Technological innovations for sustainable business success: enhancing processes and performance

Technological innovation plays a decisive role in promoting sustainability and democratizing access to finance and facilitates the high-quality transformation of economic development, especially offering financial benefits to environmentally friendly and sustainable investors. Sustainable value creation through technological innovation is one of the most significant factors that offer investors advantages while addressing critical environmental challenges. The increased interest of enterprises in adopting technological innovation and their perseveration in reaching environmental sustainability leads to introducing new environment-related technologies by providing environmentally friendly and socially responsible solutions.

Based on these landmarks, the current study aims to examine how environment-friendly and sustainable investors within the European Union (EU) can leverage technology and digital innovation to enhance sustainable business processes and financial performance. To achieve this, we implement an innovative and complex three-fold methodological approach: first, we develop robust regression models with Huber and biweight iterations and dynamic GMM models with Arellano-Bond estimations; second, we use cluster analysis through the Ward method inset on hierarchical clustering to group technological innovators at the European Union level; and finally, we use alternative network analysis through Gaussian Graphical Models (GGMs) for each specific group of technological innovators. This enables us to capture the overall interlinkages between technology, environment, and investment credentials and draw relevant connections and inferences in a complex, comprehensive setting. Therefore, the research questions that are pursued entail the following: (1) What are the specific factors that drive firms’ investments in innovation and technology at the EU level, and what are the outcomes of these actions on environmental sustainability? (2) What is the intensity of technological innovation performance associated with EU countries? (3) What are the discrepancies between the EU Member States that have associated low or very high levels of technological innovation performance?

Although there are several studies that have concentrated on aspects related to technological innovation, sustainable development and firm investments (Ahmad et al., 2023; Ahmed et al., 2022; Omri, 2020; Wang et al., 2021; Xiong and Dai, 2023; Zhang et al., 2022), the novelty of the present study is entailed by an original approach regarding the effects of technology and innovation on achieving business performance emphasizing the firm investments in innovation and assessing the effects on environmental sustainability for EU countries. Furthermore, the study outlined the significant differences at the EU level regarding technological innovation determinants. Moreover, an essential contribution of this study to research in this area is the use of sophisticated and advanced statistical and econometric methods, which provide statistically significant results, along with detailed and insightful knowledge of the explored relationships.

Therefore, we examine the technological innovations perceived from four distinct perspectives: firstly, on the indicators related to the framework conditions, related to human resources (educated workforce with relevant high skills); secondly, the investments exerted both in the private and public sector (finance and support, company investments, personnel and specialist with high ITC skills); thirdly, the innovation activities (innovation capabilities, the introduction of innovations on the market as regards the company) based on six indicators, including some new ones in comparison with the previous scientific research; last but not least, the impacts generated by the enterprises in terms of employment and environmental sustainability.

In this innovative approach, the main results highlight a major impact of two dimensions of the innovation and technology sectors on attracting environmentally friendly and sustainable investors. Consequently, based on the complex set of selected variables concerning European innovations, our results indicate that only framework conditions, investments, and innovation activities have significantly impacted the firm investments and environmental-friendly and sustainable investors, with a particularly slightly touched impact from the employment as regards the knowledge-intensive and innovative companies’ dimensions.

Along the same lines, the main results regarding the performance of EU Member States (MS) in terms of technological innovation denote those nine countries registered soaring performances. The other eighteen EU-27 MS accounted for significant and medium performances but also the lowest achievements in terms of technological innovations. The GGMs results revealed positive interconnections between most indicators except for environmental sustainability, which had strong negative linkages with attractive research systems. Furthermore, the research highlights the need for boosting environmental sustainability through human resources.

The research provides valuable information regarding the innovation and technology dimensions that significantly affect investment decisions and influence sustainable investors. These findings can be helpful for practitioners, managers, entrepreneurs, or policy makers, who are focused on achieving environmental sustainability through innovative processes and new technologies, understanding how and evaluating with what intensity different aspects of businesses can attract investments in innovative solutions and create positive environmental outcomes. Moreover, our research highlights the importance of adopting and practising technological innovation tools and processes. This fact leads companies to increase sustainable practices and develop new investment strategies to attract more socially responsible investors.

The structure of this paper comprises four distinct, yet interconnected sections designed to provide a comprehensive understanding of the theme being discussed. The first section introduces this topical subject and outlines the main objectives of the study. The second section provides a critical literature review, which involves a systematic analysis of existing research related to the topic. The third section captures the methodology and data employed in the empirical analysis. Finally, the fourth section contains the results and conclusions, which summarize the key findings of the study and provide relevant strategic directions and recommendations.

The process of increasing propagation and the manifestation of globalization, including foreign direct investment (FDI) and the increasing openness to foreign trade, manifested by countries around the world, together with climate change and the degree of air pollution, are currently the three main points of concern with a high level of gravity globally (Van Tran et al., 2024; Tariq et al., 2023). Industrial activities of all kinds generate increasing emissions of pollutants; therefore, viable long-term methods are needed to lessen the adverse outcomes and improve air quality, notably in conglomerated areas and highly industrialized sectors.

The evolution of technology creates the opportunity to handle environmental issues, such as emissions of pollutants or climate change (Omri, 2020). The concept of technological innovation indicates a set of actions performed to generate an original or an advanced version of a technology that brings significant improvements in terms of productivity and has the potential to generate added value for companies (Liu et al., 2020). We often refer to technological innovations as instruments for optimizing and efficiently using our environment’s resources, considering the constant pursuit of societies for socioeconomic growth (Cancino et al., 2018). According to authors Cancino et al. (2018), the sustainable value of organizations can be divided into the following categories: (1) environmental: renewable or sustainable resources, the reduction of pollutants generated by organizational activities, including emissions that create the greenhouse effect and industrial waste; (2) social: promoting diversity, equal rights for all stakeholders, safe environment and working conditions, welfare and labour standards; (3) economic: profitability, rentability of organizational investments, the long-term adaptation to market conditions, and the ability to maintain a consistent financial performance.

It has been demonstrated, remarkably in developing countries, that globalization, regional economic integration, and FDI can bring representative benefits to economies but can also generate adverse effects, increasing emissions of pollutants and greenhouse gases. Authors Farooq et al. (2020) pointed out that globalization and FDI illustrated positive environmental outcomes while considering high-income countries and presented adverse effects on environmental quality while studying the impact on low-income states. Omri (2020) also investigates the effects of technological innovation on economic growth, society and the environment, considering low-, middle-, and high-income countries, indicating the difference between the results according to each country’s specific stage of economic growth. The results suggest that innovations within technology significantly contribute to economic, social and environmental sustainability in high-income countries, affecting the economic and environmental sustainability for countries that present medium incomes, and concluding that for countries with low earnings, the influence of technological innovation is not significant. Moreover, in order to ensure technological progress, competitiveness and economic development, it is necessary to have sustainable public policies that are guided by the permanent support of the public administration (Costea et al., 2022).

Even if it fosters economic expansion, foreign investment frequently results in environmental degradation in emerging nations. Lobonț et al. (2023) state that corporate investment decisions largely depend on the key factor, namely fiscal policy, thus reflecting the desire of investors or companies to engage in various situations with a high degree of risk. In addition, several emerging nations have made environmental preservation and economic growth their top priorities in their pursuit of economic development, turning them into “Pollution Havens”. In this case, the environment is harmed by increased economic activity, improved by technical advancement, and the impact of shifting economic structures on the environment depends on the direction of change. Foreign investment harms the environment in developing nations not only by increasing the volume of economic activity but also by changing the nature of that activity (Wu and Li, 2024). Such solutions obviously and necessarily involve technological innovation and environmentally friendly practices so that future major investments are sustainable and play a major role in the conservation of natural resources.

We are currently at the centre of the fourth industrial revolution. The present and future actions aim at a new era of technology, of innovation on all levels, in which the development of activities involves the preponderance of robots and especially artificial technology, which will conquer and take over many fields and economic branches, becoming even indispensable (Xu et al., 2018). Authors Zhang et al. (2022) consider that to accomplish sustainable objectives, companies need to capitalize on their investments in renewable energy sources and take into consideration the diverse implications of firm risk and return, especially in emerging markets (Pirtea et al., 2014). Furthermore, foreign direct investments can lead to increased incomes, economic development, and growth for the beneficial country. Firms, clients and managers are the central beneficiaries of these positive outcomes due to the increased effectiveness that generates additional earnings, which can be assigned for further development actions (Zhang et al., 2022). Technological progress can generate reduced energy consumption and has the potential to introduce new tools for customers and firms that are focused on environmental sustainability and desire to minimize their negative impact. Furthermore, the shift to a more tenable process of energy usage is essential to decrease environmental degradation, and the government should focus on introducing more sustainable power systems (Zhang et al., 2022).

Chege and Wang (2020) study the connection between technological innovation, sustainable environmental practices and the outcomes on firm performance, viewed from several perspectives: firms’ profitability, image, impact on the environment, and shareholder satisfaction. Their research outlines that firms that invest in eco-friendly projects and protect the welfare of the community can achieve higher financial performance. Furthermore, innovation practices and employee participation contribute to increasing enterprise performance and improve its image (Chege and Wang, 2020). Asset managers are continuously investigating how the use of new technologies can enhance sustainable investment decisions, trying to reduce the biases linked to human evaluation, such as subjectivity (Hughes et al., 2021). The study of Indriastuti and Chariri (2021) has demonstrated that corporate social responsibility (CSR) and green investments significantly influence companies’ sustainability and financial performance. CSR investments can support innovative activities or human resources and can also sustain actions to improve the reputation and protect the company culture (Indriastuti and Chariri, 2021). Authors Shabbir and Wisdom (2020) further analyse the connection between CSR, environment-friendly investments, and the financial performance of manufacturing firms. Their findings suggest a positive and statistically significant linkage between the firms’ internal environment-friendly investments and financial performance. Additionally, the research underlines that enterprises with increased sustainable investments tend to present a higher level of profitability (Shabbir and Wisdom, 2020).

The correlation between technology, innovation and business performance was previously studied by author Koellinger (2008). This research provides evidence that in European firms’ context, technology is an essential factor that drives innovation, which is positively connected to turnover and increasing employment. In the context of EU firms, the research of Ionaşcu et al. (2022) suggested that digital transformation progress is compensated and appreciated by investors, along with firms’ social responsibility and environmental preservation. Furthermore, the research field that covers the influence of technological innovations on environmental sustainability has previously been studied by authors Wang et al. (2023) using panel data from 14 EU countries. Their findings revealed that green energy and innovation within technology can lessen the degree of environmental deterioration.

Environmentally conscious behaviours and “green” mindsets are progressively becoming a rapidly expanding megatrend and a commercial necessity for all social and economic actors. Connecting ecologically conscious behaviours with economic activity can lead to novel business models as well as untapped potential for value generation (Pirtea et al., 2015). The concept of sustainable investing, which refers to investment practices that focus on the environmental, social, and governance aspects (Martini, 2021), has made several important advances in the realm of financial and economic studies, representing the new line of inquiry into the social, economic, financial, and institutional aspects of sustainability (Marszk and Lechman, 2024).

Green technology, which addresses the balance between economic growth and environmental protection, represents a modern technological approach that promotes environmental preservation, resource efficiency, and recycling. Companies must weigh the costs and advantages of transitioning to greener practices due to internal or external demands, deciding whether to invest in research and development or engage in “greenwashing” behaviours that hinder green technological advancements. In the immediate future, businesses might resort to spreading misinformation to overstate their progress toward sustainability to appease stakeholders (Li et al., 2023).

As authors Ali et al. (2024) pointed out, the biodegradation of polymers has been a highly researched topic over the last decades, showing that some plastic kinds are soluble, and the process by which they break down is becoming increasingly apparent. Aiming to reduce the environmental outcomes of pollution, the emerging technologies and studies regarding this topic tend to focus on achieving more sustainable and eco-friendly methods. Thus, employing biologically derived organic waste to produce bioplastics not only lessens our need for edible feedstock but also helpfully contributes to solid waste management. The main obstacles are their low mechanical strength, low production volume, lack of infrastructure, and expensive feed for large-scale manufacturing (Ali et al., 2024).

The optimal utilization of natural resources is now a critical determinant of sustainable development that encompasses both economic growth and environmental protection (Hariram et al., 2023). Natural resource efficiency can be a crucial driver to achieving sustainable economic development, and it is considered to be a valuable topic in the following years, especially for adjusting countries’ economic policies. Nidumolu et al. (2013) propose a paradigm shift where businesses perceive environmental and social challenges as opportunities for growth, innovation, and transformation. Authors Sovacool et al. (2022) study the complications and outcomes of introducing innovative technologies within society. Their research divided technological innovations and sustainable behaviours into four categories: upgraded and improved heating systems, batteries and electric vehicles, solar panels for residential usage, and food-sharing. Their study concluded that technological innovations that pursue reducing emissions are not naturally equitable and sustainable, suggesting that specific policies and actions need to be implemented in this regard.

Green technologies have advanced significantly with the onset of the Fourth Industrial Revolution, leading to the restoration of environmental conditions in modern economies. Studies indicate that long-term green growth relies on technological innovation, GDP, human capital, economic globalization, and R&D investments (Ahmad and Wu, 2022; Söderholm, 2020). Ahmed et al. (2022) examined the influence of public investments in research and development linked to renewable energy and technology advancements on pollution and renewable energy usage within selected countries, and their findings suggest that to improve the renewable energy supply shares and decrease the air pollution, public expenditures in sustainable technologies and processes are necessary. Taking into account that technological innovation has the potential to boost green energy resources and benefit environmental welfare, industrial development should focus on producing and adopting modern technology and improving innovation skills within organizations (Ahmed et al., 2022). In addition, private investments in research and development are also an essential driver for environmental sustainability. The study of Pradhan et al. (2020) and Plakaj Vërbovci et al. (2024) also indicate that information and communication technology (ICT) diffusion and innovation practices significantly influence sustainability and economic development in the European Union countries.

According to Xiong and Dai (2023), green finance has the potential to contribute to the long-term development of the country. Their study reveals that sustainable investments can lead to economic growth within countries. Although green finance has the potential to sustain opportune financial support (Xiong and Dai, 2023), which can be obtained taking into consideration the costs involved and the current financial structure of the firm, and in order to get more remarkable economic development, it is necessary to include technological innovation but also consider appropriate environmental regulations (Xiong and Dai, 2023). Renewable energy can represent the starting path for introducing innovations, as emphasized by highly developed companies and sustainability rates in states with rigorous renewable policy regulations. This results in more advanced economic stimulation, commerce openness, and personnel training and development (Zhang et al., 2022).

The effects of green energy finance and ecological progress on eco-friendly surroundings set the model for other regions, contributing to a green recovery. The impact of economic expansion and urbanization on environmentally friendly environments and the reciprocal relationship are notable. Intensifying the integration of ecological advancement and environmental prosperity with green energy finance is essential to further enhancing green recovery. Creating a corporate ecological framework to oversee local governments and improve the efficiency of green energy financing can help achieve this. Creating an effective long- and medium-term approach as an external financial market involvement measure can also help finance green energy and ecological development, ultimately creating eco-friendly surroundings and green recovery (Sun et al., 2022).

In line with the main purpose of this study, namely, to analyse the role of technological innovation in attracting environment-friendly and sustainable investors in the European Union, we extracted a large dataset from the European Innovation Scoreboard (EIS) 2023 (European Commission, 2023) comprising specific and relevant indicators for the 27 EU countries from 2016 to 2023. We analysed the nexus between sustainable investment choices and technological innovation by considering two advanced statistical software, respectively Stata and R.

The complete set of indicators includes:

1. Framework conditions: Human resources (HR), which assesses the accessibility of a workforce that is highly skilled and educated, and Attractive research systems (AT_RES);

2. Investments dimension: Finance and support (FIN_S), Firm investments in innovation (FINV), and Use of information technologies (U_I_TECH);

3. Innovation activities representative credentials: Innovators (INNOV), Linkages (LINK), Intellectual assets (INTEL_A);

4. Impacts dimension: Employment impacts (EMPL_I), and Environmental sustainability (ENVS).

The descriptive statistics of the indicators used in the empirical endeavour can be found in Table 1.

The Panel unit-root test (LLC) is identified in Table 2 and entails stationarity for almost all series. Panel data methods have several advantages. One of the most significant benefits is that they enable the use of data from multiple countries combined in a panel, which is useful for studying hypotheses of interest that would otherwise be precluded due to insufficient period-series data. Furthermore, many issues studied in economics, naturally lend themselves to being studied in a panel context (Barbieri, 2006).

Figure 1 illustrates the evolution of the indicators used in the empirical analysis in each EU country between 2016 and 2023. We note that the dynamic of these indicators presents both positive and negative trends over time, and the indicators that register keen fluctuations over the years are identified in several countries, such as Cyprus, Estonia, Ireland, Lithuania, Luxemburg, Malta, and Portugal.

Table 3 contains the pairwise correlations and the Pearson and Spearman correlation matrix. As Schober et al. (2018) previously mentioned, the Pearson and Spearman correlation, with values ranging from −1 to 1, indicates the strength of the connections between the variables.

The Pearson correlation presents the linear linkages between the considered variables. In contrast, the Spearman correlation determines the monotonic linkages emphasized by the variables’ connection, this connection increasing as the values tend to get closer to 1.

We note strong correlations exist between the indicators selected as proxies for technological innovation, firm investments, human resources, employment outcomes, and environmental sustainability.

Figure 2 identifies the graph matrix of the correlations. This matrix is extensively used to reflect the linkages in the data and represent the connections between the variables.

The methodological endeavour is based on a three-fold approach:

1. First, robust regression models with Huber and biweight iterations and dynamic GMM estimations are developed;

2. Second, cluster analysis through the Ward method is employed to capture the specific groups of technological innovators at the European Union level;

3. Third, alternative network analysis through Gaussian graphical models (GGMs) is configured for each specific group of technological innovators to capture the overall interlinkages between technology, environment, and investment credentials considered in the empirical analysis, drawing relevant connections and inferences in a complex setting.

Looking into empirical methods applied by economists to study the role of technological innovation in attracting environment-friendly and sustainable investors, we focused on some advanced modelling procedures and important alternatives used to obtain unbiased estimators by coping with potential endogeneity. Hence, to avoid the weaknesses of models, “the alternative methods for dealing with endogenous explanatory variables” (Lozano and Steinberger, 2012) are Generalized Methods of Moments (GMM) estimations and quantile or robust regressions. On these lines, in the initial phase of the empirical analysis, we applied robust regression with Huber and biweight iterations and dynamic GMM models.

Multiple methods and instruments were used to conduct this analysis, consistently comparing the results obtained. This practice not only ensures a comprehensive analysis but also facilitates the identification of the most reliable and effective methods and instruments for achieving the desired objectives. Consequently, this approach is significant for achieving rigorous evaluations and suitable results.

Thus, in the final stages of the research endeavour, we also implemented cluster and network analyses to capture the differentials and specificities between the EU Member States. The methodology also relies on a robustness analysis built based on the hierarchical clustering executed, following the method delineated by Ward (1963). This agglomerative approach incrementally builds cluster hierarchies, enabling the detailed examination of data structures. A dendrogram is constructed to visually represent the clustering process, offering insights into the natural groupings of countries. Cluster analysis represents a statistical method generally used for efficiently comparing a set of variables and determining the similarities and differences between them. Cluster analysis brings empirical results which distribute the considered variables and allocate them into different groups. This technique also explores and measures the collected data to create clusters or groups of related variables and individuals that have unique characteristics compared to other clusters. Cluster forming and analysis were conducted using the standardized values of indicators through the Ward method, which is specific for hierarchical clusters. This method performs a global analysis of statistical units by using a high number of characteristics. It determines that the distance between two clusters A and B is based on how much the sum of squares will increase when they are cumulated. In hierarchical clustering, the sum of squares starts from zero since each point is placed within its cluster and increases as the clusters are combined. The Ward method keeps this increase to the lowest possible level. Thus, the Euclidian distance between the subjects is applied in current research. The measurement scale of considered variables plays an important role in the analysis, as changing the scale modifies the distance between subjects. Additionally, if a variable has a wider range of variation than the others, it tends to dominate. To ensure precise and accurate research, each variable has been standardized. However, this standardization method reduces the variability (distance) between clusters. Considering two clusters (A and B), the Euclidean distance (d) of these clusters can be indicated by applying the square root of the sum of squared differences d(A,B) = $∑|Ai−Bi|2$⁠. The Euclidean metric is using the squaring function (d²) on d = $|Ai−Bi|$⁠, that generates differences <1 to turn out smaller, and differences >1 eventually be increased values of d² (Ultsch and Lötsch, 2022).

Finally, Gaussian Graphical Models (GGMs) are generally used for network analysis. They create an undirected network of partial correlation coefficients, which can be positive or negative. The strength of the connections between nodes is shown through the width and saturation of the edges. This helps to avoid spurious correlation and provides a network model of conditional associations. GGMs use a variance-covariance matrix to identify how variables are related to each other, similar to path analysis, and have supported the current research endeavour by determining the direct and indirect effects of one variable on another in a comprehensive setting.

A Gaussian graphical model (GGM) is “a model for a random vector X = (X1, … …., Xp) that is defined by a graph G consisting of p nodes” (Crăciun et al., 2023, p. 13). The model includes all multivariate normal distributions $N(μ,θ−1)$ whose inverse correlation matrix satisfies certain conditions, namely that $θjk=0$ when ${j,k}$ is not an edge in G (Foygel and Drton, 2010, p. 1). The undirected graph $G=(V,E)$ includes both a vertex set $V={1,....,p}$ and an edge set $E⊂V×V$ (Williams, 2019, p. 3). “Let $Ωd=(ωij,d)=Σd−1$ for d = 1.2 be the precision matrix for $Χ=[x1,...,xn1]T∈Rn1xp$ and $Y=[y1,...,yn2]T∈Rn2xp$⁠. X and Y denote the data matrices. The precision matrix (inverse covariance matrix) $Ω=Σ−1$ represents a GGM. A GGM associated with X is a graph, where the node set $V={x1,x2,.....,xp}$ has p components and the edge set E such that any edge between xk and xj if and only if xk and xj are conditionally dependent given all other variables” (Crăciun et al., 2023, p. 13). Similarly, a GGM associated with Y is also a graph (He et al., 2019, p. 1).

The hypotheses designed in the present study, grounded accordingly to the primary objective of the paper and the research questions presented in the introduction section, are the following:

The empirical results provide new evidence to attest to the fundamental role of technological innovation in heightening firm investments in a sustainable perspective. We ensured the robustness of our findings by performing several statistical methods. We used econometric procedures to draw our conclusions. The conclusions and results of these econometric procedures were aligned, validating the accuracy of our results.

The results of the robust regression models (Table 4) reveal that the framework conditions, reflected by human resources (HR) and attractive research systems (AT_RES), have an unfavourable and statistically significant influence on firm investments (Model 1) and favourable and statistically significant on environmental sustainability (Model 3). In this light, Anagnostopoulou and Avgoustaki (2020) also demonstrated that human resources are negatively associated with firm investment efficiency, given that the human resource system is associated with more direct cash costs. On the other hand, similar to our results, Alcaraz et al. (2017) discussed the essential role of human resources in creating value regarding both social responsibility and environmental sustainability. Thus, their results highlight the importance of creating sustainable human resources commitment agendas and outline a model centred on corporate priority, community development, and ecosystem resilience.

The investment credentials, respectively finance and support, also positively and statistically significantly shaped the firm investment and environmental sustainability (Model 1 and Model 3). In contrast, the use of information technologies (Model 1 and Model 3) exerts favourable effects on firm investments alongside a negative and significant influence on environmental sustainability. Nowadays, there is growing global concern on the environmental front. Thus, our results align with those obtained by Chakraborty and Mukherjee (2013), who mention that investment flows positively affect environmental sustainability. Similarly, the results obtained by Guo et al. (2023) align with ours. The author reveals that information technology plays the main role in firm innovation, employees are considered a necessary complementary resource, and the use of information technology has a particularly positive role in the firm’s investments and intellectual assets. Also, Raghupathi et al. (2014) identified some key challenges regarding the negative impact of information technologies on environmental sustainability, particularly depending on how the information technologies are used.

Both firm investments and environmental sustainability are significantly influenced by innovation activities, such as intellectual assets capturing different property rights characteristics mainly generated through the innovation process. In contrast, the results do not capture innovation activities and the variable that measures the innovation capabilities to create significant influence (Model 1 and Model 3). Further, a slightly favourable impact, exerted by the employment credentials, was also induced on environmental sustainability (Model 3). These findings align with those obtained by Chen et al. (2006), which also demonstrated that investing in green products and process innovation was beneficial to businesses and positively correlated with competitive advantage. Moreover, our results align with those of Ceptureanu et al. (2020), confirming that innovation activities have the characteristic of sustainability, respectively innovation activities have a positive effect on environmental sustainability through the practice of clean production activities, the proper recycling of waste regularly, the use of ecological raw materials, and the creation of sustainable products.

Unlike the robust regression results, the dynamic GMM captures various and multiple influences between our analysed dimensions. Instead, the linkages (LINK) exerted only a favourable and statistically significant influence on the firm investment (Model 2). At the same time, we noticed a negative influence of the linkages (LINK) on environmental sustainability (ENVS) (Model 4). Nevertheless, our results are sustained by those obtained by West and Gallagher (2006), which identify three significant challenges for companies in the application of the concepts of innovation and intellectual assets, namely the identification of the way to exploit internal innovation, the alignment of external innovation in internal development and the motivation of people from the external environment towards the continuous supply of a flow of innovations, emphasizing the issue of collaboration efforts in research and development with the external environment and competitive companies.

As regards the framework conditions, respectively the measurement of the international competitiveness regarding science domain (AT_RES) (Model 4), mainly, favourable impacts were attained on the considered credentials of impacts dimensions, respectively the effects of companies’ innovation activities, expressed by the environmental sustainability (ENVS) (Model 4). Accordingly, Della Corte et al. (2013) has also identified that most SMEs have limited dimensions, especially those regarding financial resources. This fact generates difficult access to innovation and global competition, highlighting the need for greater cooperation and well-developed networking, collaboration efforts between innovating firms, and research collaboration, thus sustaining our obtained results. Hence, we can attest that hypotheses H1 and H2 are fulfilled.

Furthermore, in this framework, we conducted the cluster analysis using the Ward method (Härdle and Simar, 2019) to capture the specific groups of technological innovators at the European Union level in 2023. As regards the cluster technique, we followed the same methodology as Cristea et al. (2021) and Noja (2018). The analysis aims to provide a relevant summary of the data sets (Majerova and Nevima, 2017). Cluster analysis results obtained using the Ward method can be found in Figure 3 and Table 5.

Based on the results obtained from Figure 3 and Table 5 we identified that the highest performance achieved by two groups of EU member states included in Cluster 1 (C1 – very high performance): Ireland, Austria, Luxembourg, Finland, Netherlands, Germany, Den-mark, Sweden, Belgium and Cluster 2 (C2 – high/significant performance) for Lithuania, Greece, Italy, Slovenia, Portugal, Spain, Estonia, Cyprus, France, Malta, Czechia. Moreover, these clusters are followed by Cluster 3 (C3 – medium (of the niche) performance), which includes countries such as Hungary, Croatia, Slovakia, and Poland.

In contrast, low performance was observed in Cluster 4 (C4 – low performance), which included Bulgaria, Romania, and Latvia as EU member states. Our results are attested and reassured by the results of Leogrande et al. (2023), who conclude in their study that the EU is constrained to solve its technological gap between Northern Central Europe and Southern and Eastern Europe by considering different policies through which public decision-makers can increase the innovation index at the level of the EU-27 member states, results indicating a positive upward trend in the future of the innovation index in European Union.

Our findings (Table 5) entail significant dissimilarities between 27 European countries as regards the technological innovations connected to environment-friendly and sustainable companies’ implications, noticing that developed countries registered high performance compared to developing countries that are facing different challenges and barriers in the adoption and implementation of technological innovations, with an associated low level in terms of performance. Therefore, we can attest that the H3 research hypothesis is fulfilled.

Crane (1977) sustains our main findings by affirming that the inability to develop the potential of technological innovation in developing countries is primarily caused by inadequate coordination between different sectors of the respective society.

Moreover, in the same methodological rationale, to capture the interlinkages between all the variables/credentials implemented in current research for each specific cluster of EU Member States, we applied the Gaussian Graphical Models (GGMs) based on several relevant studies in the complex, specialized literature, such as Crăciun et al. (2023) and Gînguță et al. (2023).

By considering the results from the cluster analysis (Figure 4), we have grouped the countries into four categories and tested the interlinkages (positive/negative) between the technological innovation representative indicators for each group through a Gaussian Graphical Model (GGMs), as follows:

1. Cluster 1 (C1): Ireland, Austria, Luxembourg, Finland, Netherlands, Germany, Denmark, Sweden, Belgium;

2. Cluster 2 (C2): Lithuania, Greece, Italy, Slovenia, Portugal, Spain, Estonia, Cyprus, France, Malta, Czechia;

3. Cluster 3 (C3): Hungary, Croatia, Slovakia, Poland;

4. Cluster 4 (C4): Bulgaria, Romania, Latvia.

Figure 4 presents the network analysis results obtained through the Gaussian Graphical Models (GGMs) processed based on partial correlations for the four clusters identified previously through the Ward method.

The GMM results revealed a complex and comprehensive perspective on the interlinkages and correlations between all the coordinates/indicators selected in our models. Firstly, the most notable positive interconnections are highlighted between all the indicators from the framework conditions, investments, innovations activities, and impacts domains, except the environmental sustainability (ENVS) indicator that was highlighted with strong negative linkages with attractive research systems (AT_RES). Nevertheless, these negative linkages are mainly because human resources involved in the research activities present a lack of capabilities regarding the maintenance of ecological balance and proper use of natural resources that can significantly affect environmental conditions. Therefore, human resources must boost environmental sustainability. On the other hand, environmental sustainability can also exert a negative impact on human resources due to the multiple adverse effects.

Consequently, the results foreground that Cluster 3 (C3) has the most unfavourable interconnection networks induced between human resources (HR), finance and support (FIN_S), use of information technologies (U_I_TECH), intellectual assets (INTEL_A), and environmental sustainability (ENVS).

As regards Cluster 1 (C1) and Cluster 2 (C2), the finance and support (FIN_S) positively acted on human resources (HR), attractive research systems (AT_RES), firm investments (FINV), innovators (INOV), and linkages (LINK). The opposite, unfavourable interconnections were established between innovators (INOV), human resources (HR), and the use of information technologies (U_I_TECH), both at the level of Cluster 1 (C1) and Cluster 2 (C2).

Finally, our results from the Gaussian Graphical Models (GGMs) are supported by previous studies that also demonstrate that technological innovation has a significant positive impact on promoting sustainable development (Ahmad et al., 2023). Policymakers can support green growth by implementing initiatives focusing on technological innovation, globalization, research and development, and human capital development. According to Wang et al. (2021), the effectiveness of these policies is anticipated to take more than a year to manifest. Besides this, Omri (2020) attest that the impact of technological innovation on sustainable development depends on the stages of development.

Looking into how framework conditions, financial support, the use of IT, innovation and employment credentials affect firms’ investments in innovation and environmental sustainability in the context of EU countries, our results outline positive and statistically significant connections between attractive research systems (AT_RES), that captures the international publications, the citations of scientific research, and international students from doctoral schools, finance and support (FIN_S), which refers to the private funding, R&D spendings in university and public research, as well as the government support for firms’ research and development, intellectual assets (INTEL_A), which covers the intellectual property rights associated with the created innovations, and employment in innovative companies (EMPL_I), with environmental sustainability (ENVS). These positive associations suggest that EU countries attempt to address environmental concerns through research, therefore providing good quality papers that are highly cited and attracting doctoral students to continue this process by providing a deeper understanding of ecological problems and introducing innovative practices that aim to promote sustainability. The results also suggest that EU member states support eco-friendly solutions by funding companies’ R&D and increasing the employment rate of innovative companies. Firm investments in innovation are positively associated with finance and support (FIN_S), the use of information technology (U_I_TECH), and intellectual assets (INTEL_A), showing that in EU member states, the companies are more attracted to implementing technologies in their workflow and focus their investments on creating new products and services.

The clusters of EU countries analysed through Gaussian Graphical Models (GGMs) provided valuable information regarding the factors contributing to environmental sustainability and enhancing firm investments in countries that have associated a very high level of technological innovation (C1). Environmental sustainability is positively related to employment impacts (EMPL_I) and attractive research systems (AT_RES), indicating that the research endeavours and employees who work in innovative companies are contributing to addressing environmental concerns and finding solutions to decrease the ecological footprint. On the other hand, firms’ investments in R&D and innovation are positively connected to the innovator’s dimension (INNOV), which captures the small and medium enterprises that present innovative processes and products, and finance and support (FIN_S), underlining the importance of the government of these EU countries that offered a very high level of technological innovation, in encouraging the creation of new businesses aiming to innovate and introduce new products on the market.

The research provides new empirical evidence and insights into the factors that drive technological innovation and attract sustainable investments in EU member states. Our findings could be helpful for enterprises by providing a better understanding of how technology adoption and environmental outcomes affect investment decisions across EU companies. In addition, the results may also be relevant to policymakers, who could develop policies that foster technological innovation within European firms and provide additional support through research and development investments.

Therefore, the results encompass a series of practical implications. Firstly, our results highlight the need for capacity building as concerns human resources, alongside skills in sustainable practices, eco-friendly innovations or resource management. Thus, the positive relationship between environmental sustainability (ENVS) and attractive research systems (AT_RES) could indicate a balance between ecological concerns and the resources that are used, underlying the need to foster collaborations with environmental scientists in order to be able to integrate ecological aspects into innovation activities. Secondly, the results highlight the need for the integration of sustainability in the research and innovation activities of the company, noting that current systems may omit environmental sustainability by focusing only on technological or economic growth. Thus, the practical implications of the results obtained consider the paradigm shift imperative in order to integrate sustainability into the activities undertaken (research/innovation) by allocating funds for ecological innovation and encouraging the development of ecological principles in R&D projects. Thirdly, the results show a weak interconnection between environmental sustainability and firm investments in innovation, thus highlighting the need to prioritize sustainable-focused investments more. Thus, the government can focus on a series of fiscal incentives, grants, or subsidies aimed at projects that contain and develop activities based on environmental sustainability.

The potential use of the research results highlights that the integration of sustainability into attractive research systems is hindered by the lack of capacity to maintain ecological balance. Public policy can also be influenced based on the results obtained and according to which there is a weak connection between environmental sustainability (ENVS) and attractive research systems (AT_RES), a key perspective underlining the need to influence public policy through a series of structural reforms. Financing, support, and investments in innovation are paramount, as they stimulate environment-friendly and sustainable investors to make public and private investments in attractive research systems, which can create suitable framework conditions for resources involved in lifelong learning activities. On the other perspective, if we concentrate on the negative interlinkage between environmental sustainability and human resources, we can notice that decision-makers need to have and implement robust policy interventions, such as incentives and actions that can sustain and spur green practices, policy regulations, education and training, and upskills initiatives.

Moreover, our main results reveal that companies have a major role and contribution to society. At the same time, the adoption and use of technological innovation practices lead to significant environmental improvements, alongside the reduction of multiple negative effects on the environment, while the exposure to air pollution can also be reduced, thus positively impacting the whole society’s health and well-being. In addition, our findings suggest that the existence of sustainable businesses could increase the creation of more job opportunities. At the same time, more educated and trained human resources and a more attractive research system can stimulate the results obtained regarding the higher-quality staff. Moreover, we observe that the company’s innovation activities and the improvements exerted by sustainable investors to reduce the negative impact on the environment could help, on the one hand, to decrease poverty and, on the other hand, to increase the standard of living.

Our research endeavour aimed to investigate the nexus between technological innovation and environment-friendly and sustainable companies within the European Union by considering the period between 2016 and 2023.

The study presented several advanced modelling approaches, embedding robust regression, dynamic GMM, and cluster and network analyses. The robust regression models revealed that human resources and attractive research systems had a negative impact on firm investments but a positive influence on environmental sustainability. The use of information technologies had a positive linkage with firm investments but a negative influence on environmental sustainability. The dynamic GMM model captured different and multiple connections between the analysed dimensions, with linkages positively influencing firm investments but negatively influencing environmental sustainability. According to the study, competitiveness in science had positive impacts on most dimensions.

By applying the cluster analysis, we have identified that the performances achieved in terms of technological innovation have denoted that in many countries of the EU-27 MS (Cluster C1 and Cluster 2), namely Ireland, Austria, Luxembourg, Finland, Netherlands, Germany, Denmark, Sweden, Belgium, Lithuania, Greece, Italy, Slovenia, Portugal, Spain, Estonia, Cyprus, France, Malta, Czechia there are both very high and significant levels of performance, especially for C1 (the mean is 136.36), being closed followed by C2 (the mean is 99.93), considered the most representatives countries in terms of technological innovation performances. On the opposite, we notice that medium (of the niche) and low levels of performance were registered by the countries of C3 (the mean is 72.75) and C4 (the mean is 47.81), namely Hungary, Croatia, Slovakia, Poland, Bulgaria, Romania, Latvia.

The GGMs’ results showed that most indicators were positively interrelated, except for environmental sustainability, which had substantial negative connections with attractive research systems. Additionally, the research emphasized the importance of improving environmental sustainability through human resources.

Our results validate the three research hypotheses and suggest that environmentally friendly technologies and the level of digital technologies are essential in a company’s identification of the balance between sustainability, innovative products, and meeting investors’ demands, a fact also stated by Ch’ng et al. (2020). Moreover, the results obtained emphasize that investors differ in prioritizing economic, social, or environmental performance, even if all these steps lead to the company’s success, as Fernando et al. (2018) affirmed. Thus, investors attracted by environmentally friendly technologies are based on the capacity for innovation, which is achieved by encouraging knowledge-based and high-quality workers to apply eco-innovative principles in developing environmental-related technologies, results also confirmed by Hu et al. (2022). Besides a commitment to provide sustainable environmental standards, our results highlighted the need to continuously seek new digital technologies and innovative business processes to gain competitiveness while also having well-skilled human resources. Thus, based on the results obtained through the firm investment domain, investing, especially in internal research and development, is essential to generate innovations and design a new framework of business opportunities. However, spurring collaboration efforts between innovative companies generates high value. Therefore, our main findings concentrate on decision-makers’ critical role in increasing the finance and support offered, as Alraja et al. (2022) demonstrated.

Broadly, our study also includes some recommendations. Firstly, to increase innovation at the European level, decision-makers should adopt a series of levers, even considering public investment to improve education and training systems regarding human capital. Secondly, European institutions and decision-makers need to develop sustainable and appropriate economic policies, aiming for an important role for Europe in terms of the production and accumulation of technological innovation in the global context. Based on the findings of our research study, decision-makers could establish a series of strategies that aim to support managers/firm investors in using technological innovation to achieve a balance between innovation drivers and sustainable business performance, which mainly depends on government regulations. Additionally, decision-makers should be strongly connected to financial institutions for providing industry loans, particularly to small and medium-sized companies. Therefore, another line-up for the decision-makers concentrates on developing strategies and regulations that can further sustain productive competition. Moreover, the decision-makers should be strongly linked and proactive in offering a regulated environment alongside a well-defined infrastructure that can further encourage, maintain, and reward environmentally friendly and sustainable investors.

Despite the main results and contributions presented and discussed above, we are cautious to point out that our analysis and findings may suffer from an array of limitations that should be mentioned, such as the use of a limited number of proxies for the complex dimensions and credentials targeted throughout the research endeavour, which may not capture the amplitude of the economic inferences.

Future research will target an in-depth assessment of the pivotal role of intellectual capital in enhancing firm performance in a sustainable development framework. Ultimately, researchers could investigate the complex impact of technological innovation on how developing economies achieve the major goals of innovation performance, which also has implications for environmentally friendly and sustainable investors. Our study will focus on identifying additional relevant indicators to include in the research framework, setting new measurement criteria, and establishing new procedures to guide future research directions. We aim to continuously expand our research by updating our data and adapting our measured dimensions, potentially incorporating new areas such as environmental and innovation indicators. Moreover, future research will investigate additional technological innovation indicators, focusing on factors such as technological advancements, sustainable investment choices, and firm investments in innovations. Another future direction considers expanding our research to a more extensive area to compare EU Member States with other global regions over a longer time span, which will provide deeper insights into the statistical relationship between sustainable investment choices and technological innovation.

Our research is not without limitations, and we recognize several restraints regarding our study. Firstly, in certain conditions, one limitation of our study may embody the relatively reduced number of observations, respectively, a dataset with low availability in terms of more extensive panel data regarding all the included samples of variables in our analysis. Secondly, focusing only on the EU-27 European Union Member States can also be considered another limitation, as we may not generalize our findings to other geographical regions. Thirdly, the few specific indicators for each analysed dimension can restrict the measurement’s influence regarding the respective phenomena. At the same time, the applied research framework could have been more complex. Last but not least, another limitation resides in the fact that we may offer a general, not a specific, path for a company in a particular sector across the European Union (EU) to enhance sustainable business processes and financial performance through technology and digital innovation.

This work was supported by a grant from the Romanian Ministry of Research, Innovation, and Digitalization, the project with the title “Economics and Policy Options for Climate Change Risk and Global Environmental Governance” (CF 193/28.11.2022, Funding Contract no. 760078/23.05.2023), within Romania’s National Recovery and Resilience Plan (PNRR) – Pillar III, Component C9, Investment I8 (PNRR/2022/C9/MCID/I8) – Development of a program to attract highly specialised human resources from abroad in research, development and innovation activities.