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Article

Does ESG Performance Drive Firm-Level Innovation? Evidence from South Korea

by
Hyunah Lee
School of Business, Gachon University, 1342 Seongnam daero, Sujeong-gu, Seong-nam-si 13120, Gyeonggi-do, Republic of Korea
Sustainability 2025, 17(4), 1727; https://doi.org/10.3390/su17041727
Submission received: 10 January 2025 / Revised: 15 February 2025 / Accepted: 18 February 2025 / Published: 19 February 2025
(This article belongs to the Special Issue Innovation Management, Competition Strategies and Corporate Sustainability)
Versions Notes

Abstract

:
This study investigates the relationship between environmental, social, and governance (ESG) performance and firm-level innovation, with a particular focus on how this relationship evolves in response to external shocks such as the COVID-19 pandemic. Using intellectual capital as a measure of innovation, the study employs Tobit regression analysis on a panel dataset of South Korean listed firms from 2013 to 2023. The findings reveal a positive association between ESG performance and firm-level innovation, which becomes statistically significant only in the post-pandemic period. A detailed examination of ESG sub-dimensions shows that environmental and social performance are positively associated with firm-level innovation exclusively in the post-pandemic period, while governance performance maintains a consistently positive relationship with innovation across both periods, becoming more pronounced after the pandemic. These findings suggest that ESG practices foster firm-level innovation and highlight the shifting dynamics of this relationship during crises such as the COVID-19 pandemic, when stakeholder engagement and networks become crucial for organizational resilience. This study provides valuable insights for various stakeholders including managers, investors, and policymakers, emphasizing the importance of integrating ESG considerations into corporate strategies to enhance innovation capacity and long-term competitiveness.

1. Introduction

In light of increasing environmental risks and intensifying business competition, adopting sustainable business practices and fostering innovation have become essential strategies for corporate survival. This growing emphasis on sustainability and innovation has garnered significant attention from academics and practitioners, particularly regarding the role of environmental, social, and governance (ESG) practices in driving innovation. Understanding the relationship between ESG practices and innovation is crucial, as it demonstrates how companies can achieve sustainability goals while simultaneously gaining a competitive advantage, which is critical for their survival. Despite its importance, the relationship between ESG practices and innovation remains unclear, with conflicting theoretical perspectives and mixed empirical evidence that varies across methodological approaches [1]. To address this ambiguity, the study aims to clarify the relationship between ESG practices and firm-level innovation by validating conflicting theoretical perspectives and employing a sophisticated methodology that includes a novel measure of innovation.
Conflicting perspectives on the relationship between ESG and innovation are evident in the literature. Stakeholder theory suggests that firms engage in responsible business practices to build trust and strengthen relationships with diverse stakeholders [2]. Social network theory complements this perspective by emphasizing that stakeholder relationships enable firms to develop networks that facilitate the exchange of ideas and foster innovative solutions [3]. Together, these theories suggest a positive relationship between ESG practices and innovation, driven by enhanced stakeholder engagement and dynamic networks [4]. Conversely, agency theory presents a contrasting view, arguing that ESG practices may serve as a facade for opportunistic managerial behavior [5]. Such behavior may lead to the misallocation of resources, diverting funds from R&D and other innovative pursuits, potentially resulting in a negative relationship between ESG and innovation [6]. To assess these conflicting theoretical perspectives, this study examines not only the overall relationship between ESG practices and firm-level innovation, but also how this relationship shifted in the context of the COVID-19 pandemic.
The COVID-19 pandemic created unprecedented conditions that compelled firms to engage more deeply with stakeholders to address economic and social challenges [7,8]. This unique context reinforced the importance of stakeholder relationships and networks in fostering resilience and sustainability [9]. If stakeholder and social network theories effectively explain the relationship between ESG practices and innovation, it is likely that this relationship was strengthened during the pandemic, as the crisis intensified stakeholder engagement and network interactions. Thus, the pandemic provides a valuable opportunity to assess whether these theories provide more compelling explanations than agency theory.
Furthermore, this study extends previous research by incorporating the concept of intellectual capital (IC), thereby broadening the scope of firm-level innovation. Innovation is generally understood as the creation of new knowledge or technology to increase sales or business value [10]. For instance, the US Advisory Committee on Measuring Innovation defines it as “the design, invention, development, and/or implementation of new or altered products, services, processes, systems, organizational structures, or business models for creating new value for customers and financial returns for the firm”.
However, traditional measures such as R&D expenditures or patent data often fail to capture the full range of intangible factors that drive firm performance but may not result in patents [11,12]. To address these limitations, this study adopts IC as an alternative measure of innovation. Defined as the total stock of intangible assets and capabilities that provide competitive advantage, IC emphasizes intangible factors that generate financial returns [13]. This approach shifts the understanding of ESG-driven innovation from emphasizing traditional, tangible outcomes to focusing on capability-based intangible assets, providing a more comprehensive perspective on how ESG practices contribute to sustainable competitive advantage.
This study measures IC using the Calculated Intangible Value (CIV) method developed by Stewart [14]. As the IC data derived from the CIV method are censored at zero, a Tobit regression model is employed for the analysis. The Tobit model overcomes the limitations of ordinary least squares regression in analyzing censored data, providing more precise and reliable results [15,16]. To increase the robustness of the analysis, both pooled and random effects Tobit models are applied. This comparative approach strengthens the validity of the results by addressing potential biases arising from unobserved firm-specific factors.
The analysis is based on a panel dataset of 8478 firm-year observations from South Korean listed companies between 2013 and 2023. According to the Global Innovation Index (GII), published annually by the World Intellectual Property Organization (WIPO), South Korea ranked sixth globally in 2024 with a score of 60.9, marking its fifth consecutive year in the top 10 [17]. This ranking is based on the assessment of 133 economies using 78 indicators across seven categories, including human capital, research, infrastructure, and knowledge and technology output. Additionally, with ESG disclosure requirements set for implementation in 2026, South Korean companies are increasingly prioritizing sustainability initiatives to enhance stakeholder trust [18]. Given its high level of innovation performance and strong emphasis on sustainable business practices, South Korea provides an ideal context for studying ESG and innovation. Building on this landscape, this study aims to provide valuable insights into the relationship between ESG and innovation. Its findings will have meaningful implications for various stakeholders, including managers, investors, and policymakers.
This study makes several contributions to the literature on ESG practices and innovation. First, this study addresses the ambiguity in the ESG–innovation relationship by empirically testing conflicting theories in the context of the COVID-19 pandemic. The findings support stakeholder and social network theories, demonstrating that the positive relationship between ESG and innovation is strengthened during crises such as the pandemic, when stakeholder engagement and networks become critical for organizational survival. Second, the study introduces IC as a novel and comprehensive measure of innovation that captures the intangible factors essential to sustainable competitive advantage. This approach broadens the concept of innovation to include knowledge assets and capabilities, providing a deeper understanding of how ESG practices drive innovation. Third, it advances the methodology by employing a Tobit regression model tailored to the censored nature of IC data, ensuring more precise and reliable results. Using both pooled and random effects Tobit models further enhances the robustness and validity of the results. Finally, by analyzing a panel dataset of South Korean listed companies, this study offers valuable context-specific insights. South Korea’s strong focus on ESG practices and its innovation-driven economy provide an exemplary context for examining the relationship between ESG and innovation.
The remainder of this paper is organized as follows. Section 2 reviews the relevant literature and proposes hypotheses. Section 3 describes the data and methodology used to test these hypotheses. Section 4 presents the results of the analysis, and Section 5 discusses the theoretical and practical implications of the findings. Finally, Section 6 concludes the study.

2. Literature Review and Hypothesis Development

2.1. Literature on ESG

The concept of ESG gained prominence after the 2004 report ‘Who Cares Wins’, which was published by 20 financial institutions at the request of United Nations Secretary-General Kofi Annan [19]. The report emphasized the role that effective management of ESG issues could play in creating long-term value for shareholders, which is crucial to a firm’s future competitiveness and financial performance. While corporate social responsibility (CSR) represents a sustainability framework adopted by companies, ESG provides a quantifiable approach to assessing a firm’s sustainability, as increasingly demanded by investors and other stakeholders. The terms ‘ESG’ and ‘CSR’ are often used interchangeably when discussing sustainable business strategies [20].
Studies have typically approached ESG or CSR through two primary theoretical frameworks: agency theory and stakeholder theory. Agency theory, introduced by Meckling and Jensen [21], posits that managers may occasionally act in their own self-interest, rather than prioritize the interests of shareholders, because of the inherent conflicts of interest between managers and shareholders. From this perspective, managers may strategically engage in ESG or CSR activities to mask their opportunistic or self-serving behavior because these activities enhance their image and reputation among stakeholders [22,23]. This behavior can come at the expense of shareholder value or lead shareholders to make suboptimal investment decisions [24,25,26]. Therefore, ESG or CSR initiatives that do not align with shareholder interests can ultimately reduce firm value. Several studies support this perspective by providing evidence that ESG/CSR has a negative impact on firm performance and profitability [27,28,29].
In contrast, stakeholder theory, proposed by Freeman [2], suggests that firms have responsibilities not only to shareholders but also to a broader range of stakeholders who can influence the outcomes of the firm [30]. Companies benefit from engaging in socially responsible activities by fostering long-term relationships with stakeholders and ensuring continuous support [31,32]. Therefore, firms that integrate ESG or CSR into their business strategies may secure a competitive advantage and drive long-term value creation [33,34]. From this perspective, firms committed to ESG/CSR activities are likely to achieve superior financial performance [35]. Several studies support this view by demonstrating a positive relationship between ESG/CSR and financial performance or profitability [36,37,38,39].
Li et al. [40] provide a systematic literature review of studies on the relationship between ESG and financial performance, showing that findings in this area are often conflicting. Some studies suggest that this relationship may be mediated by factors such as innovation, emphasizing the need for a more in-depth examination of a firm’s innovation capacity, as it may play a crucial role in shaping this relationship [41,42,43,44].

2.2. Literature on Innovation

Innovation is a multifaceted concept that broadly encompasses the implementation of new or significantly improved products (goods or services), processes, marketing methods, or organizational practices, including workplace organization and external relations. In today’s rapidly evolving markets, innovation is crucial for firms to maintain their competitiveness and sustainability [45]. As firms increasingly recognize the significance of innovation for survival and growth, effectively managing it becomes a central challenge [46]. While measuring a company’s innovation is fundamental for enhancing its value and informing decision-making processes, assessing firm-level innovation remains complex and challenging [47,48].
The literature on innovation has traditionally emphasized the link between R&D investment and innovation, leading to the use of R&D expenditures or patents as primary innovation indicators [49,50]. However, several studies have questioned the direct link between R&D investment and innovation output, suggesting that this relationship is not straightforward, and may even be negative [51,52]. Zambon and Monciardini [12] argued that this simplistic approach fails to account for essential complementary resources such as marketing, organizational, and human resources, which can render R&D efforts merely a cost without such support.
Patent data also have limitations, although they provide a quantifiable metric [53]. Patents primarily capture formalized and documented innovations, neglecting crucial informal innovation processes, such as learning-by-doing [54,55]. Not all innovations are patentable, and these measures exclude trial-and-error learning, which contributes significantly to a firm’s knowledge base and drives change [56,57]. Patent analysis often prioritizes quantity over quality, focusing on the number of patents rather than their impact or commercial success [58]. This approach overlooks the inherent diversity and complexity of innovation processes within firms, leading to an incomplete understanding of innovation activities. Given these limitations, relying on R&D expenditures or patent data can obscure the diverse and complex nature of firms’ innovation processes. These shortcomings highlight the need for a broader and more evolutionary framework that captures the full range of innovation activities [59,60].
Intellectual capital (IC) theory, pioneered by Edvinson and Malone [61], Stewart [14], and Sveiby [62], provides a comprehensive framework for understanding innovation by emphasizing intangible assets that drive long-term corporate success. IC refers to intangible resources, including knowledge assets, organizational structures, and human capital, all of which are crucial for shaping a firm’s innovation performance [63,64,65]. It serves as the primary input in a company’s knowledge production process, enabling firms to enhance performance when combined with other resources [66]. Additionally, it captures both exploitative innovation (improving existing knowledge) and exploratory innovation (generating new knowledge), providing a more comprehensive picture of a firm’s innovation efforts [67,68].
Social network theory provides a complementary framework for understanding innovation, emphasizing knowledge exchange and collaboration among institutional and social actors [3]. It posits that innovation emerges from dynamic interactions among firms and various actors, with a pivotal role assigned to the knowledge held by each network member [69,70]. The strength and frequency of these interactions foster innovation by facilitating the exchange of ideas, technologies, and processes [71]. Networks enable firms to access diverse perspectives, resources, and expertise, thereby enhancing innovation potential [72]. By integrating IC and social network theories, this study examines firm-level innovation, moving beyond traditional metrics to offer a comprehensive view of innovation processes.

2.3. Literature on ESG and Innovation

Sustainable business practices, including ESG and CSR, are often considered to enhance a firm’s capacity for innovation [45]. However, evidence from prior studies is mixed, leaving the relationship between sustainable business practices and innovation a subject of ongoing debate [1,73].
Several studies report a positive relationship between ESG/CSR initiatives and innovation. For example, McWilliams and Siegel [44] provided empirical evidence of a positive correlation between CSR and R&D investment, suggesting that firms actively engaged in CSR are also more likely to invest in complementary strategic R&D activities. Luo and Du [74] found that firms with greater CSR engagement tend to be more innovative and launch more new products. They argued that CSR enables firms to build broader and deeper relationships with stakeholders, facilitating the exchange of external knowledge, which drives innovation. This aligns with the knowledge-based view, which asserts that a firm’s innovation capability is driven by its knowledge assets, with external knowledge playing a crucial role [75]. In the context of Chinese listed firms, Tang [76] found that ESG performance enhances both the quantity and quality of corporate innovation, attributing this to ESG practices that send positive signals to the capital market, thereby reducing financial constraints and providing R&D support. Similarly, Chen et al. [77] observed that ESG disclosures enhances technological innovation capabilities, as evidenced by an increase in patent applications. In a study of Spanish firms, Caballero-Cerviño and Mendi [4] found that ESG-driven firms demonstrate superior future innovation performance in terms of labor productivity, exports, and survival compared to non-ESG firms.
Conversely, other studies have questioned the positive relationship between ESG/CSR and innovation, arguing that this relationship is not straightforward and varies across contexts. Broadstock et al. [1] demonstrated a nonlinear relationship between ESG and innovation capacity, suggesting that while ESG activities can initially stimulate innovation, beyond a certain threshold, further adoption of ESG practices become counterproductive, potentially hindering process innovation. Bocquet et al. [41] examined the impact of different types of CSR on innovation and found that reactive CSR practices negatively impact product or process innovation and create barriers to innovation, whereas firms with strategic CSR practices are more likely to innovate. Costa et al. [42] analyzed the effects of CSR on different types of innovation and their impact on export performance, revealing that CSR practices enhance the impact of exploratory innovation, which involves developing new knowledge, on export performance. However, these practices can have a detrimental effect on exploitative innovation, which focuses on refining existing knowledge. Cohen et al. [78] highlighted a paradox within the energy sector: oil-, gas-, and energy-producing firms, which often have lower ESG scores and are typically excluded from ESG-focused investment funds, are nonetheless significant contributors to green innovation in the United States. Given these mixed empirical findings, this study aims to investigate the relationship between ESG practices and firm-level innovation.

2.4. Hypothesis Development

Theoretical frameworks offer contrasting predictions about the relationship between ESG practices and innovation. Agency theory suggests that managers may engage in ESG activities primarily to enhance their personal reputations or mask opportunistic behaviors. This could result in prioritizing image-enhancing actions over genuine innovation [22,24]. Such behavior could lead to resource misallocation, diverting funds and attention away from R&D and other innovative pursuits [6,23]. Consequently, focusing on ESG could hinder a firm’s ability to effectively pursue and achieve innovation, leading to a negative relationship between ESG and firm-level innovation.
Conversely, stakeholder theory anticipates a positive relationship between ESG and innovation. This theory suggests that firms should engage with a broad range of stakeholders, whose support is crucial for long-term success [2]. ESG initiatives promote active engagement with diverse groups, building trust and strengthening relationships. Enhanced trust and collaboration can facilitate greater information sharing and cooperative efforts, which are essential for fostering innovation [79,80,81].
Social network theory further elucidates how stakeholder interactions drive innovation [3]. Stakeholders can be represented as nodes within a social network, and the strong connections between these diverse stakeholder groups, facilitated by ESG practices, create channels for knowledge exchange. The dynamic exchange of diverse perspectives and expertise through these channels can stimulate new ideas and collaborative innovation efforts [74]. Therefore, ESG practices can lead to innovation by creating a dense network with strong connections between the firm and various stakeholders, thereby facilitating the flow of knowledge and ideas.
Based on these conflicting theories and empirical evidence in the existing literature, the relationship between ESG practices and innovation remains a subject of ongoing debate. Agency theory argues that ESG practices hinder innovation due to resource misallocation, while stakeholder and social network theories suggest that ESG practices enhance trust, cooperation, and knowledge sharing, thereby fostering innovation. These conflicting perspectives underscore the need for further research to clarify this relationship. To empirically investigate the relationship between ESG practices and firm-level innovation, this study proposes the following null hypothesis without presupposing the direction of the relationship:
Hypothesis 1:
ESG practices have no significant relationship with firm-level innovation.
Crises often present both threats and opportunities for organizations, driving them to learn, adapt, and innovate to mitigate risks and manage disruptions [82]. The COVID-19 pandemic is a particularly notable crisis that disrupted economic systems, threatened organizational survival, and exposed the vulnerabilities of various stakeholders. Unlike the gradual evolution of global ESG trends, the pandemic represents a profound external shock that accelerated certain organizational practices and reshaped stakeholder priorities. This context provides a unique opportunity to examine how ESG practices relate to innovation during crises.
While the importance of ESG practices has grown steadily over the past decade—particularly since the United Nations adopted the Sustainable Development Goals (SDGs) in 2015—the COVID-19 pandemic introduced an unprecedented urgency for organizations to engage more deeply with stakeholders. The crisis profoundly affected various stakeholder groups: employees faced job losses; customers encountered health risks when accessing services such as restaurants and gyms; suppliers experienced operational disruptions; and government agencies implemented new policies and regulations [9]. This environment underscored the critical need for firms to actively engage with diverse stakeholders, whose support is vital for resilience and long-term sustainability [8].
From a theoretical perspective, stakeholder and social network theories provide a rationale for how ESG practices can foster innovation during crises. These theories posit a positive relationship between ESG practices and innovation, emphasizing the role of stakeholder engagement and networks in facilitating the efficient flow of information, resource sharing, and collaborative problem-solving to foster innovation. Accordingly, if these theories accurately capture the relationship between ESG and innovation, it is likely that this relationship was strengthened during the COVID-19 pandemic, as the crisis intensified stakeholder engagement and network interactions.
In contrast, agency theory presents a different view, suggesting that ESG practices may hinder innovation due to resource misallocation or managerial opportunism [22,23]. From this perspective, the relationship between ESG practices and innovation is unlikely to differ significantly between the pre- and post-COVID periods, as inefficiencies such as resource misallocation and opportunistic behavior persist regardless of external conditions. Given these contrasting theoretical perspectives, this study formulates the following null hypothesis to empirically test whether and how the relationship between ESG practices and firm-level innovation has shifted in the context of the COVID-19 pandemic.
Hypothesis 2:
The relationship between ESG practices and firm-level innovation does not differ significantly between the pre-COVID and post-COVID periods.

3. Data and Methodology

3.1. Data

In this study, financial data were sourced from the FnGuide and TS2000 databases, while ESG ratings were obtained from the Korea Institute of Corporate Governance and Sustainability (KCGS). The initial dataset comprised firm-year observations of companies listed on the Korean Stock Exchange (KOSPI and KOSDAQ) from 2013 to 2023, totaling 20,557 observations.
To ensure the reliability and relevance of the dataset, several exclusion criteria were applied. First, firms without ESG ratings from KCGS were removed, resulting in the exclusion of 11,764 observations. Since KCGS ESG ratings serve as a key indicator of ESG performance, firms without these ratings were excluded to maintain consistency in measuring ESG practices. Second, firms lacking the financial data required for variable construction were excluded, leading to the removal of 63 observations. Financial data are essential for generating key variables, including firm-level innovation, and missing values could compromise the completeness of the dataset and the robustness of the statistical analysis. Third, firms with impaired capital were excluded, resulting in the removal of 252 observations. These firms are often in financial distress, which can lead to irregular financial reporting and abnormal business conditions that may distort the analysis. Excluding these firms helps maintain the robustness and reliability of the study by focusing on firms with stable financial conditions. After applying these exclusions, the final dataset comprised 8478 firm-year observations. The summary of the data selection process is presented in Table 1.

3.2. Measurement of Variables

In measuring firm-level innovation, IC provides a more comprehensive and dynamic metric than traditional measures such as R&D expenditures or patent counts. IC encompasses the total stock of intangible assets and capabilities within a company that can generate value and provide competitive advantage [13]. This metric is particularly useful for assessing innovation because it aligns with social network theory by capturing the knowledge assets, capabilities, and dynamic interactions that drive innovation. To measure IC, the study utilizes the CIV method developed by Stewart [14]. The fundamental assumption of the CIV method is that an investment in physical capital yields only the average returns prevailing in the industry, and any returns above this average can be attributed to the application of IC. In other words, the portion of a company’s profits that exceeds the industry average can be credited to its IC.
The process of measuring a firm’s IC using the CIV model comprises seven steps. The initial step is to calculate the firm’s average pre-tax earnings over the past three years. Second, the average year-end tangible assets of the firm for the same period must be determined. Third, the return on assets (ROA) is calculated by dividing earnings by assets from the preceding two steps. Subsequently, the average ROA for the sub-industry in which the firm operates over the same period must be ascertained. In the fifth step, the excess return is calculated by multiplying the sub-industry’s average ROA by the company’s average tangible assets. This indicates that the company performs above the industry average, suggesting the presence of excess earnings power. If the calculated value is less than zero, it is treated as zero. Subsequently, the three-year average income tax rate should be calculated, multiplied by the excess return, and subtracted from the excess return to obtain the after-tax figure, which represents the premium attributable to intangible assets. Ultimately, the present value of this premium must be calculated by dividing it by an appropriate discount rate, such as the company’s cost of capital. These steps allow for the quantification of a firm’s IC, reflecting its capacity to leverage intangible assets for competitive advantage and innovation. As the CIV model provides an absolute value that does not account for the firm’s size, this study standardizes the measure by dividing the absolute value by the firm’s total assets [83]. This relative CIV per book value allows for comparison between companies of different sizes [84].
This study utilizes ESG ratings from the KCGS. The KCGS, an independent ESG rating agency in Korea, began providing corporate governance ratings in 2003 and expanded its scope to include ESG ratings in 2011. The ESG rating models developed by the KCGS are aligned with international standards, such as the OECD Principles of Corporate Governance and ISO 26000 [85], while also reflecting Korea’s unique legal and management circumstances. The data for these ratings are collected from disclosed and publicly available sources, and assessments are conducted across environmental, social, and governance dimensions using a customized and proprietary system.
The KCGS assigns ratings for the individual environmental, social, and governance areas, as well as an integrated ESG rating. The ratings range from S to D, with seven levels in total. Scores are based on absolute assessments and ratings are assigned according to specific scoring criteria. These ratings provide a comprehensive evaluation of a firm’s commitment to sustainable and responsible environmental, social, and governance practices.

3.3. Research Model

This study uses the Tobit regression model introduced by Tobin [86] to analyze the relationship between ESG and innovation. The Tobit model is a censored regression approach well-suited to data where the dependent variable has values censored at a specific point, frequently zero [87]. The Tobit model is expressed as follows:
y* = Xβ + ε, with ε~N (0, σ2)
y = y* if y* ≥ 0, y = 0 if y* < 0
A key limitation of the Tobit model is that it assumes the underlying latent variable follows a single normal distribution, whereas the observed outcome is subject to censoring due to the detection limit [88,89]. Non-normality in the latent variable can significantly affect the reliability of Tobit estimators [90]. Despite this limitation, the Tobit regression model is appropriate for this study because innovation, measured using IC values calculated through the CIV model, follows a normal distribution but is censored at zero. According to the CIV model, any excess return below zero, indicating returns beneath the industry average, is set to zero because negative CIVs are considered meaningless. Thus, employing the Tobit regression model enables us to manage the censored nature of our innovation data and ensures an accurate estimation of the relationship between ESG practices and firm-level innovation.
This study employs two Tobit model specifications: pooled and random effects. The pooled Tobit model aggregates data across different time periods or cross sections without accounting for individual-specific effects. This model assumes that all observations are independent and identically distributed with no unobserved firm-specific characteristics influencing the dependent variable. While this approach is simple to estimate and interpret, it may underestimate standard errors if unobserved firm-specific characteristics influence innovation, potentially leading to misleading conclusions regarding the significance of the relationships [91]. The RE Tobit model incorporates random effects to account for individual-specific heterogeneities [15,92]. The inclusion of random effects allows the model to account for unobserved firm-specific characteristics that may influence the dependent variable (innovation) alongside the observed factors (ESG practices). This approach can provide more reliable estimates of the relationship between ESG practices and innovation, particularly when significant unobserved differences exist between firms.
By utilizing and comparing both pooled and RE Tobit models, this study assesses whether unobserved characteristics influence the relationship between ESG practices and firm-level innovation, and determines which model is more appropriate for the dataset. A fixed effects model is not employed because applying fixed effects methods to nonlinear models such as Tobit often leads to inconsistent estimates due to the incidental parameter problem [15,91]. The Tobit regression model used in this study is as follows:
INNt = β0 + β1 ESGt (ENVt or SOCt or GOVt) + β2 SIZEt + β3 ASLACKt + β4 PSLACKt + β5 OCFt + β6 RND + β7 AGEt + β8 MRKt + β9 ∑YR + β10 ∑IND + εt
where INN = firm-level innovation measured by IC values calculated by the CIV model, standardized by division by total assets; ESG = overall ESG rating, assigned a number from 1 to 7, with D being the lowest and S the highest; ENV = environmental rating, assigned a number from 1 to 7; SOC = social rating, assigned a number from 1 to 7; GOV = governance rating, assigned a number from 1 to 7; SIZE = natural logarithm of total assets; ASLACK = available slack, measured by the ratio of net income to total assets; PSLACK = potential slack, measured by the debt-equity ratio; OCF = operating cash flows divided by total assets; RND = R&D expenditures divided by total assets; AGE = natural logarithm of firm age; MRK = 1 if a firm is listed on KOSPI, 0 otherwise; YR = year indicators; and IND = industry indicators.
The main independent variable in this study is the overall ESG rating (ESG), with separate analyzes conducted for environmental (ENV), social (SOC), and governance (GOV) ratings. To analyze the relationship between ESG and firm-level innovation, this study incorporates several control variables to account for factors that might independently influence innovation. Firm size (SIZE) is included because larger firms often have more resources to invest in R&D and innovation activities [58], whereas smaller firms may be more agile and adaptable, allowing them to innovate more rapidly in response to market changes. Available slack (ASLACK) denotes organizational slack that proxies the availability and flexibility of internal resources for innovation [93,94]. Potential slack (PSLACK) or the capability to obtain external resources is also considered influential in determining the degree of innovation [93,95]. ASLACK and PSLACK are measured as the ratio of net income to total assets and the debt-equity ratio, respectively, following Chiu and Liaw [93] and Sung [53]. Operating cash flow (OCF) is included because higher cash flows provide the financial resources necessary to fund innovation activities [96]. R&D expenditures (RND) control for the resources allocated towards innovation, such as developing new products, services, or processes [97]. The age of the firm (AGE) is another control variable, as younger firms may be more innovative owing to their flexibility and willingness to take risks, while older firms may have fewer resource constraints in terms of assets and their ability to borrow [58]. We also include the types of securities markets (MRK) because different listing requirements might attract firms with different innovation strategies and capabilities. Year (YR) and industry (IND) dummies are added to control for unobserved factors that may influence innovation and vary by year (e.g., economic conditions) or industry (e.g., technological advancements).
To test Hypothesis 2, Model (2) is applied separately to data from the pre-COVID-19 period (2013–2019) and post-COVID-19 period (2020–2023). By comparing the significance of the ESG coefficients across these two periods, this study investigates whether the relationship between ESG practices and firm-level innovation has shifted during the pandemic.

4. Results of the Analysis

4.1. Descriptive Statistics

Table 2 presents the descriptive statistics for the variables employed in the analysis. The firm-level innovation (INN) exhibits a mean of 0.539 and a median of 0.292, with values ranging from a minimum of 0 (due to censoring) to a maximum of 3.344. The overall ESG rating (ESG) displays a mean of 2.816 and a median of 3.000, with a range from 1 (lowest rating, D) to 6 (highest rating, A+). The environmental rating (ENV) shows a mean of 2.584 and a median of 3.000, also ranging from 1 to 6. Similarly, the social rating (SOC) has a mean of 3.021 and a median of 3.000, while the governance rating (GOV) reflects a mean of 2.964 and a median of 3.000, both with identical ranges from 1 to 6. Thus, ESG ratings (ESG) and their subcomponents (ENV, SOC, GOV) exhibit a relatively balanced distribution, centering around a median of 3.000.
For the control variables, the mean (median) values of firm size (SIZE), available slack (ASLCK), and potential slack (PSLCK) are 20.006 (19.776), 0.022 (0.026), and 0.842 (0.605), respectively. The mean (median) values of operating cash flows (OCF), R&D expenditures (RND), and firm age (AGE) are 0.046 (0.043), 0.014 (0.003), and 3.450 (3.689), respectively. Finally, the binary variable indicating whether a company is listed on the KOSPI (MRK) has a mean (median) of 0.828 (1.000), indicating that approximately 82.8% of the sample consists of companies listed on the KOSPI, with the remainder listed on the KOSDAQ.

4.2. Correlation Analysis

Table 3 presents the results of Pearson’s correlation analysis for all variables in the study. Firm-level innovation (INN) shows a positive correlation with the overall ESG rating (ESG), governance rating (GOV), and social rating (SOC), while no significant correlation is observed with the environmental rating (ENV). The overall ESG rating (ESG) is highly correlated with the environmental (ENV), social (SOC), and governance (GOV) ratings, as it is a composite measure of these three sub-dimensions. To mitigate multicollinearity, the study separately incorporates the ratings of these ESG sub-dimensions in the regression model.
Among the control variables, firm-level innovation (INN) is positively correlated with firm size (SIZE), available slack (ASLACK), operating cash flows (OCF), and R&D expenditures (RND). Conversely, it exhibits a negative correlation with potential slack (PSLACK), firm age (AGE), and the binary variable indicating whether a company is listed on the KOSPI (MRK).
The results in Table 3 indicate that firm-level innovation is positively associated with ESG performance, particularly in the governance and social dimensions, while no significant correlation is found with the environmental dimension. However, since these correlations do not account for control variables, they offer only a preliminary insight into the relationship between ESG and innovation. This analysis serves as a basis for the subsequent regression analysis, which will provide a more comprehensive assessment of these relationships.

4.3. Regression Analysis

Table 4 presents the results of both the pooled and the RE Tobit models, which examine the relationship between overall ESG ratings and firm-level innovation. In the pooled Tobit model, the coefficient of ESG is positive and significant at the 1% level, indicating a significant association between overall ESG ratings and the level of firm innovation (INN). Similarly, in the RE Tobit model, the coefficient of ESG remains positive and significant at the 5% level, further confirming the positive relationship between overall ESG ratings and firm-level innovation.
The log-likelihood values for the pooled and RE Tobit models assess the models’ goodness of fit, with less negative values indicating a better fit. Higher log-likelihood values for the RE Tobit model suggests a superior fit to the data compared with the pooled Tobit model. This difference in fit is further supported by a likelihood ratio (LR) test, which yields a statistic of 1356.90 with a p-value of 0.000. This result indicates that the RE Tobit model significantly improves upon the pooled Tobit model, making it the preferred model for inferring the relationship between ESG practices and firm-level innovation. The RE Tobit model results demonstrate a positive association between overall ESG ratings and firm-level innovation.
The findings in Table 4 provide empirical evidence that firms with higher ESG performance exhibit greater levels of innovation. Additionally, the results for the control variables suggest that firm size, available slack, and operating cash flows positively influence innovation, while firm age, potential slack, and KOSPI-listed firms are negatively associated with innovation. However, since overall ESG ratings do not distinguish the individual effects of the environmental, social, and governance dimensions, this study decomposes ESG into its three sub-dimensions in the subsequent analysis.
Table 5 presents the findings from both the pooled and RE Tobit models, which explore the relationship between individual ESG sub-dimension ratings and firm-level innovation. The table is divided into three panels, each focusing on a distinct ESG sub-dimension: environmental (ENV), social (SOC), and governance (GOV). This structure allows for a more detailed analysis of how each ESG component rating is independently associated with firm-level innovation.
Panel A shows that the pooled Tobit model indicates a positive but statistically insignificant ENV coefficient in relation to firm-level innovation (INN). However, in the RE Tobit model, the coefficient of ENV is positive and significant at the 10% level, suggesting a positive relationship between environmental ratings and firm-level innovation when unobserved firm-specific effects are considered. Panel B reveals that both the pooled and RE Tobit models report positive and statistically significant coefficients for SOC at the 5% level, indicating a positive association between social ratings and firm-level innovation. Panel C indicates that the pooled Tobit model finds a positive and significant coefficient for GOV at the 1% level, whereas the RE Tobit model demonstrates significance at the 5% level, implying a positive relationship between governance ratings and firm-level innovation.
Across all panels, the RE Tobit model consistently demonstrates a superior fit to the data, as indicated by the higher log-likelihood values and significant LR tests with p-values below 0.05. This implies that accounting for unobserved firm-specific effects enhances the reliability of the estimates. The results from the RE Tobit models indicate that all three ESG sub-dimensions—environmental (ENV), social (SOC), and governance (GOV)—were positively and independently associated with firm-level innovation, aligning with the findings for overall ESG performance. Therefore, firms with higher ratings in each ESG sub-dimension tend to exhibit greater levels of innovation.
Table 6 presents the results from the pooled and RE Tobit models, which analyze the relationship between overall ESG ratings and firm-level innovation during the pre- and post-COVID periods. In the pre-COVID period (Panel A), the coefficients of ESG are positive but statistically insignificant in both the pooled and RE Tobit models, indicating that before the pandemic, overall ESG ratings did not have a significant impact on firm-level innovation. By contrast, in the post-COVID period (Panel B), the coefficients of ESG are both positive and statistically significant in both models, suggesting that firms with higher ESG ratings were more likely to innovate after the onset of the pandemic.
These results indicate that the COVID-19 pandemic significantly altered the relationship between ESG practices and firm-level innovation. Before the pandemic, ESG practices did not have a substantial impact on innovation, implying that pre-pandemic ESG efforts were not necessarily translated into firms’ innovative capabilities. However, in the post-COVID period, ESG emerged as a key driver of innovation, as firms with strong ESG performance were better positioned to adapt, restructure, and develop innovative capabilities in response to economic disruptions and evolving stakeholder expectations. This shift suggests that the pandemic, as an external shock, amplified the importance of stakeholder engagement and networks, thereby strengthening the relationship between ESG and innovation. These findings align with stakeholder theory and social network theory, both of which emphasize the critical role of stakeholder relationships and networks in fostering innovation.
Table 7 presents the results from the pooled and RE Tobit models, examining the relationship between the environmental (ENV) rating within the ESG and firm-level innovation across the pre- and post-COVID periods.
In Panel A (pre-COVID period), both models reveal a negative and statistically insignificant coefficient for ENV, indicating that environmental ratings had no significant impact on firm-level innovation before the pandemic. This suggests that, in a stable economic environment, firms may not have actively leveraged environmental initiatives to drive innovation. However, in Panel B (post-COVID period), both models yield a positive and statistically significant coefficient for ENV, indicating that environmental performance positively influences firm-level innovation.
This notable change in the ESG-innovation relationship across the pre- and post-COVID periods suggests that, while environmental performance was not a significant factor in driving innovation before the pandemic, it became a critical determinant of firm-level innovation in the post-COVID era. This shift likely stems from increased corporate emphasis on environmental responsibility and heightened stakeholder expectations, which encouraged firms to integrate sustainability-driven strategies into their innovation processes. Consequently, firms with higher environmental performance appear to have fostered stronger stakeholder engagement and collaboration, thereby enhancing their innovation capabilities in the post-pandemic business landscape.
Table 8 presents the results from the pooled and RE Tobit models, analyzing the relationship between the social rating (SOC) of ESG and firm-level innovation across both the pre- and post-COVID periods. Panel A, which covers the pre-COVID period, demonstrates that the coefficients of SOC are positive but statistically insignificant in both the pooled and RE Tobit models, suggesting that social ratings did not significantly influence firm-level innovation before the pandemic. Panel B, focusing on the post-COVID period, indicates that while the pooled Tobit model generates a positive yet insignificant coefficient for SOC, the RE Tobit model reveals a positive and statistically significant coefficient at the 5% level. The LR test further confirms that the RE Tobit model is a better fit for this dataset.
These results imply that, although social performance was not significantly linked to innovation before the pandemic, it became a significant driver of firm-level innovation in the post-COVID period. This change likely reflects the increased emphasis on social responsibility for sustainability during the pandemic, which may have reinforced the link between social activities and innovation. Firms with strong social performance may have been better equipped to navigate workforce challenges, mitigate supply chain disruptions, and adapt to evolving stakeholder expectations, ultimately strengthening their capacity for innovation during times of crisis. Therefore, in the post-pandemic era, companies with higher social performance appear to have fostered greater stakeholder engagement and collaboration, thereby enhancing their innovation capabilities.
Table 9 presents the results from the pooled and RE Tobit models, analyzing the relationship between the governance rating (GOV) of ESG and firm-level innovation across both the pre- and post-COVID periods. Panel A (pre-COVID period) shows that the coefficients of GOV are positive and statistically significant at the 1% level in the pooled Tobit model and at the 10% level in the RE Tobit model, indicating that strong governance practices contributed to firm-level innovation even before the pandemic. Panel B (post-COVID period) reveals that the coefficients of GOV remain positive and significant at the 1% level in the RE Tobit model, although they are insignificant in the pooled Tobit model. The significant LR test (p-value < 0.05) confirms that the RE model is more appropriate for this dataset, further reinforcing the importance of accounting for unobserved firm-specific effects in analyzing the relationship between governance and innovation.
The findings from the RE Tobit models suggest that governance performance was significantly associated with firm-level innovation, both before and after the pandemic, with the strength of this relationship increasing in the post-COVID period. The positive relationship between governance and innovation suggests that robust governance creates an environment that is conducive to innovation by ensuring transparency and accountability. This relationship appears to have been reinforced during the COVID-19 period, likely due to the heightened importance of governance in managing the uncertainties and managing challenges posed by the external crisis.
Firms with strong governance may be more effective in allocating resources toward innovative projects, adapting to rapidly evolving market conditions, and implementing strategic changes necessary for long-term resilience. Accordingly, well-governed firms are better equipped to manage risk, maintain transparency, and strengthen stakeholder trust and engagement, all of which are critical during crises such as the pandemic and can contribute to increased innovation.

5. Discussion

The findings of this study make several significant theoretical contributions. First, they reveal that ESG practices align more closely with stakeholder theory than agency theory. The positive relationship observed between ESG practices and firm-level innovation challenges the agency theory perspective, which often views ESG initiatives as primarily driven by managerial self-interest. This finding is consistent with Luo and Du [74], who argue that socially responsible firms cultivate long-term relationships with stakeholders, thereby fostering innovation.
Second, the study shows that the positive relationship between ESG practices and innovation became significantly stronger in the post-COVID-19 period. This finding suggests that the increased importance of stakeholders and their networks during the crisis may have facilitated improved communication and collaboration, enabling companies with stronger ESG performance to innovate more effectively. These results further reinforce the theoretical underpinnings of stakeholder and social network theories, which posit that ESG practices drive innovation by strengthening stakeholder relationships and enabling the efficient flow of information and resources through networks. The results demonstrate how crises act as a catalyst, strengthening the relationship between ESG and innovation and validating the relevance of stakeholder and social network theories in explaining this relationship. This conclusion aligns with Al Amosh and Khatib [7] and Gromis di Trana et al. [8], who found that the COVID-19 pandemic strengthened stakeholder relationships and increased the importance of ESG performance.
Finally, while the use of IC represents a methodological advancement, it also contributes to the theoretical understanding of firm-level innovation by shifting the focus from traditional, tangible metrics such as patents or R&D expenditures to a broader conceptualization of innovation capacity. By incorporating intangible assets that drive financial returns, this study shows how ESG practices influence not only visible innovation outcomes, but also the development of intangible capabilities that are critical for sustained competitive advantage. This approach broadens the theoretical discourse on ESG-driven innovation by moving beyond traditional, quantifiable outcomes to emphasize intangible, capability-based advantages. This study extends the findings of Tang [76] and Chen et al. [77] by demonstrating that firms with strong ESG performance enhance innovative capabilities not captured by traditional, tangible metrics.
The findings of this study have significant practical implications for various stakeholders, including managers, investors, policymakers, and society. First, managers should recognize the importance of integrating ESG practices into their business strategies, particularly during times of crisis. By prioritizing robust ESG practices, they can cultivate and sustain strong relationships with diverse stakeholders. This enhances information exchange and collaborative efforts, both of which are essential for driving innovation and securing long-term competitive advantage. Second, investors can leverage these insights to identify companies with strong ESG practices as promising investment opportunities. Firms with superior ESG performance are more likely to exhibit higher levels of innovation, which typically translate into enhanced long-term performance. Therefore, investors may consider incorporating ESG ratings into their investment criteria to identify companies that are well-positioned for sustainable growth. Third, policymakers can develop regulations and incentives to encourage adoption and active implementation of ESG practices, thereby fostering a business environment conducive to innovation. Finally, strong ESG practices contribute to the development of robust networks of stakeholder interaction, thereby facilitating the flow of knowledge, resources, and support. This dynamic can lead to broader societal benefits, such as job creation and improved local infrastructure, ultimately fostering sustainable economic growth.

6. Conclusions

This study explores the relationship between ESG practices and firm-level innovation using a dataset of 8478 firm-year observations from South Korean listed companies spanning from 2013 to 2023. By employing IC as a measure of innovation, calculated through the CIV model, this study provides a broader perspective on firm-level innovation beyond traditional metrics such as R&D expenditures and patent counts. The use of Tobit regression models ensures a more precise estimation of the ESG-innovation relationship by appropriately handling censored IC data.
The findings reveal a positive association between ESG performance and firm-level innovation, reinforcing the notion that companies committed to sustainability can simultaneously enhance their innovative capabilities. This supports the idea that ESG initiatives contribute to long-term value creation by fostering an environment conducive to innovation. The results provide practical implications for firms seeking to integrate ESG considerations into their corporate strategies, demonstrating that sustainability efforts are not merely compliance-driven, but can also serve as a source of competitive advantage.
Additionally, this study examines how the ESG–innovation relationship has evolved over time, particularly in response to external shocks such as the COVID-19 pandemic. The comparison between pre- and post-pandemic periods reveals that the relationship has strengthened significantly since the pandemic. These results illustrate how ESG practices enhance innovation by fostering stakeholder engagement and networks, facilitating knowledge exchange, resource sharing, and collaborative problem-solving. The pandemic reinforced the importance of stakeholder relationships and networks for organizational resilience and survival, further strengthening the link between ESG and innovation.
This study contributes to the broader discourse on sustainability and innovation by demonstrating that ESG practices not only fulfill corporate social responsibility goals, but also serve as key enablers of firm-level innovation. ESG initiatives foster stakeholder collaboration and provide firms with critical resources and networks, thereby creating an environment conducive to innovation. The findings offer valuable insights for managers, investors, and policymakers, highlighting the necessity of integrating ESG strategies into corporate decision-making to enhance innovation capacity, resilience, and long-term competitiveness.
Despite its contributions, this study has several limitations that should be addressed in future research. First, while the findings provide valuable insights into the relationship between ESG performance and firm-level innovation, they are based on a sample of South Korean listed firms. As a result, the generalizability of these findings to other economies may be limited, particularly in countries with different regulatory frameworks, business environments, and cultural contexts. Given that ESG policies, stakeholder expectations, and institutional structures vary significantly across nations, the ESG–innovation relationship may manifest differently in other regions. Future research could conduct cross-country comparative analyses to assess whether the observed relationship holds across diverse institutional and economic settings.
Second, while this study establishes a positive association between ESG performance and innovation, it does not empirically investigate the mechanisms through which ESG fosters innovation. ESG practices may drive innovation through multiple channels, such as stakeholder engagement, knowledge exchange, financial incentives, and enhanced corporate reputation. Future research employs social network analysis, mediation models, or qualitative case studies to explore the pathways through which ESG influences innovation and whether these mechanisms vary across different sectors and industries.
Lastly, this study does not account for the potential bidirectional relationship between ESG performance and innovation. While the findings indicate that ESG enhances innovation, it is also possible that highly innovative firms are more likely to adopt stronger ESG practices due to their ability to allocate resources toward sustainability initiatives. To address this, future research could employ dynamic panel models, instrumental variable approaches, or causality tests to explore the reciprocal nature of the ESG–innovation relationship and mitigate potential endogeneity concerns. By addressing these limitations, future studies can provide a more comprehensive and globally relevant understanding of the ESG–innovation relationship, offering deeper insights for academics, practitioners, and policymakers.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to proprietary restrictions.

Conflicts of Interest

The author declares no conflicts of interest.

References

  1. Broadstock, D.C.; Matousek, R.; Meyer, M.; Tzeremes, N.G. Does Corporate Social Responsibility Impact Firms’ Innovation Capacity? The Indirect Link between Environmental & Social Governance Implementation and Innovation Performance. J. Bus. Res. 2020, 119, 99–110. [Google Scholar]
  2. Freeman, R.E. Stakeholder Management: A Stakeholder Approach; Pitman Publishing: Marshfield, MA, USA, 1984. [Google Scholar]
  3. Wasserman, S. Social Network Analysis: Methods and Applications; Cambridge University Press: Cambridge, UK, 1994; Volume 2, pp. 131–134. [Google Scholar]
  4. Cabaleiro-Cerviño, G.; Mendi, P. ESG-Driven Innovation Strategy and Firm Performance. Eur. Bus. Rev. 2024, 14, 137–185. [Google Scholar] [CrossRef]
  5. Dhaliwal, D.S.; Li, O.Z.; Tsang, A.; Yang, Y.G. Voluntary Nonfinancial Disclosure and the Cost of Equity Capital: The Initiation of Corporate Social Responsibility Reporting. Account. Rev. 2011, 86, 59–100. [Google Scholar] [CrossRef]
  6. Bhandari, A.; Javakhadze, D. Corporate Social Responsibility and Capital Allocation Efficiency. J. Corp. Financ. 2017, 43, 354–377. [Google Scholar] [CrossRef]
  7. Al Amosh, H.; Khatib, S.F. ESG Performance in the Time of COVID-19 Pandemic: Cross-Country Evidence. Environ. Sci. Pollut. Res. 2023, 30, 39978–39993. [Google Scholar] [CrossRef] [PubMed]
  8. Gromis di Trana, M.; Fiandrino, S.; Yahiaoui, D. Stakeholder Engagement, Flexible Proactiveness and Democratic Durability as CSR Strategic Postures to Overcome Periods of Crisis. Manag. Decis. 2022, 60, 2719–2742. [Google Scholar] [CrossRef]
  9. Gopalakrishnan, S.; Kovoor-Misra, S. Understanding the Impact of the COVID-19 Pandemic Through the Lens of Innovation. BRQ Bus. Res. Q. 2021, 24, 224–232. [Google Scholar] [CrossRef]
  10. Roper, S.; Hewitt-Dundas, N. Knowledge Stocks, Knowledge Flows and Innovation: Evidence from Matched Patents and Innovation Panel Data. Res. Policy 2015, 44, 1327–1340. [Google Scholar] [CrossRef]
  11. Beneish, M.D.; Harvey, C.R.; Tseng, A.; Vorst, P. Unpatented Innovation and Merger Synergies. Rev. Account. Stud. 2022, 27, 1–39. [Google Scholar] [CrossRef]
  12. Zambon, S.; Monciardini, D. Intellectual Capital and Innovation: A Guideline for Future Research. J. Innov. Econ. Manag. 2015, 2, 13–26. [Google Scholar] [CrossRef]
  13. Stewart, T.A. Intellectual Capital: The New Wealth of Organization; Crown Currency: New York, NY, USA, 2010. [Google Scholar]
  14. Stewart, T.A. Trying to Grasp the Intangible. Fortune Mag. 1995, 132, 157–161. [Google Scholar]
  15. Amore, M.D.; Murtinu, S. Tobit Models in Strategy Research: Critical Issues and Applications. Glob. Strategy J. 2021, 11, 331–355. [Google Scholar] [CrossRef]
  16. Austin, P.C.; Escobar, M.; Kopec, J.A. The Use of the Tobit Model for Analyzing Measures of Health Status. Qual. Life Res. 2000, 9, 901–910. [Google Scholar] [CrossRef]
  17. World Intellectual Property Organization. Global Innovation Index 2024: Unlocking the Promise of Social Entrepreneurship. World Intellect. Prop. Organ. 2024. [CrossRef]
  18. Kim, D.; Shin, D.; Lee, J.; Noh, G. Sustainability from institutionalism: Determinants of Korean companies’ ESG performances. Asian Bus. Manag. 2024, 23, 1–33. [Google Scholar] [CrossRef]
  19. Gillan, S.L.; Koch, A.; Starks, L.T. Firms and social responsibility: A review of ESG and CSR research in corporate finance. J. Corp. Financ. 2021, 66, 101889. [Google Scholar] [CrossRef]
  20. Garcia, A.S.; Mendes-Da-Silva, W.; Orsato, R.J. Sensitive industries produce better ESG performance: Evidence from emerging markets. J. Clean. Prod. 2017, 150, 135–147. [Google Scholar] [CrossRef]
  21. Jensen, M.C.; Meckling, W.H. Theory of the firm: Managerial behavior, agency costs, and ownership structure. In Corporate Governance; Gower: Farnham, UK, 2019; pp. 77–132. [Google Scholar]
  22. Almubarak, W.I.; Chebbi, K.; Ammer, M.A. Unveiling the connection among ESG, earnings management, and financial distress: Insights from an emerging market. Sustainability 2023, 15, 12348. [Google Scholar] [CrossRef]
  23. Barnea, A.; Rubin, A. Corporate social responsibility as a conflict between shareholders. J. Bus. Ethics 2010, 97, 71–86. [Google Scholar] [CrossRef]
  24. Bénabou, R.; Tirole, J. Individual and corporate social responsibility. Economica 2010, 77, 1–19. [Google Scholar] [CrossRef]
  25. Hemingway, C.A.; Maclagan, P.W. Managers’ personal values as drivers of corporate social responsibility. J. Bus. Ethics 2004, 50, 33–44. [Google Scholar] [CrossRef]
  26. Masulis, R.W.; Reza, S.W. Agency problems of corporate philanthropy. Rev. Financ. Stud. 2015, 28, 592–636. [Google Scholar] [CrossRef]
  27. Duque-Grisales, E.; Aguilera-Caracuel, J. Environmental, social and governance (ESG) scores and financial performance of multilatinas: Moderating effects of geographic international diversification and financial slack. J. Bus. Ethics 2021, 168, 315–334. [Google Scholar] [CrossRef]
  28. Garcia, A.S.; Orsato, R.J. Testing the institutional difference hypothesis: A study about environmental, social, governance, and financial performance. Bus. Strategy Environ. 2020, 29, 3261–3272. [Google Scholar] [CrossRef]
  29. Landi, G.; Sciarelli, M. Towards a more ethical market: The impact of ESG rating on corporate financial performance. Soc. Responsib. J. 2019, 15, 11–27. [Google Scholar] [CrossRef]
  30. Freeman, R.E. Strategic Management: A Stakeholder Approach; Cambridge University Press: Cambridge, UK, 2010. [Google Scholar]
  31. Donaldson, T.; Preston, L.E. The stakeholder theory of the corporation: Concepts, evidence, and implications. Acad. Manag. Rev. 1995, 20, 65–91. [Google Scholar] [CrossRef]
  32. McWilliams, A.; Siegel, D.S.; Wright, P.M. Corporate social responsibility: Strategic implications. J. Manag. Stud. 2006, 43, 1–18. [Google Scholar] [CrossRef]
  33. López, M.V.; Garcia, A.; Rodriguez, L. Sustainable development and corporate performance: A study based on the Dow Jones sustainability index. J. Bus. Ethics 2007, 75, 285–300. [Google Scholar] [CrossRef]
  34. Rojo-Suárez, J.; Alonso-Conde, A.B. Short-run and long-run effects of ESG policies on value creation and the cost of equity of firms. Econ. Anal. Policy 2023, 77, 599–616. [Google Scholar] [CrossRef]
  35. Veeravel, V.; Sadharma, E.K.S.; Kamaiah, B. Do ESG disclosures lead to superior firm performance? A method of moments panel quantile regression approach. Corp. Soc. Responsib. Environ. Manag. 2024, 31, 741–754. [Google Scholar] [CrossRef]
  36. Aydoğmuş, M.; Gülay, G.; Ergun, K. Impact of ESG performance on firm value and profitability. Borsa Istanb. Rev. 2022, 22, S119–S127. [Google Scholar] [CrossRef]
  37. Fatemi, A.; Glaum, M.; Kaiser, S. ESG performance and firm value: The moderating role of disclosure. Glob. Financ. J. 2018, 38, 45–64. [Google Scholar] [CrossRef]
  38. Velte, P. Does ESG performance have an impact on financial performance? Evidence from Germany. J. Glob. Responsib. 2017, 8, 169–178. [Google Scholar] [CrossRef]
  39. Xie, J.; Nozawa, W.; Yagi, M.; Fujii, H.; Managi, S. Do environmental, social, and governance activities improve corporate financial performance? Bus. Strategy Environ. 2019, 28, 286–300. [Google Scholar] [CrossRef]
  40. Li, T.T.; Wang, K.; Sueyoshi, T.; Wang, D.D. ESG: Research progress and future prospects. Sustainability 2021, 13, 11663. [Google Scholar] [CrossRef]
  41. Bocquet, R.; Le Bas, C.; Mothe, C.; Poussing, N. Are firms with different CSR profiles equally innovative? Empirical analysis with survey data. Eur. Manag. J. 2013, 31, 642–654. [Google Scholar] [CrossRef]
  42. Costa, C.; Lages, L.F.; Hortinha, P. The bright and dark side of CSR in export markets: Its impact on innovation and performance. Int. Bus. Rev. 2015, 24, 749–757. [Google Scholar] [CrossRef]
  43. Hull, C.E.; Rothenberg, S. Firm performance: The interactions of corporate social performance with innovation and industry differentiation. Strateg. Manag. J. 2008, 29, 781–789. [Google Scholar] [CrossRef]
  44. McWilliams, A.; Siegel, D. Corporate social responsibility and financial performance: Correlation or misspecification? Strateg. Manag. J. 2000, 21, 603–609. [Google Scholar] [CrossRef]
  45. Nidumolu, R.; Prahalad, C.K.; Rangaswami, M.R. Why sustainability is now the key driver of innovation. Harv. Bus. Rev. 2009, 87, 56–64. [Google Scholar]
  46. Bessant, J.; Lamming, R.; Noke, H.; Phillips, W. Managing innovation beyond the steady state. Technovation 2005, 25, 1366–1376. [Google Scholar] [CrossRef]
  47. Figueiredo, P.N.; Cabral, B.P.; Silva, F.Q. Intricacies of firm-level innovation performance: An empirical analysis of latecomer process industries. Technovation 2021, 105, 102302. [Google Scholar] [CrossRef]
  48. Ponta, L.; Puliga, G.; Manzini, R. A measure of innovation performance: The Innovation Patent Index. Manag. Decis. 2021, 59, 73–98. [Google Scholar] [CrossRef]
  49. Danneels, E.; Vestal, A. Normalizing vs. analyzing: Drawing the lessons from failure to enhance firm innovativeness. J. Bus. Ventur. 2020, 35, 105903. [Google Scholar] [CrossRef]
  50. Dziallas, M.; Blind, K. Innovation indicators throughout the innovation process: An extensive literature analysis. Technovation 2019, 80, 3–29. [Google Scholar] [CrossRef]
  51. Lööf, H.; Heshmati, A. On the relationship between innovation and performance: A sensitivity analysis. Econ. Innov. New Technol. 2006, 15, 317–344. [Google Scholar] [CrossRef]
  52. Hammar, N.; Belarbi, Y. R&D, innovation and productivity relationships: Evidence from threshold panel model. Int. J. Innov. Stud. 2021, 5, 113–126. [Google Scholar]
  53. Sung, B. Do government subsidies promote firm-level innovation? Evidence from the Korean renewable energy technology industry. Energy Policy 2019, 132, 1333–1344. [Google Scholar] [CrossRef]
  54. Moser, P. Innovation without patents: Evidence from World’s Fairs. J. Law Econ. 2012, 55, 43–74. [Google Scholar] [CrossRef]
  55. Peters, M.; Schneider, M.; Griesshaber, T.; Hoffmann, V.H. The impact of technology-push and demand-pull policies on technical change–Does the locus of policies matter? Res. Policy 2012, 41, 1296–1308. [Google Scholar] [CrossRef]
  56. Baum, J.A.; Dahlin, K.B. Aspiration performance and railroads’ patterns of learning from train wrecks and crashes. Organ. Sci. 2007, 18, 368–385. [Google Scholar] [CrossRef]
  57. Greve, H.R. Organizational Learning from Performance Feedback: A Behavioral Perspective on Innovation and Change; Cambridge University Press: Cambridge, UK, 2003. [Google Scholar]
  58. Plank, J.; Doblinger, C. The firm-level innovation impact of public R&D funding: Evidence from the German renewable energy sector. Energy Policy 2018, 113, 430–438. [Google Scholar]
  59. Doz, Y.L.; Wilson, K. Managing Global Innovation: Frameworks for Integrating Capabilities Around the World; Harvard Business Press: Cambridge, MA, USA, 2012. [Google Scholar]
  60. Lev, B.; Zambon, S. Intangibles and Intellectual Capital: An Introduction to a Special Issue. Eur. Account. Rev. 2003, 12, 597–603. [Google Scholar] [CrossRef]
  61. Edvinsson, L.; Malone, M.S. Intellectual Capital: Realizing Your Company’s True Value by Finding Its Hidden Brainpower; Harper Business: New York, NY, USA, 1997. [Google Scholar]
  62. Sveiby, K.E. The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets; Barrett-Kohler Publishers: Oakland, CA, USA, 1997. [Google Scholar]
  63. Clarke, M.; Seng, D.; Whiting, R.H. Intellectual capital and firm performance in Australia. J. Intellect. Cap. 2011, 12, 505–530. [Google Scholar] [CrossRef]
  64. Marr, B.; Gray, D.; Neely, A. Why do firms measure their intellectual capital? J. Intellect. Cap. 2003, 4, 441–464. [Google Scholar] [CrossRef]
  65. Kianto, A.; Sáenz, J.; Aramburu, N. Knowledge-based human resource management practices, intellectual capital and innovation. J. Bus. Res. 2017, 81, 11–20. [Google Scholar] [CrossRef]
  66. Montresor, S.; Vezzani, A. Intangible Investments and Innovation Propensity: Evidence from the Innobarometer 2013. Ind. Innov. 2016, 23, 331–352. [Google Scholar] [CrossRef]
  67. Lee, J.; Lee, J.; Lee, H. Exploration and Exploitation in the Presence of Network Externalities. Manag. Sci. 2003, 49, 553–570. [Google Scholar] [CrossRef]
  68. Piao, M. Thriving in the New: Implication of Exploration on Organizational Longevity. J. Manag. 2010, 36, 1529–1554. [Google Scholar] [CrossRef]
  69. Amara, N.; Landry, R. Sources of Information as Determinants of Novelty of Innovation in Manufacturing Firms: Evidence from the 1999 Statistics Canada Innovation Survey. Technovation 2005, 25, 245–259. [Google Scholar] [CrossRef]
  70. Fang, S.C.; Wang, M.C.; Chen, P.C. The Influence of Knowledge Networks on a Firm’s Innovative Performance. J. Manag. Organ. 2017, 23, 22–45. [Google Scholar] [CrossRef]
  71. Mendi, P.; Moner-Colonques, R.; Sempere-Monerris, J.J. Cooperation for Innovation and Technology Licensing: Empirical Evidence from Spain. Technol. Forecast. Soc. Chang. 2020, 154, 119976. [Google Scholar] [CrossRef]
  72. Wang, M.C.; Chen, P.C.; Fang, S.C. A Critical View of Knowledge Networks and Innovation Performance: The Mediation Role of Firms’ Knowledge Integration Capability. J. Bus. Res. 2018, 88, 222–233. [Google Scholar] [CrossRef]
  73. Wan, X.; Pan, F.; Liu, C.; Zhao, J.; Li, C. The Impact of Profitability Sustainability on Innovation in Dairy Companies: The Multiple Moderating Effects of Corporate Social Responsibility. Sustainability 2024, 16, 5935. [Google Scholar] [CrossRef]
  74. Luo, X.; Du, S. Exploring the Relationship between Corporate Social Responsibility and Firm Innovation. Mark. Lett. 2015, 26, 703–714. [Google Scholar] [CrossRef]
  75. Cassiman, B.; Veugelers, R. In Search of Complementarity in Innovation Strategy: Internal R&D and External Knowledge Acquisition. Manag. Sci. 2006, 52, 68–82. [Google Scholar]
  76. Tang, H. The Effect of ESG Performance on Corporate Innovation in China: The Mediating Role of Financial Constraints and Agency Cost. Sustainability 2022, 14, 3769. [Google Scholar] [CrossRef]
  77. Chen, L.; Khurram, M.U.; Gao, Y.; Abedin, M.Z.; Lucey, B. ESG Disclosure and Technological Innovation Capabilities of the Chinese Listed Companies. Res. Int. Bus. Financ. 2023, 65, 101974. [Google Scholar] [CrossRef]
  78. Cohen, L.; Gurun, U.G.; Nguyen, Q.H. The ESG-Innovation Disconnect: Evidence from Green Patenting. Natl. Bur. Econ. Res. 2020, w27990. [Google Scholar] [CrossRef]
  79. Fliaster, A.; Kolloch, M. Implementation of Green Innovations–The Impact of Stakeholders and Their Network Relations. R&D Manag. 2017, 47, 689–700. [Google Scholar]
  80. Martínez, J.B.; Fernández, M.L.; Fernández, P.M.R. Corporate Social Responsibility: Evolution through Institutional and Stakeholder Perspectives. Eur. J. Manag. Bus. Econ. 2016, 25, 8–14. [Google Scholar] [CrossRef]
  81. Tseng, C.Y.; Lin, S.C.; Pai, D.C.; Tung, C.W. The Relationship between Innovation Network and Innovation Capability: A Social Network Perspective. Technol. Anal. Strateg. Manag. 2016, 28, 1029–1040. [Google Scholar] [CrossRef]
  82. Antonacopoulou, E.P.; Sheaffer, Z. Learning in Crisis: Rethinking the Relationship between Organizational Learning and Crisis Management. J. Manag. Inq. 2014, 23, 5–21. [Google Scholar] [CrossRef]
  83. Aho, S.; Ståhle, S.; Ståhle, P. A Critical Assessment of Stewart’s CIV Method. Meas. Bus. Excell. 2011, 15, 27–35. [Google Scholar] [CrossRef]
  84. Volkov, D.; Garanina, T. Intangible Assets: Importance in the Knowledge-Based Economy and the Role in Value Creation of a Company. Electron. J. Knowl. Manag. 2007, 5, 539–550. [Google Scholar]
  85. ISO 26000; Social Responsibility. ISO: Geneva, Switzerland, 2010.
  86. Tobin, J. Estimation of Relationships for Limited Dependent Variables. Econom. J. Econom. Soc. 1958, 24, 24–36. [Google Scholar] [CrossRef]
  87. Maddala, S.G. Censored Data Models. Econometrics 1990, 54, 54–57. [Google Scholar]
  88. Moffitt, R. The Tobit Model, Hours of Work and Institutional Constraints. Rev. Econ. Stat. 1982, 64, 510–515. [Google Scholar] [CrossRef]
  89. Smith, D.A.; Brame, R. Tobit Models in Social Science Research: Some Limitations and a More General Alternative. Sociol. Methods Res. 2003, 31, 364–388. [Google Scholar] [CrossRef]
  90. He, H.; Tang, W.; Kelly, T.; Li, S.; He, J. Statistical Tests for Latent Class in Censored Data Due to Detection Limit. Stat. Methods Med. Res. 2020, 29, 2179–2197. [Google Scholar] [CrossRef] [PubMed]
  91. Greene, W.H. Econometric Analysis; Prentice Hall: Englewood Cliffs, NJ, USA, 2003; pp. 116–125. [Google Scholar]
  92. Wooldridge, J.M. Simple Solutions to the Initial Conditions Problem in Dynamic, Nonlinear Panel Data Models with Unobserved Heterogeneity. J. Appl. Econ. 2005, 20, 39–54. [Google Scholar] [CrossRef]
  93. Chiu, Y.C.; Liaw, Y.C. Organizational Slack: Is More or Less Better? J. Organ. Chang. Manag. 2009, 22, 321–342. [Google Scholar] [CrossRef]
  94. Cheng, J.L.; Kesner, I.F. Organizational Slack and Response to Environmental Shifts: The Impact of Resource Allocation Patterns. J. Manag. 1997, 23, 1–18. [Google Scholar] [CrossRef]
  95. Suzuki, O. Enabling or Constraining? Unraveling the Influence of Organizational Slack on Innovation. Ind. Corp. Change 2018, 27, 555–575. [Google Scholar] [CrossRef]
  96. Deng, Z.; Guo, H.; Zhang, W.; Wang, C. Innovation and Survival of Exporters: A Contingency Perspective. Int. Bus. Rev. 2014, 23, 396–406. [Google Scholar] [CrossRef]
  97. Bilbao-Osorio, B.; Rodríguez-Pose, A. From R&D to Innovation and Economic Growth in the EU. Growth Change 2004, 35, 434–455. [Google Scholar]
Table 1. Data selection process.
Table 1. Data selection process.
Firm-year observations of listed firms on the Korean Stock Exchange, including both KOSPI and KOSDAQ, during the period 2013–2023 20,557
Less:
Firms without ESG rating from KCGS 11,764
Firms without financial data for variables63
Firms with impaired capital252
Final firm-year observations8478
Table 2. Descriptive statistics of variables.
Table 2. Descriptive statistics of variables.
VariablesNumberMeanMedianStandard DeviationMINMAX
INN84780.539 0.292 0.688 0.000 3.344
ESG84782.816 3.000 1.088 1.000 6.000
ENV84782.584 3.000 1.286 1.000 6.000
SOC84783.021 3.000 1.301 1.000 6.000
GOV84782.964 3.000 1.060 1.000 6.000
SIZE847820.006 19.776 1.387 17.503 24.216
ASLACK84780.022 0.026 0.076 −0.304 0.228
PSLACK84780.842 0.605 0.855 0.019 4.870
OCF84780.046 0.043 0.071 −0.181 0.246
RND84780.014 0.003 0.025 0.000 0.132
AGE84783.450 3.689 0.705 1.099 4.443
MRK84780.828 1.000 0.377 0.000 1.000
Definition of variables
INN: Firm-level innovation measured by IC values calculated by the CIV model, standardized by division by total assets
ESG: Overall ESG rating, assigned a number from one to seven, with D being the lowest and S the highest
ENV: Environmental rating, assigned a number from one to seven, with D being the lowest and S the highest
SOC: Social rating, assigned a number from one to seven, with D being the lowest and S the highest
GOV: Governance rating, assigned a number from one to seven, with D being the lowest and S the highest
SIZE: Natural logarithm of total assets
ASLACK: Available slack, measured by the ratio of net income to total assets
PSLACK: Potential slack, measured by the debt-equity ratio
OCF: Operating cash flows divided by total assets
RND: R&D expenditures divided by total assets
AGE: Natural logarithm of firm age
MRK: 1 if a firm is listed on KOSPI, 0 otherwise
Table 3. Correlation matrix of variables.
Table 3. Correlation matrix of variables.
INNESGENVSOCGOVSIZEASLACKPSLACKOCFRNDAGEMRK
INN1.0000 0.0793 −0.0041 0.0695 0.1217 0.0560 0.5348 −0.2378 0.3954 0.0441 −0.1602 −0.1205
<0.00010.7090 <0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001
ESG 1.0000 0.7478 0.8283 0.7966 0.6106 0.1183 0.1041 0.1603 0.0239 −0.0885 0.1565
<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.00010.0276 <0.0001<0.0001
ENV 1.0000 0.6468 0.3976 0.5605 0.0842 0.1603 0.1430 −0.0148 0.0127 0.2246
<0.0001<0.0001<0.0001<0.0001<0.0001<0.00010.1737 0.2412 <0.0001
SOC 1.0000 0.5702 0.6427 0.1056 0.1035 0.1497 0.0429 −0.0726 0.1393
<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001
GOV 1.0000 0.4406 0.1216 0.0245 0.1127 0.0261 −0.1078 0.0986
<0.0001<0.00010.0243 <0.00010.0162 <0.0001<0.0001
SIZE 1.0000 0.1500 0.1748 0.1512 −0.0736 −0.0098 0.1655
<0.0001<0.0001<0.0001<0.00010.3666 <0.0001
ASLACK 1.0000 −0.2603 0.4946 −0.0229 −0.0623 −0.0298
<0.0001<0.00010.0351 <0.00010.0061
PSLACK 1.0000 −0.1039 −0.0662 0.0226 0.1163
<0.0001<0.00010.0378 <0.0001
OCF 1.0000 0.0307 −0.0964 −0.0594
0.0048 <0.0001<0.0001
RND 1.0000 −0.1549 −0.2495
<0.0001<0.0001
AGE 1.0000 0.2641
<0.0001
MRK 1.0000
Note: The variable definitions are provided in Table 2.
Table 4. Pooled and RE Tobit regression results on the relationship between overall ESG ratings and firm-level innovation.
Table 4. Pooled and RE Tobit regression results on the relationship between overall ESG ratings and firm-level innovation.
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
Intercept−0.396−2.44 **−1.208−4.03 ***
ESG0.0232.60 ***0.0202.16 **
SIZE0.0192.67 ***0.0725.89 ***
SLACK8.46353.36 ***6.12540.96 ***
PSLACK−0.205−17.82 ***−0.263−16.26 ***
OCF1.86814.65 ***0.8657.16 ***
RND−0.054−0.15−0.315−0.65
AGE−0.086−7.88 ***−0.111−5.67 ***
MRK−0.074−3.42 ***−0.141−3.77 ***
YRIncludedIncluded
INDIncludedIncluded
Log likelihood −6821.35 −6142.89
LR test 1356.90 (0.000)
N8478
Note: The variable definitions are provided in Table 2. ** and *** indicate significance levels at the 5% and 1% levels, respectively, based on two-tailed tests.
Table 5. Pooled and RE Tobit regression results on the relationship between ESG sub-dimensions ratings and firm-level innovation.
Table 5. Pooled and RE Tobit regression results on the relationship between ESG sub-dimensions ratings and firm-level innovation.
Panel A. Environmental Dimension
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
ENV0.006 0.82 0.015 1.85 *
ControlIncludedIncluded
Log likelihood −6824.38 −6143.53
LR test 1361.70 (0.000)
N8478
Panel B. Social Dimension
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
SOC0.015 1.99 ** 0.020 2.56 **
ControlIncludedIncluded
Log likelihood −6822.72 −6141.95
LR test 1361.55 (0.000)
N8478
Panel C. Governance Dimension
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
GOV0.033 3.99 *** 0.021 2.42 **
ControlIncludedIncluded
Log likelihood −6816.76 −6142.29
LR test 1348.93 (0.000)
N8478
Note: The variable definitions are provided in Table 2. *, **, and *** indicate significance levels at the 10%, 5%, and 1% levels, respectively, based on two-tailed tests.
Table 6. Pooled and RE Tobit regression results on the relationship between overall ESG ratings and firm-level innovation during the pre- and post-COVID periods.
Table 6. Pooled and RE Tobit regression results on the relationship between overall ESG ratings and firm-level innovation during the pre- and post-COVID periods.
Panel A. Pre-COVID Period
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
ESG0.024 1.57 0.009 0.57
ControlIncludedIncluded
Log likelihood −4166.87 −3706.40
LR test 920.94 (0.000)
N5072
Panel B. Post-COVID Period
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
ESG0.019 1.74 *0.027 2.41 **
ControlIncludedIncluded
Log likelihood −2548.10 −2200.84
LR test 694.51 (0.000)
N3406
Note: The variable definitions are provided in Table 2. * and ** indicate significance levels at the 10% and 5% levels, respectively, based on two-tailed tests.
Table 7. Pooled and RE Tobit regression results on the relationship between environmental ratings and firm-level innovation during the pre- and post-COVID periods.
Table 7. Pooled and RE Tobit regression results on the relationship between environmental ratings and firm-level innovation during the pre- and post-COVID periods.
Panel A. Pre-COVID Period
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
ENV−0.014 −1.17 −0.003 −0.18
ControlIncludedIncluded
Log likelihood −4167.42 −3706.55
LR test 921.75 (0.000)
N5072
Panel B. Post-COVID Period
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
ENV0.021 2.08 **0.030 3.02 ***
ControlIncludedIncluded
Log likelihood −2547.46 −2199.18
LR test 696.54 (0.000)
N3406
Note: The variable definitions are provided in Table 2. ** and *** indicate significance levels at the 5% and 1% levels, respectively, based on two-tailed tests.
Table 8. Pooled and RE Tobit regression results on the relationship between social ratings and firm-level innovation during the pre- and post-COVID periods.
Table 8. Pooled and RE Tobit regression results on the relationship between social ratings and firm-level innovation during the pre- and post-COVID periods.
Panel A. Pre-COVID Period
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
SOC0.009 0.64 0.011 0.76
ControlIncludedIncluded
Log likelihood −4167.90 −3706.28
LR test 923.26 (0.000)
N5072
Panel B. Post-COVID Period
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
SOC0.013 1.39 0.019 2.05 **
ControlIncludedIncluded
Log likelihood −2548.66 −2201.65
LR test 694.01 (0.000)
N3406
Note: The variable definitions are provided in Table 2. ** indicates significance levels at the 5% level, based on two-tailed tests.
Table 9. Pooled and RE Tobit regression results on the relationship between governance ratings and firm-level innovation during the pre- and post-COVID periods.
Table 9. Pooled and RE Tobit regression results on the relationship between governance ratings and firm-level innovation during the pre- and post-COVID periods.
Panel A. Pre-COVID Period
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
GOV0.051 3.83 *** 0.024 1.68 *
ControlIncludedIncluded
Log likelihood −4160.78 −3705.15
LR test 911.27 (0.000)
N5072
Panel B. Post-COVID Period
VariablesPooled Tobit ModelRE Tobit Model
Coef.t-statCoef.z-stat
GOV0.017 1.60 0.029 2.77 ***
ControlIncludedIncluded
Log likelihood −2548.33 −2199.90
LR test 696.86 (0.000)
N3406
Note: The variable definitions are provided in Table 2. * and *** indicate significance levels at the 10% and 1% levels, respectively, based on two-tailed tests.
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Lee, H. Does ESG Performance Drive Firm-Level Innovation? Evidence from South Korea. Sustainability 2025, 17, 1727. https://doi.org/10.3390/su17041727

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Lee H. Does ESG Performance Drive Firm-Level Innovation? Evidence from South Korea. Sustainability. 2025; 17(4):1727. https://doi.org/10.3390/su17041727

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Lee, Hyunah. 2025. "Does ESG Performance Drive Firm-Level Innovation? Evidence from South Korea" Sustainability 17, no. 4: 1727. https://doi.org/10.3390/su17041727

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Lee, H. (2025). Does ESG Performance Drive Firm-Level Innovation? Evidence from South Korea. Sustainability, 17(4), 1727. https://doi.org/10.3390/su17041727

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