level, indicating the existence of a stable long-run equilibrium relationship among intellectual capital, social capital, physical capital, resource rents, and GDP per capita, consistent with the panel cointegration framework proposed by Kao et al. (1999). Although cointegration was established, the Panel ARDL model was used because it already embeds the error-correction mechanism and simultaneously estimates both long-run and short-run dynamics, making a separate ECM or alternative cointegration estimator unnecessary.

The cointegrating specification contributes to determining both short-run dynamics and long-run equilibrating interactions. In the short term, the effect of intellectual capital on economic growth is both positive and statistically significant (Δln(IC), t = 7.761), which means that improvements in intellectual capital have a short-term positive effect on the economy. Bontis (2004) stresses the importance of the intellectual-capital-driven growth mechanism, which is what this finding is based on. Gashe et al. (2024) confirm this by showing that human, market, process, and renewal capital components have a direct impact on productivity and output dynamics.

Social capital has positive and significant short-run effects, as indicated by (Δln(SC), t = 4.103). This indicates that the existence of social cohesion, trust, and networks is likely to enhance coordination and efficiency, even in the short term. This finding supports the idea that social and relational capital are important for economic actions (Xue et al., 2025). Resource rents, on the other hand, have a negative and statistically significant short-term effect on GDP (Δln(RR), t = −4.456), and even though its effect is smaller and delayed, it is still important (Δln(RR(−1)), t = −1.915). This is based on short-term changes caused by volatility, rent-seeking, and misallocation, similar to what is seen in today’s resource-curse research (Rahim et al., 2021; Wang et al., 2024). The short-term impact of physical capital is marginally positive (Δln(K), t = 1.759).

The negative sign and value of CointEq(−1) = −0.143 (t = −5.254) show that there is a long-term cointegrating link between the variables. This suggests that approximately 14% of the disequilibrium is adjusted over each period, indicating a moderate adjustment rate toward long-run equilibrium. Over the long term, Intellectual Capital is substantially associated with gross domestic product (ln(IC), t = 3.070), and socio-capital, even more so, exhibits a strong and significant long-run relationship (ln(SC), t = 4.822); thus, both IC and SC impact productivity and output beyond physical capital effect (Bontis, 2004; Ståhle et al., 2015; Gashe et al., 2024). However, resource rents have no significant long-term effect on GDP (ln(RR), t = −0.984), nor does the physical capital variable (ln(K), t = 0.552). This supports the notion that long-term development primarily relies on the accumulation of intellectual capital as well as social capital, which can mitigate and offset the negative impacts of resource dependence (Rahim et al., 2021; Wang et al., 2024). Further tests in

Table 7. Panel Ardl Model 1.

Variable	Coefficient	Std. Error	t-Statistic	p-Value
Δ(ln(IC))	0.223	0.029	7.761	0.000 ***
Δ(ln(SC))	0.047	0.011	4.103	0.000 ***
Δ(ln(RR))	−0.025	0.006	−4.456	0.000 ***
Δ(ln(RR(−1)))	−0.011	0.006	−1.915	0.057 *
Δ(ln(K))	0.017	0.01	1.759	0.080 *
CointEq(−1)	−0.143	0.027	−5.254	0.000 ***
ln(IC)	0.256	0.084	3.07	0.002 ***
ln(SC)	0.327	0.068	4.822	0.000 ***
ln(RR)	−0.019	0.019	−0.984	0.326
ln(K)	0.011	0.02	0.552	0.581
C	3.049	0.443	6.89	0.000 ***

R Studio. Version: 4.2.1. Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

confirm these results; as part of our model, non-linear tests and simulations are also performed in the next part.

3.4.4. Model Validation and Threshold Simulation (Non-Linear Tests)

Using a Panel-PSTR framework, this section analyzes the impact of IC on Europe’s GDP per capita and determines the model’s robustness and soundness. A Monte Carlo smooth-transition simulation alleviates uncertainty by generating confidence bands around projected GDP, while preserving social capital, investment capital, and resource rents at their mean levels. The transition parameters, specifically the anticipated threshold (τ) and transition speed (γ), are integrated to ensure that GDP evolves smoothly as IC advances along the transition path. The results depicted in Figure 1 indicate that when IC is moderate, fluctuations in gross domestic product are minimal. As IC nears the expected threshold, development escalates. The seamless transition at this juncture signifies that the alteration is occurring progressively rather than abruptly. The simulation illustrates that the impact of intellectual capital on growth significantly escalates once reaching a specific accumulation level. The figure demonstrates the nonlinear and regime-dependent characteristics of intellectual capital’s impact on economic performance, reinforcing the notion that knowledge accumulation is only significantly effective after beyond a critical developmental threshold.

To further ensure robustness, an additional hybrid linear–threshold specification was estimated using the PSTR framework, where Intellectual Capital (IC) enters the model both linearly and through a smooth logistic transition mechanism using interaction terms. In this setting, the logistic transition function

$$g\left(ln\left(IC\_\left\{it\right\}\right); \gamma , c\right) = \left(1\right)/\left(1 + e^\left\{-\gamma \left(ln\left(IC\_\left\{it\right\}\right) - c\right)\right\}\right)$$

governs how the marginal effect of IC changes across different accumulation levels.

Accordingly, the extended model is expressed as:

ln(GDP_{it}) = α_i + β_K × ln(K_{it}) + [β_{SC₀} + β_{SC₁} × g(ln(IC_{it}); γ, c)] × ln(SC_{it}) + [β_{RR₀} + β_{RR₁} × g(ln(IC_{it}); γ, c)] × (RR_{it}) + ε_{it}.

Here, the transition function g(IC) captures the nonlinear and regime-dependent role of intellectual capital, while γ and τ determine the speed and location of the transition, respectively. The large and statistically significant value of γ indicates a rapid but smooth transition, implying that coefficients adjust continuously rather than abruptly across regimes. The estimated intellectual capital threshold (τ = 1.504, p < 0.001,

Table 8. PSTR Model.

Variable	Regime	Coefficient	t-Statistic	p-Value
Ln(K)	Low-IC	0.0207	1.18	0.238
Ln(SC)	Low-IC	0.2356	4.18	<0.001 ***
RR	Low-IC	−0.10740	−2.54	0.011 **
Ln(K) × g(ln(IC))	Transitional effect	0.02553	3.79	<0.001 ***
Ln(SC) × g(ln(IC))	Transitional effect	−0.23710	−2.73	0.006 ***
RR × g(ln(IC))	Transitional effect	0.081	1.88	0.060 *
Ln(K)	High-IC	0.04623	2.66	0.008 ***
Ln(SC)	High-IC	−0.00143	−0.02	0.984
RR	High-IC	−0.02639	−2.39	0.017 **
γ (transition speed)	Threshold effect	33.32	3.39	<0.001 ***
c1 (ln(IC)) (threshold)	Threshold effect	1.504	84.7	<0.001 ***

R Studio. Version: 4.2.1. Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

) is precisely identified, defining the point around which the transition between regimes occurs. Accordingly, the PSTR specification allows the marginal effects of physical capital, social capital, and resource rents to vary with intellectual capital levels, rather than assuming constant effects across all observations (Figure 1).

The PSTR model results presented in Table 8 show a positive correlation with economic growth that explains one of the highest variances with an adjusted R² of close to 0.81. The calculated threshold for intellectual capital (IC) (c₁ = 1.504) reflects nonlinear characteristics, demonstrating that IC is an important growth factor. While IC unquestionably has a positive and significant impact on all observations, the development effect of the additional variables is almost entirely dependent on the level of IC, which is in line with endogenous growth theory and the human-capital spillover effect (γ = 33.32, p < 0.001).

As long as investment capital (K) varies across regimes, it has a more pronounced growth-enhancing effect once IC crosses a certain threshold. For example, it increases from β = 0.021 (p = 0.238) in the low-IC regime to β = 0.046 (p = 0.008) in the high-IC regime, suggesting that physical investment becomes more productive in economies with sufficiently developed intellectual capital. Permitting the investment capital coefficient to differ across intellectual capital regimes does not mean capital accumulation is contingent on intellectual capital. Instead, it reflects regime-specific variations in capital productivity, resulting from different levels of absorptive capacity and institutional efficiency (Romer, 1990; Benhabib & Spiegel, 2005).

In the low-IC regime, resource rents, as expected, hinder growth (β = −0.107, p = 0.011) confirming the resource-curse hypothesis, and their negative effect is constrained once IC is above the threshold (β = −0.026, p = 0.017). In the low-IC regime, social capital positively affects growth (β = 0.236, p < 0.001), but its effect is in the high-IC regime is negative (β = −0.001, p = 0.984). The non-linear specification, overall, captures more effectively than a linear model the heterogeneous growth responses.

SEM-Model

The below

Table 9. SEM-PLS model.

Path	Original Sample (O)	t Statistic	p Value
ln(K) → ln(GDP)	0.213	5.997	0.000 ***
Ln(SC) → ln(GDP)	0.158	7.014	0.000 ***
RR → ln(GDP)	−0.083	4.173	0.000 ***
Ln(SC) → (IC)→ ln(GDP)	0.158	7.014	0.000 ***
RR → (IC)→ ln(GDP)	−0.083	4.173	0.000 ***
ln(K) → (IC)→ ln(GDP)	0.213	5.997	0.000 ***
Ln(IC) → ln(GDP)	0.438	8.871	0.000 ***
ln(K) → (IC)	0.486	11.513	0.000 ***
ln(K) → ln(GDP) (total)	0.365	15.735	0.000 ***
Ln(SC) → LN(IC)	0.36	7.176	0.000 ***
Ln(SC) → ln(GDP) (total)	0.582	20.499	0.000 ***
RR → LN(IC)	−0.189	4.799	0.000 ***
RR → ln(GDP) (total)	−0.186	6.317	0.000 ***

Source: Smart PLS. Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

, confirms the outcomes of the structural equation model (SEM) using the partial least Squared (PLS) approach highlighting the key relationships among IC, SC, RR, and GDP as estimated:

SEM-PLS model results presented in Table 9 above show that social capital (SC) exhibits the most significant correlation between intellectual capital (IC) and economic growth. SC has a good impact on IC (β = 0.360) and a significant overall impact on GDP (β = 0.582). Furthermore, SC affects economic growth indirectly via IC (β = 0.158), suggesting that intellectual capital serves as the primary conduit through which social institutions, trust, and cooperation enhance economic performance.

Resource rents (RR) have an inverse correlation with both the production of intellectual capital (IC) and economic growth. RR lowers IC (β = −0.189) and GDP (β = −0.186), which has a negative indirect effect on growth through IC (β ≈ −0.083). These findings align with the resource-curse hypothesis, indicating that increased dependence on natural resources undermines knowledge accumulation and sustainable development when revenues are ineffectively allocated to productive and innovative endeavors.

Investment Capital (K) has a positive effect on IC (β = 0.486) and a positive total effect on GDP (β = 0.365). But its effect on economic growth works mostly through intellectual capital, not just through direct accumulation. This shows that IC completely connects Investment Capital and economic growth.

The findings indicate that both RR and K influence GDP predominantly via IC. This elucidates the reason RR and K seem inconsequential in the long-term ARDL estimates, which emphasize equilibrium linkages, yet they retain significance in the SEM-PLS framework that encapsulates structural transmission and mediation effects. Consequently, the two techniques complement one other instead of opposing one another, so reinforcing the model’s internal consistency. Social capital (SC) exhibits the most significant correlation between intellectual capital (IC) and economic growth. SC has an adequate impact on IC (β = 0.360) and a significant overall impact on GDP (β = 0.582). Furthermore, SC affects economic growth indirectly via IC (β = 0.158), suggesting that intellectual capital serves as the primary conduit through which social institutions, trust, and cooperation enhance economic performance.

4. Conclusions

The study examines econometric techniques including linear panel modeling, the panel autoregressive distributed lag model, panel smooth transition regression, and partial least squares structural equation modeling to elucidate the relationships among social, intellectual, and investment capital, resource rents, and the economic performance (assessed by GDP per capita) of European Union member states. The findings of the Panel Smooth Transition Regression indicate the presence of many non-linear and regime-specific growth patterns while taking intellectual capital as the threshold variable. In the low-threshold tier, the impact of investment capital on GDP per capita is minimal and statistically insignificant, whereas social capital exerts a positive influence, and resource rents have a statistically significant negative effect, consistent with the resource curse hypothesis. In the elevated IC regime, the beneficial influence of investment capital on income substantially intensifies, whereas the detrimental effect of resource rents considerably diminishes. In the advanced IC regime, the impact of social capital diminishes and becomes statistically insignificant upon the integration of threshold dynamics.

Monte Carlo smooth-transition simulations indicate that at low threshold variable values, fluctuations in per capita GDP are negligible. Conversely, once the threshold is surpassed, a progressive increase in the marginal growth response is observed, thus supporting the concept of a gradual transition in development regimes rather than an abrupt structural shift. Findings indicate that intellectual capital mostly influences smooth transitions in per capita income levels rather than long-term abrupt growth rates when calculating the IC threshold effect, hence endorsing semi-endogenous growth theory (Jones, 2022). Consequently, European nations often yield augmented revenue from intellectual capital as they advance towards elevated levels of knowledge and intellectual assets.

Linear panel models and panel autoregressive distributed lag models indicate that, in the long term, intellectual and investment capital positively influence GDP per capita, but resource rents adversely affect economic growth, a conclusion supported by additional models. In linear models, social capital favorably influences income; but, in the presence of nonlinear factors, this effect seems to wane. This indicates that productive investment combined with knowledge enhances economic performance, whereas reliance on natural resources does not (Bontis, 2004; Ståhle et al., 2015; Rahim et al., 2021; Gashe et al., 2024).

The conclusions are further validated by the results of structural equation modeling and additional confirmatory factor analysis. The measurement method for the dimensions of intellectual and social capital is substantiated using confirmatory factor analysis. Structural equation modeling indicates that social capital, investment capital, and resource rents influence GDP per capita, with intellectual capital acting as a mediator.

For policy makers and managers, the results underscore the importance of integrated strategies to improve the intellectual and social aspects of education, healthcare, research and development, innovation ecosystems, digital skills, institutional quality, civic engagement, and both voluntary and involuntary civic participation. Especially on policy level, revenue from resources should be allocated to strategically prioritized areas, including human capital development, innovation, digital systems, and enhancement of institutional frameworks. This will facilitate the modification of path dependencies and enhance GDP per capita via intellectual capital.

Future researchers are recommended to incorporate other variables such as the rule of law, governmental efficiency, and institutional trust as indicators of social capital. This study employs a thorough macro approach and integrates country-specific dynamics into the econometric framework; nevertheless, it omits tax-based innovation incentives, as proposed in prior growth models (e.g., Marchese et al., 2019). Subsequent study may broaden the notion by exploring many components of intellectual capital and integrating additional dimensions, including research and development tax incentives, patent systems, digital capabilities, and various environmental aspects. Integrating these traits would enhance the analysis of the relationships and interdependencies of intellectual, social, and natural capital and their collective influence on per capita income within the changing economic landscape of the European Union.