Fostering competitiveness through knowledge management and sustainability-oriented resources: insights from manufacturing industries

Nowadays, the global business landscape has undergone a rapid and sweeping transformation, driven by heightened demands for sustainability, technological innovation, and evolving regulatory frameworks. Despite political setbacks, sustainability efforts are set to persist (Bick, 2025; Mackay et al., 2025; Rudolf and Schmidt, 2025; United Nations, 2024). For instance, recent insights from the Harvard Business Review suggest that, notwithstanding political volatility in countries such as the USA, the momentum of global sustainability initiatives remains resilient (Mandyck, 2024). This durability stems from the recognition that sustainability is essential for the long-term success of firms, investors, and nations alike (Corvino et al., 2024; Nam et al., 2024; Romano et al., 2024). International regulations, stakeholder expectations, and financial market pressures continue to reinforce these practices, making their neglect extremely difficult even amid political volatility (Gomez-Trujillo et al., 2024; Intenza et al., 2025; Ning and Shen, 2024).

From a theoretical standpoint, our study is grounded in the resource-based view (RBV) (Barney, 1991; Wernerfelt, 1984), which posits that firms achieve a competitive advantage through valuable, rare, inimitable, and non-substitutable (VRIN) resources. However, given our focus on sustainability and innovation, two well-recognised theoretical developments of the RBV need to be considered. First, the natural-resource-based view (NRBV) (Hart, 1995; Hart and Dowell, 2011) extends the RBV by emphasising sustainability-oriented resources as critical drivers of competitiveness. Second, the knowledge-based view (KBV) (Grant, 1996; Spender, 1996) highlights knowledge, learning, and innovative capabilities as the most strategic resources for firms, particularly in dynamic environments. Integrating these theoretical lenses allows us to investigate how both sustainability-oriented resources (via NRBV) and innovative capabilities (via KBV) contribute to long-run firms’ competitiveness (Branca et al., 2025; Foss and Mazzelli, 2025).

Despite the relevance of these issues, the ongoing academic debate still presents at least two shortcomings. First, while the RBV (Barney, 1991; Wernerfelt, 1984), its natural extension through the NRBV (Hart, 1995; Hart and Dowell, 2011), and the KBV (Grant, 1996; Spender, 1996) have been widely investigated, they have generally been treated as distinct theoretical perspectives. Consequently, the way sustainability-oriented resources and innovative capabilities jointly shape long-term competitiveness remains under-examined. Second, although responsible innovation (Owen et al., 2012; Stilgoe et al., 2013; von Schomberg, 2013) and organisational support (Eisenberger et al., 1986; Rhoades and Eisenberger, 2002) are increasingly acknowledged as valuable drivers (Andersén, 2021; Chesbrough, 2020; Intenza et al., 2025), little is known about how these mechanisms operate simultaneously to translate resources and capabilities into long-term competitiveness. Moreover, most existing studies adopt a single-country perspective, overlooking how these relationships may vary across different institutional and regulatory contexts.

To address these gaps, we investigate how sustainability-oriented resources and innovative capabilities influence long-term competitiveness via the mediating roles of responsible innovation and organisational support. Analysing 493 manufacturing small- and medium-sized enterprises (SMEs) across the European Union (EU), the UK, and the USA, allows us to compare settings that significantly differ in regulatory stringency, stakeholder expectations, and political uncertainty.

Accordingly, our research questions are formulated as follows:

Our study offers many theoretical contributions. In line with the broader literature highlighting the relevance of sustainability-driven resources for competitive advantage (Čater et al., 2025; Liu, 2025), this analysis provides timely insights into how different contexts influence internal resource deployment and strategic outcomes. In particular, by integrating the RBV, KBV, and NRBV, we show how sustainability-oriented resources and innovative capabilities contribute to long-term competitiveness through responsible innovation and organisational support.

From a practical standpoint, we underscore the relevance of embedding sustainability into the core strategies of SMEs to foster competitiveness. By combining sustainable practices, firms can improve their market positioning and respond more effectively to stakeholder demands. In this context, Top Management Teams (TMT) play a central role in cultivating a culture of sustainability within their organisations, supporting learning and capability development, and aligning corporate goals with sustainable outcomes.

The remainder of the paper is organised as follows. Section 2 delves into the theoretical grounds. Section 3 formulates the research hypotheses. Section 4 details the methodological approach undertaken. Section 5 shows the findings and associated robustness checks. Section 6 interprets the key results, as well as highlights the theoretical contributions and practical implications, and Section 7 concludes by acknowledging the study’s limitations and suggesting directions for future research.

RBV has long been a cornerstone in strategic management research, arguing that firms achieve sustainable competitive advantage when they possess the so-called VRIN resources (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984). From this perspective, firms are heterogeneous bundles of internal configuration where resources drive strategic differentiation and value creation (Barney et al., 2001; Crook et al., 2008).

Over time, RBV has evolved to address new competitive challenges. Notably, NRBV (Hart, 1995; Hart and Dowell, 2011) emphasises sustainability-oriented resources as critical drivers of long-term competitive advantage. Many studies have shown that environmental resources and capabilities – such as pollution prevention, clean technologies, and product stewardship – diminish operating costs, strengthen legitimacy, and enhance resilience (Corvino et al., 2024; Intenza et al., 2025; Netti et al., 2025; Romano et al., 2024). These outcomes represent critical pathways through which sustainability-oriented resources contribute to long-term competitiveness (Christmann, 2000; Dangelico, 2016; Oliver, 1997; Sharma and Vredenburg, 1998). Consistently with the NRBV, we conceptualise sustainability-oriented resource availability as the stock of tangible and intangible assets that support firms to embed environmental priorities into their core strategies (Do et al., 2022; Liu and Wang, 2025).

At the same time, the KBV moves the RBV forward by positing that knowledge is the most strategically significant factor to gain a competitive advantage (Grant, 1996; Spender, 1996). Within this perspective, innovative capabilities – defined as the ability to generate new ideas, develop differentiated products, introduce more efficient processes or adopt novel business models – are essential for attaining competitiveness (Miller and Friesen, 1983; Verona and Ravasi, 2003; Zahra and Covin, 1993). More recent works stress that in dynamic contexts, and particularly in sustainability-driven industries, innovative capabilities enable firms to align technological development with emerging societal and environmental expectations (Chesbrough, 2020; Perotti et al., 2025).

By integrating the NRBV and KBV, we can better grasp how internal assets translate into competitive outcomes through two main mechanisms: responsible innovation and organisational support. On the one hand, responsible innovation is strictly aligned with ethical acceptability, social desirability, and sustainability goals. Specifically, complementary dimensions that shape innovation processes are characterised by anticipation, reflexivity, inclusion, and responsiveness, thus accounting for future impacts, involving different stakeholders and remaining adaptable to evolving societal needs (Owen et al., 2012; Stilgoe et al., 2013). Prior research has shown that firms with plentiful and flexible resources are better positioned to mobilise financial, technological, and human capital to sustain such innovation pathways (Bouguerra et al., 2024; Do et al., 2022). Moreover, responsible innovation enhances stakeholder legitimacy and long-term resilience, making it a strategic driver of long-run competitiveness (Porter and Kramer, 2011). On the other hand, organisational support is understood as the leadership, culture, structures, and routines that allow firms to activate and coordinate their internal conditions (Lee et al., 2018). For instance, change-sensitive leadership and a learning-oriented culture strengthen adaptability in dynamic contexts (Eisenhardt and Martin, 2000; Schein, 2010), while human resource practices such as teamwork, shared decision-making, and continuous training help transform static resource endowments into dynamic performance outcomes (Andersén, 2021). Organisational support, therefore, represents the infrastructure through which resources and capabilities are effectively deployed and combined to sustain competitiveness (Grant, 1996; Makadok, 2001).

To infer, we adopt a multidimensional perspective of long-run competitiveness, which extends beyond financial performance to include resilience, adaptability, and legitimacy (Barney, 1991; Dangelico, 2016; Porter and Kramer, 2011). This broader view reflects the growing consensus that firms must pursue not only financial outcomes, but also environmental and social legitimacy (Hart and Dowell, 2011). From this standpoint, we argue that competitiveness is not simply a matter of possessing valuable resources, but of strategically configuring them through responsible innovation and organisational support, thereby transforming internal conditions into long-term sustainable advantage and reinforcing legitimacy with key stakeholders (Donaldson and Lee, 1995; Freeman, 1984). Such a perspective is particularly relevant for SMEs, which often operate under resource constraints yet are increasingly required to balance financial performance with societal expectations. Accordingly, throughout this study, we consistently refer to competitiveness to highlight the multidimensional nature of competitive advantage in sustainability-oriented contexts.

Building on the RBV, long-run competitiveness emerges from the effective configuration and deployment of VRIN resources through organisational processes that facilitate differentiation, cost efficiency, and market adaptation (Barney, 1991; Crook et al., 2008; Peteraf, 1993; Ray et al., 2004; Wernerfelt, 1984). Assets such as managerial competences, technological bases, and reputational capital yield superior performance when effectively orchestrated inside the firm (Newbert, 2007; Sirmon et al., 2011). Even under environmental volatility, well-managed resource endowments act as buffers that enhance resilience and enable innovation, ultimately strengthening competitive positions over time (Do et al., 2022; Wu, 2010).

In line with this reasoning, the NRBV underscores sustainability-oriented resources, including clean technologies, eco-efficient equipment, and green human capital as main sources of competitive advantage, as they combine operational efficiency with legitimacy vis-à-vis regulators and stakeholders (Christmann, 2000; Dangelico, 2016; Hart, 1995; Lichtenthaler, 2022; Sharma and Vredenburg, 1998). Accordingly, we define long-run competitiveness as the integration of firm performance with environmental and social legitimacy outcomes recognised by stakeholders (Hart and Dowell, 2011).

In our study, sustainability-oriented resource availability refers to the firm’s access to qualified human resources for sustainable operations, reliable eco-materials, relevant knowledge and technologies, energy-efficient equipment, and a supportive external environment through alliances and collaborations, which together expand the feasible set of strategic actions (Jiao et al., 2020). These endowments allow resources to be embedded into organisational practices, transforming internal conditions into competitive outcomes (Sirmon et al., 2011; Helfat and Peteraf, 2003). Similarly, the extent to which resources contribute to sustainable competitive advantage is shaped by the surrounding institutional and stakeholder environment, which reinforces the role of resource availability in sustaining competitiveness (Oliver, 1997).

Though there has been a great deal on RBV and NRBV, relatively little is known about how these mechanisms apply to SMEs and whether their relevance persists across different institutional contexts. To address this gap, we propose the following hypothesis:

On the same wavelength, innovative capabilities denote the firm’s ability to transform knowledge into new products, processes, and business practices (Miller and Friesen, 1983; Verona and Ravasi, 2003; Zahra and Covin, 1993). In particular, the KBV highlights knowledge as the most significant asset for building long-term advantage (Grant, 1996; Spender, 1996). In our study, this construct captures the capacity to mobilise and recombine knowledge to generate ideas and implement novel business models (Ávila, 2022).

Innovative capabilities allow firms to renew their resource base and adapt to shifting conditions. They are particularly valuable when oriented towards sustainability and/or encompass a broad range of initiatives such as product and process improvements, organisational changes, and business model innovations (Chesbrough, 2020; Perotti et al., 2025). Indeed, prior research has shown that firms with strong innovative capabilities are more likely to anticipate environmental shifts, align with stakeholder expectations, and respond proactively to sustainability transitions (Bouguerra et al., 2024; Galbreath, 2005; Zahra and Covin, 1993).

While innovation is widely recognised as a driver of competitiveness, empirical studies have predominantly focused on its effects on financial performance (Calantone et al., 2002; Rosenbusch et al., 2011). The direct contribution of innovative capabilities to competitiveness remains under-developed, especially within resource-constrained contexts like SMEs. Building on this reasoning, we propose the following hypothesis:

Responsible innovation is recognised as a paradigm that requires firms to anticipate the social and environmental impacts of innovation, involve stakeholders in the implementation of solutions, and align technological development with ethical and sustainability principles (Owen et al., 2012; Stilgoe et al., 2013). This perspective supplements traditional views of innovation by embedding principles of inclusiveness, responsiveness, and sustainability into the innovation process.

The NRBV suggests that sustainability-oriented resources provide both tangible and intangible foundations for responsible innovation practices. Clean technologies, eco-efficient processes, and skilled human capital allow firms to pursue anticipatory and inclusive innovation pathways, balancing economic efficiency with legitimacy outcomes (Do et al., 2022; Hart and Dowell, 2011). Similarly, from a KBV standpoint, these resources also facilitate learning, recombination, and stakeholder engagement, thereby covering a pivotal role for responsible innovation.

Despite the growing attention devoted to responsible innovation in recent years, prior research has largely conceptualised it as a normative or policy-driven ideal (Burget et al., 2017; Owen et al., 2012; Stilgoe et al., 2013). Only a thin number of studies have examined the organisational and resource conditions that enable its implementation within firms (Holm et al., 2025). In particular, it remains unclear whether and how the availability of sustainability-oriented resources fosters the adoption of responsible innovation practices in SMEs, which often operate under resource constraints yet face heightened stakeholder pressures. More specifically, sustainability-oriented resources may act as a critical enabler of responsible innovation within SMEs, as limited access to human, technological, and relational assets often hinders their ability to anticipate impacts and engage stakeholders. This highlights the relevance of examining whether sustainability-oriented resources systematically support the adoption of responsible innovation practices in smaller firms. On this basis, we formulate the following hypothesis:

On the same page, innovative capabilities represent the organisational capacity to integrate knowledge across domains, experiment with new ideas, and adapt processes and models in response to changing environments (Miller and Friesen, 1983; Zahra and Covin, 1993). The KBV emphasises that the competitive advantage arises not simply by possessing knowledge, but from the capability to generate, recombine, and apply it in novel ways (Grant, 1996; Verona and Ravasi, 2003).

In sustainability-oriented contexts, these capabilities allow firms to tackle ethical and societal pressures through continuous learning and stakeholder engagement (Chesbrough, 2020). By fostering exploration and adaptation, innovative capabilities provide the basis for anticipating future impacts and aligning technological development with societal values (Owen et al., 2012; Stilgoe et al., 2013). Recent work suggests that firms with stronger innovative capabilities are better positioned to integrate environmental foresight, assess potential impacts in advance, and adjust technologies in line with stakeholder expectations (Harrison et al., 2021).

While conceptually related, little empirical research has explicitly examined the link between innovative capabilities and responsible innovation. Different authors have examined responsible innovation as a normative principle or policy framework (Burget et al., 2017), leaving underexplored the organisational capacities that enable firms to operationalise it. This gap is particularly relevant within SMEs, where resource constraints make the mobilisation of knowledge and the development of innovation more challenging. Examining this relationship is therefore crucial to understanding how smaller firms can convert knowledge into socially responsive and sustainable innovation pathways. These considerations lead to the following hypothesis:

As outlined above, responsible innovation is a process through which firms transform internal resources and capabilities into outcomes that generate technological advancement, legitimacy, and stakeholder trust (Do et al., 2022; Porter and Kramer, 2011; Stilgoe et al., 2013). It serves as the bridge linking firm-level conditions to societal expectations (Owen et al., 2012).

From the NRBV perspective, sustainability-oriented resources provide the foundation for responsible innovation by equipping firms with the assets needed for inclusive innovation pathways (Hart and Dowell, 2011). In this vein, responsible innovation may serve as a mediating factor that converts resource availability into long-term competitiveness by integrating economic goals with social legitimacy.

From the KBV perspective, innovative capabilities strengthen the firm’s ability to create and integrate knowledge, pursue novel opportunities, and involve stakeholders in reflective learning processes (Chesbrough, 2020; Grant, 1996; Verona and Ravasi, 2003). These capabilities are particularly suited to operationalise responsible innovation, since they provide the organisational conditions required to anticipate impacts, adapt technologies, and embed social values into innovation practices (Harrison et al., 2021; Owen et al., 2012). Accordingly, responsible innovation can be expected to function as the linking mechanism through which innovative capabilities translate into long-term competitiveness. In light of the above considerations, the following two hypotheses are formulated:

From both NRBV and KBV perspectives, resources drive competitive advantage only when they are effectively embedded in organisational support systems that promote their deployment (Barney, 1991; Grant, 1996; Makadok, 2001). Organisational support can be understood as the infrastructures, processes, and practices that ensure resources and knowledge are activated and coordinated across the firm (Eisenhardt and Martin, 2000; Schein, 2010).

In sustainability-oriented contexts, such support is pivotal for incorporating environmental and social objectives into operational routines. Firms endowed with greater sustainability-oriented resources are more likely to invest in training, information systems, and cross-functional initiatives that foster collaboration and learning, thereby facilitating the effective mobilisation of those resources (Hart and Dowell, 2011).

Empirical evidence shows that resource availability often leads to more collaborative and learning-oriented organisational contexts (Andersén, 2021). However, it remains unclear whether SMEs with richer sustainability-oriented resources systematically develop stronger organisational support systems. Previous research has typically conceived organisational support as a contextual condition for performance rather than an outcome of resource endowments. Consistently with this argument, we advance the following hypothesis:

In keeping with this line of thought, innovative capabilities involve more than generating ideas; they require an organisational context that fosters collaboration, autonomy, and change (Eisenhardt and Martin, 2000; Verona and Ravasi, 2003). The KBV suggests that these capabilities stem from the firm’s ability to create and disseminate knowledge through structures that support experimentation and learning (Grant, 1996). They represent a dynamic process in which knowledge is generated, shared, and transformed into routines, practices, and innovations that continuously renew the organisational system.

Organisational support, in turn, provides the enabling environment in which innovative capabilities flourish. Leadership commitment and participatory cultures reinforce the knowledge diffusion and its integration into operations (Schein, 2010). Firms with stronger innovative capabilities are more likely to create these support mechanisms, as the pursuit of innovation requires cross-functional coordination and the institutionalisation of learning routines (Verona and Ravasi, 2003). Empirical research suggests that innovative firms tend to develop both collaborative and adaptive organisational contexts (Andersén, 2021), but the mechanisms through which capabilities translate into such support remain underexplored.

In spite of these insights, scant studies have examined whether innovative capabilities systematically foster the development of organisational support in SMEs, where resource constraints often limit the ability to institutionalise supportive practices. This gap calls for greater attention to the reciprocal relationship between innovation and the organisational conditions that sustain it. These considerations lead to the following hypothesis:

Organisational support represents the structural and cultural context through which resources and capabilities are mobilised towards strategic aims (Grant, 1996; Makadok, 2001). It encompasses the infrastructures, processes, and practices that nurture collaboration, knowledge sharing, and learning across the organisation (Eisenhardt and Martin, 2000; Schein, 2010).

By providing training, information systems, and cross-functional platforms, organisational support facilitates the alignment of business activities with broader sustainability goals, thus allowing firms to transform resource endowments into competitive outcomes (Hart and Dowell, 2011).

Building on NRBV and KBV perspectives, we argue that organisational support functions as a mechanism through which both sustainability-oriented resources and innovative capabilities are translated into long-term competitiveness. When supported by organisational support, sustainability-oriented resources can be effectively coordinated and deployed, while innovative capabilities are more likely to be institutionalised in routines and practices that reinforce adaptability and stakeholder responsiveness (Andersén, 2021; Verona and Ravasi, 2003).

Although prior studies acknowledge the relevance of organisational support, little is known about its mediating role in linking resources and capabilities to competitiveness. This issue is particularly salient in the realm of SMEs, where supportive structures often determine how effectively internal resources and capabilities can be mobilised. Taken together, these insights motivate the following hypotheses:

The data collection involved administering an anonymous online survey using the Prolific platform (Link to ProlificLink to the website of Prolific.), which is one of the most used tools by academic researchers in social science studies (Marzi et al., 2023; Peer et al., 2017; Rinderknecht et al., 2025). Our sampling procedure applied strict filters for company size, industry type, location, and professional role. Participants who did not meet these prerequisites were automatically excluded at the screening stage. Specifically, the target respondents were SME managers (i.e., top management, middle management, junior management, researchers, and self-employed/partners) operating in the manufacturing sector (i.e., the computer, electronics and other manufacturing sectors), and residing in the EU, the UK and the USA. These settings were chosen to facilitate a comparison across distinct geographical contexts with heterogeneous regulatory environments, cultural attitudes and stakeholder expectations towards sustainability. As is well known, they are characterised by diverse political, economic and support frameworks, resulting in peculiar factors that can significantly impact the key drivers of SMEs’ competitiveness (Akkerman et al., 2009; Aureli et al., 2020; Jensen et al., 2007). The EU has been at the forefront of proactive sustainability regulations through the European Green Deal and the Corporate Sustainability Reporting Directive (CSRD) – 2022 / 2464/EU, promoting strategic integration of responsible innovation within firms (Deloitte, 2024; Hummel and Jobst, 2024). The UK’s post-Brexit landscape represents an intriguing context in which to investigate strategic sustainability after the de-Europeanising process (Gravey and Jordan, 2023). Finally, the USA provides a market-driven, yet less regulated environment for exploring whether sustainable factors underpin the competitive advantage, particularly for SMEs (Latella et al., 2025).

The questionnaire was developed based on the established literature to ensure the use of validated measurement scales (Adomako and Tran, 2022; Ávila, 2022; Jiao et al., 2020; Lee et al., 2018). Specifically, the constructs were measured using a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Content validity was established through different techniques. First, the survey was designed to be anonymous to minimise social desirability bias (Fisher, 1993). Second, the authors’ expertise in the field, combined with rigorous methodological approaches and a thorough literature review, significantly contributed to the robustness of the research framework. Third, we performed a pilot study in February to examine the survey’s functionality and refine the instrument. After addressing any identified issues in this preliminary run, the survey was launched in March and ended in April 2023. Data collection achieved a high response rate of 67.26% ^[1]. A total of 494 questionnaires were initially gathered. Afterwards, responses were excluded if participants failed to pass instrumental manipulation checks, designed to maintain engagement and assess attention (Shahzad et al., 2020; Trabucchi et al., 2023). Precisely, the questionnaire was discarded if he/she failed a maximum of two out of three designed attention checks. After applying these quality control measures, the final sample consisted of 150 respondents from the EU, 249 from the UK, and 94 from the USA, thus achieving a total sample of 493 observations (Table 1).

Dependent variable.

Competitiveness refers to the firm’s perceived ability to outperform competitors across several dimensions, such as research and development (R&D), capacity management, profitability, corporate reputation, and its overall ability to maintain a competitive advantage. Based on prior research, it was computed through a composite measure derived from five survey items (Ávila, 2022). The label is: COMP.

Independent variables.

Resource availability regards the firm’s access to qualified human resources, reliable sources of sustainable materials, relevant knowledge and technologies, energy-efficient equipment, and a supportive external environment (such as technological alliances and collaborations), that collectively enable the adoption and implementation of sustainable operations. Following Jiao et al. (2020), it was operationalised by way of five survey questions. The label is: RES_AV.

Innovation capabilities pertain to the organisation’s capacity to deploy new ideas and skills aimed at developing innovative products, services and processes that enhance competitiveness, and support effective recruitment and talent development. Building on previous studies (Ávila, 2022; Liao et al., 2007), it was measured using a set of five survey items. The label is: INN_CAP.

Mediating factors.

Responsible innovation concerns the development and launch of new products and services that prioritise social well-being, environmental sustainability, and ethical issues, thus demonstrating a commitment towards green solutions that contribute to a better future and address significant societal challenges. Drawing on Adomako and Tran (2022), we used six items to compute this construct. The label is: RESP_INN.

Organisational support for environmental initiatives encompasses management’s proactive efforts to implement environmental strategies, the allocation of resources to address environmental issues, and the provision of written guidelines to ensure the adoption of green practices across the organisation. Building on Lee et al. (2018), it was calculated using three items. The label is: ORG_SUPP.

Control variables.

Both individual- and firm-level control variables were incorporated into the analysis (Adomako and Tran, 2022; Ávila, 2022; Branca et al., 2025; Lei et al., 2024).

At the individual level, the age of each interviewee was self-reported and subsequently coded as follows: 1 for ages 18–30 years, 2 for 31–45 years, 3 for 46–60 years, and 4 for those older than 60 years. This variable is labelled as: CEO_AGE. A nominal variable was included to indicate the manager’s gender. It was coded as 1 for male managers, 2 for female managers, and 3 for other or unspecified genders. The label is: CEO_GENDER. Finally, the manager’s experience was assessed using an ordinal variable, with the following coding: 1 for less than one year, 2 for one to five years, 3 for six to ten years, and 4 for more than ten years. The label is: CEO_EXP.

At the firm level, a dummy variable was included to differentiate between low- and high-technology industries, coded as 0 for low-technology and 1 for high-technology sectors. This classification relies on the notion that firms operating within high-tech industries are better positioned to reconfigure their operations and capabilities, conceivably resulting in increased competitiveness. The label is: SECTOR. Firm size was operationalised using an ordinal variable, with the following coding framework: 1 for firms with fewer than 5 employees; 2 for firms employing between 5 and 20 employees; 3 for those with 21–50 employees; 4 for firms employing between 51 and 250 employees; and 5 for firms with more than 250 employees. The label is: FIRM_SIZE. At last, the operational market was included as a dummy variable, where 1 points out mass-market customers, and 0 denotes business-to-business customers.

For additional details concerning constructs and items, please refer to Table 2.

To evaluate our hypotheses, we conducted a mediation analysis based on single-mediator models using OLS regressions. Building upon the framework established by Baron and Kenny (1986), the process of mediation involves three steps. First, it requires demonstrating that the independent variable significantly influences the mediators. Second, it is needed to prove that the independent variable also impacts the dependent variable; however, for mediation to be present, the strength of this direct effect should diminish when the mediators are included in the model. Specifically, the coefficient from the initial regression of the independent variable on the dependent variable should be larger in absolute value than the coefficient obtained when both the independent variable and the mediators are included in the model, thus suggesting that part of the effect may be conveyed through the mediators. Finally, to confirm mediation, the mediators themselves must significantly affect the dependent variable when controlling for the independent variable (Baron and Kenny, 1986; MacKinnon et al., 2007; Zhao et al., 2010). As depicted in Figure 1, the independent variables (RES_AV and INN_CAP) are assumed to exert a positive effect on the mediators (RESP_INN and ORG_SUPP), which subsequently influence the dependent variable (COMP).

The hypothesis testing proceeded through three stages.

The initial step involved examining the baseline relationships by estimating the following regression models (H1a–H1b):

The second step was devoted to assessing the association between the independent variables (RES_AV and INN_CAP) and the mediators (RESP_INN and ORG_SUPP). Specifically, the econometric specifications for these relationships are outlined as follows (H2a–H2b; H3a–H3b):

The third step consisted of evaluating the mediating variables’ role as significant predictors of the primary relationships by incorporating them together with the independent variables in the regression models for the dependent variable. Accordingly, the following models were specified (H4a–H4b; H5a–H5b):

In addition to the causal steps approach described above, we used the Sobel test and path analysis to strengthen our empirical findings (Baron and Tang, 2011; Sobel, 1982; Zhao et al., 2010).

Common method bias (CMB) may have potentially existed in such a survey-based cross-sectional analysis (Podsakoff et al., 2003). To safeguard our results from the potential influence of CMB, we adopted a comprehensive analytical approach encompassing confirmatory factor analysis (CFA), tests of reliability, measures of sampling adequacy, and normality checks.

CFA was used to examine the underlying structure of the data (Brown and Moore, 2012; Harrington, 2009). It specifically assesses the extent to which the proposed factorial structure can accurately reproduce the observed covariance matrix among a set of variables. Table 3 presents the factor loadings derived from the CFA for a single-factor solution, where notably high positive loadings corroborate a strong association with the latent construct. All factor loadings are statistically significant at p < 0.001, suggesting that the probability of these patterns arising by random chance is exceedingly low, thus reinforcing the robustness and reliability of the findings.

Cronbach’s alpha coefficients were calculated for each latent construct to evaluate internal consistency (Anderson and Gerbing, 1988). All constructs demonstrated Cronbach’s alpha values surpassing the recommended threshold of 0.70, indicating acceptable levels of reliability. Besides, the average variance extracted (AVE) scores surpass the 0.50 threshold (Table 3), thus corroborating the convergent validity of the construct measurements (Hair et al., 2021). Kaiser–Meyer–Olkin (KMO) measure was used to assess sampling adequacy (Nunnally, 1978). The KMO values exceeded the critical benchmark of 0.50 (Hair et al., 1979), thereby satisfying the requisites for factor analysis and confirming the suitability of the data for such procedures. Furthermore, Bartlett’s test of sphericity produced highly significant results across all constructs, further supporting the appropriateness of the data for factor analysis.

Finally, after assessing the multivariate normality through the Shapiro–Wilk test, latent variables were transformed using the Box–Cox procedure ^[2] (Box and Cox, 1964). The latter is a commonly used methodological approach to transform the data distribution into a normal distribution.

Multicollinearity was assessed using Pearson correlation coefficients alongside Variance inflation factors (VIFs). As shown in Table 4, the majority of Pearson correlation values fall below ± 0.7. Still, all VIF values are below the commonly accepted threshold of 5, pointing out that the selected explanatory variables likely represent different underlying latent constructs. Consequently, multicollinearity does not pose a significant issue within our OLS regression models, thereby enabling more dependable interpretation of the relationships among the variables (Hair, 2014).

Table 5 shows the descriptive statistics of the variables included in our research design (Figure 1). The dependent variable, COMP, exhibits a minimum score of 5 and a maximum of 35, with an average of 21.64 (SD = 6.38). Among the independent latent variables, RES_AV ranges from 5 to 35, with a mean of 22.33 (SD = 6.54), while INN_CAP presents a mean of 22.93 (SD = 6.30), also spanning the same range. The mediating variables, RESP_INN and ORG_SUPP, report a means of 27.09 (SD = 7.62) and 17.42 (SD = 6.61), respectively, with scores covering their respective ranges.

Control variables enable us to detect the demographic features of our sample. For instance, CEO_AGE has an average value of 2.11 (SD = 0.76), suggesting that the bulk of managers are between 31 and 60 years old. Moreover, CEO_EXP averaged 3.06 (SD = 0.92), suggesting that most managers reported their industry experience to be in the range of 6–10 years. CEO_GENDER ranges from 0 to 3 with a mean value of 1.25 (SD = 0.45), thus suggesting that the sample includes a balanced mix of genders. FIRM_SIZE has a mean of 3.56 (SD = 1.07), further corroborating that our sample focused on SMEs. Additional categorical controls include SECTOR and MARKET, with mean values of 0.48 and 0.23, respectively. This figures out that the majority of the SMEs in our sample operate within the business-to-business (B2B) segment, and that the sample encompasses firms from both high- and low-technology industries.

Table 6 sets out the regression results for the EU sub-sample. Model A refers to the results when resource availability acts as the key explanatory variable; alternatively, Model B illustrates the empirical evidence when innovative capabilities are considered as the independent variable. Starting with Model 1A, the results show that RES_AV significantly predicts COMP (β = 0.27, p < 0.01), fulfilling the first condition of Baron and Kenny (1986). Models 2A-3A highlights that RES_AV also significantly influences RESP_INN (β = 1.09, p < 0.01) and ORG_SUPP (β = 0.16, p < 0.01). These results support the second condition, confirming that the independent variable is significantly associated with the mediators. Moving to the mediating models, Models 4A-5B display that RESP_INN positively mediates both the relationship between RES_AV and COMP (β = 0.11, p < 0.01), as well as INN_CAP and COMP (β = 0.07, p < 0.01), thus satisfying the third condition. Moreover, ORG_SUPP positively mediates the basic relationships between RES_AV and COMP (β = 0.30, p < 0.10), as well as INN_CAP and COMP (β = 0.25, p < 0.05). Indeed, when the mediators are included in the regressions, the coefficient for the independent variables (RES_AV and INN_CAP) decreases but remains significant.

To infer, partial mediation rather than full mediation was found in Models 4A-4B-5A-5B (Zhao et al., 2010).

Table 7 presents the findings for the UK sub-sample, reflecting a similar pattern to the EU context. In particular, RES_AV exerts a significant positive effect on COMP (Model 1A: β = 0.20, p < 0.01), thereby meeting the first condition of mediation. Subsequent models (2A-3A) demonstrate that RES_AV significantly predicts both RESP_INN (β = 0.83, p < 0.01) and ORG_SUPP (β = 0.23, p < 0.01), confirming the second condition. In models incorporating the mediators, both RESP_INN and ORG_SUPP positively mediate the relationship between RES_AV and COMP (Model 4A: β = 0.14, p < 0.01; Model 5A: β = 0.31, p < 0.05). Furthermore, when considering INN_CAP as an independent variable, both RESP_INN and ORG_SUPP positively mediate the baseline relationship (Model 4B: β = 0.09, p < 0.01; Model 5B: β = 0.21, p < 0.10). Indeed, the coefficients for RES_AV and INN_CAP considerably diminish upon inclusion of the mediating variables, satisfying the second condition for mediation.

Thus, these results suggest partial mediation rather than full mediation prevails in Models 4A-4B–5A-5B (Zhao et al., 2010).

Table 8 sets out the results regarding the US sub-sample. Notably, RES_AV demonstrates a significant positive effect on COMP (Model 1A: β = 0.19, p < 0.01), thus meeting the first criterion of mediation. Focussing on Models 2A-3A, RES_AV significantly predicts both RESP_INN (β = 1.00, p < 0.01) and ORG_SUPP (β = 0.24, p < 0.01), thereby confirming the second condition. When mediators are included in the models, the results from Models 4A-4B and 5A-5B indicate that, within the US context, neither RESP_INN nor ORG_SUPP effectively mediate the baseline relationships. Unlike the patterns observed in the EU and UK samples, such empirical evidence suggests that sustainable drivers do not play a substantial mediating role in the USA, implying that other mechanisms might be driving the relationship between resource endowments, innovation capabilities, and competitiveness in this context.

To substantiate our empirical evidence, we conducted three main robustness checks. The former two, namely the path analysis and Sobel, tests were performed to appraise the statistical significance of the indirect effects of the independent variables (RES_AV and INN_CAP) on competitiveness via responsible innovation and organisational support (Baron and Kenny, 1986; MacKinnon and Dwyer, 1993; Sobel, 1982; Wright, 1934); whereas, the fit indices values were calculated to assess the overall adequacy of each regression model (Yin et al., 2022).

Table 9 presents the path analysis results with resource availability as the main independent variable, while Table 10 presents those with innovative capabilities as the main explanatory variable. However, both are performed by using the bootstrap technique across the three sub-samples. Specifically, Table 9 exhibits the path analyses with RESP_INN and ORG_SUPP as mediators, unveiling a positive and significant total effect of RES_AV on COMP in most of the three subsamples [Panel A (EU): c + (a * b) = 0.50, p < 0.01; Panel A (UK): c + (a * b) = 0.48, p < 0.01; Panel A (USA): c + (a * b) = 0.32, p < 0.01; Panel B (EU): d + (e * f) = 0.88, p < 0.05; Panel B (UK): d + (e * f) = 0.82, p < 0.05], thereby corroborating H1a. The results fully corroborate H2a and H4a for the EU and UK subsamples when RESP_INN mediates the basic relationship. In particular, the direct effect of RES_AV on RESP_INN [Panel A (EU): a = 1.02, p < 0.01] and the mediating effect of this variable [Panel A (EU): a * b = 0.23, p < 0.01] are both positive and statistically significant. Similar empirical evidence is observed for the UK setting [Panel A (UK): a = 0.83, p < 0.01; Panel A (UK): a * b = 0.27, p < 0.01]. However, the findings when ORG_SUPP is included as a mediating factor do not attain the same level of statistical significance. Even though H3a is supported across all sub-samples [Panel B (EU): e = 0.22, p < 0.01; Panel B (UK): e = 0.23, p < 0.01; Panel B (EU): e = 0.24, p < 0.01], the mediating hypothesis H5a is not confirmed.

Table 10 reveals a positive and significant total effect of INN_CAP on COMP in almost all subsamples [Panel A (EU): c + (a * b) = 0.14, p < 0.05; Panel A (UK): c + (a * b) = 0.18, p < 0.05; Panel B (EU): d + (e * f) = 0.76, p < 0.01; Panel B (UK): d + (e * f) = 0.62, p < 0.05], thereby corroborating H1b. The empirical findings fully support H2b and H4b solely for the EU and UK subsamples when RESP_INN acts as a mediating variable. Specifically, the direct effect of INN_CAP on RESP_INN [Panel A (EU): a = 0.92, p < 0.01; Panel A (UK): a = 0.72, p < 0.01] and the mediating outcomes [Panel A (EU): a * b = 0.14, p < 0.05; Panel A (UK): a * b = 0.18, p < 0.05] are both positive and statistically significant. Nonetheless, the findings involving ORG_SUPP as a mediator do not reach the same levels of statistical significance. Although H3b is supported across all sub-samples [Panel B (EU): e = 0.15, p < 0.01; Panel B (UK): e = 0.13, p < 0.01; Panel B (EU): e = 0.15, p < 0.01], the mediating hypothesis H5b is only confirmed in the EU subsample [Panel B (EU): e * f = 0.50, p < 0.10].

The Sobel statistic is calculated as the ratio of the indirect effect to its standard error, which can then be tested against the standard normal distribution to determine significance (Zhao et al., 2010). As shown in Table 11, the results predominantly indicate partial mediation across most pathways. These findings provide robust evidence supporting the mediating roles of both responsible innovation and organisational support within the EU and UK geopolitical contexts. Conversely, in the US sub-sample, these drivers do not exhibit a significant mediating influence, suggesting that their role in facilitating the relationship between resources, innovation capacity, and competitiveness is scant or missing in this setting.

To evaluate the goodness of the models, Table 11 presents the fit indices across the three sub-samples (i.e. EU, UK, and USA). The root mean square error of approximation (RMSEA) measures the discrepancy between the model and the data, with values below 0.08 pointing out a good fit (Steiger, 1990). All RMSEA values are roughly around 0.07 or 0.09, suggesting an acceptable fit across all groups. The standardised root mean square residual (SRMR) reflects the difference between observed and model-implied correlations, with values under 0.08 generally deemed acceptable (Hu and Bentler, 1999). In our analysis, SRMR values mostly range between 0.07 and 0.09, supporting the effectiveness of the models. The comparative fit index (CFI) and Tucker–Lewis Index (TLI) values above 0.90 are typically regarded as indicative of a good fit (Hu and Bentler, 1999). In this context, CFI and TLI indices exceed 0.84, with many approaching or surpassing 0.90, denoting satisfactory model adequacy. Values of PCMIN/DF below 3 are preferred (Kline, 2016); in our case, ratios range from roughly 1.68 to 2.91, further endorsing the suitability of the models. These fit indices, on the whole, hint at the validity and robustness of the estimated models.

Concerning the hypothesis testing (Table 11), the empirical evidence meets most of the assumed relationships. In the EU sample, H4a, H5a, and H5b are completely supported, suggesting strong validation. Conversely, H5a and H5b related to the mediating role of organisational support are only moderately supported in the EU and UK samples, while in the US sample, the hypotheses are rejected.

This study deepens our understanding of how internal resources and innovation capabilities translate into long-term competitiveness in manufacturing SMEs. The empirical results corroborate the core tenets of the RBV, which suggest that competitiveness depends on the effective mobilisation of VRIN resources (Barney, 1991; Wernerfelt, 1984). At the same time, the findings enrich this perspective by showing that resources and capabilities gain strategic relevance only when they are activated through mechanisms that reflect sustainability and knowledge dynamics. This evidence, therefore, posits the RBV within a broader theoretical domain that also encompasses the NRBV and the KBV, thereby highlighting how environmental orientation and knowledge integration jointly drive long-term advantage.

While previous research often examined innovation or environmental commitment in isolation (e.g. Do et al., 2022; Halme and Korpela, 2014; Porter and Kramer, 2011), our results provide new empirical insights into their interactive effects. We demonstrate that sustainability-oriented resources and innovative capabilities are most effective when channelled through organisational and ethical processes that align firms’ internal conditions with societal expectations. This integrated view supports a dynamic understanding of competitiveness, where environmental legitimacy and knowledge creation operate as complementary forces rather than separate strategic domains.

The cross-country comparison further nurtures this interpretation. The partial mediation identified in the EU and UK samples, contrasted with the absence of significant effects in the US context, indicates that institutional and cultural frameworks dictate how internal resources are leveraged. Such evidence underscores the relevance of a context-sensitive reading of the RBV and aligns with research calling for a more embedded approach to competitiveness that accounts for institutional diversity and stakeholder pressures (Brouthers et al., 2008; Liu and Wang, 2025).

Overall, the empirical evidence highlights that while internal resources remain the cornerstone of competitive advantage, their transformation into long-term performance needs ethical, organisational, and contextual mechanisms. These insights bridge established theories on resource value with emerging perspectives on sustainability and knowledge management. The following sections elaborate on these findings by presenting their theoretical implications (§ 6.1) and managerial and policy implications (§ 6.2).

The results offer intriguing insights into how sustainability-oriented resource availability and innovation capabilities contribute to the competitiveness of manufacturing SMEs (El Nemar et al., 2022; Halme and Korpela, 2014; Lukovszki et al., 2021). Grounded in the RBV (Barney, 1991; Wernerfelt, 1984) and extended through the complementary lenses of the NRBV (Hart, 1995; Hart and Dowell, 2011) and KBV (Grant, 1996; Spender, 1996), the proposed dual-path model assumes that responsible innovation (Owen et al., 2012; Stilgoe et al., 2013) and organisational support (Aarons et al., 2014; Schein, 2010) act as mediators, transforming internal resource endowments into strategic outcomes. Therefore, the findings strongly corroborate the RBV: while direct effects on competitiveness are evident across all sub-samples, the mediating roles of responsible innovation and organisational support – characterised by partial mediation – are statistically confirmed only in the EU and UK.

These results substantiate and extend the RBV’s central tenets while introducing novel elements that integrate sustainability-oriented and knowledge-based dimensions into the theory. In line with prior studies (Do et al., 2022; Porter and Kramer, 2011), our findings reinforce the strategic relevance of responsible innovation and organisational support in shaping SMEs’ competitiveness. However, the absence of mediation effects in the US sub-sample suggests that the RBV’s internalist stance cannot be universally generalised. Consistently with the NRBV’s focus on environmental legitimacy and the KBV’s emphasis on knowledge diffusion and learning, contextual contingencies can significantly affect how internal resources lead to competitive advantage. Consequently, our empirical evidence challenges a strictly firm-centred interpretation of the RBV and advocates for a more embedded, context-sensitive view (Brouthers et al., 2008).

This study offers several theoretical contributions. First, we enrich the RBV by testing a dual-path model that accounts for how internal resource endowments are converted into competitive advantage not only through direct effects but also via sustainability-oriented mechanisms (Lichtenthaler, 2022). The integration of knowledge-based mediators – responsible innovation and organisational support – enhances the explanatory strength of the RBV in sustainability-sensitive environments. Second, our empirical evidence contributes to the growing body of literature that embeds sustainability into strategic and knowledge management (Carayannis et al., 2017; Nguyen and Kanbach, 2024). On the one hand, responsible innovation emerges not as a passive response to regulatory or reputational pressures but as an active process through which firms engage stakeholders and refine their innovation practices in a socially conscious way (Owen et al., 2012). On the other hand, organisational support strengthens the internal conditions – such as culture, leadership, and coordination – that allow knowledge and capabilities to be effectively deployed (Makadok, 2001). These findings move the RBV forward by highlighting the role of behavioural and relational mechanisms through which capabilities are transformed into competitive outcomes.

Finally, our results reinforce the recent call for a context-sensitive and socially embedded interpretation of the RBV (Liu and Wang, 2025). The observed cross-country differences confirm that the effectiveness of knowledge-based capabilities is shaped not only by firm-specific routines but also by institutional configurations, stakeholder pressure, and cultural expectations. By perceiving sustainability as an internal driver of strategic alignment – rather than as an external constraint – our model integrates the NRBV’s ethical and environmental focus with the KBV’s knowledge-centric logic, thereby offering a more comprehensive and geopolitically aware understanding of how competitive advantage emerges (Brouthers et al., 2008).

The results of this study provide relevant insights for managers and entrepreneurs seeking to enhance the competitiveness of manufacturing SMEs through sustainability-oriented strategies. From a managerial perspective, our findings confirm that the availability of internal resources and innovation capabilities, while essential, is not sufficient to ensure long-term competitive advantage. Managers should therefore invest in developing enabling mechanisms – such as organisational structures, cultural support, and ethical leadership – that allow these resources to be mobilised effectively. This involves embedding sustainability and responsibility into everyday routines, thereby transforming potential into performance. Consistently with the NRBV and KBV, firms should view sustainability-oriented practices not as additional costs or external constraints, but as knowledge-intensive processes that strengthen adaptive capacity and innovation effectiveness.

The mediating role of responsible innovation further highlights that innovation processes must be designed to be both efficient and ethically aware. Managers should move beyond short-term, market-driven innovation opportunities to embrace broader corporate governance models that integrate sustainability, stakeholder participation, and reflexivity into product and process development. Encouraging cross-functional collaboration and participatory decision-making can ensure that innovation trajectories remain aligned with both internal capabilities and external expectations, ultimately strengthening legitimacy and long-term competitiveness. As emphasised by the KBV, this requires not only technical knowledge, but also relational competencies and ethical awareness that can be nurtured through continuous learning and knowledge-sharing practices.

Furthermore, the role of organisational support underscores the significance of internal alignment between strategic intent, leadership commitment, and cultural orientation. Managers should foster a climate that encourages collective responsibility and supports experimentation, learning, and adaptation. Environmental and social objectives should be integrated into performance evaluation systems and incentive structures, reinforcing the behavioural and motivational dimensions of sustainability. In doing so, organisational support becomes the infrastructure that enables resources and capabilities to operate effectively as a source of sustainable competitive advantage.

From a policy perspective, the findings suggest that institutional frameworks can play a decisive role in shaping how SMEs convert resources and knowledge into competitiveness. The cross-country differences observed in our study point to the need for context-sensitive policy interventions. Policymakers should design targeted instruments that incentivise responsible innovation and strengthen SMEs’ organisational capabilities, especially in less regulated environments. Support mechanisms could include fiscal incentives for sustainability-oriented R&D, public–private partnerships for knowledge exchange and training programmes promoting environmental and social responsibility. These measures would help SMEs – often constrained by limited resources – to develop the needed capabilities to navigate complex, sustainability-driven markets.

Ultimately, both managers and policymakers should perceive sustainability not as a compliance exercise but as a strategic lever for innovation, legitimacy, and growth. By operationalising the dual-path mechanisms of responsible innovation and organisational support, SMEs can transform their resource endowments into sustained competitiveness, contributing to a broader, system-wide transition towards responsible and knowledge-based economic development.

This study explored how sustainability-oriented resource availability and innovation capabilities affect SMEs’ competitiveness, emphasising the mediating roles of responsible innovation and environmental organisational support. The findings underscored that while internal resources are fundamental, their strategic value is significantly enhanced when channelled through sustainability and knowledge-oriented mechanisms. Notably, the mediating effects were statistically significant in the EU and UK sub-samples, whereas no statistically significant effects were found in the US context. This divergence suggests that institutional and cultural environments critically shape how firms leverage their resources for competitive advantage. Such results reinforce the relevance of adopting a context-sensitive interpretation of the RBV, acknowledging that the effectiveness of internal capabilities is contingent upon external institutional frameworks and stakeholder expectations (Oliver, 1997). The divergence observed between the US and EU sub-samples may reflect differences in regulatory landscapes, cultural attitudes towards sustainability, and stakeholder pressure.

Although our study provides valuable insights, some limitations warrant acknowledgment. Firstly, even if the cross-sectional design is frequently employed in management and sustainability research (Branca et al., 2025; Erena et al., 2023; Marzi et al., 2023), it may potentially restrict the ability to investigate the evolution of sustainability practices or establish definitive causal inference (Maxwell and Cole, 2007). Nevertheless, the breadth of the sample and the robustness of the statistical checks lend reliability to the observed relationships. Indeed, beyond the Baron and Kenny (1986) methodological approach, we conducted a comprehensive path analysis estimating all direct and indirect pathways (Wright, 1934), as well as Sobel tests examining the statistical significance of the mediating effects (Zhao et al., 2010). Moreover, we calculated the fit indices for these models, reinforcing the validity of our assumptions (Yin et al., 2022). To overcome this limitation, future research should investigate longitudinal dynamics to elucidate how firms reconvert their internal resources and competencies over time in response to changing sustainability demands, as well as examining how responsible innovation and organisational support come into play when shifts in institutional and stakeholder pressures occur.

Secondly, relying on self-reported survey data may introduce common perceptual distortions. For this reason, several precautions were adopted to diminish the likelihood that CMB significantly affected our results, such as the anonymity of answers, the implementation of attention checks, and the reliance on validated measurement scales. Future studies might address this limitation by employing multi-method strategies, combining large-scale surveys with qualitative approaches, such as case studies or interviews. Moreover, other scholars can combine primary subjective data with secondary objective data – such as financial reports or market performance metrics – to offer a deeper analysis of the findings.

Thirdly, the empirical scope of this research is circumscribed to manufacturing SMEs. While this choice ensures theoretical consistency, it inevitably narrows the generalisability of the results. Future research could therefore extend the analysis beyond the manufacturing sector, examining whether the proposed dual-path model holds in service-based SMEs or other industries characterised by different competitive dynamics. In addition, comparative studies between family and non-family firms may offer further insights, as variations in ownership structures, governance arrangements, and value orientations are likely to influence how resources are channelled through responsible innovation and organisational support.

Finally, the geographical coverage is limited to mature economies (EU, UK, and USA). While these provide a useful comparison, they do not capture the diversity of institutional environments worldwide. Expanding the analysis to emerging economies, where institutional voids, regulatory uncertainty, and informal stakeholder pressures are more pronounced, would enable us to test the robustness of our model under different socio-economic and institutional conditions and to explore alternative pathways through which firms leverage resources for sustainable competitiveness.