How Do Dynamic Capabilities Enable a Firm to Convert the External Pressures into Environmental Innovation? A Process-Based Study Using Structural Equation Modeling

Abstract

Recently, dealing with environmental issues has emerged as a critical part of various corporate social responsibility activities. To effectively address the environmental problems along with their generic purposes of increasing competitive advantages, firms pay attention to environmental innovation. Despite the growing importance of environmental innovation for achieving competitive advantages, there remains a significant gap in understanding how firms actually accomplish this innovation. This study aims to fill this gap by leveraging Teece’s theoretical framework to identify three key components of dynamic capabilities—sensing, seizing, and reconfiguring—that facilitate the development of an effective managerial system. Specifically, this study proposes that sensing and seizing guide a firm to correctly respond to the external requests of dealing with the environmental problems so that the firm may incorporate the external pressure in the environmental innovation outcomes, while reconfiguring leads directly to the realization of environmental innovation. Using a Korean Innovation Survey that includes direct questions about environmental innovation, we construct a structural equation model, PLS-SEM, to test our hypotheses, and the findings support all the hypotheses. The contributions and managerial implications are discussed based on the findings, and some limitations in methodology are also addressed.

1. Introduction

Addressing environmental challenges has become one of the most pressing responsibilities for contemporary profit-driven organizations [1,2,3,4,5,6]. Since the onset of the Industrial Revolution in the late 18th century, various modern firms have significantly enhanced human well-being by integrating unprecedentedly innovative technologies into industrial processes and devising efficient methods of wealth generation. However, by the close of the 20th century, it became evident that the economic advancements achieved by these organizations were profoundly incomplete, failing to account for their substantial environmental impact [7,8,9]. A significant portion of firm activities are blamed as the principal cause of the current climate change problems, and the firms can no longer dismiss such criticisms [10,11].

To address these severe environmental issues, the global system has also instituted various environmental regulations over the recent decades, compelling firms to actively respond to these challenges [12,13,14,15,16,17,18]. These environmental regulations have also become a significant task, requiring firms to find solutions to environmental problems in addition to their core business activities [19,20]. Firms that fail to cultivate the strategic resources and capabilities necessary to actively address environmental issues may find themselves constrained by such institutional pressures and ultimately be unable to prolong their survival [12,21,22]. Despite the urgency of environmental issues, firms still seem to be struggling to find effective solutions [23,24].

In this study, we suggest that environmental innovation can be a significant strategy for firms to address environmental challenges within their business operations and processes [8,25,26]. Environmental innovation involves the enhancement and development of products, services, and processes that are environmentally oriented, with the goal of delivering greater value to both producers and consumers while reducing environmental impact [8,20,26,27]. This form of innovation may include transforming operations and processes to support environmental sustainability, addressing objectives such as waste management, environmental efficiency, emission reduction, recycling, and eco-design [28,29]. Through environmental innovation, firms can potentially find effective solutions to respond to criticisms and contribute to addressing environmental issues.

For contemporary firms, environmental innovation is not merely a passive response to external pressures. Many firms are becoming more aware of their roles within a broader social context and are seeking to strengthen their relationships with various stakeholders, both internally and externally, to explore ways to create benefits for multiple stakeholders [19,26,30,31,32]. Corporate social responsibility has become an important concept in reconfiguring business operations to meet the demands of these stakeholders, with environmental innovation being a key component of this practice [32,33]. In other words, some firms are evolving from solely profit-driven entities to organizations that consider their responsibilities in addressing societal issues, including environmental challenges [33]. However, the extent and effectiveness of this transformation can vary widely among firms, and there is a need for further research on the internal factors that firms need to develop to effectively implement environmental innovation.

In this study, we focus particularly on dynamic capabilities as essential for a firm to evolve into a social system that adeptly understands the complex demands of diverse stakeholders, swiftly formulates strategic responses, and adapts its structures and processes accordingly [34]. Our choice of dynamic capabilities, as initially proposed by Teece and his colleagues in their seminal work [35], is grounded in the concept’s emphasis on the ability to respond to rapidly changing external environments. Dynamic capabilities underscore a firm’s strategic competencies in adapting, integrating, and reconfiguring organizational resources to align with evolving environmental demands [31,36]. Given these characteristics, we propose that the development of dynamic capabilities is a prerequisite for firms aiming to enhance their environmental innovation effectively. More specifically, we argue that the three components of dynamic capabilities—namely sensing, seizing, and reconfiguring—fulfill distinct roles in facilitating environmental innovation. Organizations that exhibit heightened sensing and seizing capabilities are likely to respond more acutely to external pressures concerning environmental challenges, thereby indirectly enhancing environmental innovation. Conversely, organizations with advanced reconfiguring capabilities are expected to directly contribute to the outcomes of environmental innovation. Building on these ideas, we develop a research model that illustrates how each primary component of dynamic capabilities contributes to environmental innovation through different pathways. We empirically test this model by using a reliable dataset collected by an authoritative research institute.

Our study aims to make the following contributions. In this study, we first attempt to develop a theoretical framework that illustrates how a firm can proactively address environmental issues through the use of dynamic capabilities. This research is also one of the pioneering empirical investigations to examine the role of dynamic capabilities in enhancing environmental innovation by promptly responding to external pressure related to environmental challenges. While some existing studies have suggested the role of dynamic capabilities in fostering environmental innovation through qualitative approaches, such as in-depth interviews or case studies [30,37,38,39], there is a dearth of empirical research examining the substantive impact of dynamic capabilities on the enhancement of environmental innovation [40]. The findings from our well-structured empirical analysis will also provide firm policymakers with valuable insights, enabling them to develop effective strategies for responding to environmental challenges and advancing environmental innovation.

2. Hypothesis Development

Environmental issues have consistently been a responsibility that contemporary firms must address within their business strategies [19,41,42]. From the traditional perspective of business strategy, environmental challenges are considered part of the general environment, which comprises various dimensions within the broader society that affect an industry or market and the firms operating within it [43]. The conventional logic regarding the current environmental challenges assumes that macro environmental conditions, including the environmental issues discussed in our research interest, are relatively steady and stable, making them easier to analyze and understand. Once these conditions are identified through strategic analysis of the external environment, firms can devise and implement appropriate responses. In this context, environmental issues are perceived as stable external factors to which firms must respond appropriately. This approach aligns with what is known as the generic analysis of business strategy [44].

Nonetheless, the reality of the current environmental challenges faced by firms is notably distinct. Firms are increasingly compelled to address the urgent and rapidly changing nature of current environmental issues [43]. This urgency is further intensified by considerable institutional pressures and the escalating concerns of diverse stakeholders, including regulatory authorities, consumers, investors, and advocacy groups [30]. The accelerated pace of environmental degradation, driven by industrial emissions, habitat destruction, and unsustainable resource utilization, has resulted in amplified demands for accountability and sustainable practices [45]. Regulatory bodies, such as international organizations or governmental institutes, are enacting more stringent environmental standards, while consumers and investors emphasize environmental responsibility in their purchasing and investment choices [6,19,26]. Advocacy groups are vigorously promoting enhanced environmental stewardship. Consequently, firms must respond not only to the ecological imperatives but also to the multifaceted expectations of stakeholders, necessitating the adoption of innovative, sustainable strategies to maintain legitimacy and competitive advantage in a rapidly changing landscape.

Given the complex characteristics of today’s environmental challenges, firms must undertake the task of innovatively altering their strategic and organizational processes. This transformation is necessary to effectively address externally imposed requirements while concurrently maintaining competitive advantages in the market. Environmental innovation, in this regard, can facilitate “win-win” outcomes, offering both economic and ecological benefits [26,46]. It involves improving and innovating products, services, and processes with an environmental focus, thereby providing greater value to both producers and consumers while gradually reducing environmental impact [38]. To enhance their capacity for environmental innovation, firms must engage in activities that transform existing organizational routines by proactively reallocating resources to incorporate the externally mandated environmental tasks within their organizational boundaries.

From an organizational learning perspective, dynamic capabilities, often characterized as ‘routines to learn routines’, may function as the fundamental organizational and strategic routines that enable firms to adjust their resource bases to develop new value-generating strategies, particularly in response to environmental mandates [47]. Additionally, the strategic management literature suggests that a firm’s strategic core competencies are shaped by the resources it possesses and the capabilities it has developed as learning abilities. These abilities allow firms to search for new knowledge relevant to their organizational routines and modify existing routines to effectively respond to various internal and external demands. Aligned with these well-established theoretical viewpoints, we propose that firms with dynamic capabilities can facilitate effective learning routines to better understand external demands, such as environmental issues, seize opportunities to integrate these demands, and transform existing routines to innovate their manufacturing processes. As Eisenhardt and Martin point out, the strengths of dynamic capabilities arise not only from their capability of learning-by-doing but also from their capability of learning-before-doing [47]. This suggests that dynamic capabilities do not merely rely on past experiences within a firm’s boundary but originate from an ability to sense what is happening externally and what will occur in the future. Therefore, we propose that dynamic capabilities represent an extended form of learning that surpasses the traditional scope of organizational learning literature. In this regard, we further argue that firms with dynamic capabilities can rapidly change their routines due to their agile learning characteristics and expand their knowledge sources by broadening and deepening their learning scopes [48,49]. Given these effective characteristics, dynamic capabilities are posited to serve as an effective means for firms to overcome emerging environmental challenges from outside. We propose that firms can adeptly manage the increased complexity of environmental innovation by focusing on dynamic capabilities.

Dynamic capabilities enable organizations not only to recognize potential technological shifts but also to adapt to these changes through innovation [50]. Teece categorizes dynamic capabilities into three coherent yet distinct sub-components: sensing, seizing, and reconfiguring [51]. While each component has been theoretically distinguished in its functions, the three components tend to be seamlessly linked in a higher learning process. Simply put, sensing involves identifying and evaluating opportunities in a changing environment at the initiation stage. Seizing encompasses the mobilization of internal and external resources and competencies to address opportunities and derive value from them. Reconfiguring entails the continuous renewal and orchestration of resources to align the company’s resource base with changes in the business environment. This study adopts Teece’s threefold classification of dynamic capabilities to conceptualize and argue that firms with greater dynamic capabilities are likely to enhance environmental innovation more effectively [51]. Consequently, we propose a process model of firms’ dynamic capabilities for environmental innovation. Our aim is to build a theoretical framework to effectively investigate how each component of dynamic capabilities leads to environmental innovation. Drawing on dynamic capabilities, we elaborate on each of the three components and the underlying mechanism of each component and develop our hypotheses.

2.1. Sensing

The ordinary capabilities that tend to focus on basic operational abilities that allow a firm to function effectively on a day-to-day basis, such as manufacturing efficiency or administrative processes, are not typically sufficient for navigating the complexities and uncertainties of rapidly changing environments. In contrast, dynamic capabilities are higher-order skills that enable firms to integrate, build, and reconfigure internal and external competencies to address swiftly changing environments [35,52]. These capabilities allow firms to adapt to new challenges and opportunities, such as shifting market demands or regulatory changes, by fostering innovation and strategic renewal [53]. Dynamic capabilities are particularly useful for firms aiming to respond to external pressures, such as those related to sustainability and environmental issues, as they provide the foresight needed to transform environmental challenges into opportunities for innovation and competitive advantages [51]. Among the components of dynamic capabilities suggested by Teece and his colleagues [35], we first focus on sensing because it is especially essential for understanding the criticality of external pressures related to environmental issues.

Sensing is a crucial aspect of dynamic capabilities that allows firms to detect changes outside their boundaries ahead of their competitors. According to Teece, sensing is embedded in the organizational routines and processes that enable a firm to identify and interpret signals from the external environment, such as customer needs, technological opportunities, competitive actions, and broader market trends [51,53]. It encompasses the ability to collect, analyze, and utilize information from various external sources to anticipate and respond to changes that could affect the firm’s strategic position [35]. In a turbulent environment where numerous demands for addressing environmental issues emerge beyond the firm’s boundary, firms with advanced sensing capabilities can detect early changes, often serving as indicators of larger trends. These characteristics of sensing capabilities are theoretically aligned with the well-known concept of exploration in organizational learning literature [54]. As an adaptive system, a firm engages in exploring new opportunities from the outside. Exploration is characterized by various organizational activities such as “search, variation, risk taking, experimentation, play, flexibility, discovery, or innovation” [54]. Instead of relying solely on their existing competencies, firms employing the exploration mode proactively look outward to identify opportunities by effectively responding to external environmental changes. In this sense, sensing capabilities serve as an effective learning routine to encourage exploration-related activities and are particularly meaningful for addressing environmental challenges in our context.

Environmental issues can arise in various external contexts, including rapidly institutionalized environmental regulations, shifts in industry-specific technologies addressing environmental problems, and increasing customer concerns about environmental issues in market domains [51,53]. Sensing capabilities can effectively monitor and scan changes in various external stakeholders’ demands for tackling environmental problems, leveraging insights from these diverse stakeholders. The existing literature highlights that external sources of information and knowledge are vital for innovating toward sustainability and environmental innovation [55,56]. Sensing capabilities specifically involve the ability to actively gather information from external sources and develop knowledge links with a wide range of external parties to discover innovative solutions by appropriately responding to external pressures related to environmental challenges [57]. In this regard, we first propose that sensing capabilities may help firms prepare to “do the right things” for environmental innovations by overcoming incomplete information about environmental challenges [58].

Addressing environmental challenges necessitates a firm’s ability to comprehend the requirements and expectations of external stakeholders, and this comprehension is primarily achieved through the deployment of sensing capabilities. By leveraging sensing capabilities, a firm can accurately perceive the critical nature of external pressures related to environmental issues, thereby enhancing its capacity for responsiveness. Without a proactive response to external pressures, a firm may find it challenging to leverage its sensing capabilities to drive environmental innovation.

As such, we argue that this enhanced responsiveness acts as a significant mediative step that enables the firm to align its sensing capability more closely with its strategic initiatives of environmental innovation. That is, this alignment facilitates the identification of a coherent and strategic direction for environmental innovation. By highlighting the mediating role of the responsiveness to the external pressures, we propose an indirect pathway where sensing capabilities, while not directly leading to environmental innovation, play a critical role by enhancing the firm’s responsiveness to external pressures. This enhanced responsiveness subsequently acts as a conduit, indirectly enhancing environmental innovation through its mediating influence. Therefore, we propose our first hypothesis as follows:

Hypothesis 1. 

Sensing capabilities are likely to indirectly enhance environmental innovation effectiveness, mediated by increased responsiveness to the external pressures of environmental issues.

2.2. Seizing

Seizing capabilities concentrate on the execution and realization of opportunities identified through sensing. This involves mobilizing resources and making strategic decisions to capture value from these opportunities [52]. Seizing is about transforming potential into reality by developing and implementing new products, services, or processes. It necessitates the orchestration of organizational routines and the strategic allocation of resources to effectively capitalize on sensed opportunities [59,60]. While sensing is crucial for recognizing and understanding external pressures and opportunities, seizing is vital for taking concrete actions to address them [58]. In this context, seizing is understood as a blend of exploration and exploitation from the perspective of organizational learning literature [54]. Opportunities newly identified through exploration activities in the sensing process are channeled into the seizing process, which fundamentally relies on exploitation activities. Unlike exploration, which seeks new opportunities from the outside, exploitation is characterized by a series of internal organizational routines such as “refinement, choice, production, efficiency, selection, implementation, or execution” [54]. A set of exploitative activities that focus on enhancing internal efficiency can form a solid foundation to capture external opportunities and effectively integrate them into established organizational routines. In this regard, seizing capabilities function as learning mechanisms by which a firm incorporates the opportunities sensed from outside into its existing organizational routines. Therefore, seizing is linked with both exploration and exploitation in its role of enabling external opportunities to become internally established routines that ultimately generate desired outcomes.

In the context of environmental challenges, seizing capabilities also play a pivotal role in enhancing a firm’s responsiveness to external pressures. Environmental innovation is often fraught with uncertainties, stemming from the lack of standardized metrics and the nascent nature of many green technologies. It requires substantial investments in research and development, as well as a deep understanding of both market dynamics and technological trends. By leveraging seizing capabilities, firms can effectively mobilize internal and external resources to respond to the external pressures for environmental issues and to pursue sustainability-driven initiatives. This involves engaging in exploratory and exploitative learning to refine internal capabilities, allowing firms to respond swiftly and strategically to environmental pressures [54,59,60]. We first propose that by making such investments aimed at capitalizing on opportunities identified through sensing capabilities, firms can also generate extra resources by which they can address environmental challenges posed by various external stakeholders by employing seizing capabilities.

Increased responsiveness, facilitated by seizing capabilities, subsequently can lead to environmental innovation by empowering firms to initiate and execute projects that are in line with sustainability objectives. It involves not only improving the efficiency of existing products and services but also driving the development of new solutions that address environmental concerns [61]. Firms with strong seizing capabilities are better equipped to respond to various stakeholders in terms of environmental challenges because they may effectively utilize the investments and resources to realize the opportunities identified by sensing-related activities. Their proper and timely responses to the external pressures regarding environmental issues also enable firms to collaborate with various external partners, such as suppliers and research institutions, to co-create innovative solutions. Such collaborations eventually enhance information exchange and reduce the uncertainties associated with environmental innovation [61]. By systematically leveraging external partnerships and internal resources, firms can better navigate the complexities of environmental innovation, turning external pressures into opportunities for growth and differentiation [51]. Given this, we suggest that enhanced responsiveness to the external pressures preceded by seizing capabilities will subsequently lead to environmental innovation by which firms can effectively address environmental problems. In summary, we propose an indirect pathway where seizing capabilities play a critical role in the advancement of environmental innovation by indirectly enhancing the firm’s responsiveness to external pressures. This leads us to propose the following hypothesis:

Hypothesis 2. 

Seizing capabilities are likely to indirectly enhance environmental innovation effectiveness, mediated by increased responsiveness to the external pressures of environmental issues.

2.3. Reconfiguring

When dealing with the problems arising in a rapidly changing environment, it is not sufficient for firms to simply adapt to changes incrementally. Environmental challenges requested by various stakeholders are also such kinds of problems. To effectively cope with the environmental challenges, firms must significantly reshape themselves. Reconfiguring capabilities, a component of dynamic capabilities, are essential for transforming firms in terms of structures and processes so that they may address the shifts in the environment. These capabilities consist of organizational routines designed to adjust and modify innovation processes previously utilized to a dramatic extent [51,53]. Reconfiguring capabilities play a crucial role, especially in fostering innovation by integrating resources obtained through exploratory learning with existing ones, thereby adapting to environmental changes. Unlike other dynamic capabilities, such as sensing and seizing, reconfiguring capabilities can enhance organizational processes and drive environmental innovation independently of external pressures. They exert a direct influence on environmental innovation by enabling firms to rethink and renew practices and routines, effectively responding to changing contexts [62]. Innovations in business practices and management strategies are among the most effective renewals, positioning firms to focus on environmental innovation [53]. For example, implementing novel marketing methods and strategies, which necessitate significant alterations in product design, labeling, placement, promotion, or pricing, can facilitate sustainable environmental product innovation [63].

In this context, successful environmental innovation also relies on a firm’s capacity to renew its organizational resources and competencies in response to evolving environmental demands [64]. Environmental innovation often requires radical transformations, which, although potentially disruptive, are essential for establishing new sustainability-focused practices and routines [65]. The literature highlights the establishment of cross-functional teams as a managerial practice that supports sustainability innovation [66,67]. Firms must engage in reconfiguring capabilities to maintain evolutionary fitness, enabling swift responses to unpredictable contingencies and shifting demands [51]. Thus, reconfiguring capabilities empower firms to implement comprehensive changes across the corporate value chain, management practices, and external relationships, achieving higher integration and driving environmental innovation. Therefore, we propose the following hypothesis:

Hypothesis 3. 

Reconfiguring capabilities are likely to directly enhance environmental innovation effectiveness.

Figure 1 illustrates our research model in which the impacts of sensing and seizing capabilities on environmental innovation are mediated by responsiveness to external pressures, and reconfiguring capabilities directly affects environmental innovation.

3. Methods

3.1. Data

To test our hypotheses, we used the “Korean Innovation Survey (KIS) 2010: Manufacturing Industry”, which is systematically collected biennially by the Science and Technology Policy Institute (STEPI), a government-operated research institution in Korea. The survey’s design was originally based on the Organization for Economic Cooperation and Development’s (OECD) Oslo Manual, which also informs the structure of the European Statistical Office’s (EUROSTAT) “Community Innovation Survey (CIS)”. As a result, the KIS questionnaire is largely parallel to that of the CIS, with certain modifications to reflect the unique Korean context. The KIS, akin to the CIS used in the European Union, serves as a credible and authoritative data source, enabling the Korean government to monitor the innovation activities and capabilities of firms and to craft national innovation policies. The public availability of these data has also facilitated its widespread use in empirical studies concerning innovation [18,68,69]. In line with previous research efforts that have employed this dataset, we have integrated it into our study’s analytical approach. Among the Korean Innovation Survey datasets collected since 2008, KIS 2010 holds particular significance for studying organizational environmental innovations, as it uniquely includes a dedicated section on green and environmental innovations. At the time of initiating this research, it represented the most recent dataset that included such questions, as no similar surveys had been conducted in the preceding decade [18,23,70]. This specific section is absent from all other KIS datasets, both prior and subsequent. Therefore, KIS 2010 is particularly well-suited for empirically testing hypotheses related to environmental innovation.

The KIS 2010 data were developed using a stratified sampling approach. According to STEPI, the stratified sample was constructed using two main criteria: industry classification based on the Korean Standard Industry Code (KSIC) and firm size, categorized by the number of regular employees (10–49, 50–99, 100–299, 300–499, and 500 or more). The sample selection process involved a multi-stage stratified systematic sampling method, with industry as the primary stratification layer and firm size as the secondary layer. Utilizing these stratified features, STEPI created the final KIS 2010 sample through a random sampling technique, resulting in an original sample size of 4000. From this initial sample, the final sample size for our study was 2206 firms, after excluding some coding errors and missing data from the 4000 sampled firms.

3.2. Measurement

3.2.1. Dependent Variable: Environmental Innovation Effectiveness

To measure environmental innovation effectiveness, our dependent variable, we used a specific question from the KIS survey that inquires whether the respondent firm has incorporated environmental benefits into its innovation activities. The question covers various environmental benefits, including: “reduced material use per unit output”, “reduced energy use per unit output”, “reduced CO₂ footprint (total CO₂ production) by your enterprise”, “replaced materials with less polluting or hazardous substitutes”, “reduced soil, water, noise, or air pollution”, “recycled waste, water, or materials”, “reduced energy use”, “reduced air, water, soil, or noise pollution”, and “improved recycling of product after use”. We constructed a count variable, ranging from zero to nine, to indicate the number of environmental benefits integrated into the firm’s innovation activities. A higher value of this variable signifies greater environmental innovation effectiveness achieved by the firm.

3.2.2. Independent Variables: Sensing, Seizing, and Reconfiguring

The main independent variables in our analysis are the three components of dynamic capabilities: sensing, seizing, and reconfiguring. Following a recent study that used CIS data to examine the impact of these components on environmental innovation [53], we defined and operationalized each component of dynamic capabilities and incorporated these measures into our structural equation model. The measures for each component were developed based on a classification of organizational routines that are closely linked to each aspect of dynamic capabilities.

Sensing. As detailed in the hypothesis development section, sensing capabilities rely on organizational routines that monitor and scan environmental changes, often depending heavily on various sources of information and knowledge. To operationalize sensing capabilities, we focused on firms’ sensitivity to diverse informational sources. Following the approach of Mousavi et al., who emphasize reliance on diverse information sources, we used the data from a KIS 2010 survey question that asks firms about the information sources they use for innovation activities, presenting four categories: internal sources (three question items labeled as A1 to A3 in analysis), market sources (four question items labeled as B1 to B4 in analysis), institutional sources (three question items labeled as C1 to C3 in analysis), and other sources (two question items labeled as D1 and D2 in analysis). The survey question includes 11 items about different information sources, asking respondents to rate their usage on a six-point scale from zero (“not used”) to five (“used a lot”). We categorized these 11 items under the four source categories and incorporated them into our structural equation modeling estimation.

Seizing. Seizing refers to the process of identifying the necessary resources and competencies to create value from an opportunity and organizing those resources effectively to make appropriate technological decisions. To measure a firm’s seizing capabilities, we followed the way Mousavi et al. used, which evaluates various organizational activities through which new products or processes have been developed and commercialized [53]. According to this method, seizing capabilities in our analysis include adoption of the best practices (two question items labeled as E1 and E2 in the analysis), cooperation with knowledge partners (four question items labeled as F1 to F4), and market introduction activities (two question items labeled as H1 and H2). We constructed seizing capabilities by identifying the questions representing each category from the KIS 2010 survey data. The questions were all asked on a dichotomous scale (1 = yes; 0 = no).

Reconfiguring. Reconfiguring means continuously adjusting and organizing resources to keep up with changes in the business environment while adapting technologies to fit users’ needs [53]. It includes new management practices (three question items labeled as I1 to I3 in analysis), new marketing methods and strategies (three question items labeled as J1 to J3 in analysis), new manufacturing related processes (three question items labeled as K1 to K3 in analysis). To measure reconfiguring, we used the same question that was used in the 2010 survey in the process innovation, marketing innovation, and organizational innovation sections: “During the three years 2007 to 2009, did your enterprise introduce?” and we coded a 1 if introduced and a 0 if there was no introduction. A total of 9 items were extracted, and a binary scale of 0 and 1.

3.2.3. Mediator: Responsiveness to External Pressures About Environmental Issues

In this study, we define a firm’s responsiveness to external environmental pressures as the degree to which it acknowledges and acts upon the demands from various external stakeholders to promote environmental innovation in its operations. To measure this variable, which we propose as a key mediator between dynamic capabilities and environmental innovation, we used a question that asks which external pressures the firm attempts to address. This question presented five different external factors that firms consider as external pressures of environmental issues: “existing environmental regulations or taxes on pollution”, “anticipated future environmental regulations or taxes”, “availability of government grants, subsidies, or other financial incentives for environmental innovation”, “current or expected market demand from customers for environmental innovations”, and “voluntary codes or agreements for environmental best practices within the industry sector”. Respondent firms answered each factor on a dichotomous scale (1 = yes; 0 = no). We created a count variable ranging from 0 to 5 to indicate the level of responsiveness to these external pressures.

3.2.4. Control Variables

In this study, we control for 13 variables that might affect the estimation of the main variables. We control for firm size, as fluctuations in size can influence the likelihood of environmental innovation [23]. Additionally, we account for firm characteristics like firm type and whether the firm is publicly listed, as these factors can impact the results. We also control for the effect of cost reduction, acknowledging that firms may innovate to cut costs, as noted by Hojnik and Ruzzier [71]. The diversity of external pressures is another control, considering that different pressures might simultaneously influence a firm’s response. Furthermore, we include controls for the firm’s average annual sales and both internal and external R&D expenditures to address the possibility that these factors provide a financial advantage for environmental innovation. Finally, we consider the impact of a firm’s previous innovation failures, controlling for how these experiences might affect its approach to new challenges.

4. Results

This study used partial least squares structural equation modeling (PLS-SEM) to estimate our research model. PLS-SEM is a variance-based, nonparametric technique commonly used for examining complicated relationships between observed and latent variables [72]. It is particularly suitable for estimating second-order factor models with both reflective and formative indicators, as it does not require the assumption of normality in data distribution [73]. While covariance-based structural equation modeling (CB-SEM) is primarily utilized to validate established theories, PLS-SEM supports both confirmatory and exploratory research with an emphasis on prediction [74]. Recent studies reveal that 13 out of 25 investigations adopted second-order factor models, often estimating the first-order factors as reflective and the second-order factors as formative [75]. In this study, it is important to explore the validity of the reconstructed questionnaire because the explanatory variables are derived from survey data. Additionally, given that the explanatory variables include three sub-dimensions, a second-order factor model should be estimated through a two-step analytical process.

Therefore, this study used SmartPLS 4.0 software to analyze the proposed hypotheses. Initially, first-order latent variables were generated by estimating the model with all constructs treated as reflective indicators. This step aims to evaluate and verify factor loadings, reliability, and validity to determine whether each measurement item appropriately reflects the latent variables. Subsequently, the study assessed how these first-order latent variables contribute to the formation of second-order constructs by treating them as formative indicators and estimating second-order latent variables. Finally, path coefficients between the endogenous and exogenous variables were derived according to the study’s theoretical model, and the hypotheses were evaluated using the PLS algorithm and bootstrapping technique.

4.1. First and Second-Order Hierarchical Measurement Model Result

First, this study employed the path weighting scheme in the PLS algorithm to estimate the first-order constructs of dynamic capability, as it maximizes the explanatory power of endogenous latent variables and is broadly applicable across different model structures [76]. The first-order model included 12 reflectively specified subcomponents representing the overarching dynamic capability construct. Among the three available weighting schemes in the PLS algorithm—centroid, factor, and path weighting—the path weighting scheme was chosen for its ability to generate weights based on directional relationships in the path model, with weights acting as regression coefficients. The PLS algorithm was then applied to evaluate the reflective measures following the three-step approach suggested by Hair et al. [77,78]. First, convergent validity and internal consistency reliability were assessed using metrics such as Cronbach’s alpha, reliability ρ_A, and composite reliability ρ_C. Second, the outer loadings of the reflective construct indicators were examined. Finally, discriminant validity was evaluated using the HTMT (heterotrait-monotrait ratio) criterion.

Following the analysis, we removed four items based on the initial analysis results. Specifically, two items (A1 and A2) were deleted from “Internal Sources”, and two items (K2 and K3) were deleted from “New Manufacturing Related Processes”, as their Cronbach’s alpha values failed to meet the required threshold of 0.7 recommended by Hair et al. [77]. Consequently, “Cooperation with Market Partners” and “Internal Capabilities” were removed from the model due to insufficient reliability and validity. These adjustments were made to enhance the overall reliability, validity, and robustness of the measurement model for subsequent analyses.

As illustrated in

Table 1. First-order hierarchical measurement model results.

	First-Order Constructs	Items	Loading	Cronbach’s Alpha	CR ρC	AVE
Sensing	Internal Sources VIF = 2.647	A1 (delete) A2 (delete) A3	1
	Institutional Sources VIF = 3.737	B1 B2 B3 B4	0.884 0.875 0.849 0.841	0.885	0.921	0.744
	Market Sources VIF = 3.718	C1 C2 C3	0.929 0.896 0.924	0.905	0.940	0.840
	Public Sources VIF = 3.229	D1 D2	0.946 0.947	0.884	0.945	0.896
Seizing	Adoption of the Best Practices VIF = 2.338	E1 E2	0.929 0.923	0.833	0.923	0.857
	Cooperation With Knowledge Partners VIF = 1.721	F1 F2 F3 F4	0.851 0.860 0.816 0.730	0.833	0.888	0.666
	Market Introduction Activities VIF = 1.513	H1 H2	0.957 0.865	0.811	0.863	0.832
Reconfiguring	New Management Practices VIF = 2.026	I1 I2 I3	0.884 0.894 0.779	0.814	0.889	0.729
	New Marketing Method and Strategy VIF = 1.409	J1 J2 J3	0.820 0.849 0.822	0.776	0.870	0.690
	New Manufacturing-Related Processes VIF = 1.448	K1 K2 (delete) K3 (delete)	1

, the results revealed the reliability and validity of the remaining reflectively measured constructs. Table 1 includes only the constructs retained in the final analysis, ensuring clarity and focus on the validated constructs. Except for “internal sources” and “new manufacturing-related processes”, Cronbach’s alpha, reliability ρ_A, and composite reliability ρ_C values consistently exceeded 0.7 [77,78], supporting a sufficient level of construct reliability to confirm the first measure evaluation. And all factor loadings for the model components exceeded the minimum threshold of 0.7, as suggested by prior studies [77,78,79]. In addition, AVE values above the critical value of 0.5, except for internal sources and manufacturing-related processes [77,78,80]. In the final step, as shown in

Table 2. Discriminant validity and collinearity results.

	1	2	3	4	5	6	7	8	9	10	VIF	Out.wt	Conf. Interval
Internal Sources											2.407	0.211 ***	[0.135; 0.289]
Institutional Sources	0.637										3.187	0.421 ***	[0.315; 0.520]
Market Sources	0.794	0.833									3.570	0.234 ***	[0.137; 0.334]
Public Sources	0.676	0.896	0.824								3.172	0.257 ***	[0.154; 0.354]
Adoption of the Best Practices	0.731	0.737	0.790	0.716							1.556	0.486 ***	[0.472; 0.502]
Cooperation With Knowledge Partners	0.463	0.715	0.559	0.587	0.578						1.380	0.547 ***	[0.532; 0.561]
Market Introduction Activities	0.502	0.558	0.570	0.573	0.594	0.469					1.409	0.180 ***	[0.174; 0.187]
New Management Practices	0.552	0.722	0.667	0.681	0.713	0.657	0.663				1.578	0.590 ***	[0.531; 0.646]
New Manufacturing-Related Processes	0.503	0.509	0.545	0.484	0.729	0.475	0.414	0.535			1.331	0.385 ***	[0.331; 0.439]
New Marketing Method and Strategy	0.434	0.539	0.516	0.553	0.512	0.443	0.948	0.633	0.384		1.361	0.255 ***	[0.196; 0.311]

Note: The values labeled from 1 to 10 in the table represent the HTMT. HTMT (Heterotrait–Monotrait ratio) values below 0.85 confirm sufficient discriminant validity, indicating that the constructs are distinct from each other. VIF (variance inflation factor) values below 5 suggest the absence of significant multicollinearity issues among the predictor variables. Outer weights (Out.wt) with *** indicate significant contributions (p < 0.001) to the respective constructs, with confidence intervals (Conf. Interval) further validating the reliability of measurement items.

, the HTMT criterion was applied to evaluate discriminant validity. The results demonstrated that all HTMT values were below the recommended threshold of 0.85, indicating sufficient discriminant validity across all constructs [77,78].

Second, this study estimated a second-order model using a formative approach. The variables included in the second-order model and their final item counts are as follows: internal sources (one item), institutional sources (four items), market sources (three items), public sources (two items), adoption of best practices (two items), cooperation with knowledge partners (four items), market introduction activities (two items), new management practices (three items), new marketing methods and strategies (three items), and new manufacturing-related processes (one item). To evaluate the formative measures of the second-order model, this study followed the three-step approach suggested by Hair et al. [77,78]. First, convergent validity was assessed through redundancy analysis. Second, multicollinearity among formative indicators was examined using VIF values. Third, the outer weights of the indicators were analyzed for their significance and relevance.

Redundancy analysis also confirmed that all formatively measured constructs also have convergent validity, as their values exceeded the threshold of 0.70 [77,78,81]. This analysis utilized latent variable scores generated by the reflective model as a proxy for global measures. While redundancy analysis typically requires a single-item global measure incorporated during the survey process, hierarchical models with both reflective and formative measures allow the use of latent variable scores from the reflective model [78,81]. In summary, redundancy analysis supports the convergent validity of all formatively measured constructs, confirming the robustness of the measurement model.

Collinearity is not a concern in the formative measurement models, as all VIF values remain below the acceptable threshold of 5, which is less stringent than the stricter threshold of 5 suggested by previous researchers [77,78,81]. The highest VIF, observed for market sources, is 3.570, as shown in Table 2. These results indicate that collinearity does not pose a critical issue, ensuring the reliability of PLS path model estimation.

We assessed the significance of the indicators’ outer weights by means of bootstrapping. As shown in Table 2, we estimated the original outer weight estimates and the confidence intervals derived from the 2.5% to 97.5% percentiles using the percentile method, all significant at the 5% level [77,78]. This final evaluation of the formative measurement estimates confirms the robustness and validity of the constructs, ensuring their suitability for further structural model analysis.

4.2. The Results of Path Analysis

We first evaluated the overall model’s explanatory power and predictive validity. The structural model explains 62.4% (Adjusted R² = 0.624) of the variance in environmental innovation when including sensing, seizing, reconfiguring, and response to external pressure as predictors. Initially, sensing and seizing explained 20% (Adjusted R² = 0.200) of the variance in response to external pressure (see Figure 2). The inclusion of reconfiguring significantly enhances the model’s explanatory power for environmental innovation. To assess the model’s predictive validity, we conducted blindfolding procedures. The Q² values obtained were 0.666 for sensing, 0.576 for seizing, 0.573 for reconfiguring, and 0.157 for external pressure. All Q² values are greater than zero, indicating satisfactory predictive relevance for each construct. We evaluated the significance of the path coefficients using bootstrapping with 5000 subsamples, following the default settings in SmartPLS. The bootstrapping results, presented in Figure 2, include the estimated path coefficients, corresponding t-values, and significance levels.

From the path coefficient estimation and hypothesis testing results in

Table 3. Description of hypothesis results.

Hypothesis	Relationships	Path Coefficient	Sample Mean	STDEV	t Values	Confidence Intervals		Result
						Lower	Upper
Control	Age −> Environmental Innovation	0.026	0.026	0.018	1.465	−0.008	0.062
Control	Comtype −> Environmental Innovation	−0.049	−0.051	0.096	0.514	−0.235	0.142
Control	Diversity of external pressure −> Environmental Innovation	−0.435	−0.436	0.038	11.514 *	−0.511	−0.363
Control	GovSupport −> Environmental Innovation	0.050	0.051	0.045	1.118	−0.041	0.134
Control	Listed −> Environmental Innovation	−0.056	−0.049	0.078	0.726	−0.209	0.091
Control	Cost for capital goods −> Environmental Innovation	0.025	0.010	0.049	0.506	−0.035	0.208
Control	Cost for external knowledge −> Environmental Innovation	−0.012	−0.007	0.054	0.213	−0.089	0.135
Control	External cost for R&D −> Environmental Innovation	0.001	0.015	0.033	0.025	−0.069	0.054
Control	Failure experience −> Environmental Innovation	−0.013	−0.014	0.019	0.715	−0.049	0.024
Control	Internal cost for R&D −> Environmental Innovation	0.022	0.013	0.028	0.783	−0.034	0.071
Control	Ln_annualsales −> Environmental Innovation	0.010	0.010	0.025	0.400	−0.041	0.056
Control	Ln_employeesforr&D −> Environmental Innovation	0.020	0.022	0.027	0.763	−0.035	0.071
Control	Ln_numberofemployees −> Environmental Innovation	−0.003	−0.002	0.025	0.110	−0.051	0.045
H1: Indirect	Sensing −> external pressure −> environmental innovation	0.181	0.181	0.036	5.029 *	0.112	0.252	supported
H2: Indirect	Seizing −> external pressure −> environmental innovation	0.834	0.837	0.102	8.183 *	0.636	1.033	supported
H3: Direct	Reconfiguring −> environmental innovation	0.385	0.381	0.064	5.995 *	0.264	0.513	supported

Note: STDEV represents the standard deviation of the estimated parameter. t-values marked with * indicate significant contributions (p < 0.05).

, we confirm that Hypothesis 1 is supported. The indirect effect of sensing shows that sensing has a positive effect on response to external pressure, and response to external pressure has a positive effect on environmental innovation. In the process, we analyzed whether response to external pressure has a mediating effect, and the t-value (5.029) was significant with a confidence interval of 0.112 to 0.252, confirming the mediating effect of response to external pressure. This suggests that firms with higher external sensitivity and sensing capabilities are more likely to respond more quickly to responsiveness to external environmental pressure and thus achieve environmental innovation.

Our findings for Hypothesis 2 indicate that seizing has a positive effect on the response to external pressure, which subsequently enhances environmental innovation. The mediating effect of response to external pressure is confirmed by a significant t-value (8.813) and a confidence interval ranging from 0.636 to 1.033 (see Table 3). Therefore, Hypothesis 2 is supported. This suggests that firms with a higher ability to recognize opportunities arising from external environmental pressures are more likely to respond quickly to these pressures, thereby achieving environmental innovation.

In the case of Hypothesis 3, the results demonstrate that reconfiguring capability directly and positively affects environmental innovation. The t-value of 5.995 indicates a significant positive effect, with a confidence interval of 0.264 to 0.513 (refer to Table 3). Therefore, Hypothesis 3 is supported. This implies that firms capable of creating new resources or opportunities by reorganizing existing resources—without needing intermediary steps to respond to external pressures—are more effective at achieving environmental innovation.

In addition to these, control variables were also included in the model. Among them, the diversity of external pressure had a significant negative effect on environmental innovation (t = 11.514 *, path coefficient = −0.435), indicating that a higher diversity of external pressures may hinder environmental innovation.

5. Discussions

This study extends the discourse on dynamic capabilities as crucial drivers of environmental innovation within firms, particularly through the framework of corporate social responsibility (CSR). Contemporary firms are not merely profit-generating entities but integral components of a broader social system, tasked with actively interacting with external entities to enhance societal well-being [3,82]. Given the pressing nature of current environmental challenges, firms must take proactive measures as responsible participants in this social ecosystem. Dynamic capabilities emerge as crucial assets for firms committed to addressing these challenges, enabling them to provide effective solutions proactively. Furthermore, in the context of contemporary business, where sustainability has evolved from a compliance requirement to a strategic imperative, firms are compelled to address multifaceted external pressures [12,13,14,15,16,17,18,83]. These pressures include stringent regulatory frameworks, heightened market demands for sustainable products, and increasing societal expectations for corporate accountability. Our study underscores the pivotal role of dynamic capabilities as internal enablers that allow firms not merely to react to these pressures but to harness them as catalysts for innovative transformation.

Our main contribution is in broadening the theoretical framework of corporate social responsibility by focusing more on the rapidly emerging environmental issues. This study underscores the essential role that internal core capabilities play in effectively tackling urgent environmental challenges. Although recent corporate social responsibility literature has emphasized the importance of environmental issues for modern firms, there is a notable lack of research connecting these external pressures with the development of internal capabilities [26]. Much of the existing literature tends to focus on reactive strategies, prioritizing swift responses to stakeholder demands. This research tendency stems from a gap in studies that establish a theoretical foundation for how enhancing a firm’s internal core capabilities can effectively respond to external pressures, ultimately leading to successful environmental innovation. By emphasizing the importance of dynamic capabilities in addressing external environmental challenges, our study is among the first to bridge this gap in the corporate social responsibility literature with a focus on environmental issues [9,40]. We demonstrate that significant achievements in environmental innovation are possible when firms proactively cultivate dynamic capabilities related to their core operations. We contend that firms possessing dynamic capabilities, especially those adept at continuous learning and adaptation, can proactively confront external pressures and significantly advance their environmental innovations. Moreover, by examining each component of dynamic capabilities through the lens of exploration and exploitation from an organizational learning perspective [54], our study also extends the theoretical understanding of dynamic capabilities within the framework of organizational learning theory.

Our contribution also lies especially in empirically elucidating the mechanisms through which dynamic capabilities enhance environmental innovation. By employing PLS-SEM analysis on a robust dataset from Korean manufacturing firms, we provide quantitative evidence that illustrates the interplay between a firm’s internal capabilities and external environmental factors. This study confirms that sensing capabilities are integral for systematically identifying and interpreting environmental signals, forming the foundation for informed strategic decision-making [35,51,53,55,56]. This capability allows firms to align their innovation strategies with external demands, thereby contributing indirectly to environmental innovation. Seizing capabilities empower firms to capture and exploit identified opportunities through strategic actions and investments. This involves channeling resources towards innovative projects that align with environmental goals and pursuing collaborations that amplify a firm’s capacity to innovate sustainably [51,52,58,59]. Furthermore, reconfiguring capabilities are essential for facilitating the internal adjustments necessary to sustain environmental innovation [51,53,64,65]. These capabilities ensure that firms remain agile and resilient, capable of realigning their resources, processes, and structures in response to both anticipated and unforeseen environmental challenges. This adaptability is crucial for transforming firms from economic entities focused solely on profit into social systems responsible for addressing societal issues, including environmental challenges.

From a managerial perspective, our study offers substantial insights into the strategic management of dynamic capabilities. It is imperative for managers to adopt a holistic approach that integrates dynamic capabilities into the firm’s strategic framework, ensuring that environmental objectives are central to corporate strategy. Managers should foster a culture of continuous learning and adaptability, honing sensing capabilities to keep pace with evolving external trends and stakeholder expectations. Additionally, seizing capabilities should be leveraged not only to meet immediate environmental challenges but also to explore new markets and technologies that promise long-term sustainability. Reconfiguring capabilities, meanwhile, are vital for maintaining internal coherence and alignment, enabling firms to innovate sustainably and maintain a competitive edge in an increasingly eco-conscious market.

Our findings highlight the strategic advantage firms gain by transforming environmental challenges into growth opportunities [35,84]. By effectively managing their dynamic capabilities, firms can exceed stakeholder expectations and position themselves as leaders in sustainable innovation [58]. This proactive approach aligns with a broader societal shift towards sustainability, recognizing firms as key contributors to global environmental solutions.

6. Conclusions

In conclusion, this study enhances our understanding of the critical role dynamic capabilities play in fostering environmental innovation. By providing empirical insights into how these capabilities can be strategically managed, we offer a roadmap for managers seeking to align business strategies with environmental objectives. Firms that adeptly manage their dynamic capabilities will be well-positioned to lead in sustainable innovation, setting benchmarks for others to follow [85].

Despite its contributions, this study also has limitations. First, the focus on Korean manufacturing firms may limit the generalizability of the findings to other geographical or industrial contexts. Future research could enhance the applicability of these findings by incorporating a more diverse sample that includes firms from different countries and sectors. Such studies would be instrumental in examining the universality of dynamic capabilities in fostering environmental innovation across varied environmental and regulatory landscapes. Second, while our study concentrated on dynamic capabilities, future research should explore the interplay of other organizational factors such as culture, leadership, and employee engagement, which may also significantly influence environmental innovation [26]. Understanding how these elements interact with dynamic capabilities could provide a more comprehensive view of the internal mechanisms driving sustainable innovation. Third, the use of survey-based data introduces potential biases stemming from respondent characteristics, item design, and contextual influences, as well as artificial covariances among survey items. These factors may contribute to common method bias, particularly when subjective responses are involved [86]. While previous studies have validated KIS as a reliable data source, future research should adopt more robust methodologies or incorporate datasets that minimize these biases. Fourth, the study acknowledges that relying on data from 2010 may limit the relevance of its findings in addressing current trends and challenges in environmental innovation. At the time of initiating this research, the 2010 survey represented the most recent dataset that included questions related to environmental innovation, as no similar surveys had been conducted in the preceding decade. Future research should validate these insights using more recent data, ensuring greater applicability to today’s dynamic environmental and regulatory contexts, and addressing issues of timeliness in light of evolving trends. Fifth, the study used PLS-SEM for the analysis, which is suitable for complex models with formative and reflective constructs and multiple mediation effects. However, PLS-SEM has been criticized for biased parameter estimates, ignoring measurement errors, and lacking proper tools to assess model fit. To address these issues, future studies should use statistical programs (e.g., R) that support both PLS-SEM and CB-SEM. This combined approach can improve estimation accuracy and strengthen the reliability of the results [87].

Ultimately, firms that strategically cultivate dynamic capabilities can transform sustainability from a regulatory obligation into a source of competitive advantage, ensuring long-term success in a rapidly evolving global market. Our research offers firm policymakers valuable insights, enabling them to develop effective strategies for responding to environmental challenges and advancing environmental innovation.