Can Executives with Environmental Expertise Promote Open Innovation?

Abstract

This paper examines whether executives’ environmental expertise shapes firms’ engagement in open innovation. Open innovation requires long-term, uncertain, and relationship-specific investments, yet such investments are often underprovided due to manage rial short-termism. Based on stewardship theory, we argue that environmental expertise embeds a long-horizon cognitive orientation in executives and alleviates managerial myopia. Based on the upper echelons theory, we posit that executives with environmental expertise could significantly influence corporate strategies, specifically enhancing corporate open innovation activities. Using a panel of 40,133 firm–year observations from Chinese listed firms over 2002–2022, we find that firms led by environmentally expertised executives exhibit significantly greater open innovation, measured by co-patenting activities. To address endogeneity concerns, we implement a stacked difference-in-differences design and propensity score matching. Our results remain robust in a battery of robustness tests. Consistent with the managerial myopia alleviation argument, we show that the effect of executives with green expertise on open innovation is stronger in settings where short-term pressures are more pronounced, including firms with weaker governance and higher capital market pressure. This study highlights and provides micro-level evidence on how environmental human capital enables firms to adopt sustainability-oriented business models through open innovation. Overall, our findings contribute to the literature on sustainable entrepreneurship and open innovation and offer practical implications for firms and policymakers seeking to support long-term, innovation-driven sustainability transitions.

1. Introduction

Firms have increasingly relied on open innovation to address complex technological and environmental challenges [1]. Open innovation is defined as a paradigm where firms integrate external knowledge and resources across organizational boundaries [2]. Unlike traditional innovation perspectives that emphasize internal R&D investment and firm-specific resource accumulation (closed innovation), open innovation represents a paradigm of deliberate boundary-spanning. Empirical research by Cheng and Huizingh (2014) emphasizes the importance of open innovative activities by showing their significant positive effects on new product innovativeness, market success rates, and financial performance [3]. Open innovation enables firms to develop sustainable solutions and maintain long-term competitive advantage [4].

However, despite its strategic importance, open innovation poses substantial implementation challenges. Compared with internal research and development, open innovation requires long-term, uncertain, and relationship-specific investments, often involving high coordination costs and delayed returns [5,6]. These features make open innovation especially sensitive to managerial short-termism. A large body of literature shows that managers facing capital market pressure and career concerns tend to prioritize short-term performance, leading to the underinvestment in long-horizon innovative activities [7,8]. As a result, a fundamental tension emerges: while open innovation is essential for sustainable value creation, its long-term and high-risk nature makes it particularly vulnerable to managerial myopia. Therefore, it is essential to investigate how the characteristics of executives affect open innovation activities.

To explain how executives affect the firms’ strategies on open innovation, we draw on upper echelons theory, which posits that executives’ expertise shapes their cognitive frameworks and, in turn, influences strategic choices [9]. For example, building on the upper echelons theory, the prior literature has shown ample empirical evidence that educational background, career expertise, age, and gender gradually shape the cognitive framework, values, and risk preferences of senior executives. This information is then transmitted to the organizational level, directly influencing the strategic direction and the quality of results [9]. Thus, executives’ distinct past expertise fundamentally shapes their values, time horizons, and subsequent strategic choices. In this situation, the cognitive abilities of managers, particularly the time span for making decisions, play a crucial role in determining whether an enterprise adopts a sustainable innovation strategy [10,11].

Recent studies have shifted research interests towards the importance of one particular executive characteristic, the executives’ environmental expertise. For example, Xie et al. (2023) point out that the pro-environmental educational backgrounds help executives accumulate knowledge-based resources and enhance eco-friendly production capabilities [12]. Environmental expertise endows executives with specialized expertise, which enables them to improve corporate environmental governance and enhance their green reputation, thereby meeting green credit standards and attracting environment-oriented capital [13]. Although the existing literature has examined the impact of green executives on information disclosure quality [14], ESG performance [15], and environmental performance [16], how green executives affect corporate open innovation remains underexplored. Therefore, this paper fills in the gap by investigating the proposition that the appointments of executives with environmental expertise (hereafter referred to as green executives) promote open innovation activities.

Our proposition is grounded in both upper echelons theory and stewardship theory. First, based on stewardship theory, we argue that green executives develop long-term cognitive orientation [11]. Stewardship theory posits that managers are intrinsically motivated to act in the best interests of the organization, prioritizing long-term value creation over short-term personal gains [17]. A recent series of studies demonstrates that green executives are oriented toward the long-term interests of the firm and place greater emphasis on sustainability-driven objectives that generate enduring value [10,11]. In contrast to conventional executives who may be constrained by short-term performance pressures or monitoring mechanisms, green executives are more likely to support investments in sustainability initiatives. By prioritizing long-term environmental and economic objectives, green executives foster organizational resilience and sustainable growth, effectively counterbalancing short-term decision biases.

Second, drawing on the upper echelons theory, we argue that green executives could affect the corporate open innovation. Environmental expertise enhances executives’ ability to process complex, uncertain, and long-horizon problems, as environmental issues typically involve delayed feedback and intertemporal trade-offs. This cognitive capability promotes forward-looking decision-making and reduces short-term bias. Open innovation, which relies on boundary-spanning collaboration and long-term resource commitment, is particularly dependent on managerial willingness to forgo short-term gains in favor of long-term value creation. By extending decision-making horizons, environmentally expertised executives enable firms to engage more actively in open innovation and to better internalize the benefits of sustainability-oriented investments. We therefore propose that green executives promote corporate open innovation by alleviating managerial myopia.

To empirically test our hypotheses, we utilize a comprehensive panel of 40,133 firm–year observations from China’s A-share listed firms between 2002 and 2022. We choose the Chinese market as our empirical setting for two primary reasons. First, as the world’s center of the manufacturing industry, China provides an unparalleled context to observe the profound tensions between traditional industrial scale and the urgent need for green transformation. Second, as we established, Chinese listed firms face strong capital market pressures alongside an increasing policy emphasis on sustainability. In such a rapidly transforming market, corporate strategic choices are highly sensitive to managerial time horizons, making it an ideal setting to examine how environmentally expertised executives navigate the long-term challenges of open innovation.

Our results show that firms with environmentally expertised executives exhibit significantly higher levels of open innovation. To address endogeneity issues, we employ a multi-period difference-in-differences (DID) design combined with propensity score matching (PSM). Our findings do not alter in a series of robust checks. Mediation mechanism analyses indicate that this effect operates through the alleviation of managerial myopia.

Furthermore, we examine the managerial myopia mechanism using the cross-sectional heterogeneity tests. We hypothesize that the effect of appointing green executives will be greater in firms with stronger managerial myopia issues. First, we expect the positive effect of green executives to be more pronounced in firms with weak supervision. In such environments, the lack of formal monitoring exacerbates myopic behaviors [18,19], thereby elevating the strategic necessity for green executives to act as a form of “substitute” cognitive governance [16,20,21]. Second, we posit a stronger effect of green executives on firms facing higher capital market pressure. Highly capital market pressure imposes severe short-term performance pressures and heightened takeover threats [22,23]. The cross-sectional tests reveal that the effect is more pronounced in firms with weaker governance and greater capital market pressure, where short-term incentives are more severe. Therefore, green executives play a crucial role in alleviating the myopia and promoting open innovation [24]. Their cognitive framework effectively mitigates managerial excessive sensitivity to short-term financial fluctuations and reduces myopic discounting when firms face high-risk innovation projects [11].

The contribution of this paper is threefold. First, it contributes to the open innovation and sustainability literature by identifying managerial cognition as a key determinant of firms’ ability to engage in sustainability-oriented innovation. Although existing studies have explored their impact in environmental and social dimensions [15,16], to the best of our knowledge, this is the first paper investigating the path of green executives promoting open innovation by alleviating myopia. This paper deepens our understanding of how green executives create financial and strategic value. Second, it extends the upper echelons literature by highlighting environmental expertise as an important source of heterogeneity in managerial time horizons. We provide empirical evidence that green executives can mitigate short-term bias and facilitate long-term investment strategies. Third, our findings highlight the role of environmental human capital in enabling firms to adopt open innovation and develop sustainable business models.

2. Literature Review and Hypotheses Development

2.1. Environmental Expertise and Myopia

In corporate innovation, managers who rely on conventional financial logic often perceive long-term, high-risk investments as immediate costs, a cognitive bias known as managerial myopia. This leads to compressed decision horizons and a preference for short-term financial performance over long-term value creation [7,25]. Often driven by capital market pressures and career concerns, such myopia results in the systematic underinvestment in long-horizon innovation activities [26].

To explain how executives’ environmental expertise addresses this bias, we draw on stewardship theory. We argue that green executives promote open innovation by mitigating managerial myopia. Under this framework, executives are intrinsically motivated to prioritize long-term organizational interests and sustainable value creation over short-term financial performance [17,27].

First, environmental expertise reinforces executives’ intrinsic environmental values and long-term orientation. Executives with environmental backgrounds are more likely to prioritize substantive environmental performance rather than symbolic actions such as greenwashing [28,29]. This value-based orientation shifts managerial focus away from short-term financial outcomes toward long-term strategic objectives, thereby alleviating managerial myopia.

Second, the stewardship-driven reduction in managerial myopia increases firms’ willingness to engage in open innovation. Open innovation requires sustained investment, tolerance for uncertainty, and coordination with external partners, making it particularly sensitive to short-term managerial biases. In contrast, environmentally oriented executives are more likely to support long-term and uncertain innovation strategies and to commit resources to boundary-spanning collaboration and knowledge integration [30].

Furthermore, such executives tend to view environmental regulation not as a constraint but as an opportunity for sustainable growth and competitive advantage, which further strengthens their commitment to long-term innovation investments [14,31].

Taken together, stewardship-driven motivations embedded in environmental expertise reshape managerial priorities toward long-term value creation, reduce short-term bias, and facilitate the adoption of open innovation strategies that require sustained commitment and cross-boundary collaboration.

2.2. Environmental Expertise and Open Innovation

Building on upper echelons theory, prior research establishes that executives’ backgrounds, particularly their education and professional expertise, systematically form their cognitive bases and values, thereby influencing corporate decisions [9,32]. For instance, Ali et al. (2022) and Yang et al. (2025) find that CEOs’ financial background and professional expertise significantly improve the efficiency of investment decisions and resource allocation [33,34]. Kallias et al. (2023) and Nawaz (2022) show that executive educational backgrounds such as MBA degrees profoundly shape corporate strategic posture and IPO performance [35,36]. Similarly, executives with overseas or international experience tend to enhance firms’ sustainable development capabilities, internationalization, and financial performance [37,38]. Additionally, executives with political experience play a significant role in securing green subsidies, providing an important impetus for firms to improve their environmental performance [39].

Recently, growing attention in academia has been paid to a specific group known as green executives. Green executives are defined by their environmental education and professional work experience, which provide them with specialized knowledge and shape their values toward sustainability and long-term development [10].

Existing studies document that these green executives generate positive effects for firms through multiple channels. First, they promote managerial environmental concerns to facilitate eco-friendly production [12]. Second, they significantly curb corporate environmental violations, mitigate agency problems, and enhance ESG performance through green innovation [15]. Third, they implement substantive sustainable development strategies to enhance organizational legitimacy [40], while attracting green investors and obtaining government environmental subsidies [16,41]. Additionally, they adopt a leadership style centered on environmental protection to win favor and support from stakeholders [42].

Recall that the stewardship theory implies that green executives tend to be less myopic; we posit that this positive effect could contribute to the corporate open innovation activities. Open innovation intensifies this intertemporal trade-off. It requires long-term, uncertain, and relationship-specific investments, necessitating coordination with external partners under high uncertainty [43]. More than a technological choice, it is an intertemporal investment decision that is particularly vulnerable to managerial myopia, as managers tend to undervalue its long-term benefits relative to short-term performance metrics [8,44].

Therefore, we argue that the dual forces of upper echelon cognition and stewardship motivation uniquely position green executives to champion open innovation in firms. Open innovation requires long-term, uncertain, and relationship-specific investments, which are inherently hindered by managerial short-termism.

The stewardship theory explains the underlying behavioral shift in these executives. By internalizing environmental protection as a core value, they are intrinsically motivated to act as responsible stewards of the organization. They prioritize long-term collective value creation over short-term opportunistic gains [11,17]. Driven by this stewardship orientation, green executives actively foster cooperative organizational cultures [42] and signal long-term commitment to stakeholders [10], which are essential conditions for building trust with external innovation partners.

Based on upper echelons theory, environmental issues are characterized by long feedback cycles, intertemporal trade-offs, and systemic complexity [45]. Exposure to such issues cognitively imprints green executives with a greater tolerance for delayed returns [46]. This enhanced cognitive capacity to navigate systemic complexity enables them to effectively identify and attract external innovation-related resources [21].

Taken together, the cognitive ability to manage complexity and the stewardship motivation to pursue long-term collaboration work synergistically to overcome managerial short-termism. Therefore, green executives are fundamentally equipped to support the sustained commitments and external partnerships required for open innovation activities.

H1. 

Firms with green executives exhibit higher levels of open innovation.

2.3. Cross-Sectional Implications

We focus on two cross-sectional corporate features demonstrated in prior studies that are closely associated with the managerial myopia level, namely the corporate governance and the capital market pressure.

First, corporate governance plays a critical role in constraining managerial behavior and mitigating agency problems [18]. In firms with strong governance, monitoring mechanisms and incentive structures can partially limit managerial myopia [18]. In contrast, weak governance environments provide managers with greater discretion, exacerbating short-term behavior [7,19].

In such settings, executive cognition becomes more important. When formal governance mechanisms are weak, the long-term orientation of environmentally expertised executives can serve as a substitute for external monitoring, leading to a stronger effect on open innovation [27]. Therefore, we hypothesize:

H2. 

The positive effect of green executives on open innovation is stronger in firms with weaker corporate governance.

Second, capital market pressure is another key determinant of managerial time horizons. Firms facing strong short-term pressure from investors are more likely to exhibit myopic behavior, as managers seek to meet earnings expectations and avoid market penalties [26]. Managers facing severe capital market pressures in firms often succumb to managerial myopia by cutting long-term R&D expenditures to boost current earnings [24].

In high-pressure environments, the tendency toward short-termism is more pronounced, increasing the importance of cognitive factors. The long-term orientation associated with environmental expertise can act as a countervailing force [47], mitigating myopia and supporting open innovation. Therefore, we hypothesize:

H3. 

The positive effect of green executives on open innovation is stronger in firms with greater capital market pressure.

3. Data, Methodology, and Descriptive

3.1. Data

We utilize several databases to construct our sample. The data on executive characteristics and accounting is from the China Stock Market & Accounting Research (CSMAR) database. The data on open innovation is from the Chinese Research Data Services (CNRDS) platform. Our initial sample consists of 5338 A-share listed firms, corresponding to 57,522 firm–year observations. To ensure the reliability and comparability of our empirical results, we apply several standard exclusion criteria. First, we exclude 2569 observations of firms designated for Special Treatment (ST or *ST), as these firms are in abnormal financial distress and their operational decisions are often distorted. Second, we exclude 1151 observations from financial firms (representing approximately 2.00% of the initial population) due to their unique accounting standards, distinct capital structures, and different regulatory environments. Third, we exclude 2045 observations of delisted firms, 1472 pre-IPO observations, and 122 observations of insolvent firms (negative equity) to ensure data continuity, public market scrutiny, and normal operational status. Finally, after removing observations with missing primary data for our main variables, the final sample consists of 4256 firms with 40,133 firm–year observations between 2002 and 2022. There are 13,739 firm–years with green executives and 26,394 firm–years without green executives. Our Appendix A 

Table A1. Sample selection.

Selection Criteria	Number of Firms	# of Observations	Number of Industries
Panel A: Firm–year sample
Initial A-share listed companies during the sample period	5338	57,522	85
After excluding ST or *ST firms	5327	54,953	85
After excluding firms in the financial industry	5233	53,802	81
After excluding delisted firms	5035	51,757	80
After excluding pre-IPO observations	4958	50,285	80
After excluding insolvent firms (negative equity)	4958	50,163	80
Final sample (excluding missing variables or singletons)	4256	40,133	79

Note: This table presents the process of changes in our sample screening. # denotes ‘Number of’; * in *ST refers to firms under delisting risk warning (special treatment) in China’s A-share market.

reports the entire process of variable elimination we carried out.

3.2. Methodology

This study constructs the following model to examine the impact of CEOs’ environmental expertise on firm open innovation:

$${\mathrm{O}\mathrm{I}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}{\mathrm{n}}_{\mathrm{i},\mathrm{t}}+\mathsf{\gamma }{\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}+\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+{\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}}_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}},$$

(1)

where ${\mathrm{O}\mathrm{I}}_{\mathrm{i},\mathrm{t}}$ is the logarithm of the number of open innovations in a firm–year. The key independent variable of interest, $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}{\mathrm{n}}_{\mathrm{i},\mathrm{t}}$, is a dummy variable indicating whether the firm has green executives (i.e., executives with green expertise) in that year. ${\mathrm{C}\mathrm{V}}_{\mathrm{i},\mathrm{t}}$ is a vector of firm, executive, and governance characteristics. ${\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}}_{\mathrm{i}}$ represents industry-fixed effects, and $\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}$ captures the year-fixed effects.

3.2.1. Measuring the Dependent Variable

The measurement of open innovation in academia has evolved from an early conceptual framework to a multi-dimensional and multi-method empirical measure. Huizingh (2011) emphasizes that open innovation is a multi-dimensional continuum rather than a simple dichotomy [48], which points out the direction for subsequent quantitative research. On this basis, subsequent research has developed a variety of operational measurement methods. One mainstream method is to quantify open innovation behaviors by using multi-indicator scales through questionnaires. For example, Mubarak et al. (2025) use the maturity scale derived from the classic literature to measure the level of open innovation activities of firms, ensuring the reliability and validity of measurement [49]. Another type of research focuses more finely on specific types of open innovation activities for measurement. Inauen and Schenker-Wicki (2011) operationalize open innovation as the cooperation intensity between firms and six different types of external partners (such as customers and universities) [50].

We measure open innovation by firms’ co-patenting activities [51,52]. On this basis, our indicator is the natural logarithm of one plus the total number of co-patent applications filed by the firm each year ($\mathrm{O}\mathrm{I}$).

3.2.2. Measuring the Independent Variable

Our key independent variable of interest is the presence of green executives. A green executive is defined via textual analysis of executive resumes. Following the method of Walls and Hoffman (2013) [53], we extract 1298 provincial government work reports from 2002 to 2022 and generate a high-frequency dictionary of green terms such as “environmental protection,” “low-carbon,” and “clean energy”. An executive is classified as having environmental expertise if his or her résumé contains any of these keywords. $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ is defined as 1 if the firm appoints executives who possess either an educational degree relevant to environmental fields or work expertise pertinent to environmental domains. These executives include directors, supervisors, and senior managers. Otherwise, it is defined as 0. Importantly, our textual analysis strictly extracts objective, historical educational, and occupational facts rather than subjective environmental claims, effectively mitigating concerns regarding executive greenwashing.

3.2.3. Control Variables

Based on established practices in the literature, this study incorporates a set of control variables to account for both firm-specific characteristics and executive-level attributes. At the firm level, we control for return on total assets ($\mathrm{R}\mathrm{O}\mathrm{A}$), revenue growth rate ($\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}$), the shareholding ratio of the largest shareholder ($\mathrm{T}\mathrm{o}\mathrm{p}1$), CEO-Chairman duality ($\mathrm{D}\mathrm{u}\mathrm{a}\mathrm{l}$), the book-to-market ratio ($\mathrm{B}\mathrm{M}$), the leverage ratio ($\mathrm{L}\mathrm{e}\mathrm{v}$), firm listing age ($\mathrm{L}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{A}\mathrm{g}\mathrm{e}$), state ownership ($\mathrm{S}\mathrm{O}\mathrm{E}$), and firm size ($\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}$). Furthermore, several executive-level characteristics are included, namely, whether directors, supervisors, or senior managers possess financial expertise ($\mathrm{F}\mathrm{i}\mathrm{n}\mathrm{B}\mathrm{a}\mathrm{c}\mathrm{k}$), the proportion of female executives ($\mathrm{F}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{e}$), and the average age of the management team ($\mathrm{T}\mathrm{M}\mathrm{T}\mathrm{A}\mathrm{g}\mathrm{e}$). The detailed definitions of the main variables are presented in Appendix A 

Table A2. Variables definition.

Category	Variable Name	Symbol	Definition	Source
Dependent variables	Open innovation	OI	The natural logarithm of one plus the total count of invention patents, utility model patents, and design patents.	(Laursen and Salter, 2006; Yun et al., 2024) [51,52].
		OI2	The natural logarithm of one plus the number of invention patents.	(Laursen and Salter, 2006; Yun et al., 2024) [51,52].
		OI3	The natural logarithm of one plus the number of utility model patents.	(Laursen and Salter, 2006; Yun et al., 2024) [51,52].
Independent variables	Executives’ Environmental Background	$\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$	A dummy variable that equals 1 if the firm employs senior executives (directors, supervisors, or senior managers) with an environmental education or work background, and 0 otherwise.	(Luo et al., 2026; Walls and Hoffman, 2013) [10,53].
		$\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}\_\mathrm{n}\mathrm{u}\mathrm{m}$	The natural logarithm of one plus the number of executives with an environmental background.	(Zhang et al., 2025) [21].
Control variables	Return on total assets	$\mathrm{R}\mathrm{O}\mathrm{A}$	Net profit is divided by the average balance of total assets. This variable is standardized.	(Hu and Shi, 2025) [42].
	Revenue Growth	$\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}$	The growth rate of operating income is calculated as (Current year’s operating income/Previous year’s operating income)—1. This variable is standardized.	(Lévesque et al., 2012) [61].
	Top Shareholder Ownership	$\mathrm{T}\mathrm{o}\mathrm{p}1$	The natural logarithm of the percentage of shares held by the largest shareholder.	(Luo et al., 2026) [10].
	CEO Duality	$\mathrm{D}\mathrm{u}\mathrm{a}\mathrm{l}$	A dummy variable that equals 1 if the Chairman also serves as the CEO, and 0 otherwise.	(Wang et al., 2021) [62].
	Book-to-market ratio	$\mathrm{B}\mathrm{M}$	The ratio of the book value of equity to the total market value of equity.	(Wang et al., 2021) [62].
	Asset-liability ratio	$\mathrm{L}\mathrm{e}\mathrm{v}$	The ratio of total liabilities to total assets at the end of the year.	(Hu and Shi, 2025) [42].
	Listing Age	$\mathrm{L}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{A}\mathrm{g}\mathrm{e}$	The natural logarithm of (Current year—Year of listing + 1).	(Hu and Shi, 2025) [42].
	Executives’ Financial Background	$\mathrm{F}\mathrm{i}\mathrm{n}\mathrm{B}\mathrm{a}\mathrm{c}\mathrm{k}$	A dummy variable indicating whether current senior executives have a financial background (e.g., experience in regulatory authorities, banks, securities, futures, trusts, etc.).	(Liu et al., 2020) [63].
	Proportion of women in management	$\mathrm{F}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{e}$	The percentage of female managers in the top management team.	(Kou et al., 2020) [64].
	Average management age	$\mathrm{T}\mathrm{M}\mathrm{T}\mathrm{A}\mathrm{g}\mathrm{e}$	The average age of Executives.	(Lu et al., 2022) [65].
	State-owned firm	SOE	A dummy variable that equals 1 if the firm is state-controlled (the ultimate controller is the state), and 0 otherwise.	(Wang et al., 2021) [62].
	Firm size	Size	The natural logarithm of one plus total asset.	(Hu and Shi, 2025) [42].

Appendix A Table A2 lists the description of variables employed in this paper. We have indicated the sources of each control variable at the end.

.

3.2.4. Descriptive Statistics

Table 1. Descriptive statistics results.

Variables	N	Mean	P50	Sd	Min	Max
OI	40,133	0.732	0	1.242	0	5.333
Green	40,133	0.342	0	0.474	0	1
Green_num	40,133	0.333	0	0.522	0	2.197
ROA	40,133	0	−0.0380	1	−4.013	2.905
Growth	40,133	0	−0.152	1	−1.894	5.538
Top1	40,133	3.501	3.531	0.445	2.262	4.323
Dual	40,133	0.245	0	0.430	0	1
FinBack	40,133	0.546	1	0.498	0	1
BM	40,133	0.642	0.647	0.249	0.124	1.170
Lev	40,133	0.438	0.437	0.200	0.0590	0.880
Female	40,133	18.20	16.67	11.20	0	50
ListAge	40,133	2.146	2.303	0.750	0.693	3.332
TMTAge	40,133	48.98	49.06	3.265	40.93	56.57
SOE	40,133	0.434	0	0.496	0	1
Size	40,133	22.13	21.94	1.279	19.82	26.15

Note: Table 1 presents descriptive statistics of the variables used in the regression analyses. Roa and Growth have been standardized. Details of the definition can be seen in Appendix A Table A2.

presents the descriptive statistics of the baseline sample. The mean of the green executive indicator ($\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$) is 0.342, and the median is 0. This indicates that, during the entire sample period, 34.2% of the firm–year observations involve firms with green executives, meaning that firms lacking green executives constitute most of the sample. The minimum and maximum values of the logarithm of open innovation ($\mathrm{O}\mathrm{I}$) are 0 and 5.333, respectively, suggesting that our sample covers a wide range. Additionally, the mean of the state-owned firm indicator ($\mathrm{S}\mathrm{O}\mathrm{E}$) is 0.434, indicating that in our sample, non-state-owned firms account for a relatively high proportion. The logarithm of the firm’s listing age ($\mathrm{L}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{A}\mathrm{g}\mathrm{e}$) ranges from 0.693 to 3.33, covering both newly listed firms and those that have been in operation for many years. On average, our sample firms are large and have considerable growth potential. In terms of firm characteristics, an average firm in our sample has a firm size ($\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}$) of 22.13 and a financial leverage ($\mathrm{L}\mathrm{e}\mathrm{v}$) of 0.438. The mean of the book-to-market ratio (BM) is 0.642. Regarding corporate governance and top management team characteristics, the logarithm of the largest shareholder’s ownership ($\mathrm{T}\mathrm{o}\mathrm{p}1$) has a mean of 3.501, and CEO duality (Dual) exists in 24.5% (mean = 0.245) of the observations. The average age of the TMT ($\mathrm{T}\mathrm{M}\mathrm{T}\mathrm{A}\mathrm{g}\mathrm{e}$) is approximately 49 years (mean = 48.98), and female executives ($\mathrm{F}\mathrm{e}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{e}$) account for 18.20% of the board on average. These data distributions are largely consistent with the typical governance and financial structures of listed firms.

It is worth noting that several variables in Table 1 exhibit standard deviations greater than their means. This variation is driven by the inherent statistical characteristics of the firm-level data: binary indicators (e.g., $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n},\mathrm{D}\mathrm{u}\mathrm{a}\mathrm{l},\mathrm{S}\mathrm{O}\mathrm{E}$) mathematically exhibit this trait when the sample mean is below 0.5; variables with a zero lower bound (e.g., $\mathrm{O}\mathrm{I},\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}\_\mathrm{n}\mathrm{u}\mathrm{m}$) are naturally right-skewed. To ensure robust estimations and appropriately address the natural skewness, we apply a natural logarithm transformation to the highly skewed variables. Since the standard deviations of $\mathrm{R}\mathrm{O}\mathrm{A}$ and $\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}$ are greater than their means, we have standardized them. Furthermore, to mitigate the potential influence of extreme outliers, all continuous variables are strictly winsorized at the 1st and 99th percentiles prior to the regression analysis.

4. Empirical Results

4.1. Main Results

Table 2. Results of the impact of executives’ environmental expertise on open innovation.

	(1)	(2)	(3)	(4)
Variables	OI	OI	OI	OI
Green	0.346 ***	0.183 ***	0.122 ***	0.078 ***
	(0.013)	(0.012)	(0.028)	(0.022)
ROA		−0.026 ***		−0.007
		(0.007)		(0.010)
Growth		−0.019 ***		−0.021 ***
		(0.006)		(0.006)
Top1		−0.104 ***		−0.007
		(0.013)		(0.031)
Dual		0.049 ***		−0.001
		(0.014)		(0.025)
FinBack		−0.083 ***		−0.041 *
		(0.011)		(0.023)
BM		−0.461 ***		−0.400 ***
		(0.027)		(0.065)
Lev		−0.474 ***		−0.377 ***
		(0.035)		(0.070)
Female		−0.004 ***		−0.002 **
		(0.001)		(0.001)
ListAge		−0.066 ***		0.025
		(0.009)		(0.020)
TMTAge		0.029 ***		0.017 ***
		(0.002)		(0.004)
SOE		−0.020		0.126 ***
		(0.013)		(0.033)
Size		0.487 ***		0.505 ***
		(0.006)		(0.020)
Constant	0.613 ***	−10.398 ***	0.690 ***	−10.884 ***
	(0.008)	(0.127)	(0.018)	(0.437)
Observations	40,133	40,133	40,133	40,133
R-squared	0.018	0.224	0.129	0.313
Industry FE	NO	NO	YES	YES
Year FE	NO	NO	YES	YES

Note: Standard errors clustered at the firm level are in parentheses. *** p < 0.01, ** p < 0.05, and * p < 0.1.

contains the baseline regression results testing whether the presence of green executives promotes corporate open innovation. The base specifications are ordinary least squares (OLS) panel regressions, where the dependent variable is corporate open innovation. Column (1) presents the results of the baseline model without any control variables or fixed effects. Column (2) incorporates firm and executive characteristics as control variables. Column (3) excludes control variables but incorporates industry and year-fixed effects. Finally, Column (4) includes the full set of control variables along with industry and year-fixed effects. In all specifications, standard errors are clustered at the firm level. Across all specifications, the coefficient on the $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ variable is positive and statistically significant at the 1% level. In terms of economic significance, the coefficient in Column (4) indicates that having green executives is associated with an approximately 7.8% increase in corporate open innovation. This consistent and statistically significant relationship across specifications robustly supports Hypothesis 1, confirming that green executives enhance corporate open innovation.

4.2. Robustness Tests

The nature and technical content of innovation vary significantly across different patent types. For example, the process for invention patents is naturally longer and riskier than for utility models or design patents, representing higher innovation quality. To account for such heterogeneity and focus on high-quality innovation endeavors, we prioritize $\mathrm{O}\mathrm{I}2$, defined as the natural logarithm of one plus the number of jointly applied invention patents, as an alternative dependent variable. We define $\mathrm{O}\mathrm{I}3$ as 1 plus the natural logarithm of the total number of jointly applied utility model patents. We also measure open innovation output using the number of jointly granted invention patents ($\mathrm{L}\mathrm{n}\mathrm{G}\mathrm{a}\mathrm{i}\mathrm{n}2$), jointly granted utility models ($\mathrm{L}\mathrm{n}\mathrm{G}\mathrm{a}\mathrm{i}\mathrm{n}3$), and an aggregate measure of total jointly granted patents (including inventions, utility models, and designs) ($\mathrm{L}\mathrm{n}\mathrm{G}\mathrm{a}\mathrm{i}\mathrm{n}$), all computed as the natural logarithm of the count plus one. Our main inferences remain qualitatively unchanged. Our conclusions are unaffected by using these alternative measures of open innovation.

We are conducting an additional test to address potential concerns regarding inherent variations in innovation opportunities across different sectors. We replace our baseline dependent variable with an industry-adjusted measure. Specifically, we calculate the annual average of raw patents for each industry. We then subtract the logged industry average from the firm’s logged patents. This approach effectively normalizes the dependent variable by the total innovation output of the respective sector. It captures the relative open innovation performance of the firm compared to its industry peers. The regression results using this adjusted measure are reported in

Table 3. Regressions using alternative measures for the dependent variable.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	OI2	OI3	LnGain	LnGain2	LnGain3	OI_industry
Green	0.065 ***	0.056 ***	0.063 ***	0.047 ***	0.049 ***	0.066 ***
	(0.019)	(0.018)	(0.020)	(0.014)	(0.017)	(0.022)
Constant	−9.022 ***	−7.493 ***	−9.513 ***	−6.347 ***	−7.147 ***	−12.094 ***
	(0.387)	(0.377)	(0.407)	(0.313)	(0.365)	(0.429)
Observations	40,133	40,133	40,133	40,133	40,133	40,133
R-squared	0.299	0.281	0.311	0.264	0.279	0.313
Control Variables	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES

Note: Standard errors clustered at the firm level are in parentheses. *** p < 0.01.

. The coefficient of green executives remains significantly positive.

We perform a series of other robust tests. First, we examine various approaches to defining the green executive variable. In the baseline results, we primarily focus on the presence of green executives. In

Table 4. Regressions using alternative measures for the independent variable.

	(1)	(2)	(3)	(4)
Variables	OI	OI	OI	OI
Green_num	0.345 ***	0.192 ***	0.148 ***	0.094 ***
	(0.012)	(0.011)	(0.031)	(0.024)
Constant	0.617 ***	−10.366 ***	0.682 ***	−10.884 ***
	(0.007)	(0.127)	(0.018)	(0.437)
Observations	40,133	40,133	40,133	40,133
R-squared	0.021	0.226	0.130	0.314
Control Variables	NO	YES	NO	YES
Industry FE	NO	NO	YES	YES
Year FE	NO	NO	YES	YES

Note: Standard errors clustered at the firm level are in parentheses. *** p < 0.01.

, following Zhang et al. (2025) [21], we take an additional step and consider an alternative measure, $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}\_\mathrm{n}\mathrm{u}\mathrm{m}$. This variable is defined as the natural logarithm of one plus the total number of green executives in a firm year. This measure captures not only the existence but also the intensity of green expertise within the top management team. The results show that the positive relation between green executives and corporate open innovation incentives is robust to using this alternative measure.

4.3. Identification

4.3.1. DID

Reverse causality may exist between executives’ environmental expertise and corporate open innovation. Firms with higher levels of open innovation or fewer resource constraints may be more inclined to appoint green executives to spearhead a green transformation. Furthermore, endogeneity may arise from omitted variable bias. To mitigate these issues and address endogeneity in testing Hypothesis 1, we adopt the approach of Nunn and Qian (2011) [54] and employ a multi-period DID model.

The study utilizes the first appointment of an executive with environmental expertise as an external shock. Since the timing of the first introduction of such executives varies across firms, using a single year as the cut-off for the treatment is inappropriate. Instead, we define a relative time dummy variable for each firm. The variable $\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{t}$ indicates whether a given year is after the first appointment of an executive with environmental expertise; it equals 1 if it is after the shock and 0 if otherwise. The variable Treat indicates whether a firm hires an executive with environmental expertise during the sample period, with the treatment group coded as 1 and the control group coded as 0. This specification allows us to identify the causal effect of green executives on corporate open innovation.

The validity of the DID estimation relies on the parallel trend assumption, which requires that, before the appointment of green executives, the open innovation trends of treated and control firms would have evolved similarly. To test this assumption and examine the dynamic effects of green executives on open innovation, following the methodology of Li et al. (2016) [55], we bin observations beyond six years and use the year immediately preceding the appointment (t = −1) as the reference period. The results of the parallel trend test are plotted in Figure 1. The results show that, before the appointment of green executives, the estimated coefficients for all relative years are statistically indistinguishable from zero and small in magnitude. This indicates that there are no significant systematic differences in open innovation between the treated and control firms before the treatment, thereby supporting the validity of the parallel trend assumption. In contrast, the regression coefficients become significantly positive in the year of the appointment and subsequent years, exhibiting a continuously increasing trend over time. This pattern further confirms that green executives have a significant and sustained promoting effect on corporate open innovation, rather than the results being driven by pre-existing trends.

4.3.2. PSM-DID

Next, we employ the Propensity Score Matching (PSM) approach to generate control groups to address potential sample self-selection bias. Specifically, we match the treatment group of firms that appoint green executives with the control group of firms that never appoint green executives. We calculate the propensity scores using a Logit model that includes a comprehensive set of covariates: firm size ($\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}$), return on assets ($\mathrm{R}\mathrm{O}\mathrm{A}$), growth rate ($\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}$), largest shareholder ownership ($\mathrm{T}\mathrm{o}\mathrm{p}1$), CEO duality ($\mathrm{D}\mathrm{u}\mathrm{a}\mathrm{l}$), book-to-market ratio ($\mathrm{B}\mathrm{M}$), leverage ($\mathrm{L}\mathrm{e}\mathrm{v}$), listing age ($\mathrm{L}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{A}\mathrm{g}\mathrm{e}$), and TMT average age ($\mathrm{T}\mathrm{M}\mathrm{T}\mathrm{A}\mathrm{g}\mathrm{e}$). To ensure matching quality, we employ a one-to-one nearest neighbor matching algorithm with a caliper distance of 0.04. This strict criterion ensures that the matched control observations do not have structural differences with the treated observations. Using this matched subsample, we re-estimate the baseline DID specification.

Table 5. Regression results of the DID model.

	(1)	(2)
	Panel A: DID	Panel B: PSM-DID
Variables	OI	OI
Treat * Post	0.079 ***	0.080 **
	(0.029)	(0.034)
Constant	−10.834 ***	−10.636 ***
	(0.436)	(0.491)
Observations	40,133	19,508
R-squared	0.313	0.302
Control Variables	YES	YES
Industry FE	YES	YES
Year FE	YES	YES

Note: Standard errors clustered at the firm level are in parentheses. *** p < 0.01, ** p < 0.05, and * p < 0.1.

presents the robustness test results using the propensity-score matched subsample. The results indicate that, after controlling for self-selection bias, the coefficient on the treatment variable remains positive and statistically significant. Consistent with our baseline results, the PSM estimates further demonstrate that appointing green executives significantly enhances corporate open innovation.

4.4. Mechanism

4.4.1. Mediating Effect

To test the mediating role of managerial myopia, we first construct the measurement of this variable using textual analysis. Following the standard procedure, we download the annual financial reports of all A-share listed firms from the CNINFO website. We utilize the WinGo financial text data platform to convert PDF documents into TXT format and extract the Management Discussion and Analysis (MD&A) section. To ensure data accuracy, we remove tables and scanned files that contain little incremental textual information. Subsequently, we employ the Jieba Chinese segmentation system combined with specialized financial dictionaries to tokenize the MD&A content and remove stop words, transforming unstructured text data into word vectors. Finally, we calculate the total frequency of the seed words related to short-termism in the MD&A section to measure managerial myopia.

We predict the mediating role of managerial myopia in the relationship between green executives and corporate open innovation. We adopt the approach suggested by Baron and Kenny (1986) to test this hypothesis [56]. This approach involves a causal strategy. First, the total effect is computed by regressing the dependent variable ($\mathrm{O}\mathrm{I}$) on the independent variable ($\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$). Then, the mediator ($\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}$) is regressed on the independent variable. In the last step, the dependent variable is regressed on both the independent variable and the mediator. The regression results are reported in

Table 6. Regression results of the mediation analysis.

	(1)	(2)
Variables	Myopia	OI
Myopia		−0.350 ***
		(0.104)
Green	−0.004 ***	0.076 ***
	(0.001)	(0.022)
Constant	0.081 ***	−10.952 ***
	(0.020)	(0.424)
Observations	39,177	39,177
R-squared	0.155	0.303
Control Variables	YES	YES
Industry FE	YES	YES
Year FE	YES	YES

Note: Standard errors clustered at the firm level are in parentheses. *** p < 0.01.

. In the first step, green executives are significantly and positively associated with corporate open innovation (β = 0.078, p < 0.01). In the second step, the results show that green executives had a significant negative effect on managerial myopia (β = −0.004, p < 0.01), indicating that such executives help mitigate managerial short-termism. In the final step, after controlling for the mediator ($\mathrm{M}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}$), the negative effect of managerial myopia on open innovation is significant (β = −0.35, p < 0.01). Meanwhile, the direct effect of green executives on open innovation (β = 0.076, p < 0.01) is reduced in magnitude compared to the total effect but remains significant. This reduction suggests that managerial myopia partially mediates the relationship between green executives and corporate open innovation. The Sobel test statistic (Z = 2.415, p < 0.01) further confirms the significance of this mediating effect.

4.4.2. Regulatory Effect

When facing severe capital market pressures, managers are more inclined to pursue short-term objectives rather than invest in long-term and risky innovation projects. To empirically capture this setting, we use high stock liquidity as a primary proxy for such capital market pressures. As documented by Fang et al. (2014) [26], high liquidity increases the risk of hostile takeovers and attracts transient institutional investors who focus on short-term gains, forcing managers to cut long-term R&D expenditures. By contrast, for firms characterized by low stock liquidity, such a shift would be less appealing because of the lower marginal benefit from reducing short-termism. Therefore, we hypothesize that the appointment of green executives significantly improves corporate open innovation only for firms that are exposed to excessive short-term pressure (i.e., $\mathrm{H}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{L}\mathrm{i}\mathrm{q}\mathrm{u}\mathrm{i}$), but may not affect performance for firms that are not.

To test this hypothesis, we construct a dummy variable, $\mathrm{H}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{L}\mathrm{i}\mathrm{q}\mathrm{u}\mathrm{i}$, which equals one if a firm’s stock liquidity is above the industry–year median and zero if otherwise. We use the effective spread estimator of Roll (1984) to proxy for liquidity [57], where a lower Roll value indicates higher liquidity. Following this, we classify firms with Roll values below the median as High Liquidity firms. We posit that a High Liquidity firm is more likely to engage in myopic behavior and would benefit more from the long-term horizon brought by green executives. We augment the open innovation models by including the interaction between $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ and $\mathrm{H}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{L}\mathrm{i}\mathrm{q}\mathrm{u}\mathrm{i}$.

Extant research shows that weak corporate governance is detrimental to the firm because it exacerbates managerial myopia and leads executives to prioritize short-term financial goals over long-term value creation. In the absence of effective monitoring, managers are more prone to cut R&D expenditure and avoid complex open innovative activities [24,44]. Hence, it is possible that green executives can mitigate the executives’ myopic bias by bringing a long-term sustainability orientation and thereby fostering open innovation [10]. Then, it follows that the role of green executives may be particularly important in firms with weak corporate governance. Consequently, the moderate effects of green executives on corporate open innovation should be more prominent in these firms.

To examine the moderating role of corporate governance, we first construct a composite governance index following the framework of Schweizer et al. (2019) [58]. We conduct principal component analysis on seven governance factors from the CSMAR database. These include CEO duality, board size, independent director ratio, management shareholding, executive compensation, equity balance, and institutional ownership. The first principal component serves as our proxy for governance quality. Based on this index, we construct an indicator variable named $\mathrm{W}\mathrm{e}\mathrm{a}\mathrm{k}\mathrm{G}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}$. It equals one if a firm’s governance score is lower than the industry–year median and zero if otherwise. We posit that weakly governed firms suffer from more severe managerial myopia due to ineffective oversight. These firms thus benefit more from the corrective role of green executives. To empirically test this prediction, we augment the baseline model with the interaction term between $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ and $\mathrm{W}\mathrm{e}\mathrm{a}\mathrm{k}\mathrm{G}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}$.

In what follows, we report on the regression results in

Table 7. Regression results of the cross-sectional tests.

	Stock Liquidity	Corporate Governance
Variables	OI	OI
Green * HighLiqui	0.100 ***
	(0.028)
Green * WeakGover		0.132 ***
		(0.038)
Green	0.030	0.010
	(0.023)	(0.026)
HighLiqui	−0.008
	(0.016)
WeakGover		−0.045 *
		(0.027)
Constant	−10.837 ***	−11.034 ***
	(0.434)	(0.455)
Observations	40,133	36,237
R-squared	0.314	0.307
Control Variables	YES	YES
Industry FE	YES	YES
Year FE	YES	YES

Note: Standard errors clustered at the firm level are in parentheses. *** p < 0.01, * p < 0.1.

. For each specification, we estimate the regression controlling for fixed effects. The results show that the coefficient in the interaction term between $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}$ and $\mathrm{W}\mathrm{e}\mathrm{a}\mathrm{k}\mathrm{G}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}$ is positive and significant at the 1% level. This finding is consistent with the conclusion that the positive impact of green executives on open innovation is significantly stronger in firms characterized by weak governance. Green executives play a compensatory role. Their presence becomes crucial for refocusing on long-term innovation when weak governance allows managerial myopia to prevail. There is a less pronounced effect in firms with strong governance, where monitoring mechanisms are already effective in curbing myopia.

In Column 1, open innovation is the dependent variable. We find that the coefficient for interaction is positive and significant at the 1% level. This finding is consistent with the view that the presence of green executives improves innovative outcomes only for firms that are likely to engage in excessive short-termism due to market pressure. For High Liquidity firms, having green executives increases their open innovation by roughly 10%.

Taken together, the mechanism and regulatory tests validate Hypotheses 2 and 3. The findings confirm that green executives promote open innovation primarily by correcting managerial myopia. Furthermore, this corrective role acts as a crucial substitute mechanism, offering the highest marginal benefits in environments characterized by weak corporate governance and high short-term market pressure.

4.5. Heterogeneity

Heterogeneity in pollution intensity across industries. Since environmental regulations and public scrutiny exert differential pressures on firms, we examine the heterogeneity in pollution intensity across industries in this section. Following the literature (e.g., Liu et al., 2025) [59], we categorize industries into heavy-polluting and non-heavy-polluting sectors based on the guidelines issued by the Ministry of Environmental Protection of China. The rationale is that firms in heavy-polluting industries face greater legitimacy threats and transition urgency and thus have stronger incentives to leverage the expertise of green executives to drive open innovation. Therefore, for firms in heavy-polluting industries, the marginal benefit of appointing green executives is expected to be higher. Based on this classification, we construct an indicator variable, $\mathrm{I}\mathrm{n}\mathrm{d}$, which equals one if the firm belongs to a heavy-polluting industry and zero if otherwise. We then augment the baseline model by including the interaction term between green executives and heavy pollution. A positive estimated coefficient on the interaction term would support our hypothesis.

Table 8. Regression results of the heterogeneity.

	(1)	(2)
Variables	OI	OI
Ind	0.062
	(0.136)
Green * Ind	0.160 ***
	(0.048)
type		−0.133
		(0.138)
Green * type		0.055 **
		(0.026)
Green	0.037	−0.023
	(0.026)	(0.050)
Constant	−11.162 ***	−11.032 ***
	(0.431)	(0.427)
Observations	40,133	40,133
R-squared	0.314	0.313
Control Variables	YES	YES
Industry FE	YES	YES
Year FE	YES	YES

Note: Standard errors clustered at the firm level are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

reports the estimation results. As Column (1) shows, the coefficient for $\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}\ast \mathrm{I}\mathrm{n}\mathrm{d}$ is positive and statistically significant at the 1% level. This finding is consistent with our hypothesis, suggesting that the positive relationship between green executives and corporate open innovation is significantly stronger for firms in heavy-polluting industries. Overall, these findings confirm that the strategic value of green executives is more pronounced when firms face higher environmental compliance costs and transformation pressures.

Heterogeneity in factor intensity across industries. Since the production function and innovation reliance vary significantly across sectors, we examine the heterogeneity in factor intensity in this section. Following the classification guidelines of the China Securities Regulatory Commission (CSRC), we categorize industries ($\mathrm{t}\mathrm{y}\mathrm{p}\mathrm{e}$) into three groups based on their primary factor inputs: labor-intensive (coded as 1), technology-intensive (coded as 2), and capital-intensive (coded as 3).

Capital-intensive firms typically possess high asset specificity and face severe short-term financial pressures due to massive sank costs. For these firms, green executives provide a critical buffer and the strategic patience needed to steer the firm toward sustainable open innovation. Therefore, we hypothesize that the promoting effect of green executives on open innovation is more prominent in capital-intensive industries compared to labor-intensive ones.

To test this, we augment the baseline model by including the interaction terms between green executives and the factor intensity indicators. Table 8 reports the estimation results. The coefficients on the interaction term are positive and statistically significant. This finding is consistent with our hypothesis, confirming that the strategic value of green executives in fostering open innovation is significantly amplified in sectors driven by capital investment.

5. Conclusions and Discussion

5.1. Conclusions

This study examines how executives’ environmental expertise influences firms’ engagement in open innovation. Building on upper echelons theory, we conceptualize open innovation as an intertemporal investment decision that is particularly vulnerable to managerial myopia. We show that environmental expertise shapes managerial cognition by extending decision-making time horizons, thereby mitigating managerial myopia and enabling firms to undertake long-term, uncertain, and collaborative innovation activities.

Using a panel of 40,133 firm–year observations from Chinese listed firms over 2002–2022, our results corroborate the hypothesis that firms led by environmentally expertised executives exhibit significantly higher levels of open innovation. This effect operates through the alleviation of managerial myopia and is more pronounced in firms facing stronger short-term pressures.

Additionally, we identify specific environments that are more likely to induce managerial myopia and demonstrate how environmental expertise interacts with these conditions. The promoting effect of green executives on open innovation is stronger in firms with weaker corporate governance and higher capital market pressure. Suggesting that in environments lacking robust formal monitoring, the long-term cognitive orientation of green executives serves as a form of substitute cognitive governance to alleviate myopia. Moreover, the strategic value of green executives is more pronounced when firms operate in heavy-polluting and capital-intensive industries. Firms in these sectors face higher environmental compliance costs and severe short-term financial pressures, making the long-term strategic vision of green executives particularly critical for navigating such constraints.

Importantly, corporate sustainability is closely relevant to the firm’s ability to generate long-term economic and strategic value through continuous innovation and capability renewal. In this context, open innovation serves as a critical mechanism for internalizing sustainability-related externalities by facilitating knowledge sharing, stakeholder collaboration, and the co-creation of sustainable solutions. Our findings imply that green executives enhance open innovation and corporate sustainability by alleviating managerial myopia.

5.2. Discussion

Our empirical results offer significant theoretical contributions by examining the causal link between environmentally expertise executives (green executives) and open innovation in firms. Moving beyond simply documenting an empirical relationship, our analysis unpacks the underlying cognitive mechanisms and theoretical boundaries. This extends upper echelons theory, stewardship theory, and the literature on sustainable innovation. Specifically, we make three primary contributions: extending the innovation context, revealing the mediating channel of managerial myopia, and identifying critical boundary conditions.

First, we bridge the theoretical gap between general innovation and boundary-spanning collaborative strategies in firms. Our findings are closely related to Luo and Zhang (2024) [41]. Their study documents that executives with environmental expertise promote corporate innovation. However, our study extends this line of research by examining the more demanding context of open innovation. As our study notes, open innovation requires long-term, uncertain, and relationship-specific investments. By demonstrating that environmental expertise facilitates these specific collaborative strategies, we complement the existing literature and show that green executives are vital for navigating the unique complexities of open innovation.

Second, we unpack the mechanism of this relationship by providing direct evidence that the effect of green executives operates through the alleviation of managerial myopia. Drawing on upper echelons and stewardship theories, we show how environmental expertise shapes managers’ time horizons. Because open innovation is highly vulnerable to managerial short-termism, green executives act as a corrective force by developing a long-term cognitive orientation. By extending decision-making horizons, these executives mitigate short-term bias and prioritize sustainable value creation, thereby contributing directly to the literature on managerial myopia.

Third, our analysis refines established governance frameworks by identifying the environments where this cognitive orientation is most critical. Based on our cross-sectional and heterogeneity results, the promoting effect of green executives is stronger in firms with weaker corporate governance and higher capital market pressure. In these settings, the long-term orientation of green executives serves as a substitute cognitive governance mechanism to buffer against severe short-term incentives. Furthermore, we find that this strategic value is more pronounced in heavy-polluting and capital-intensive industries, confirming that environmental human capital is especially critical when firms face high compliance costs and severe sunk cost pressures.

From a practical perspective, our findings provide important implications. For policymakers, promoting sustainability-oriented innovation requires not only external regulation but also the development of managerial human capital aligned with long-term sustainability goals. Policymakers and boards of directors may consider encouraging the integration of environmental expertise into top management teams through governance guidelines, disclosure frameworks, and talent development programs. Such initiatives can help firms overcome short-termism, strengthen sustainable business models, and accelerate the transition toward innovation-driven and collaborative sustainability.

Our study also has limitations that point to avenues for future research. In particular, while we focus on executives’ green expertise as a form of knowledge-based human capital acquired through education and professional experience, we do not directly capture executives’ exposure to environmental conditions in their personal histories. The prior literature suggests that environmental experiences may shape managerial cognition and behavior through an experiential channel; however, the direction of this effect remains theoretically ambiguous. Adverse environmental experiences, such as exposure to pollution or climate-related shocks, may either foster resilience and prosocial orientation or induce greater caution and risk aversion, depending on the nature and salience of exposure. Meanwhile, executives may also benefit from positive environmental experiences. For example, growing up in regions with strong environmental quality or sustainability practices, which could reinforce pro-environmental preferences and long-term orientation through a different mechanism [60]. Future research could more precisely distinguish between positive and negative environmental experiences and examine how these distinct experiential channels interact with knowledge-based green expertise in shaping corporate innovation and sustainability strategies.

Furthermore, our analysis does not classify sectors that offer different challenges and possibilities for innovation. We acknowledge this limitation and suggest that future research should consider differentiations by activity, examining whether the influence of green executives varies across technological regimes with distinct innovation requirements.