1. Introduction
The speedy growth of human society has led to increasingly serious problems involving global environmental pollution and shortage of resources, and in such a complex and difficult situation, green development is now the key theme of today’s world [
1]. As an important contributor to global ecological civilization construction, China is actively promoting ecological civilization construction and regards green innovation development as an important strategy for advancing China’s modernization construction. In the process of enterprise innovation and development, both external pressure and internal governance are particularly important. The construction industry is not only a high carbon emission and high pollution industry but also a pillar industry that has a significant impact on a country’s national economy. To survive and develop in fierce competition, construction enterprises should strengthen their ability to cope with external pressures and internal governance, enhance their awareness of green innovation, and achieve sustainable development.
There are various factors that can influence the behavior of companies in terms of green innovation, including the economy and social environment, and it is critical to explore the factors that influence enterprise development. Schaefer confirmed that the primary force driving CGI is institutional pressure [
2]. According to stakeholder theory, enterprise development cannot be achieved without stakeholders’ support, and at the same time, enterprise behavior will be driven by stakeholders. Enterprises are under pressure from stakeholders such as consumers, competitors, and suppliers during their operational and production processes, prompting them to carry out green innovation in energy conservation and environmental protection [
3]. Hojnik and Ruzzier conducted empirical research on data from 223 companies, confirming enterprises have to undertake green innovation to keep their competitive advantage in an increasingly competitive environment [
4]. Internal drivers mainly include factors such as corporate environmental awareness, firm size, and human capital. The positive attitude of executives towards green innovation creates a favorable cooperation environment, which helps enterprises achieve green innovation [
5]. Combining qualitative and quantitative methods, Wang et al. concluded through empirical research that internal drivers such as environmental leadership, environmental culture, and environmental competence motivate firms to take the initiative in green innovation [
6]. The resource-based view holds that resources are an important asset of a company and an important indicator of its competitiveness [
7]. Compared to large-scale enterprises, small-scale enterprises require more funding and technical support for green innovation, making it more difficult to achieve green innovation [
8].
At present, the interweaving of new communication technologies and new media patterns allows the public to easily access news and public events and express their opinions. The widespread dissemination of public opinion by online media constitutes a new type of public opinion discourse pattern [
9]. Public opinion is a double-edged sword. Positive public opinion can increase the legitimacy of a company, while negative public opinion can lead to a decrease or loss of its legitimacy status [
10]. It has been found that media public opinion on corporate governance monitoring mechanisms is mainly realized through the following four paths. First, agenda-setting. The concept of “Pseudo-environment” was first proposed by the American sociologist Lippmann in his treatise “Public Opinion” [
8], who argued that “the media’s choice of content and the way it reports on events affects our perception of the outside world”. In the new media era, the theme of agenda-setting has undergone a transformation, and the public has the right to set their own agenda. Second, is the reputation mechanism. MPO performs an essential function in shaping and accumulating reputation for enterprises, negative MPO damages corporate image, thus corporate management will actively solve related issues to reduce reputation losses [
11]. Third, information intermediaries. What is revealed in the media can help to diminish the information gap between firms and their stakeholders. Bushee et al. found that media coverage had a significant impact on information asymmetry in 1182 medium-sized NASDAQ growth companies through a sample of 27,987 quarterly news reports from 1993 to 2004 [
12]. Fourth, public supervision. Media opinion motivates companies to improve their governance through stakeholder participation and involvement. Tang and Tang found that the media influences the polluting behavior of companies through two main stakeholders: the government and the public [
13].
With the continuous penetration of the concept of green development into people’s hearts, GCI has become the best enterprise image in the new era. The GCI contains a large amount of corporate pollution prevention and control information, which is a reflection of corporate environmental attitudes. GCI is derived from the concept of corporate image, and some scholars equate a green image with a green reputation [
14]. Chen believed that GCI is the public’s perception and evaluation of environmentally caring behaviors displayed by companies [
15]. Favorable GCI improves the public’s green satisfaction and loyalty to a certain extent [
16], wins the first-mover advantage of green development [
17], promotes green innovation, and makes enterprises stand out in the incentive competition [
18]. At the same time, a favorable GCI can alleviate public concerns about corporate environmental behavior, reducing EPOP from the public in this way [
19]. Therefore, under EPOP, companies may promote green innovation by enhancing their green image.
Although scholars in China and abroad have achieved certain results in the research on the three elements of EPOP, GCI, and CGI, few studies have incorporated all three as a body of research, there is still room for improvement in the mechanism of EPOP on CGI. Therefore, based on theoretical perspectives such as legitimacy theory, and stakeholder theory, this article incorporates GCI into the framework of the EPOP impact study on CGI and explores its intermediary mechanism in the process of EPOP affecting CGI. At the same time, from the stakeholder theory viewpoint, examine the regulatory mechanism of MC in the relationship between EPOP and CGI. Hoping that by doing so, we can unveil the “black box” of the mechanism of EPOP on the CGI and provide feasible countermeasures for construction enterprises to strengthen green innovation.
In view of this, the subject of the study is the construction companies located in the Chengdu–Chongqing Economic Circle (CCEC). Initially, it combines the theoretical analysis to put forward the research hypotheses. Secondly, the questionnaire was designed with reference to the mature scales in China and abroad, distributed and the valid questionnaires obtained from the recovery were handled for data processing. Thirdly, the questionnaire data were statistically analyzed using SPSS 24.0 software and AMOS 27 software. Finally, based on the findings of the empirical analysis, the research conclusions and management implications of this article are drawn. The contributions of this study are: (1) Exploring the impact and mechanism of EPOP on CGI, existing research focuses more on the impact of internal governance factors on corporate green innovation. This article further explores the influence of external pressures on corporate green innovation, which can enrich empirical studies related to CGI. (2) Using GCI as an intermediary variable to verify the influence of GCI on CGI, revealing the impact mechanism of environmental public opinion pressure on corporate green innovation, which can open the “black box” of the relationship between EPOP and CGI. (3) Using MC as a moderating variable, clarify the relationship between MC and EPOP, and which can explore the effect boundary of EPOP on CGI.
4. Analyses and Result
4.1. Common Method Bias
During the survey process, due to each questionnaire being independently completed by the same subject, there may be a certain degree of common method bias. Testing according to the common method bias method proposed by Harman, the findings indicated that in the nonrotated scenario, the cumulative variance contribution rate was 48.387%, and the first factor explained the variance of the item as 30.712%, which was under 40% of the overall variation. It indicates a low degree of common method bias and can be analyzed in the next step.
4.2. Reliability and Validity Analysis
As shown in
Table 2, the reliability of the scale was evaluated using Cronbach’s alpha coefficients, which were above 0.643 for all variables. It indicates that the scale has high reliability, with good internal stability and consistency. According to the criteria suggested by Fornell and Larcker [
59], through convergent validity and discriminant validity, this article tested the scale’s validity. The factor loadings for all indicators were greater than 0.600, and the AVE extracted from each variable was greater than 0.400, indicating that the variables were measured with high convergent validity. The following are shown in
Table 3, the discriminant validity of each variable meets the requirements because the square root of AVE on the diagonal is higher than the value of the correlation coefficient between this variable and the other variables. In addition,
Table 4 results indicate that our hypothetical model (the four-factor model) has better fitting performance than other alternative models. (χ
2/df = 1.344, CFI = 0.973, RMSEA = 0.032, TLI = 0.966). Therefore, the fitting indices in
Table 4 demonstrated the convergent validity and discriminant validity of the studied constructs.
4.3. Analysis of Correlation and Descriptive Statistics
Table 3 displays the mean, standard deviation, correlation of variables, and square root of AVE. EPOP has a positive influence on GCI and CGI (r = 0.326,
p < 0.01; r = 0.294,
p < 0.01). In addition, GCI has a positive influence on CGI (r = 0.668,
p < 0.01). These findings are consistent with our hypothesis and provide preliminary support. Meanwhile, the paper tested the problem of multicollinearity by calculating the VIF values; each variable’s VIF coefficient falls below 3, indicating that there are no issues with multicollinearity in the data of this study.
4.4. Test for the Main and Mediating Effects
Table 5 reports the results of the main and mediating effects tests. Model 3 results indicate that EPOP affects the CGI significantly and positively, with a regression coefficient for EPOP of 0.259,
p < 0.001. Hence, H1 is confirmed. Putting the mediating effect of GCI to the test, first test the impact of EPOP on CGI, as confirmed by the results of Model 3. Secondly, to test the impact of EPOP on the GCI. According to Model 2 results, it indicates that EPOP affects the GCI significantly and positively, with a regression coefficient for EPOP of 0.294,
p < 0.001. Thirdly, to test the impact of GCI on CGI. According to Model 4 results, it indicates that GCI affects the CGI significantly and positively, with a regression coefficient for GCI of 0.643,
p < 0.001. Finally, the mediator variable was introduced to construct the model based on the independent variable. According to Model 5 and Model 3 results, there is a partial intermediary role for GCI between EPOP and CGI, with a regression coefficient for EPOP of 0.080,
p < 0.05, and a regression coefficient for GCI of 0.609,
p < 0.001. Furthermore, EPOP has a smaller regression coefficient in Model 5 than in Model 3. Hence, H2 is confirmed.
4.5. Moderating Effect Test
Before regression testing, to avoid multicollinearity issues between interaction terms and explanatory variables, the relevant variables were decentralized.
Table 6 reports the effectiveness of the moderating effect test. According to the regression results of Model 4, the interaction between EPOP and MC has a significant negative effect on GCI with a regression coefficient of −0.119,
p < 0.05. It indicates that MC negatively regulates the relationship between EPOP and CGI. Hence, H3a is confirmed. According to the regression results of Model 7, the interaction between EPOP and MC has a significant negative effect on CGI with a regression coefficient of −0.142,
p < 0.01. It indicates that MC negatively regulates the relationship between EPOP and CGI. Hence, H3b is confirmed.
To better demonstrate the moderating effect of MC between EPOP and GCI, as well as between EPOP and CGI, according to Toothaker’s suggestion [
60], the moderating effect diagrams were plotted in
Figure 2, with MC divided into high and low levels based on plus or minus one standard deviation from the mean. The results show that from the slope, it can be seen that MC will weaken the effect of EPOP on the GCI and CGI. In high MC, the effect of EPOP on the GCI and CGI is lower than in low MC.
4.6. Robustness Test
The main impact of EPOP, the mediating impact of GCI, and the moderating impact of MC were tested for robustness using the bootstrapping method (with a sample size of 200 and a confidence interval of 95%).
According to
Table 7, the effect value of EPOP on CGI is 0.090 with a confidence interval of [0.009, 0.171], not including 0. The moderating effect value of MC and EPOP is- 0.142 when we introduce MC as a moderating variable, with a confidence interval of [−0.246, −0.039], not including 0. It indicates that EPOP has a significant and positive impact on CGI, while MC serves as a significant negative moderator of the correlation between EPOP and CGI, further verifying H1 and H3b. The effect value of EPOP on the GCI is 0.187 when we introduce GCI as an intermediary variable, with a confidence interval of [0.097, 0.277], not including 0. Moreover, the effect value of EPOP on CGI is 0.080 with a confidence interval of [0.008, 0.151], not including 0. The effect value of the GCI is 0.179 with a confidence interval of [0.107, 0.260], not including 0. It indicates that there is a mediating effect of GCI between EPOP and CGI, further verifying H2. The moderating effect value of MC and EPOP is −0.119 with a confidence interval of [−0.233, −0.004], not including 0. It indicated that MC can significantly negatively regulate the mediation of GCI in the relationship between EPOP and CGI, further verifying H3a.
4.7. Discussions
Based on 328 valid questionnaires, this paper empirically tests the effect of EPOP on CGI, the moderating effect of MC, and the mediating effect of GCI.
The public is very attentive to corporate behavior in terms of the environment, as these are major contributors to environmental pollution. The attention of external public opinion will monitor the company’s behavior, which will make the management of the company pay attention to the EPOP and take green innovation-related activities as a measure to deal with the EPOP.
At the same time, corporate stakeholders learn about companies’ production and operation status through news media reports to reduce information asymmetry. Once an enterprise is exposed to negative environmental news, stakeholders are likely to abandon their investment in that enterprise, which forces the enterprise to improve its environmental behavior as soon as possible, increase its green innovation activities, and regain the trust of its stakeholders.
EPOP affects CGI through its green image. As a unique intangible asset of enterprises, GCI is an irreplaceable competitive advantage. Under the role of EPOP, enterprises will pay more attention to the shaping and enhancement of green image, which offers resources to support their green innovation activities.
In this study, both MC and EPOP were found to positively promote CGI. However, as the degree of MC intensifies, the impact of EPOP on CGI weakens. It indicates that MC and EPOP have a significant substituting effect on the role of CGI. Furthermore, MC also negatively moderates the mediating role of GCI in the relationship between EPOP and CGI.
5. Conclusions and Implications
Based on legitimacy theory and stakeholder theory, and taking construction companies in China as the study subject, it constructed a theoretical relationship model for EPOP, CGI, GCI, and MC, and empirically tested the effect of EPOP on CGI, the moderating effect of MC, and the mediating effect of GCI. The research conclusion shows that EPOP has a remarkably positive effect on CGI, GCI partially mediates the relationship between EPOP and CGI, and MC negatively moderates the effect of EPOP on CGI.
5.1. Theoretical Contributions and Management Implications
This study complements the existing research on EPOP, GCI, CGI, and MC, enriches the legitimacy theory and stakeholder theory, provides a new research perspective for construction company green innovation, makes up for the limitations existing in the previous research on EPOP and CGI, and makes certain theoretical contributions.
Firstly, it deepens the understanding of EPOP. Current research has mostly focused on the study of formal institutions such as environmental regulation on CGI [
6], ignoring the governance effect of EPOP as an informal institution. This paper enriches the academic dialogue on the relationship between “EPOP—corporate behavior” by studying the relationship between EPOP and CGI and provides a more theoretical basis for the research on CGI.
Secondly, the mediating effect of GCI on the relationship between EPOP on CGI was verified. Chen et al. [
20] proposed that media scrutiny can directly address corporate pollution behavior, but the impact path of environmental public opinion pressure on corporate green innovation is not clear enough. This paper relies on legitimacy theory and resource dependence theory to further enrich the path of EPOP on CGI. Facing the pressure of media public opinion, enterprises enhance their own green image to reduce public environmental concerns, and their green innovation capabilities are enhanced, thereby promoting green innovation.
Thirdly, it reveals the boundary conditions for the impact of EPOP on CGI. Shahzad et al. [
14] argue that MC promotes enterprise innovation. The paper introduces MC as a moderating variable, verifying that intense MC not only reduces the role of EPOP on CGI but also negatively affects the relationship between EPOP and GCI. Research on the moderating effects of MC has been further enriched and expanded.
This study has great significance for government departments and construction enterprises to transition from passive to proactive responses to environmental issues, including the following points:
Firstly, improving the media’s voice mechanism and fully leveraging the supervisory function of media attention. On the one hand, the country needs to regulate the media market, improve legal construction to ensure a good market environment for media operation, perfect the platform construction for media voice, and formulate corresponding protection mechanisms. On the other hand, strengthening guidance and supervision of media institutions should not only actively encourage media to report and expose corporate misconduct, but also punish evil and promote good. At the same time, it is necessary to continuously improve the professional ethics of media practitioners, enhance their professional sensitivity and sense of responsibility, and reduce reporting bias. Also, companies should pay more attention to public opinion and be proactive in green innovation to win the favor of stakeholders. For the public, it is essential to enhance their concern for environmental issues, bravely defend their own environmental rights and interests, and take an active part in the process of media environmental governance.
Secondly, strengthening GCI to enable enterprises to gain more competitive advantages. On the one hand, companies should attach importance to the establishment and maintenance of green images so that they have intangible resources that are hard to imitate and replace by rivals, and thus gain a first-mover advantage during the green innovation process. On the other hand, companies should meet the environmental demands of stakeholders and enhance their green image in the minds of stakeholders to win long-term development for the enterprise.
Thirdly, establishing a rational market order to balance public opinion and competition. Under the interaction of EPOP and MC, the united effect of the two on GCI and CGI is weakened. From this, it can be seen that if one side of EPOP and MC is too intense, whether it is for the GCI or CGI, its promoting effect is actually counterproductive. Therefore, government departments and enterprises should grasp the degree of EPOP and MC, maximize the incentive effect of the two, and promote CGI.
5.2. Limitations and Future Prospects
There are some limitations to this research. First of all, the empirical research in this paper takes construction enterprises as the object, but green innovation involves a wide range of multi-party subjects, whether the findings of this paper can represent the majority of enterprises remains to be investigated, and the scope of the research object can be expanded in subsequent research to increase the universality of the study findings. Second, the change in GCI and CGI is a process, the future needs to add panel data validation based on the existing cross-sectional data analysis, and consider the impact of the time factor, to increase the stability of the conclusions. At last, this article did not specifically classify data for different regions and ignored the differences in the effect of EPOP on CGI in different regions. Therefore, in the future, it is necessary to increase comparative research on different regions to reduce regional errors.