Green intellectual capital and organizational learning: pathways to sustainable innovation and firm performance in Pakistani SMEs Open Access

In recent years, the global discourse on sustainability has intensified, urging firms to recalibrate their strategies towards environmental responsibility, economic viability, and social well-being. As the climate crisis deepens and regulatory pressure mounts, businesses are increasingly held accountable not only for their financial outcomes but also for their contributions to environmental preservation and societal equity (Durrani et al., 2024; Tyler et al., 2024). The manufacturing sector, particularly in developing countries, plays a dual role while it drives industrialization and economic growth, it also significantly contributes to environmental deterioration through pollutants, refuse creation, and resource exhaustion depletion (Gilli et al., 2017; Ullah et al., 2021). In Pakistan, where the manufacturing industry constitutes the second-largest sector after agriculture, the urgency to balance profitability with environmental and social performance is even more critical. The country faces escalating environmental challenges, including water pollution, carbon emissions, and unchecked industrial waste, which threaten its ecological and economic stability (Ali et al., 2021; Shahbaz et al., 2025; Shahzad et al., 2020). Despite various policy measures, Pakistani SMEs continue to struggle with embedding sustainability within their strategic and operational models, underscoring the need for a stronger theoretical and empirical understanding of green-oriented capabilities.

Against these challenges, intellectual capital, in particular, green intellectual capital (GIC) has become an essential strategic intangible resource that can allow firms to develop innovation and sustainability capabilities (Ali et al., 2021; Shahbaz et al., 2025). GIC is defined as the sum of all intangible green knowledge, relationships, and structural processes that enable firms to achieve environmental and economic goals (Chen, 2008a). It comprises Green Human Capital (GHC), employees' environmental knowledge and commitment; Green Structural Capital (GSC), organizational systems, routines, and technologies that support environmental goals; and Green Relational Capital (GRC), relationships with stakeholders fostering ecological collaboration (Chen, 2008a). Prior literature has recognized GIC as a driver of green innovation (GI) and improved firm performance (financial, environmental, social), helping firms fulfil sustainability goals while maintaining competitiveness (Chen, 2008a; Yusliza et al., 2020). GI, in turn, is often conceptualized in two dimensions: green product innovation (developing eco-friendly goods) and green process innovation (adopting cleaner, more efficient production methods) (Wang et al., 2021; Yaroğlu, 2024). These dual dimensions are critical because process innovations may demand different resources and managerial capabilities than product innovations (Yaroğlu, 2024). However, limited research integrates these dimensions to understand how intellectual and learning capabilities jointly drive environmental and organizational performance in developing economies. Many empirical studies treat green innovation as a single, undifferentiated construct and neglect to distinguish between product and process innovations; this oversimplification obscures important variation in capabilities required and performance outcomes (Awwad et al., 2025; Khan et al., 2021a; Shahbaz et al., 2025). Nevertheless, GIC performance depends on the ability of a firm to learn, adapt and transform. Organizational learning capability (OLC) in this respect is a crucial enabler, where knowledge of the environment is absorbed, integrated and applied in various operations (Jerez-Gómez et al., 2005). Although there is increased international evidence on the interconnectedness of GIC, OLC, and GI, most of these studies have been conducted in developed economies where firms operate under stable regulatory systems, advanced technological infrastructures, and institutional support for sustainability initiatives. Consequently, empirical insights from such contexts cannot be readily generalized to developing economies that face resource scarcity, weaker enforcement mechanisms, and institutional voids (Yusliza et al., 2020). Empirical study in developing economies is therefore rare, particularly in the SME environment of Pakistan, where structural, financial, and knowledge constraints limit the transformation of intangible assets into sustainable innovation outcomes. This creates an important theoretical gap concerning how the mechanisms proposed by the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) operate under institutional voids, limited absorptive capacity, and weak environmental governance conditions that typify Pakistani SMEs.

Small and medium-sized enterprises (SMEs) constitute over 90% of Pakistan's industrial framework and contribute nearly 40% to GDP and 25% to exports (SMEDA, 2023). However, they often lag behind in environmental innovation due to limited resources, inadequate knowledge-sharing, and absence of formal environmental policies (Ali et al., 2025; Shahbaz et al., 2025). These contextual challenges reinforce the need to examine how knowledge-based resources, particularly GIC and OLC, can compensate for institutional and resource deficiencies and stimulate GI. By explicitly linking GIC, OLC, and GI within the Pakistani SME context, this study provides a more focused and theoretically justified investigation than prior literature.

Therefore, this study addresses a focused and critical research gap by examining how intangible assets such as GIC and OLC enable SMEs to innovate sustainably and achieve superior financial, ecological, and societal outcomes. Although previous research has examined the impact of GIC on firm performance, these studies largely consider financial outcomes in isolation and often neglect the simultaneous integration of environmental and social dimensions. The indirect mechanisms through OLC and GI, particularly in translating GIC into triple-bottom-line outcomes (Elkington and Rowlands, 1999), remain conceptually and empirically underdeveloped, especially within the context of developing-country SMEs. Secondly, in Pakistan, SMEs face multiple challenges, including limited financial and technological resources, weak regulatory enforcement, fragmented knowledge-sharing systems, and institutional voids that constrain the effective transformation of intangible assets into sustainable innovation. By explicitly investigating these pathways in Pakistani SMEs, this study provides a more contextually relevant, theoretically justified, and empirically grounded understanding of how intangible capabilities drive sustainable innovation and holistic firm performance. Building on RBV and DCT, the research extends their explanatory boundaries by illustrating how GIC, as a valuable and rare resource, is activated through learning-based dynamic capabilities to foster product and process innovations that improve triple-bottom-line performance. In doing so, the study challenges the prevailing confirmatory orientation of prior RBV/DCT applications by embedding them within the sustainability context of an emerging economy, demonstrating how dynamic capabilities can compensate for structural and institutional weaknesses. This conceptualization advances originality in three ways. First, it repositions intellectual-capital research from a resource-possession view toward a resource-mobilization perspective by highlighting learning as the key activation mechanism. Second, it contextualizes the sustainability debate within a developing-country setting that is underrepresented in mainstream IC research. Third, it integrates product and process green innovation as distinct yet interlinked mediators, enabling a richer understanding of capability formation in SMEs. From a practical standpoint, the findings are expected to inform SME managers and policymakers on how to structure knowledge systems, invest in green skills, and foster innovation culture to enhance triple-bottom-line outcomes, financial, ecological, and social. By anchoring this study in Pakistan's unique industrial and institutional landscape, the paper offers a fresh conceptual perspective on sustainability-driven innovation. It extends the application of RBV and DCT into a new empirical context. In doing so, it responds to the global call for more contextually diverse sustainability research, particularly from developing economies.

Accordingly, the following research objectives guide this study: (1) To examine the association of GIC and OLC within Pakistani manufacturing SMEs. (2) To analyze how OLC mediates the relationship between GIC and GI adoption. (3) To evaluate the direct and indirect relationship of GIC and GI with firm performance in the context of Pakistani SMEs. (4) To provide policy and managerial recommendations for leveraging GIC and OLC as strategic tools for environmental and economic sustainability. In doing so, the study provides an empirical foundation for enhancing corporate sustainability strategies in developing countries and contributes to the broader debate on the role of intangible assets in supporting the green transformation of SMEs.

In an era where knowledge and innovation define sustainable competitiveness, understanding how firms convert intangible resources into lasting performance outcomes has become increasingly vital. This study is grounded in the Intellectual Capital-Based View (ICV), an extension of the RBV that emphasizes the strategic importance of intangible, knowledge-driven assets in creating enduring value (Barney, 1991; Youndt et al., 2004). However, RBV alone has been critiqued for emphasizing resource possession without sufficiently explaining the internal mechanisms through which such resources are deployed to generate innovation (Priem and Butler, 2001). ICV extends RBV by specifying that intangible and knowledge-based resources, such as human, structural, and relational capital, are the most critical drivers of organizational value creation (Larabi, 2025; Youndt et al., 2004). This extension is analytically important because it shifts the resource discussion from generic assets to the specific intellectual configurations that enable knowledge generation, storage, and renewal (Martínez-Falcó et al., 2025). Within the sustainability context, ICV posits that when green human capital (GHC), green structural capital (GSC), and green relational capital (GRC) are integrated, they form Green Intellectual Capital (GIC), a synergistic intangible asset that strengthens a firm's environmental responsiveness and innovation capacity (Chen, 2008a; Marco-Lajara et al., 2023). Empirical studies further demonstrate that GIC enhances a firm's ability to develop eco-innovative products and processes by leveraging employees' environmental expertise and embedding green routines across the organization (Baquero, 2024; Rana et al., 2025). Nevertheless, ICV still assumes that intellectual resources automatically lead to superior outcomes, while in reality these resources must be mobilized, recombined, and reconfigured to generate innovation, especially in volatile environments. This limitation is addressed by the Dynamic Capabilities Theory (Kathleen and Martin, 2017; Teece et al., 1997), which provides the causal logic explaining how firms transform their knowledge resources into innovation. According to DCT, competitive advantage emerges not from resource possession but from a firm's ability to sense opportunities, integrate knowledge, and reconfigure capabilities in dynamic conditions (Teece, 2007).

In this study, OLC is conceptualized as a critical dynamic capability that operationalizes DCT. OLC enables firms to acquire, disseminate, and apply new knowledge through shared vision, managerial commitment to learning, open-mindedness, and intra-organizational knowledge sharing (Alegre and Chiva, 2008; Jerez-Gómez et al., 2005). By functioning as a micro-foundation of dynamic capability, OLC provides the mechanism through which GIC is converted from a static stock of knowledge into an actionable driver of GI (Hurley and Hult, 1998; Lichtenthaler, 2009). This theoretically explains why merely possessing GIC is insufficient for innovation unless firms have the learning capability to leverage and renew it. The integration of these theoretical lenses becomes even more relevant in emerging economies such as Pakistan, where SMEs face resource scarcity, technological limitations, and weak institutional support. Under such constraints, the interaction between GIC (from ICV) and OLC (from DCT) becomes essential because SMEs must rely more heavily on knowledge resources and learning processes than on capital-intensive technologies (Dyer and Singh, 1998; Zahra and George, 2002). Thus, GIC provides the foundational green knowledge base, while OLC enables its adaptive deployment toward GI, which in turn enhances sustainable firm performance. Taken together, RBV explains the value of resource possession, ICV clarifies which intangible knowledge resources constitute GIC, and DCT elucidates the dynamic learning processes through which GIC is transformed into GI and subsequently into sustainable performance. This tri-theoretical synthesis offers a coherent, analytically grounded explanation of how Pakistani SMEs can convert intangible green resources into long-term competitiveness and sustainability.

In response to the growing complexity of sustainability discourse, firm performance can no longer be evaluated solely through financial indicators (Shahbaz and Malik, 2025). Although some prior studies have emphasized a single-dimensional approach, focusing primarily on financial or economic outcomes, this study adopts a multidimensional perspective to reflect the evolving sustainability paradigm. Recent scholarly debates emphasize that firm success in contemporary markets depends not only on profitability but also on ecological responsibility and social value creation (Elkington and Rowlands, 1999; Evans et al., 2017; Kumar et al., 2025; Rehan et al., 2025; Shahbaz et al., 2025). This integrative view, often conceptualized as the “triple bottom line,” justifies the inclusion of economic, environmental, and social performance dimensions, providing a more comprehensive representation of organizational sustainability and resilience. Economic performance epitomizes an organization's proficiency in enhancing productivity, reduce operational costs, and maintain competitiveness in dynamic markets (Liu et al., 2024; Zhu et al., 2008). In SMEs, financial performance is linked to resource allocation efficiency, waste reduction, and improved cost-efficiency (Malesios et al., 2018). An organization's environmental performance underscores its dexterity in alleviating its ecological footprint, effectuating a reduction in emissions, resource exhaustion, and environmental degradation (Laosirihongthong et al., 2013; Malesios et al., 2018). Firms practicing environmentally-proactive measures not confined to improve their brand image while concurrently fostering prolonged business benefits through regulatory compliance and enhanced stakeholder trust (Rodrigues and Franco, 2023). GI significantly impacts environmental performance, improving energy efficiency and reducing hazardous material use (Jiang et al., 2025). Social performance, encompassing worker welfare, safety, ethical labor, and community engagement, also influences SME success by enhancing employee retention and customer loyalty (Zhou et al., 2024). CSR and sustainability practices correlate with improved financial performance (Le, 2023; Zhou et al., 2024). However, many SMEs in developing economies struggle with these dimensions due to limited resources, weak regulatory enforcement, and low environmental awareness (Wang et al., 2023). Therefore, integrating financial, ecological, and societal performance into SME strategy constitutes a fundamental prerequisite for enduring sustainability in dynamic market environments. Figure 1, presents the conceptual framework of the study.

The intangible assets, especially GIC are critical in determining the firm level environmental responsiveness within the context of sustainable enterprise. GIC represents the collective environmental knowledge, green organizational competencies, and stakeholder-oriented collaborations that enable firms to achieve long-term ecological and financial performance (Chen, 2008b; Kumar et al., 2025; Yusliza et al., 2020). Recent studies emphasize that firms equipped with GIC demonstrate stronger adaptability to environmental regulations and a higher capacity for GI, resource efficiency, and competitive advantage (Ali et al., 2021; Truong et al., 2024). GIC is usually divided into three dimensions that are interrelated to each other: GHC, GSC and GRC. These dimensions are a synergistic triad in which GHC offers eco-sensitive solutions to problems, GSC incorporates the environmental values in decision-making and GRC generates innovation by means of external relationships and regulatory collaboration (Chang and Chen, 2012; Kumar et al., 2025). The incorporation of GIC in SMEs, where formal approaches to the environment are frequently held back by resource limitations, is critical to unlocking the ability to transform green (Ali et al., 2021; Kumar et al., 2025). Empirical evidence increasingly shows that GIC not only enhances financial and ecological outcomes but also strengthens the firm's learning capability and innovation performance by supporting continuous improvement and environmental strategy implementation (Ali et al., 2021; Kumar et al., 2025; Truong et al., 2024; Yusliza et al., 2020). Nevertheless, GIC is effective in different dimensions, and its effects depend on the correspondence to OLC and the implementation of the environmental strategy (Erinos NR and NR, 2018; Sabir et al., 2020). Specifically, SMEs with stronger GIC possess richer environmental knowledge bases, structured green routines, and collaborative networks that directly enhance their ability to engage in both green product and green process innovation, thereby reinforcing the argument that GIC is a central antecedent of GI (Kumar et al., 2025; Truong et al., 2024; Ali et al., 2025). Likewise, as GIC facilitates resource efficiency, regulatory compliance, and stakeholder trust, it contributes significantly to firm performance by enabling cost savings, operational excellence, and improved strategic positioning (Shahbaz and Malik, 2025; Yusliza et al., 2020). Therefore, it is expected that higher levels of GIC will positively influence firms' organizational learning capability and their subsequent engagement in GI, ultimately improving overall performance.

OLC has become a vital intangible resource in environmental management, enabling firms to absorb, transform, and apply environmental knowledge to address sustainability challenges (Aboelmaged and Hashem, 2019; Liao et al., 2017). It reflects a firm's dynamic capability to construct, internalize, decode, and restructure knowledge in alignment with contextual fluctuations (Aboelmaged and Hashem, 2019). In SMEs, where formal R&D systems may be limited, OLC facilitates agile decision-making and continuous process improvement, crucial for innovation and sustainability (Jiménez-Jiménez and Sanz-Valle, 2011; Naqshbandi and Tabche, 2018). However, despite its conceptual importance, prior studies have not sufficiently clarified how OLC translates environmental learning into concrete forms of GI. This gap becomes more pronounced when distinguishing between green product and green process innovation, particularly within developing-country SMEs where learning systems remain fragmented and largely informal. Firms with stronger OLC are more capable of converting tacit environmental insights into operational routines, fostering GI across both product and process levels through cross-functional collaboration and knowledge diffusion (Baquero, 2024; Sabir et al., 2020). Empirical studies affirm that OLC enhances the proficiency in adopting GI across products and process through environmental learning and cross-functional collaboration (Baquero, 2024; Tu and Wu, 2021). Yet, the evidence remains contextually narrow, as most empirical validations come from technologically advanced environments, leaving limited clarity on whether OLC can similarly stimulate GI under resource-poor, institutionally weak settings like Pakistan. Recent empirical evidence suggests that OLC amplifies the effectiveness of intellectual capital and external stakeholder networks by embedding environmental learning into organizational decision-making, thereby reinforcing innovation pathways, enhancing regulatory compliance, and strengthening stakeholder legitimacy in the transition toward sustainable technologies (Ali et al., 2021; Sabir et al., 2020; Rana et al., 2025). Moreover, evidence shows that OLC positively impacts all dimensions of the triple bottom line, encompassing financial, ecological, and societal outcomes (Ferreira et al., 2021; Peivand et al., 2022; Rana et al., 2025), particularly in emerging markets where SMEs face institutional challenges and resource constraints. Given these gaps and contextual inconsistencies, more focused empirical investigation is required to determine whether OLC meaningfully drives GI in developing-country SMEs, where learning-based capabilities may serve as substitutes for formal innovation infrastructures. Thus, OLC is not just a facilitator of GI but a foundational driver of long-term sustainability and competitive performance in dynamic settings.

GI has become a major principle of business sustainability, which is characterized by the design or acquisition of new products, technologies, or processes designed to curb environmental degradation while simultaneously enhancing organizational adaptability and long-term growth (Baquero, 2024; Chen et al., 2006; Hayat and Qingyu, 2024). It encompasses product or process innovation which seeks to lower emission level, resource utilization, and environment risk, thus balancing profitability and ecological accountability (Chen et al., 2006; Wang et al., 2021). Recent evidence highlights that GI not only strengthens firms' environmental legitimacy and regulatory compliance but also enhances operational efficiency and cost-effectiveness, positioning it as a strategic driver of sustainable competitiveness (Ha et al., 2024; Soewarno et al., 2019). As an illustration, green process innovation allows reconfiguring production systems to reduce energy consumption and pollution, leading to long-term cost reduction and liability (Khan et al., 2021b). Simultaneously, green product innovation enables firms to respond to shifting consumer preferences and emerging eco-conscious markets, thus creating avenues for competitive advantage and market differentiation (Chen, 2008a; Zhu et al., 2023). Other researchers claim that GI can also yield significant spillover benefits to social performance in the form of enhanced work safety, environmental well-being, and community welfare (Dang et al., 2022; Le, 2022). While GI can be influenced by firm-specific resources, such as GIC, the transformation of these resources into tangible innovation outcomes is contingent upon the firm's OLC, which acts as a dynamic mechanism that interprets, integrates, and applies environmental knowledge within organizational routines (Jerez-Gómez et al., 2005; Liao et al., 2017; Baquero, 2024; Sabir et al., 2020). SMEs, particularly in developing countries like Pakistan, often face resource scarcity and institutional voids that constrain innovation potential (Aboelmaged and Hashem, 2019; Sadaf et al., 2024). In such contexts, OLC is likely to mediate the effects of GIC on both GI and overall firm performance FP, because learning-based capabilities facilitate the practical application of intangible green knowledge into process and product innovations, thereby enhancing efficiency, market responsiveness, and environmental outcomes (Baquero, 2024; Rana et al., 2025; Sabir et al., 2020). In Addition, recent studies increasingly emphasize that GI functions as a transformative mechanism through which internal capabilities, especially OLC and GIC, are converted into both ecological and financial performance outcomes (Kumar et al., 2025; Rana et al., 2025; Romadhon et al., 2025). In contexts like Pakistan, where environmental regulations are weak and technological infrastructure remains underdeveloped, GI becomes a proactive strategic tool that enhances firm performance by enabling efficiency gains, cost reductions, and stronger market positioning (Shahbaz et al., 2025). Therefore, the literature increasingly suggests that GI acts as a critical conduit through which firms leverage internal capabilities to achieve sustainability-oriented performance in dynamic and resource-constrained contexts. This study proposed the following hypotheses:

Based on the above literature review and hypotheses, theses hypotheses are proposed.

To ensure methodological precision and contextual authenticity, the sampling framework of this study was anchored in the official SME directory maintained by the Small and Medium Enterprises Development Authority (SMEDA) of Pakistan, which functions as the primary national registry for formally recognized SMEs. Based on this authoritative database, the study adopted a cross-sectional research design, which is appropriate for examining associative relationships among constructs such as GIC, OLC, GI, and FP. This design enables the identification of structural interrelationships and theoretical linkages within a single time frame, particularly suited for resource-constrained SME contexts where longitudinal data collection is often unfeasible. While this approach limits causal inference, it provides robust empirical insight into firm-level dynamics and supports theory development in emerging economies. A purposive sampling strategy was applied, covering firms located across major industrial zones in Punjab and Sindh, regions that collectively account for over 80% of Pakistan's SME activity (SMEDA, 2023). From this population, 600 manufacturing SMEs were selected and stratified across three key industrial sub-sectors, textile and apparel, food processing, and chemical manufacturing, to ensure sectoral representativeness and analytical comparability. These industries have been chosen because they are industrially dominant, have environmental intensity and they are strategically important to GI. These areas are the most influential SME clusters in Pakistan and correspond with the priorities of the country and the need of sustainability (SMEDA, 2022–2023; Survey, 2023–2024).

The inclusion criteria required that participating firms (a) employ between 50 and 250 workers, (b) operate in manufacturing sectors with measurable environmental impact, and (c) 3have been in continuous operation for at least five years. These conditions ensured that only firms with established operational processes and environmental relevance were considered, enhancing transparency and replicability. SMEs were chosen for their central role in Pakistan's industrial economy, disproportionate contribution to environmental externalities, and limited involvement in sustainability practices, making them a critical context for examining GIC and innovation. SMEs were chosen for their central role in Pakistan's industrial economy, disproportionate contribution to environmental externalities, and limited involvement in sustainability practices, making them a valuable group for studying GIC and innovation. Manufacturing SMEs, with high material intensity, linear production, and emissions, were specifically targeted to assess the eco-efficiency potential of GI (Adel et al., 2024; Sofia et al., 2024). Each firm nominated a senior executive, typically from operations, innovation, or environmental management, with decision-making authority and sustainability expertise. Data were collected over three months (Feb–Apr 2025) via a structured, self-administered survey. Of the 600 distributed questionnaires, 481 were returned (80.1% response rate); 29 were excluded for quality issues, leaving 452 valid responses. This response exceeds SEM thresholds and supports robust statistical power and generalizability. Ethical approval for the study was obtained from the institutional review board, and all procedures adhered to the Declaration of Helsinki. Verbal informed consent was secured due to the non-invasive, professional nature of the study. Participant anonymity, confidentiality, and data privacy were rigorously upheld.

All constructs in this study were measured using established and validated instruments from prior literature, tailored to the context of SMEs. GIC was assessed through three dimensions GHC, GSC, and GRC using 19 items adapted from Chen (2008a), capturing employee competencies, internal systems, and stakeholder relationships related to environmental sustainability. GI was measured with eight items from Chen et al. (2006) and Chen (2008b), reflecting both product and process-level environmental improvements. To explore the underlying capability that enhances innovation outcomes, OLC was included as a strategic enabler and measured using a 14-item scale from Jerez-Gómez et al. (2005), covering managerial commitment, knowledge integration, and openness to experimentation. Firm performance was evaluated through the Triple Bottom Line framework: Financial, ecological, and societal performances, each captured through five items, adopted from Paulraj (2011). Questionnaire of the study is attached in  Appendix as Table A2. All responses were recorded using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), and the instruments were pretested for clarity, contextual relevance, and reliability. Table 1 presents an overview of the constructs, including their dimensions, definitions, items, and corresponding sources, providing a comprehensive framework for the study's key variables.

PLS-SEM was chosen for this study due to its effectiveness in analyzing complex data with latent variables, particularly in small samples and non-normal distributions (Hair et al., 2012). Unlike CB-SEM, which requires large samples and multivariate normality, PLS-SEM is more flexible and stable, making it suitable for the study's sample of 452 respondents (Henseler et al., 2015; Hair et al., 2012). Beyond its statistical suitability, PLS-SEM facilitates the examination of complex, hierarchical relationships among latent constructs, supporting theory advancement in emerging research domains such as sustainability and innovation. Methodologically, PLS-SEM is effective in analyzing models with latent variables, smaller samples, and non-normal data distributions (Hair et al., 2012). Unlike covariance-based SEM (CB-SEM), which emphasizes model fit, PLS-SEM prioritizes explained variance and predictive relevance, key aspects when extending theoretical understanding in resource-constrained contexts. Additionally, PLS-SEM is ideal for modeling Higher-Order Constructs (HOCs), such as GIC, which consists of first-order dimensions like GHC, GSC, and GRC. This allows for a more nuanced investigation of second-order constructs, which would be challenging with CB-SEM (Sarstedt et al., 2020). The tool's efficiency in estimating latent variable interactions and minimizing measurement errors makes it particularly valuable in sustainability research, where constructs like environmental performance and OLC play crucial roles (Hair et al., 2012; Sarstedt et al., 2020). The measurement model was rigorously tested for convergent and discriminant validity, showing strong reliability across constructs. The structural model was then evaluated to assess the hypothesized relationships between GIC and GI. Thus, PLS-SEM not only provides methodological robustness but also deepens theoretical insight into how intangible assets drive sustainability-oriented performance in emerging economies like Pakistan. The PLS-SEM criteria are summarized in Table 2.

The potential for common method bias (CMB) was carefully examined to ensure the validity of the findings. Data were collected from multiple respondents holding diverse managerial roles (operations, innovation, and environmental management), thereby minimizing single-source bias. Harman's single-factor test was applied, and results showed that the first factor accounted for only 34.7% of the total variance, well below the 50% threshold, confirming that CMB was not a critical concern. In addition, procedural remedies were adopted, including the use of multiple-item scales, clear wording of items, and assurances of anonymity and confidentiality, which further reduced the likelihood of response bias. To complement this, an independent-sample t-test was conducted to examine potential non-response bias between early and late respondents; the results revealed (Table A1 in  Appendix) no significant differences across key variables (p > 0.05), affirming the representativeness and reliability of the collected data. Hence, the integrity and robustness of the dataset are adequately ensured.

Table 3 summarizes the demographic characteristics of the respondents and firms. Most respondents were male (74.3%) and held mid-to senior-level managerial positions in operations, innovation, or environmental management. The majority were aged between 31 and 40 years (37.2%) and held at least a master's degree (46.9%). The firms represented were small and medium-sized manufacturing enterprises employing between 50 and 250 workers, primarily operating in the textile/apparel (38.9%), food processing (32.7%), and chemical manufacturing (28.3%) sectors. These sectors collectively represent Pakistan's most industrially significant and environmentally intensive SME clusters, ensuring sectoral representativeness and analytical relevance.

The findings reveal remarkable robustness and veracity across all conceptual dimensions of the study. The Cronbach's alpha coefficients surpass the prescribed benchmark of 0.7, ranging between 0.919 and 0.972, thus affirming superior internal consistency across all constructs, as depicted in Figure 2. Likewise, CR indices consistently exceed 0.9, substantiating substantial reliability, with values spanning from 0.939 to 0.974. The AVE scores surpass the 0.5 threshold, with the minimum value for GSC at 0.733, confirming satisfactory convergent validity, as illustrated in Table 4. Furthermore, all factor loadings exceed the 0.7 threshold, providing additional corroboration of the measurement model's strength. The HTMT, which gauges discriminant validity, remains well beneath the critical cutoff of 0.85, with the highest value of 0.849 observed between ECP and ENP, indicating acceptable discriminant validity across constructs, as shown in Table 5. In aggregate, these results demonstrate that the measurement model exhibits exceptional reliability, and convergent and discriminant validity, thereby underscoring the integrity of the study's constructs.

The R-square (R²) values indicate moderate to strong explanatory power across the model, with GPI, GPR, and OLC showing the highest explanatory power (R² between 0.528 and 0.615), while ECP and ENP exhibit more modest explanatory power (R² values of 0.49 and 0.395, respectively). The Q²predict values, ranging from 0.437 for ECP to 0.606 for OLC, suggest substantial predictive relevance, particularly for constructs like GPR and OLC, which show higher values of Q² predict (0.567 and 0.606) as shown in Table 6. These results highlight that the model effectively explains and predicts outcomes related to GI and organizational learning, while providing meaningful insights into economic and social performance. The f-square (f²) values in Table 6 reveal the relative strength of the effect each predictor has on its respective outcome. The path from GIC to OLC has the largest effect size, with an f² value of 0.6, indicating a large effect, suggesting that GIC significantly influences OLC. The relationship between GIC and GI demonstrates a medium effect size (f² = 0.345), suggesting a substantial association between the constructs. Likewise, GI shows a moderate association with FP (f² = 0.185), and GIC also maintains a moderate association with FP (f² = 0.168). The path from OLC to FP exhibits a small effect (f² = 0.025), suggesting a weaker influence of OLC on firm performance. Finally, OLC's effect on GI is also small (f² = 0.141), indicating a limited, yet relevant, role in fostering GI. These results highlight the varied strength of relationships within the model, with GIC emerging as a key driver of both OLC and GI.

The findings of the model fit in Table 7 indices show that the PLS-SEM model fits strongly. The SRMR (Standardized Root Mean Square Residual) value of 0.04 significantly less than the recommended value of 0.08 indicates the good fit (Henseler et al., 2015). A good model fit is also confirmed by values of d_ULS and d_G, 2.501 and 2.436, respectively, which are within reasonable limits (Henseler et al., 2015). The value of Chi-square is quite high, 5746.562, but, due to the fact that PLS-SEM does not depend much on this value, it is not such a critical point as in the case of traditional CB-SEM (Hair et al., 2012). Also, the NFI (Normed Fit Index) value of 0.809 shows a fairly good fit because the values between 0.8 and 1.0 are acceptable (Bentler, 1990). All these indices point to the same direction that the model fits the data well and this gives sufficient ground to continue with the analysis.

The results in Table 8 and Figure 3 demonstrate the direct structural relationships derived through PLS bootstrapping (5,000 subsamples). The path from GIC to OLC (H1) is strongly significant (β = 0.784, t = 32, p = 0.000), affirming the ICV assumption that integrated intellectual capital fosters organizational learning processes. GIC also exhibits significant effects on GI (H2) (β = 0.533, t = 10.651, p = 0.000) and FP (H3) (β = 0.471, t = 8.476, p = 0.000), aligning with the RBV premise that strategically deployed knowledge-based resources enhance innovation and performance outcomes. The influence of OLC on GI (H4) (β = 0.340, t = 6.111, p = 0.000) and FP (H5) (β = 0.131, t = 2.479, p = 0.014) supports the DCT perspective that learning capabilities convert static intellectual capital into dynamic, innovation-driven outcomes. Finally, the path from GI to FP (H6) (β = 0.470, t = 7.546, p = 0.000) highlights innovation as a key mediating mechanism linking knowledge resources and firm competitiveness. Collectively, these findings validate the integrated ICV–DCT model, demonstrating that GIC and OLC jointly enhance innovation and sustainable performance within resource-constrained SMEs.

The mediation analysis results in Table 9, based on a bootstrapping procedure with 5,000 resamples, unveil the intricate mechanisms through which GIC contributes to FP via OLC and GI. The indirect relation of GIC with FP through GI (H7) is strong and significant (β = 0.250, t = 5.717, p = 0.000), indicating that firms leveraging their intellectual capital can enhance innovation, which in turn drives performance, reflecting partial mediation. The indirect pathway from GIC to FP via OLC (H8) (β = 0.103, t = 2.475, p = 0.014) underscores the mediating role of learning capability in converting knowledge-based resources into performance outcomes, aligning with DCT's premise that learning enables dynamic adaptation. Similarly, the mediation of GIC on GI through OLC (H9) (β = 0.267, t = 6.045, p = 0.000) demonstrates that firms with a strong learning culture can more effectively translate intellectual capital into innovative outcomes. The indirect relation of OLC with FP via GI (H10) (β = 0.160, t = 4.959, p = 0.000) further highlights innovation as the key channel linking learning and performance. Finally, the serial mediation pathway (GIC → OLC → GI → FP; H11) (β = 0.125, t = 4.887, p = 0.000) reinforces that GIC fosters FP through the joint influence of OLC and GI. Collectively, these results reveal partial mediation across all paths, suggesting that while OLC and GI play critical intermediary roles, GIC also exerts a direct influence on FP. These findings validate the theoretical integration of ICV and DCT, emphasizing that sustainable firm performance in SMEs arises not merely from resource possession but from the firm's capacity to learn, innovate, and transform its intellectual capital into long-term competitive advantage.

The analysis demonstrates how the three key variables, GI, GIC, and OLC, are related to variations in firm performance (FP). Among these, GIC shows the strongest relationship with performance (FP = 0.744, Perf. = 72.078), indicating that firms with higher GIC levels tend to achieve better performance. GI, with a correlation of 0.47 and a performance score of 70.226, also exhibits a positive yet weaker association, highlighting its role in performance, though less pronounced than GIC. OLC, with the lowest FP value (0.029), still contributes to performance (71.741), showing a moderate but significant effect. As shown in Table 10 and Figure 4, these results accentuate the critical paramountcy of GIC in driving performance, followed by GI and OLC, suggesting that integrating IC and OL in GI initiatives can substantially enhance firm performance.

The results of the present research contribute to the developing discussion of sustainability-based innovation in the context of Pakistani SMEs based on the ICV, RBV, and DCT. All hypothesized direct relationships (H1–H6) were supported by the findings. Specifically, GIC significantly enhances OLC (H1), GI (H2), and FP (H3), while OLC significantly influences GI (H4) and FP (H5), and GI directly affects FP (H6). The findings confirm that GIC, which includes GHC, GSC, and GRC, acts as a bundle of strategically valuable and imitable assets, which improves both GI and FP, which is consistent with the claims of RBV that unique intangible resources maintain a long-term advantage (Chen, 2008a; Kumar et al., 2025; Yusliza et al., 2020). These results demonstrate that knowledge resources are strategically deployed in SMEs to foster both learning and innovation, supporting the RBV premise of resource-based competitive advantage. The study shows that GIC strengthens OLC drives green innovation, and directly improves firm performance, reflecting the strategic value of knowledge resources in SMEs. The present study extends this perspective by showing that the value of GIC is not derived merely from its possession but from its interaction with organizational learning structures, a dynamic often overlooked in earlier research that treated GIC as a relatively static knowledge stock. Nevertheless, in contrast to the previous research who conceptualized GIC as a fixed repository of knowledge (e.g. Ali et al., 2021; Chang and Chen, 2012), the study conceptualizes it by illustrating that GIC can only create value when it gets interacted with OLC, supporting the pathway from GIC to OLC and emphasizing OLC's role in translating knowledge into innovation. This aligns with emerging theoretical discourse suggesting that intellectual capital becomes strategically meaningful only when supported by organizational routines that enable firms to interpret, absorb, and apply environmental knowledge. Regarding mediating hypotheses (H7–H11), the results indicate that GI mediates the relationship between GIC and FP (H7), OLC mediates between GIC and FP/GI (H8, H9), GI mediates between OLC and FP (H10), and OLC together with GI jointly mediates the relationship between GIC and FP (H11). By demonstrating this mechanism, the study challenges linear RBV assumptions and instead positions learning capability as the operational pathway through which GIC is actualized. This study, which builds on DCT, expands the theoretical base by demonstrating how OLC can be used to transform intellectual resources into dynamic capabilities to facilitate adaptive innovation and excellent results. This observation extends past the wave of conventional RBV presumptions empirically to substantiate the micro-foundations of dynamic capabilities, demonstrating how learning practices turn intangible assets into new-fangled execution. The mediating role has strong empirical evidence of Teece et al. (1997) and Migdadi (2019), stating that the operation of innovation-based performance is the result of learning-based renewal. It also improves the prior models by showing that the ability to learn does not directly positively affect firm performance but has an impact by way of innovation; hence the contingent and sequential relationships of these associations. The findings also highlight contextual realities faced by SMEs in emerging markets, where learning systems are often informal, fragmented, and constrained by limited absorptive capacity. The comparatively weaker direct influence of OLC on performance aligns with studies in similar contexts that emphasize the delayed and indirect nature of learning benefits (Tu and Wu, 2021; Ferreira et al., 2021). These results therefore extend prior research by situating learning-based theories within the realities of resource-poor environments, showing that learning contributes to performance primarily when channeled through innovation practices.

The findings are important to further the theory by stating that OLC is more of an enabling process by which GIC does not affect FP directly but indirectly through GI. This study can shed some light on the theoretical fragmentation that has been evident in previous studies that have looked at the constructs of ICV, RBV and DCT as independent variables because they empirically connect these constructs. In addition, the fact that the study concentrated on the triple bottom line, which is economical, ecological, and social, reinforces the view of Elkington and Rowlands (1999) that GI promotes social and economic sustainability as well as environmental stewardship. This multidimensional view refines earlier work by showing that sustainability outcomes are not the result of isolated innovation initiatives but of a broader capability configuration involving knowledge, learning, and innovation. The findings together provide a unified representation of the three constructs of ICV, RBV and DCT: GIC reflects the resource base (RBV), OLC reflects the mechanism of transformation (DCT), and GI reflects the innovation product (ICV). Therefore, the study contributes to the existing body of knowledge because it demonstrates that sustainable competitiveness in SMEs is not only due to the presence of intangible assets but also due to their constant renewal through learning and GI, especially in circumstances of resource poverty which is common in developing economies.

Although this study offers valuable insights into the influence of GIC, GI, and OLC on FP in Pakistani SMEs, it has notable limitations. The cross-sectional design constrains causal inferences, suggesting that future research should adopt a longitudinal framework to examine the dynamic effects of GIC on FP. Furthermore, the sample was confined to manufacturing SMEs in Pakistan, limiting its generalizability across diverse industries or regions. Future inquiries should investigate GIC in other sectors, such as services or agriculture, to broaden the understanding of its impact on GI. Further studies are needed to narrow the theoretical explanation of the serial mediation pathway (GIC, OLC, GI and FP) with the possible presence of two-way or moderated relationships because the actual interactions in the world are not necessarily linear. This exploration would contribute to the conceptual rigor of the model and provide more information on how firms transform intellectual capital into innovation-driven performance. Finally, the research was constrained to financial, ecological, and societal performance to capture a holistic approach to sustainability in SMEs. Although this combined construct is in line with the aim of the study that seeks to analyze the general sustainability outcomes, the study can be further broken down to give a better understanding of the role of each of the dimensions individually in contributing to sustainable innovation. Moreover, the further studies might investigate other variables that can deepen the knowledge on the sustainability-oriented innovation in SMEs, including digital transformation, institutional forces, or policy-influenced dynamics that could further define the green strategic orientation of firms.

This study provides actionable insights for Pakistani SMEs, demonstrating that even within the resource constraints typical of SMEs in emerging markets, strategic management of GIC combined with strong OLC can significantly enhance GI and FP. The results show that while GIC alone has a positive impact on performance, its effect is substantially amplified when mediated through OLC and GI, indicating that SMEs can achieve meaningful outcomes by effectively leveraging intellectual capital through structured learning and innovation processes. Practically, this implies that SMEs should focus on developing organizational routines that enable employees to acquire, share, and apply environmental knowledge, turning GIC into measurable GI. Targeted green training programs, inter-departmental knowledge exchange, and integration of environmental KPIs into performance management systems can ensure that learning translates into innovation, even when financial and technical resources are limited. The findings also reveal that smaller SMEs can attain competitive advantages comparable to larger firms by strategically combining GIC with OLC to drive GI, reinforcing that sustainable FP depends more on effective utilization of knowledge and learning than on sheer resource availability. Finally, SMEs are encouraged to adopt a dual innovation strategy, optimizing existing green processes for operational efficiency while exploring new green technologies for strategic renewal, to remain adaptive, competitive, and sustainable, demonstrating that even in emerging markets with constraints, structured learning and innovation can enable substantial performance gains.

The increasing environmental challenges faced by manufacturing industries necessitate the adoption of green growth strategies, especially in nascent economies like Pakistan, where a large portion of manufacturing operations still relies on traditional resource-intensive practices. This study contributes to the RBV theory by providing an integrated framework that links GIC to GI and FP, with OLC acting as a key mediator. The findings underscore the critical role of GIC, especially its dimensions of GHC, GSC, and GRC, in fostering GI and driving sustainable FP. By accentuating the pivotal role of GIC in facilitating both innovation and organizational learning, this study furnishes profound revelations on how SMEs, especially within resource-deprived milieus, can exploit green intangible assets to augment their competitive supremacy and holistic performance. Furthermore, the results indicate that GIC's influence on FP is significantly enhanced through GI, suggesting that SMEs in developing economies can still achieve sustainability and growth by focusing on GI. However, the successful realization of these benefits requires the active participation of government and senior management in supporting and implementing green strategies across the organization.