Perceived environmental uncertainty (PEU) is a perceptual phenomenon that primarily exists in the “eyes” of the observer (Duncan, 1972). It describes entrepreneurs’ lack of critical information about the external environment. The three primary types of perceptual uncertainty are state uncertainty, effect uncertainty, and response uncertainty. State uncertainty refers to the inability to predict the probability of a particular event occurring or changing; effect uncertainty refers to the inability to predict the impact of external environmental fluctuations on the firm; and reaction uncertainty refers to the inability of entrepreneurs to predict the effect of strategic decisions in response to uncertainty. Empirical evidence suggests that this uncertainty plays a secondary role in determining a company’s strategy. What is more important is how decision-makers perceive the uncertainty.

Environmental uncertainty has a greater impact on SMEs than on large corporations. Turbulence in the external environment may easily impede the success of or lead to the failure of these businesses, as they have fewer resources and limited capabilities (García-Pérez and Yanes-Estévez, 2022). Furthermore, SME decision-makers, as the firm’s external environment analysts, are more sensitive to environmental uncertainty under the influence of a turbulent environment (Gaur et al., 2011). Specifically, in most SMEs, decision-makers are closer to the objective environment (Robinson and Simmons, 2018); they function as both analysts and collectors of environmental information (Aldehayyat, 2015); they usually rely on common sense or intuition rather than sophisticated analytical tools to analyze the environment (Wong et al., 2014); and their perception of environmental uncertainty frequently influences firms’ strategic choices (Robinson and Simmons, 2018).

As reform and opening up have progressed, tens of millions of SMEs have been developed in China. However, they face more severe external environmental uncertainties than state-owned firms or large enterprises due to flawed market mechanisms and arbitrary resource allocation (Liu et al., 2021). With regard to strategic decision-making, Chinese entrepreneurs tend to rely on their accumulated knowledge and experience to make decisions, and experience or perceptions play a pivotal role in entrepreneurial decision-making. In China, experienced managers are not necessarily well-educated and systematically trained (Parnell et al., 2012). Given the massive number of Chinese SMEs and the entrepreneurs who base their strategic decisions on experience or perception, it is appropriate to focus our research on Chinese SMEs.

Compared to conventional innovation efforts, eco-innovation has a dual externality, as firms may face “free-rider” behavior (knowledge spillover or environmental spillover) from other market participants, and the firms themselves might not fully reap the benefits of eco-innovation (Yi et al., 2020). When market mechanisms fail to address the dual externalities of eco-innovation, government policy instruments might encourage enterprises to engage in eco-innovation by reducing the disparity between social and private profits (Long and Liao, 2021). Thus, the eco-innovation of companies is internally dependent on government forces. In our research, we emphasized the moderating impacts of two distinct types of government involvement: government subsidies and government official visits (GOV).

Government subsidies are one of the most common government intervention strategies (Shao et al., 2020), and their positive influence on eco-innovation has been consistently demonstrated, despite ongoing concerns about the crowding-out effect of government subsidies on enterprises’ R&D spending (Long and Liao, 2021). In reality, most studies have found that government subsidies have an “additional effect” on enterprises’ R&D activities, reducing the cost of R&D and hastening the completion of R&D projects (Görg and Strobl, 2007), revitalizing previously unprofitable R&D programs. Furthermore, government subsidies serve as a guide, encouraging enterprises to allocate resources to production technologies and opportunities that meet society’s expectations, allowing firms to benefit economically while focusing on societal rewards (Tsai and Liao, 2017b). As a result, corporations are more willing to engage in eco-innovation activities with government subsidies (Tsai and Liao, 2017a; Tsai and Liao, 2017b; Long and Liao, 2021).

Corporate eco-innovation activities entail significant environmental uncertainty and variability, requiring a keen perception of external opportunities and threats, as well as the full utilization of current resources and the introduction of new external resources into innovation activities (Demirel and Kesidou, 2019). Due to the dual externalities of eco-innovation activities, even though SMEs have greater strategic flexibility and adaptability in uncertain environments, the lack of external resource support or meager economic returns over time may lead firms to abandon investing in eco-innovation. Government financial incentives are crucial to a company’s resource base (Melander, 2018). In a volatile market environment, SMEs that receive government subsidies can combine them with their existing knowledge and experience. These new combinations not only enhance SMEs’ resource allocation flexibility but also strengthen their organizational learning capacity. Therefore, after receiving government subsidies, companies can reallocate cross-functional teams, restructure their resource portfolios, and participate more effectively in eco-innovation activities in uncertain situations. Therefore, we propose:

Hypothesis 2a:

The positive relationship between the entrepreneurs’ PEU and corporate eco-innovation is stronger for firms that receive government subsidies.

In emerging economies, governments as social resource allocators and institutional authorities not only control scarce resources (land, industry access, etc.) but also have a strong voice in determining the legitimacy of firms’ strategies and can exert significant influence on firms’ innovation activities (Liu et al., 2021). GOV provides an opportunity to enhance enterprise and government information communication, improve corporate legitimacy, and reduce risk in strategic decision-making.

Firstly, corporations can increase their political uncertainty perception by utilizing GOV to obtain political information, such as the dynamics of government policy, changes in government officials, and the political philosophy of these officials (Yunxia and Qiu, 2021). This crucial information can strengthen firms’ willingness to take risks in implementing innovation activities, reducing their inclination to be risk-averse and exhibit short-term investment tendencies. Secondly, during government officials’ visits, companies can consult with them on major issues they face by showcasing their current achievements and long-term plans. In this manner, the corporation can assess the authorities’ basic attitude toward the company’s strategy and seek their feedback and recommendations, thereby earning the government’s recognition and support (Jia et al., 2019). Thirdly, government officials’ visits to target firms not only imply that the firms’ ability to use scarce government resources has improved but also indicate the government’s attention and support for firms’ risky behavior (Yunxia and Qiu, 2021), which will attract the support of more external stakeholders. Therefore, GOV is conducive to enhancing firms’ willingness to invest in eco-innovation and resource security.

The primary cause of entrepreneurs’ perception of uncertainty is the lack of access to vital external information. In contrast to firms, governments possess a vast quantity of critical external information and scarce resources. Therefore, GOV provides opportunities for enterprises to gain information and enhance their dynamic capacities during times of uncertainty. Specifically, during government officials’ visits, their views and suggestions on the company’s technological strengths, production processes, and product innovation provide crucial information for strategic decisions and adjustments. The government can even assist the company in identifying appropriate technologies and recruiting professional staff, which are essential for driving the company’s eco-innovation. GOV also enables corporations to learn about policy changes, turnover of officials, and other political information, improving their ability to perceive uncertainty and make their strategic decisions more rational in an uncertain environment. Moreover, similar to government subsidies, GOV can provide scarce resources to companies, enhancing their resource flexibility. Therefore, we propose:

Hypothesis 2b:

The positive relationship between the entrepreneurs’ PEU and corporate eco-innovation is stronger for firms that visited by government officials.

Based on the hypotheses developed above, the dynamic capabilities perspective is employed in this study to posit a positive relationship between entrepreneurs’ perceived environmental uncertainty and eco-innovation. Additionally, government subsidies and official visits are identified as moderators of this relationship, while firms’ dynamic capabilities are found to mediate the relationship between perceived environmental uncertainty and eco-innovation. The theoretical model of this study is illustrated in Figure 1.

The data on SMEs used in this study were obtained from the 2016 China Private Enterprise Survey (CPES) data, which is a collaborative effort between the United Front Work Department of CPC Central Committee, All-China Federation of Industry and Commerce, and State Administration for Industry and Commerce of the People’s Republic of China. The survey project began in 1993 and is conducted every 2 years, with the most recent publicly available data being from 2016. The CPES database employs a multi-stage stratified random sampling survey of private enterprises in China, which ensures data universality and representativeness and reduces the potential for sample selection bias. Additionally, the CPES database provides information on firms’ operations, entrepreneurs’ socio-economic backgrounds, and entrepreneurs’ perceived evaluation of external environmental conditions. As the founder is the provider of information, this is the most knowledgeable and effective source of information (Lechner et al., 2006). This data has been extensively employed in previous studies (Ma et al., 2015; Zhao and Lu, 2016).

We selected the 2016 survey data as the research sample because only this year’s data provided a detailed description of entrepreneurs’ perceptions of environmental uncertainty. After winsorizing some continuous variables at the 1st and 99th percentiles, we obtained a sample of 2733 (Li et al., 2020).

In our research, we utilized ordinary least squares (OLS) to estimate regression (2) and incorporated fixed effects at the industry and province levels in our estimation to investigate the relationship between entrepreneurial PEU and eco-innovation. E c o − I n n o v a t i o n i = β 0 + β 1 × P E U i + β 2 × C o n t r o l s i + ε i (2)

Where, E c o − I n n o v a t i o n i represents whether enterprise i participated in eco-innovation, P E U i denotes the level of environmental uncertainty perceived by the entrepreneur in enterprise i, Controls is a set of control variables, and ε is the error term.

To further examine the moderating effect, we estimated regression (3) by introducing interaction terms between the moderators and independent variables in regression (2). E c o − I n n o v a t i o n i = β 0 + β 1 × E P U i + β 2 × G o v _ S u b s i d y i + β 3 × G O V i + β 4 × E P U i × G o v _ S u b s i d y i + β 5 × E P U i × G O V i + β 6 × C o n t r o l s i + ε i (3)

Where, E P U × G o v _ S u b s i d y and E P U × G O V are the interaction terms of perceived environmental uncertainty with government subsidies and government official visiting, respectively, for testing Hypothesis 2a and Hypothesis 2b.

Table 1 presents the descriptive statistics for all variables included in this study. The mean value and standard deviation of Eco-Innovation among firms were 0.333 and 0.471, respectively, indicating significant variation in the extent to which firms engage in eco-innovation, with only 33.3% of firms engaging in such activities. This finding is consistent with Long and Liao (2021). Similarly, there was a significant variation in the extent to which firms received government subsidies (mean value of 0.337, standard deviation of 0.473). The mean value of entrepreneurs’ PEU was 0.583, with a standard deviation of 0.157, indicating a generally strong perception of future environmental uncertainty among entrepreneurs.

In addition, in order to depict the spatial distribution of eco-innovation levels among SMEs, we presented the average eco-innovation level at the provincial level in Figure 2. The results revealed that the average eco-innovation level in the eastern coastal region of China was significantly higher than that in the central and western regions. Specifically, the average level of eco-innovation in coastal provinces in eastern China was 0.344, while it was 0.273 in other regions. The adoption of more environmentally-friendly production processes and technologies in the eastern coastal provinces, as well as the imposition of stricter environmental regulations by the government, may be internal factors contributing to this difference.

The Pearson correlation coefficients between the variables are presented in Table 2. Consistent with our hypothesis, there is a positive correlation between Eco-Innovation and entrepreneurs’ PEU, with a correlation coefficient of 0.032. Moreover, the correlation coefficients between Eco-Innovation and the other variables are less than 0.5. In addition, we calculated variance inflation factors (VIFs) for the estimated models, which ranged from 1.04 to 2.22. These values are considerably below the acceptable level of 10 (Wang and Qian, 2011), indicating that multicollinearity was not a serious problem in this study.

The regression results are presented in Table 3. Model 1 serves as the baseline model with only control variables. In Model 2, we introduced PEU to test Hypothesis 1. The results demonstrate a positive and significant effect of PEU on Eco-Innovation ( β = 0.160, p <0.01). The analysis of the marginal effects of the regression coefficients revealed that an increase of one standard deviation in entrepreneurs’ PEU is associated with an increase in firms’ willingness to engage in eco-innovation by (0.157 × 0.160/0.471) standard deviations. These results provide strong support for Hypothesis 1. Therefore, when entrepreneurs perceive an increase in environmental uncertainty, they are likely to take proactive measures such as implementing eco-innovation to address these challenges.

The results of the moderating effect are presented in Models 3 and 4. Model 3 examined the moderating effect of government subsidies by introducing the interaction term between Gov_Subsidy and PEU, which produces a positive and significant coefficient ( β = 0.225, p <0.05), providing support for Hypothesis 2a. Similarly, Model 4 tested the moderating effect of GOV and found a positive and significant coefficient for the interaction term ( β = 0.177, p <0.1), offering support for Hypothesis 2b. The results of Models 3 and 4 show that the eco-innovation activities of enterprises under uncertainty are influenced by government intervention, which can reduce the cost of eco-innovation, enhance the dynamic capabilities of enterprises, and thus increase their innovation motivation. This also provides empirical support for the positive intervention of government as a “helping hand” for enterprise innovation in uncertain environments. Moreover, we introduced the interaction term between Gov_Intervention and PEU in Model 5 to further examine the positive effect of government intervention. The coefficient of this interaction term ( β = 0.146, p <0.01) is consistent with our previous findings.

To further illustrate the moderating effects of the selected moderators, we plotted the estimated values of these moderating relationships as shown in Figures 3, 4. The results in Figure 3 indicate that firm eco-innovation intensity is higher when they receive government subsidies, suggesting that such subsidies increase firm motivation to engage in eco-innovation activities, thus providing further support for Hypothesis 2a. Similarly, Figure 4 demonstrates that GOV significantly strengthens the relationship between entrepreneurs’ PEU and eco-innovation, thereby providing additional evidence for Hypothesis 2b.

We performed several analyses to ensure the robustness of our results. Firstly, we used the eco-innovation score (Score_Eco) as an alternative indicator for Eco-Innovation, which may more accurately reflect the eco-innovation intensity of a firm. The findings are presented in Models 1-4 in Table 4. The results of Models 1-4 show that the coefficients of PEU, PEU × Gov_Subsidy, PEU × GOV, and PEU × Gov_Intervention are positive and significant ( β = 0.246, p <0.01; β = 0.277, p <0.1; β = 0.293, p <0.05; β = 0.210, p <0.05, respectively). These results are consistent with our main findings, further indicating that the positive relationship between entrepreneurial PEU and eco-innovation is robust across different measures of eco-innovation.

Secondly, as the dependent variable in this study is a categorical variable with only two values, 0 and 1, we employed the Logit model to test the research hypotheses again for robustness. The results of the Logit model are presented in Models 5-8 in Table 4. The regression coefficients of PEU, PEU × Gov_Subsidy, and PEU × Gov_Intervention remained positive and significant ( β = 1.120, p <0.01; β = 1.372, p <0.05; β = 1.032, p <0.01). These results suggest that government subsidies and government intervention have a positive moderating effect on the relationship between PEU and Eco-Innovation. However, for PEU × GOV the regression coefficient is positive but not statistically significant.

Thirdly, we used LnPEU as an alternative independent variable for PEU. Table 5 presents the results of replacing the independent variables. Models 1-4 demonstrate that the coefficients of LnPEU, LnPEU × Gov_Subsidy, LnPEU × GOV, and LnPEU × Gov_Intervention are all positive and significant ( β = 0.080, p <0.01; β = 0.108, p <0.1; β = 0.098, p <0.1; β = 0.073, p <0.01, respectively). Overall, the findings of this study are robust to changes in the measures of key variables and regression methods.

Another issue that may affect the robustness of our research is the existence of omitted variables and reverse causality in our regressions, which can lead to endogeneity problems. For instance, firms with higher eco-innovation intensity may be more sensitive to external environmental uncertainty. To address this endogeneity problem, we utilized two-stage least squares (2SLS) estimation with an instrumental variable, the industry PEU mean.

The validity of instrumental variables depends on the relevance condition and the exclusion restriction. Firstly, regarding the relevance condition, we argue that the average level of PEU at the provincial level mainly reflects the degree of regional environmental uncertainty, and thus is correlated with entrepreneurs’ perception of external environmental uncertainty, satisfying the relevance condition. Secondly, with respect to the exclusion restriction, we contend that in the context of this study, unobserved firm-specific characteristics related to eco-innovation are unlikely to affect the regional-level environmental variable, that is, used as the instrumental variable, thereby satisfying the exclusion restriction (Fisman and Svensson, 2007).

Table 6 presents the estimation results using 2SLS, and we report the results for both stages. The findings of the first stage of Model 1 and Model 3 reveal that both instrumental variables (Industry_PEU and Industry_LnPEU) are significantly associated with entrepreneurs’ PEU. In the second stage, all coefficients, PEU in model 2 ( β = 1.620, p <0.05), and LnPEU in model 4 ( β = 1.199, p <0.05), are positive and significant at the 5% level, which remains consistent with our main conclusions. Thus, the positive relationship between PEU and Eco-Innovation remains robust even after correcting for the endogeneity problem using 2SLS estimation.

According to our theory, when entrepreneurs perceive higher levels of environmental uncertainty, the dynamic capabilities of SMEs can be enhanced, leading to a positive impact on the firm’s eco-innovation. To measure a company’s dynamic capabilities, we utilized a CPES item that asks, “What are the most significant changes that companies undertake to adapt to environmental changes?” A company’s Dynamic_Capability is coded as 1 if it has upgraded its technology, transferred its product production to high-end products, and reduced the pollution generated by its production process as part of its transformation; otherwise, it is coded as 0.

We estimated a mediation model using the firm’s dynamic capabilities as a mediator, and Table 7 reports the results of the analysis of our mediation model. In the first step, we estimated the impact of PEU on Eco-Innovation and the results are consistent with the previous ones. In the second step, we estimated the effect of PEU on firm Dynamic_Capability, and Model 2 demonstrates that PEU has a positive and significant effect on Dynamic_Capability ( β = 0.113, p <0.05). In the third step, we observed further that the effect of PEU on Eco-innovation shrinks when Dynamic_Capability is added to the model. These results suggest that the firm’s dynamic capability is an important mechanism to connect entrepreneurs’ PEU with firm’s eco-innovation.

As the primary theoretical contribution of this study, we emphasize the influence of entrepreneurs’ PEU on corporate eco-innovation from a dynamic capability viewpoint, thereby enhancing the literature on uncertainty and corporate innovation. Previous research has mainly assumed that the risk of external environmental uncertainty prompts entrepreneurs to delay innovation investment decisions. However, they primarily focused on objective macro-environmental risks rather than entrepreneurs’ attitudes towards risk (Roper and Tapinos, 2016). In fact, entrepreneurs’ attitudes or perceptions of risk are essential factors influencing business decisions (Zayadin et al., 2022). We have linked entrepreneurial PEU with corporate eco-innovation decisions based on the dynamic capabilities of the organization. Our findings not only bridge the gap between objective environmental uncertainty and PEU but also enable researchers to gain a deeper understanding of how dynamic capabilities function in uncertain environments.

Furthermore, we found that government intervention significantly contributes to improving the strategic flexibility of enterprises in an uncertain environment, providing theoretical support for the rationality of government intervention behavior in uncertain situations. Whether the influence of government interference on innovation activities is a “helping hand” or a “grabbing hand” has been the focus of academic discussion. Some scholars argue that government public funding has a crowding-out effect on firms’ innovation investments (Dimos and Pugh, 2016), while others contend that government financial support reduces firms’ R&D costs and risks (Long and Liao, 2021), thereby increasing firms’ willingness to research eco-product innovation. Our study emphasizes the positive effect of government involvement on company eco-innovation under uncertainty, providing theoretical evidence for government intervention under uncertainty. These findings further clarify the boundary conditions for the impact of PEU on eco-innovation.

More practically, this research provides new evidence and insights for corporate decision-makers and government policymakers about managing corporate eco-innovation in an uncertain environment. Based on our results, they can have a more transparent and comprehensive understanding of the risks and opportunities arising from environmental uncertainty.

First, corporate decision-makers, especially SMEs, should clearly recognize the opportunities and challenges brought by the changing environment when they perceive the uncertainty of the external environment as elevated. Although eco-innovation in an unpredictable environment carries the risk of revenue decreases, organizations can obtain a first-mover advantage and turn challenges into opportunities in a crisis if they are adaptable and have a solid dynamic capability. They should be willing to embrace technological advancements in an uncertain environment, strive to enhance their dynamic capabilities, and actively seek government resource support. Based on the resources available, companies should fully leverage their dynamic capacities to integrate and reconfigure resources to address the difficulties of uncertainty.

Second, in an unpredictable environment, policymakers should support the eco-innovation activities of businesses with financial support and strategic direction. This is because government interventions can increase the strategic flexibility of businesses and decrease the cost of eco-innovation, thereby increasing their motivation to participate in eco-innovation. In addition, the government should value government official visits as a means of government intervention, that is, significantly different from government subsidies. It improves business-government interaction and reduces the information asymmetry between the two parties. Policymakers can conduct more effective and long-term strategic planning based on corporate strategic information, a substantial amount of which is unavailable without in-depth enterprise access. Companies can make timely strategic adjustments based on government officials’ policy priorities.

Our research also has some limitations. Firstly, we used cross-sectional data, and although we eliminated some adverse effects by using instrumental variables, we cannot entirely exclude any unfavorable impacts from the use of cross-sectional data. In the future, longitudinal data could be used to further validate the relationship between entrepreneurs’ PEU and company eco-innovation. Secondly, we used a sample of SMEs to test our hypothesis, without considering large enterprises. SMEs may be compelled to engage in eco-innovation due to survival pressure or resource constraints, whereas large enterprises have fewer of these constraints and may be less sensitive to perceptions of environmental uncertainty. They may not have a positive attitude towards eco-innovation in an uncertain environment. Therefore, using a sample of large enterprises in future research could be interesting for studying this issue.