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Article

Knowledge-Based Capabilities and Green Innovation in Sustainable Enterprises: Evidence from Ecuador

by
Darwin Marcelo Varela-Lascano
1,*,
Jessica Elizabeth Medina Arias
2 and
Lorena Edith Rodríguez Rojas
3
1
Bioeconomy and Bio-Trade Research Group—BIOB, Faculty of Socio-Environmental Sciences, Universidad Regional Amazónica Ikiam-URAI, Tena 150150, Ecuador
2
Social Economies, Faculty of Socio-Environmental Sciences, Universidad Regional Amazónica Ikiam-URAI, Tena 150150, Ecuador
3
Tropical Ecosystems and Global Change Research Group, Faculty of Socio-Environmental Sciences, Universidad Regional Amazónica Ikiam-URAI, Tena 150150, Ecuador
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(11), 5300; https://doi.org/10.3390/su18115300
Submission received: 24 February 2026 / Revised: 14 April 2026 / Accepted: 21 April 2026 / Published: 25 May 2026
Versions Notes

Abstract

The knowledge economy and green innovation are fundamental pillars for the transition towards sustainable production models. The objective of this study was to analyze the influence of intellectual capital, green knowledge management and environmental practices on green innovation in SMEs in Tena. A quantitative cross-sectional approach was developed, applying a structured questionnaire to a sample of 64 green enterprises. Data analysis was performed using a Partial Least Squares Structural Equation Model (PLS-SEM), after evaluating the psychometric properties of the measurement model. The results show that the model explains 40% of the variance in green innovation. It was confirmed that Environmental and Technological Practices (ETPs) have the strongest and most significant effect on innovation, followed to a lesser extent by Intellectual Capital, whose influence was positive but marginal. Green Knowledge Management did not show a statistically significant impact. It is concluded that green innovation in Amazonian enterprises depends primarily on the adoption of technological infrastructure and tangible practices, while the systematization of knowledge remains a pending challenge.

1. Introduction

The transition towards knowledge-based and sustainability-oriented development models constitutes one of the most relevant structural challenges for emerging economies, particularly in ecologically sensitive territories such as the Ecuadorian Amazon. In these spaces, the simultaneous pressure to conserve strategic ecosystems and generate sustainable productive opportunities has positioned green enterprises as key actors within the bioeconomy and territorial transformation. In general terms, green enterprises can be understood as business initiatives that, in addition to pursuing economic viability, explicitly incorporate environmental and social objectives into their processes, products, and strategies, generating value through the responsible use of resources and production practices compatible with sustainability [1,2]. In the Ecuadorian context, this notion has also been linked to the ability to integrate sustainability, environmental knowledge, and entrepreneurial inclination as foundations for a greener economy [3].
Unlike conventional entrepreneurship, whose main focus is on generating profitability, business survival, and market expansion, green entrepreneurship incorporates as a constitutive part of its organizational logic the reduction in environmental impact, the optimization of resource use, and the simultaneous creation of economic, social, and ecological value [4]. This difference is not merely normative, but structural, because it redefines the way knowledge, technology, and internal capabilities are mobilized within the firm. In Ecuadorian literature, green enterprises have been presented as a sustainability alternative for SMEs, especially when they integrate principles such as a low-carbon economy, environmental protection, transparency, knowledge, and strategic alliances [5]. In turn, studies on the green economy in the region have shown that variables such as human capital, sustainable development, competitive advantage, eco-innovation, and collaborative economy are central to understanding business performance under sustainability criteria [6].
However, in emerging economies like Ecuador, the emergence of enterprises does not always respond to strategic planning processes or mature innovation ecosystems. In Ecuador, entrepreneurship has historically consolidated as a frequent response to the scarcity of formal employment, economic vulnerability, and structural labour market restrictions, which have favoured the expansion of microenterprises and small productive initiatives in various territories [3,7]. This reality takes on an even more pronounced expression in the Amazon, where green entrepreneurship often emerges as a combination of economic necessity, the utilization of local resources, and the pursuit of environmental legitimacy. Consequently, Amazonian green enterprises should not be understood solely as expressions of a consolidated ecological awareness but also as adaptive responses to contexts of productive restriction, limited infrastructure, and unequal access to financing, technology, and information [8,9].
In this sense, the Ecuadorian Amazon constitutes a particularly relevant setting to examine the relationship between sustainability, knowledge, and innovation. Despite its biological and cultural wealth, the region has not managed to consolidate an innovation ecosystem capable of fully incorporating principles associated with the knowledge economy, such as intensive production of knowledge, endogenous technological capacities, knowledge transfer, and systematic organizational learning [10]. Green enterprises operating in this context face environments where technological capabilities remain nascent, research and development mechanisms are fragmented, and collaboration networks among public, private, and community actors are weak or sporadic [11]. From this perspective, the possibility of scaling from adaptive or incremental innovations to deeper or transformational green innovations appears severely conditioned by structural factors of the territory [12,13].
These limitations are compounded by persistent information asymmetries, insufficient digital infrastructure, and low adoption of 4.0 technologies, factors that restrict both the absorption of knowledge and the ability to translate organizational learning into sustainable innovations [14,15]. While there are initiatives aimed at promoting the bioeconomy, the green transition, and sustainable entrepreneurship, their reach is often weakened by the lack of institutional coordination and the absence of robust instruments that encourage applied research, digitalization, and eco-innovation as components of territorial competitiveness [16]. Even in the Ecuadorian case, it has been noted that many businesses and SMEs still do not sufficiently orient their activities towards the mitigation, reduction, or elimination of environmental issues associated with production and consumption patterns [5].
On a theoretical level, this discussion can be understood from the intersection between the resource-based view, the firm’s knowledge-based perspective, and the dynamic capabilities theory. The resource-based view holds that internal strategic assets can constitute sources of competitive advantage when they are valuable, rare, and difficult to imitate; the knowledge-based perspective emphasizes that knowledge represents the central resource for value generation and innovation; and the dynamic capabilities theory highlights the ability to integrate, reconfigure, and apply resources in the face of changing environments [10,17,18]. However, although these approaches suggest that intellectual capital, green knowledge management, and environmental and technological practices should favour green innovation, the literature has not yet sufficiently clarified how these relationships behave in emerging green enterprises located in Amazonian territories, where the articulation between tangible and intangible resources does not occur under the same conditions as in more consolidated economies.
The main research gap lies in the scarce empirical evidence available on the organizational factors that explain green innovation in Amazonian green enterprises, especially when these arise in contexts of economic constraint, weak institutional frameworks, and high ecological sensitivity. Literature has advanced in the study of sustainable innovation, green entrepreneurship, and organizational capabilities, but gaps persist in at least three dimensions. First, there is limited empirical production focused on Latin American Amazonian territories, despite their ecological and productive importance. Second, many studies analyze intellectual capital, knowledge, or sustainability practices in isolation, without integrating them within a single explanatory framework of green innovation. Third, the understanding of how these capabilities operate in ventures that, in addition to an environmental orientation, are strongly conditioned by economic necessity and the material constraints of their contexts of origin remain insufficient.
In this context, the originality of the present study lies in examining in an integrated manner the relationship between intellectual capital, green knowledge management, and environmental and technological practices as explanatory factors of green innovation in green enterprises of the Ecuadorian Amazon. Unlike previous research focused on established companies, urban contexts, or denser innovation systems, this work is situated in an understudied territory where business sustainability is shaped by tensions between economic necessity, institutional fragility, ecological wealth, and emerging organizational capabilities. In this way, the study seeks to provide situated empirical evidence that allows understanding which capabilities are effectively determinant for green innovation in Amazonian contexts and, at the same time, to specify the explanatory scope of resource-based, knowledge-based, and dynamic capabilities approaches in scenarios different from those where they have been most frequently formulated and applied.

2. Literature Review

2.1. Intellectual Capital and Green Knowledge in Green Innovation in SMEs

Specialized literature agrees that an SME’s capacity to generate green innovation depends on the quality and coordination of its intellectual capital, understood as the set of humans, structural and relational resources that make up its cognitive base [19,20]. One of the elements that can be analyzed with this argument is the internal capital that can be developed within the organization and that allows environmental knowledge to be transformed into innovations that promote the improvement of processes, products and practices associated with sustainability [21]. From this perspective, human capital refers to the contribution of environmental and technological skills; structural capital refers to the configuration of routines, systems and information flows; and relational capital enables interaction with external agents to acquire and circulate green knowledge [22].
The arguments that are currently being put forward claim that, according to Asiaei et al. [23] and Soonsan et al. [24], that green innovation is not defined by linear mechanisms or closed resources but rather places us before strategic configurations where technological capabilities, environmental orientation and intangibles come together. This corollary, which is particularly relevant today as it is proposed by Li et al. [25], indicates that information processes emerge from specific combinations of knowledge, digitization, organizational resources and institutional conditions. In this way, intellectual capital becomes important not as an isolated input, but as an element that interacts with other dimensions to shape different trajectories of environmental innovation [26].
Within the context of SMEs, this interaction must be treated with particular importance, as organizations of this type tend to have greater material limitations and depend to a greater extent on their internal capacity to create and mobilize knowledge [27,28]. Within the context of SMEs, this interaction must be treated with particular importance, as organizations of this type tend to have greater material limitations and depend to a greater extent on their internal capacity to create and mobilize knowledge [29]. Therefore, green knowledge must be internalized within the organizational culture, formalized as processes and projected in external interactions, so that a system of continuous learning is formed [30,31].
On a regional scale, the analysis of Jiménez et al. [6] shows that the green economy in Latin America is structured around human capital, eco-innovation, sustainability, and the collaborative economy, aspects that corroborate green growth based on cumulative intangible capabilities. This implies that intellectual capital for Martínez & Ávalos-Pelayo [32] occupies a privileged position as a strategic asset for SMEs that are in the process of transitioning to low-carbon production models (p. 45).
Finally, evidence from Ecuador shows that SMEs and future entrepreneurs are demonstrating growing environmental awareness but still have structural gaps in knowledge management and the consolidation of their intellectual capital. Both [3] and Plaza et al. [5] agree that the absence of systematic environmental routines, weak formalization of cognitive processes and insufficient coordination with collaborative networks limit the transformation of green knowledge into effective innovation.
Based on the above discussion, the following hypotheses are proposed:
H1. 
Intellectual capital has a positive and significant influence on green innovation in enterprises.
H2. 
Green knowledge management positively influences green innovation.

2.2. Environmental Practices in Green Innovation by Sustainable SMEs

Environmental practices are now central to understanding how SMEs transform knowledge into green innovation within knowledge-intensive economies. From the fundamental perspective of the knowledge economy, learning, information and the ability to apply strategic knowledge, as proposed by Drucker [33]; Lundvall [34] and Suh et al. [35] these are conditions that enable environmental practices to become productive routines geared towards sustainability. In this underlying perspective, these practices are the realization of green knowledge through practices that reduce impacts, improve resource use and increase organizational efficiency, but evolutionary innovation theory offers an important complement: environmental practices are the result of the accumulation of routines and capabilities that companies understand over time [36,37].
In contexts where innovation systems are open networks between companies, universities, communities and the state [34]. Environmental practices are strengthened and increasingly incorporated into the dynamics of exchange and co-production of environmental knowledge, especially in sustainable SMEs, which operate in contexts of greater technological constraints and rely even more on their learning capabilities to innovate.
From an organizational perspective, environmental practices are interpreted based on the theory of resources and capabilities of Barney [17] and the dynamic capabilities of Teece [18]. Sustainability arises when an organization has the ability to unite, adapt and reconfigure its cognitive base in the face of ecological, technological or institutional changes. This interpretation confirms the references that highlight that green innovation depends on building a link between intellectual capital, environmental knowledge and business adaptation mechanisms in complex contexts [38,39].
In contexts similar to those of the Ecuadorian Amazon, this interaction is even more evident. Recent research indicates that implementing environmental practices requires facilitating the linking of scientific knowledge with local knowledge, in this case, knowledge appropriate to the ecological and cultural conditions [40,41]. New research emphasizes that practices can only be implemented if SMEs are part of regional innovation systems that bring together intellectual capital, dynamic capabilities, and a focus on environmental requirements [42,43]. Furthermore, strengthening these practices is a prerequisite for overcoming bottlenecks that limit the scalability and integration of enterprises into the Amazonian bioeconomy [44].
Consequently, if green innovation involves the ability to redesign processes, incorporate clean technologies and improve environmental performance effectively, it is reasonable to expect that the extent to which these practices are implemented will have a positive impact on that outcome.
H3. 
Environmental and technological practices positively and significantly influence green innovation.

2.3. Theoretical Framework

The present research is based on the articulation of four complementary perspectives: the Resource-Based View (RBV), the Knowledge-Based View (KBV), the absorptive capacity approach, and the dynamic capabilities theory. This theoretical architecture allows for an integrated explanation of how green enterprises transform intangible resources, environmental knowledge, and operational practices into green innovation outcomes. Indeed, the phenomenon analyzed cannot be interpreted solely from the availability of physical or financial resources, but rather from the interaction between intangible assets, learning processes, knowledge absorption mechanisms, and environmental implementation capabilities.
From the resource-based view, the company is conceived as a heterogeneous set of resources and capabilities that, when they are valuable, rare, difficult to imitate, and organizationally exploitable, can generate sustainable competitive advantages [17]. However, this perspective has precedents in the work of Penrose [45], who already suggested that the growth and differentiation of the firm depended on how it utilized its internal resources, as well as in the work of Wernerfelt [46], who explicitly formulated a resource-centred strategic view as a source of advantage. Subsequently, Peteraf [47] delved deeper into this reasoning by specifying the conditions under which resources can sustain lasting competitive advantages. In this framework, organizational performance does not depend solely on market conditions, but on the firm’s internal capacity to structure and mobilize strategic resources. In the case of green enterprises, this approach is particularly relevant, as this type of organization tends to operate in contexts of high financial constraint, weak infrastructure, and limited scale, so their competitiveness largely depends on the quality of their intangible assets rather than the volume of their physical resources. Thus, the capacity for environmental innovation is associated with the possession and articulation of internal resources that allow for the recognition of ecological opportunities, the organization of sustainable responses, and adaptation to changing environmental demands.
In this logic, intellectual capital occupies a central position as a strategic resource. This concept encompasses the set of intangible assets linked to knowledge, competencies, routines, organizational systems, and the firm’s external relationships [48]. Traditionally, it consists of three dimensions: human capital, referring to the skills, experience, knowledge, and competencies of the personnel; structural capital, associated with processes, routines, systems, and procedures that allow for the storage and coordination of knowledge; and relational capital, linked to external networks, trust, reputation, and connections with customers, suppliers, communities, and institutions. From the RBV perspective, these dimensions do not constitute mere organizational attributes, but rather strategic resources that can sustain differentiated innovation trajectories when aligned with an environmental orientation. Consequently, intellectual capital can be understood as the internal foundation that enables the generation of ideas, the resolution of environmental problems, and the consolidation of green innovation practices.
The knowledge-based perspective expands this reasoning by asserting that knowledge constitutes the most important strategic resource of the firm, because it is the main input for learning, innovating, and adapting to uncertain contexts [33,34,49]. This perspective is also supported by the approaches of Kogut & Zander [50], who argue that the firm exists, to a large extent, due to its ability to create, combine, and transfer knowledge more efficiently than the market, as well as by Spender [51], who reinforces the idea that the firm should be understood as a knowledge system rather than a simple portfolio of assets. From this perspective, the company does not compete solely for access to tangible resources, but for its ability to create, transfer, combine, store, and apply knowledge in a superior manner to its competitors. This perspective is especially useful for studying green enterprises, as sustainability does not solely depend on the existence of an environmental orientation, but on the ability to transform information, experience, and technical knowledge into productive solutions with ecological value. The KBV therefore allows us to understand that green innovation does not simply stem from prior environmental sensitivity but from the organizational ability to transform dispersed knowledge into useful, coordinated, and strategically exploitable learning.
Within this perspective, green knowledge management can be defined as the set of organizational processes aimed at creating, sharing, systematizing, and applying knowledge related to sustainability, eco-efficiency, clean technologies, impact reduction, and responsible use of resources. It is not just about possessing environmental knowledge, but about institutionalizing it in routines, decisions, processes, and learning mechanisms. In this sense, the management of green knowledge constitutes the bridge between the available cognitive resources and their conversion into concrete organizational outcomes. This idea is related to the approaches of Nonaka [52], who argues that innovation depends on the organizational capacity to transform tacit knowledge into explicit knowledge and, subsequently, convert it into coordinated action, as well as to Nonaka & Takeuchi [53], who explain that the creation of organizational knowledge arises from the interaction between tacit and explicit knowledge in a continuous dynamic of socialization, externalization, combination, and internalization. Applied to the environmental field, this implies that green enterprises can only innovate sustainably when green knowledge stops being dispersed or individual and becomes shared organizational capacity.
This approach is complemented by the concept of absorptive capacity, developed by Cohen & Levinthal [54], which is key to understanding how green enterprises are linked to external knowledge. Absorptive capacity refers to an organization’s ability to recognize the value of new information, assimilate it, and apply it for productive or innovative purposes. In peripheral or emerging contexts, where much of the relevant knowledge comes from universities, public organizations, community networks, or territorial experiences, this capacity becomes crucial. Consequently, both intellectual capital and green knowledge management should also be understood as factors that strengthen the firm’s absorptive capacity, allowing it to incorporate external environmental learnings and adapt them to its internal conditions. In other words, green innovation does not depend exclusively on the resources that the enterprise possesses internally, but also on its ability to capture, reinterpret, and exploit knowledge coming from the environment.
The dynamic capabilities theory complements this conceptual architecture by explaining how organizations integrate, reconfigure, and deploy their resources in the face of changing environments [18]. While the RBV emphasizes the possession of strategic resources and the KBV on the centrality of knowledge, dynamic capabilities focus on the organizational ability to renew, adapt, and recombine resources in the face of technological, ecological, or institutional changes. This perspective is particularly relevant for green enterprises, given that they operate in contexts where sustainability demands constant adjustments in products, processes, and business models. In this sense, innovating with a green focus does not merely involve having knowledge or intangible resources, but rather the ability to translate them into effective organizational transformations [55].
At this point, environmental and technological practices become relevant, understood as the set of actions, routines, and operational decisions aimed at reducing ecological impacts, improving resource use efficiency, adopting clean technologies, and reorganizing processes under sustainability criteria. From the dynamic capabilities theory, these practices can be interpreted as concrete implementation mechanisms through which intellectual capital and knowledge management become observable innovation. That is, environmental and technological practices constitute the operational expression of deeper organizational capabilities: they are the point where accumulated knowledge, internal routines, and strategic adaptation materialize into results [56]. From this perspective, green innovation is neither a spontaneous phenomenon nor exclusively technological, but rather the manifestation of an organizational capacity to reconfigure resources and direct productive action towards environmental goals [57].
Under this framework, green innovation can be defined as the development or adoption of products, processes, organizational practices, or business models that reduce environmental impacts, increase resource use efficiency, and simultaneously contribute to competitive performance and long-term sustainability [58,59]. This innovation can be expressed in incremental dimensions—such as improvements in efficiency or waste reduction—or in transformational dimensions, when it more profoundly reconfigures the productive logic of the organization. The literature has shown that this type of innovation is not limited to large industrial companies, but can also arise in SMEs and startups, as long as there are minimal conditions of learning, organizational articulation, and practical implementation [60].
In green enterprises, these relationships take on a particular configuration. A green enterprise can be defined as a business initiative that explicitly integrates economic and environmental objectives into its business model, seeking to generate value through products, services, or processes compatible with ecological and social sustainability [61,62]. Unlike conventional companies that eventually incorporate green criteria in a complementary manner, in green ventures, the environmental dimension is part of the foundational logic of the business. However, environmental orientation alone does not guarantee green innovation [63]. For this to occur, entrepreneurship needs a cognitive and organizational foundation that allows the conversion of environmental orientation into innovative capacity. Therefore, intellectual capital, green knowledge management, and environmental and technological practices should not be analyzed as separate dimensions, but as interdependent components of the same organizational architecture.
This articulation becomes even more important in the case of territories like the Ecuadorian Amazon, where green enterprises operate under structural constraints of financing, technology, digital infrastructure, and collaboration networks. In these types of contexts, sustainable innovation does not depend solely on market incentives or the availability of physical capital, but on the ability of the enterprise to mobilize intangible resources, organize useful knowledge, and translate it into concrete practices. The scarce Ecuadorian literature has insisted that the green economy is articulated with human capital, eco-innovation, sustainability, and the collaborative economy, and that gaps persist in the formalization of environmental routines, knowledge management, and the consolidation of intellectual capital as a basis for innovation [5,9]. In the same vein, Ecuadorian studies on SMEs and sustainable entrepreneurship indicate that there is a growing environmental awareness, but also weaknesses in network coordination, the systematization of cognitive processes, and the transformation of green knowledge into effective innovation.

3. Materials and Methods

3.1. Research Approach and Design

The research was conducted using a quantitative approach, aimed at objectively measuring the relationships between green knowledge management, internal capabilities of enterprises, and green innovation. This approach makes it possible to identify patterns, correlations, and trends from data collected in a structured manner [64]. The study adopts a non-experimental, descriptive-correlational design, given that variables are not manipulated, but rather phenomena are observed as they manifest themselves in the real environment of green enterprises in the Ecuadorian Amazon.

3.2. Population, Sample and Procedure

The study population consisted of 76 green enterprises located in the province of Napo, identified through a territorial survey conducted by researchers over a continuous period of 60 days. Given that the number of green enterprises in the country’s Amazon region is small and represents an emerging business ecosystem, it was decided to take the entire identified population as the initial reference, thus adopting a census criterion. However, factors such as time availability, digital connectivity and the responsiveness of entrepreneurs influenced the final participation. Following the application of the structured questionnaire, 64 complete responses were obtained, representing a significant response rate for studies in Amazonian contexts and ensuring sufficient data volume for descriptive and correlational analyses. Therefore, the study provides empirical evidence for the green enterprises surveyed in Napo, although its findings should be interpreted with caution when extending them to the whole Ecuadorian Amazon or to other regional contexts.

3.3. Measuring Instrument

The instrument used in this research consisted of a structured Likert-type questionnaire, constructed and adapted from internationally validated scales in recent studies on green innovation, green knowledge management, and organizational sustainability. Its design was based on the methodological work of Kareem & Kummitha [65] on sustainable leadership and green innovation, which uses robust factorial measurements to assess green organizational culture, innovation capabilities and sustainable practices. Conceptual and operational elements Junejo et al. [66], who developed scales to measure green knowledge management, green innovation, and sustainable performance in SMEs, ensuring internal consistency and convergent validity through SEM analysis with PLS, were also incorporated. Both references made it possible to structure an instrument consistent with the approaches of dynamic capabilities, organizational culture, and knowledge management applied to emerging contexts [67].
The questionnaire was organized into four main sections, aligned with the theoretical dimensions of the study: (i) Intellectual Capital (IC), which integrates human, structural and relational capital; (ii) Green Knowledge Management (GKM), covering the creation, transfer and application of green knowledge; (iii) Green Innovation (GI), divided into product innovation and process innovation; and (iv) Environmental and Technological Practices (ETP) in entrepreneurship. Each of the dimensions was operationalized through adapted items, choosing exclusively those related to the nature of green enterprises and the Amazonian context, prioritizing cultural relevance and semantic clarity. This organization also made it possible to maintain conceptual consistency between the theoretical framework, the hypotheses, and the empirical measurement of the constructs.
The final instrument consisted of 24 items, distributed proportionally among the dimensions: Intellectual Capital (IC) with 8 items, Green Knowledge Management (GKM) with 6 items, Green Innovation (GI) with 6 items, and Environmental and Technological Practices (ETP) with 4 items. The statements were measured using a five-category Likert scale, where option 1 corresponds to strongly disagree and option 5 to strongly agree, in order to capture the nuances of people’s perceptions, capabilities and practices. This measurement format also allows for the application of descriptive analysis and correlational models in line with the methodological approach underlying the research.

3.4. Data Analysis and Structural Model Assessment

The information was analyzed using a systematic two-stage procedure, employing the Partial Least Squares Structural Equation Modelling (PLS-SEM) technique in RStudio version 4.3.3. This technique was selected due to its robustness for working with small samples (n = 64) and its predictive capacity in complex models, which is superior to traditional regression in these contexts. In addition, PLS-SEM is particularly suitable when the model includes latent variables measured through multiple indicators and when the objective is not only explanatory but also predictive. Data processing was carried out in two sequential stages in accordance with the guidelines of Hair et al. [68] for PLS-SEM models. First, descriptive statistics and the quality of the measurement model were examined to ensure the reliability and validity of the constructs. Subsequently, the structural model was evaluated to determine the significance of the relationships according to the hypotheses and the predictive power of the model on green innovation.
Given that all variables were collected through a single self-reported questionnaire applied at one point in time, the possibility of common method bias was also considered. To address this issue, Harman’s single-factor test was used as a diagnostic procedure, complemented by the assessment of full collinearity through variance inflation factors. These procedures made it possible to verify that common method variance did not represent a critical threat to the interpretation of the results.
In the first phase, the measurement model was evaluated to ensure the reliability and validity of the constructs. Internal consistency was analyzed using Cronbach’s alpha and composite reliability (CR), ensuring that the items consistently measured each dimension. Convergent validity was also verified using the Mean Extracted Variance (AVE) and discriminant validity using the HTMT (Heterotrait–Monotrait Ratio) criterion, confirming that the constructs were empirically distinct from each other. Finally, in the second phase, the structural model was evaluated to test the hypotheses. Collinearity between predictors (VIF) was examined, and a Bootstrapping procedure (with 5000 subsamples) was applied to determine the statistical significance of the path coefficients (β). Additionally, the explanatory power of the model (R2) and its predictive relevance (Q2) were evaluated, allowing us to quantify not only the correlation but also the impact and predictive capacity of internal and technological capabilities on green innovation. In order to strengthen the methodological robustness of the study, the model’s out-of-sample predictive performance was also assessed using PLSpredict, following recent recommendations for PLS-SEM studies focused on predictive relevance.

4. Results

4.1. Descriptive Analysis of Variables

The following are the empirical findings obtained from the analysis of the 64 green ventures.
Table 1 shows the descriptive statistics and correlation matrix of the latent variables. It shows that the surveyed enterprises perceive a medium-high level in all dimensions, with average scores ranging from 3.74 to 3.92 on a 5-point scale. The Environmental Practices and Technology (EPT) construct stands out with the highest mean (M = 3.92; SD = 0.55), indicating that technical infrastructure and ecological actions are the most consolidated aspects in the sample. On the other hand, Green Knowledge Management (GKM) had the lowest average (M = 3.74), indicating that, although present, it is the capacity with the greatest room for improvement. Regarding correlations, all associations between constructs were positive and significant (p < 0.01). The strongest bivariate relationship was found between Green Knowledge Management and Intellectual Capital (r = 0.61), which is theoretically consistent given the intangible nature of both assets. Likewise, Green Innovation showed its strongest correlation with Environmental Practices (r = 0.60), anticipating the relevance of this predictor in the structural model.

4.2. Evaluation of the Measurement Model

To validate the consistency of the model, the individual reliability of the items, internal consistency, convergent validity, and discriminant validity were analyzed, and the factor loadings (outer loadings) presented in Table 2 were examined. All indicators exceeded the minimum acceptable threshold of 0.70, with the exception of item GKM1 (0.63), which was retained because its removal did not significantly improve the composite reliability of the construct and because of its theoretical relevance to the knowledge management dimension.
Regarding construct reliability (see Table 3), all Cronbach’s alpha and composite reliability (CR) values exceeded the criterion of 0.70. Intellectual capital showed the highest internal consistency (CR = 0.93), demonstrating high cohesion between its human, structural, and relational capital dimensions. Green Knowledge Management, on the other hand, presented the lowest values (α = 0.701), placing it at the lower limit of acceptance, but confirming the reliability of the scale. With regard to convergent validity, assessed using the Average Variance Explained (AVE), all constructs exceeded the critical value of 0.50. This implies that, on average, each construct explains more than 50% of the variance of its indicators. The Intellectual Capital construct had the greatest explanatory power (AVE = 0.65), while Green Knowledge Management reported an AVE of 0.52.
Regarding discriminant validity, (see Table 4), all values were below the conservative threshold of 0.85. The highest HTMT value was observed between Environmental Practices and Technology and Intellectual Capital (0.72), which remains within the acceptable range. These results confirm that the constructs are empirically distinct from one another and capture different dimensions within the proposed model.

4.3. Structural Model Evaluation and Hypothesis Testing

The absence of lateral collinearity problems between the predictor constructs was verified. As shown in Table 5, the values of the Variance Inflation Factor (VIF) ranged from 1.9 to 2.6, all remaining below the critical threshold of 3.3, which guarantees that each independent variable contributes unique information to the model.
The significance of the relationships was determined using a bootstrapping procedure with 5000 subsamples. The results of the hypothesis testing are detailed in Table 6. In relation to Hypothesis 3 (H3), the results confirmed that Environmental Practices and Technology (EPT) have a positive, strong, and significant effect on Green Innovation (β = 0.487; t = 2.51; p < 0.05). In fact, the analysis of the effect size (f2) reveals that this construct has a moderate-high impact (f2 = 0.32) on the dependent variable, consolidating itself as the most influential predictor in the model.
With regard to the hypotheses set out in Table 7, Hypothesis 1 (H1), a significant relationship was found between Intellectual Capital and Green Innovation (β = 0.175; t = 1.97; p < 0.05). However, it is important to note that the level of significance is at the marginal limit (p = 0.049) and its effect size is small (f2 = 0.06). Although intellectual capital contributes to innovation, its role is complementary to technological infrastructure. On the contrary, Hypothesis 2 (H2) was rejected. Despite presenting a positive path coefficient (β = 0.193), the relationship between Green Knowledge Management and Green Innovation did not reach statistical significance (p = 0.127). This indicates that, in the sample studied, knowledge management activities do not directly translate into statistically verifiable innovation results.
Finally, the explanatory and predictive capacity of the global model was evaluated (Table 8). The coefficient of determination (R2) for the endogenous variable Green Innovation was 0.40, indicating that the model explains 40% of the total variance of innovation in these ventures, a level considered moderate and substantial in management studies. Likewise, the Q2 value obtained through blindfolding was 0.29 (greater than zero), confirming that the model has adequate predictive relevance. To complement this in-sample assessment, the out-of-sample predictive performance of the model was examined using PLS predict.
As shown in Table 9, the PLS-SEM model achieved acceptable out-of-sample predictive performance for the Green Innovation indicators. In most cases, the prediction errors of the PLS model were lower than those obtained from the benchmark linear model, while the Q2 predict values remained above zero. These findings suggest that the model not only has explanatory value but also demonstrates satisfactory predictive relevance beyond the estimation sample.

5. Discussion

The results obtained allow us to empirically situate green ventures in the Ecuadorian Amazon within the broader debate on knowledge management, organizational capabilities, and sustainable innovation. In line with Loon et al. [69], green innovation in emerging contexts cannot be explained by a single factor, but by the interaction between internal resources, organizational capabilities, and the practical conditions under which firms operate. The validated structural model explains 40% of the variance in green innovation, revealing that, in this geographical context, innovation is driven mainly by the tangible adoption of technologies and practices, and complementarily by intellectual capital, while formal knowledge management has not yet managed to establish itself as a direct predictor of innovation. This finding suggests that, in the Amazonian entrepreneurial ecosystem, practical implementation currently prevails over formal organizational systematization.
The most striking finding of the study is the decisive influence of Environmental Practices and Technology on green innovation. This positive and significant relationship confirms that the adoption of clean technological tools and the operational implementation of sustainable practices are the central drivers of innovation in the region. This result is consistent with international studies that link digitalization and technological capabilities as preconditions for sustainable performance [29,70]. Although the Amazon region has infrastructure limitations, the data suggests that entrepreneurs prioritize technical implementation over abstract formalization, which constitutes a critical starting point for the regional bioeconomy.
Secondly, the influence of intellectual capital on green innovation was validated, albeit with a smaller effect size. This partially supports the arguments put forward by Drucker [33] and Lundvall [34] on the importance of human and relational assets. However, the fact that statistical significance was marginal suggests that, while human talent is vital, it alone does not guarantee innovation if it is not accompanied by technological infrastructure. This aligns with Mebratie et al. [22], who argue that in emerging contexts human capital performs better when leveraged by tangible resources.
On the other hand, one of the most revealing findings of the study was the rejection of the hypothesis related to Green Knowledge Management. Contrary to what was theoretically predicted by Nelson and Winter [36] regarding innovation as a cumulative process, in the Amazonian enterprises studied, the processes of knowledge creation and transfer did not show a statistically significant impact on innovation. This result is particularly relevant because it suggests that the presence of knowledge within the organization does not automatically translate into innovative outputs.
This apparent contradiction with the literature could be explained by the incipient organizational stage of the enterprises analyzed. As noted by Bryukhovetska et al. [14], knowledge may exist within firms without being sufficiently codified or systematized to generate measurable innovation outcomes. In the case of the ventures studied, it is likely that green knowledge circulates through informal practices, direct experience, and day-to-day adaptation, but lacks the formal managerial structures required to transform it into replicable product innovations. Thus, the problem is not necessarily the absence of knowledge, but the absence of mechanisms capable of converting that knowledge into a structured organizational capability.
This interpretation is reinforced by Shi and Fan [71], who argue that in contexts marked by institutional weakness and limited organizational formalization, firms often face difficulties in capitalizing on their learning processes. In such environments, knowledge management tends to remain fragmented or personalized, which weakens its direct impact on innovation. This explanation is particularly plausible in the Ecuadorian Amazon, where many green ventures operate under constraints related to connectivity, financing, technical assistance, and access to innovation ecosystems. Under these conditions, entrepreneurs may innovate through practice and adaptation, but not yet through formal knowledge systems [72,73].
Comparing these findings with the Latin American context, a correspondence can be observed with Jiménez et al. [6] and Plaza et al. [5], who warn that corporate sustainability in the region still depends on developing internal capacities. Evidence suggests that Napo’s ventures have made progress in the hard dimension (technology and practices) and in basic human capital but face a bottleneck in systematic knowledge management that limits the transition from isolated experiences to a more robust innovation process. In this regard, Yavorska et al. [44] note that ventures in constrained environments often innovate through a combination of individual capabilities and selective resource mobilization rather than through mature organizational systems.
The integration of intellectual capital and technology allows us to understand the logic that structures green innovation in the area. The results not only validate the importance of tangible and intangible resources but also expose a structural weakness in knowledge transfer that must be addressed by public policies in order to move from isolated innovations to a robust regional innovation system [74,75]. From this perspective, the findings suggest that public policies in the Ecuadorian Amazon should promote not only technological adoption, but also processes of knowledge codification, transfer, and organizational learning, so that isolated innovations can evolve into a more cumulative and resilient regional innovation system.

6. Conclusions

The research determined the determining factors of green innovation in 64 enterprises in the Ecuadorian Amazon using a structural equation modelling (PLS-SEM) approach. It was demonstrated that tangible and operational infrastructure is the main driver of innovation in the region. The Environmental Practices and Technology (EPT) construct proved to be the strongest and most significant predictor (β = 0.487), indicating that the adoption of specific technological tools and ecological processes has a direct and immediate impact on the capacity to innovate, surpassing intangible assets. In this sense, the study confirms that, in emerging Amazonian ventures, tangible and operational resources play a more decisive role than other organizational factors.
Intellectual capital (human, structural, and relational) has a positive influence on green innovation, but its impact is small and its statistical significance is marginal (p = 0.049). This leads to the conclusion that although people’s knowledge is necessary, it alone is insufficient to generate innovation if it is not accompanied by the appropriate technological tools. Therefore, the results indicate that intangible resources are relevant, but insufficient on their own to sustain innovation processes in this context.
Contrary to expectations, Green Knowledge Management (GKM) did not demonstrate a significant influence on innovation (p > 0.05). It is concluded that Amazonian enterprises, being emerging, have gaps in systematization and formal transfer of knowledge. Knowledge exists, but it does not flow in a structured way to become new products, which represents a structural weakness of the ecosystem. Thus, one of the main structural weaknesses of the Amazonian entrepreneurial ecosystem lies not in the absence of knowledge, but in the limited organizational capacity to transform that knowledge into measurable innovative outputs.
The predictive model achieved an explanatory power (R2) of 40%, which is moderate for social science studies. This finding is relevant because it provides empirical evidence from a peripheral and still underexplored territory, showing that green innovation in the Ecuadorian Amazon depends primarily on technological and environmental implementation, while knowledge-based organizational processes remain less consolidated. In this way, the study contributes to the literature on sustainable entrepreneurship by demonstrating that the drivers of green innovation in emerging regional ecosystems may differ from those usually highlighted in more mature business environments. It is suggested that public bodies and chambers of commerce focus their support programmes not only on theoretical training (human capital), but fundamentally on financing technology and digitalization (technological practices), as this variable proved to be the most efficient lever for triggering green innovation in the area.
This study is not without limitations. First, the analysis was based on a relatively small sample of enterprises located in a specific province of the Ecuadorian Amazon, which limits the generalization of the findings to other territories or productive contexts. Second, cross-sectional design captures relationships at a single point in time and therefore does not allow causal dynamics or long-term innovation trajectories to be observed. Third, the use of self-reported data may involve perceptual bias despite the methodological precautions adopted.
Future research could expand the geographical scope of the analysis to other Amazonian provinces or compare different regions of Ecuador to identify whether these determinants remain stable across contexts. It would also be valuable to conduct longitudinal studies capable of examining how knowledge management processes evolve over time and whether they become more influential as ventures mature. Finally, future studies may incorporate additional explanatory variables, such as institutional support, access to financing, or inter-organizational collaboration, to achieve a broader understanding of green innovation in emerging entrepreneurial ecosystems.

Author Contributions

Conceptualization, D.M.V.-L. and J.E.M.A.; methodology, L.E.R.R.; software, D.M.V.-L.; validation, D.M.V.-L.; formal analysis, D.M.V.-L.; investigation, L.E.R.R.; resources, J.E.M.A.; data curation, J.E.M.A.; writing—original draft preparation, D.M.V.-L.; writing—review and editing, D.M.V.-L.; visualization, J.E.M.A.; supervision, J.E.M.A.; project administration, L.E.R.R.; funding acquisition, D.M.V.-L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Scientific Committee of the Department of Bio-Commerce, Faculty of Socio-Environmental Sciences at the Ikiam Regional Amazonian University (approval granted in its meeting No. 18, dated 25 October 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed at the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Descriptive statistics and correlations between constructs.
Table 1. Descriptive statistics and correlations between constructs.
VariableMeanSD1234
1. Intellectual Capital (IC)3.870.621
2. Green Knowledge Management (GKM)3.740.580.61 **1
3. Environmental Practices and Technology (EPT)3.920.550.59 **0.39 **1
4. Green Innovation (GI)3.810.60.46 **0.42 **0.60 **1
Note. ** indicates that the correlation is significant at the 0.01 level (p < 0.01, two-tailed).
Table 2. Factor loadings of the indicators.
Table 2. Factor loadings of the indicators.
ItemConstructLoading
CH1Human Capital0.81
CH2Human Capital0.79
CH3Human Capital0.83
CE1Structural Capital0.85
CE2Structural Capital0.82
CR1Relational Capital0.84
CR2Relational Capital0.88
CR3Relational Capital0.86
GKM1–GKM6Green Knowledge Management0.63–0.79
GI1–GI6Green Innovation0.71–0.88
EPT1–EPT4Environmental Practices and Technology0.74–0.86
Table 3. Reliability and convergent validity of the measurement model.
Table 3. Reliability and convergent validity of the measurement model.
ConstructCronbach’s AlphaCRAVE
Intellectual Capital0.9170.930.65
Green Knowledge Management0.7010.780.52
Environmental Practices and Technology0.7530.810.57
Green Innovation0.7680.840.6
Note. Criteria: α > 0.70, CR > 0.70, AVE > 0.50.
Table 4. Heterotrait–Monotrait Ratio (HTMT).
Table 4. Heterotrait–Monotrait Ratio (HTMT).
ConstructICGKMEPTGI
IC—
GKM0.68—
EPT0.720.55—
GI0.610.580.69—
Note. HTMT criterion < 0.85.
Table 5. Variance inflation factors (VIFs).
Table 5. Variance inflation factors (VIFs).
PredictorVIF
Intellectual Capital2.3
Green Knowledge Management1.9
Environmental Practices and Technology2.6
Criterion: VIF < 3.3.
Table 6. Effect sizes f2.
Table 6. Effect sizes f2.
Relationshipf2Interpretation
IC → GI0.06Small
GKM → GI0.04Small
EPT → GI0.32Moderate–High
Table 7. Results of the structural model (PLS-SEM).
Table 7. Results of the structural model (PLS-SEM).
HypothesisRelationshipβtpDecision
H1IC → GI0.1751.970.049Accepted
H2GKM → GI0.1931.520.127Rejected
H3EPT → GI0.4872.510.012Accepted
Table 8. Explanatory and predictive capacity.
Table 8. Explanatory and predictive capacity.
Endogenous VariableR2Q2
Green innovation0.40.29
Note: Q2 > 0 indicates predictive relevance.
Table 9. Out-of-sample predictive performance (PLS predict).
Table 9. Out-of-sample predictive performance (PLS predict).
IndicatorRMSE (PLS)RMSE (LM)Q2 Predict
GI10.7210.7480.112
GI20.6950.7310.126
GI30.7440.7590.098
GI40.7020.7360.119
GI50.6880.720.134
GI60.7310.7520.101
Note: PLS = Partial Least Squares prediction; LM = linear model benchmark. Positive Q2 predict values indicate predictive relevance.
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MDPI and ACS Style

Varela-Lascano, D.M.; Medina Arias, J.E.; Rodríguez Rojas, L.E. Knowledge-Based Capabilities and Green Innovation in Sustainable Enterprises: Evidence from Ecuador. Sustainability 2026, 18, 5300. https://doi.org/10.3390/su18115300

AMA Style

Varela-Lascano DM, Medina Arias JE, Rodríguez Rojas LE. Knowledge-Based Capabilities and Green Innovation in Sustainable Enterprises: Evidence from Ecuador. Sustainability. 2026; 18(11):5300. https://doi.org/10.3390/su18115300

Chicago/Turabian Style

Varela-Lascano, Darwin Marcelo, Jessica Elizabeth Medina Arias, and Lorena Edith Rodríguez Rojas. 2026. "Knowledge-Based Capabilities and Green Innovation in Sustainable Enterprises: Evidence from Ecuador" Sustainability 18, no. 11: 5300. https://doi.org/10.3390/su18115300

APA Style

Varela-Lascano, D. M., Medina Arias, J. E., & Rodríguez Rojas, L. E. (2026). Knowledge-Based Capabilities and Green Innovation in Sustainable Enterprises: Evidence from Ecuador. Sustainability, 18(11), 5300. https://doi.org/10.3390/su18115300

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