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Article

The Impact of Environmental Public Opinion Pressure on Green Innovation in Construction Enterprises: The Mediating Role of Green Corporate Image and the Regulatory Effect of Market Competition

by
Huaming Wang
1,
Xing Huang
2,* and
Bo Wang
2,*
1
Planning and Finance Department, Southwest University of Science and Technology, Mianyang 621010, China
2
School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621010, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7286; https://doi.org/10.3390/su16177286 (registering DOI)
Submission received: 1 July 2024 / Revised: 17 August 2024 / Accepted: 22 August 2024 / Published: 24 August 2024
(This article belongs to the Special Issue Green Innovations in Sustainable Production and Consumption)
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Abstract

:
Due to growing public concern over environmental preservation and the growing development of Internet information communication platforms, media coverage of corporate environmental issues can exert certain environmental public opinion pressure (EPOP) on enterprises and influence their behaviors. However, the current study of EPOP on the influence mechanism of corporate green innovation (CGI) has not yet formed a systematic and comprehensive theoretical analysis framework. Therefore, based on legitimacy theory and stakeholder theory, this paper explores the impact mechanism and role boundary between EPOP and CGI based on the data from 328 valid questionnaires of construction enterprises of the Chengdu–Chongqing Dual City Economic Circle using hierarchical regression analysis. The findings of the research indicate that EPOP can affect construction company green innovations positively, green corporate image (GCI) plays a partial mediating effect in the relationship between EPOP on CGI; market competition (MC) negatively moderates the relationship between EPOP and CGI, in addition, MC negatively regulates the intermediary effect of GCI in the relationship between EPOP and CGI. The findings of the study serve as theoretical support and decision-making reference to promote Chinese construction enterprise’s transition to green innovation and improve environmental governance level.

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.

2. Literature Review and Hypothesis

2.1. Environmental Public Opinion Pressure and Corporate Green Innovation

Green innovation, as a vital measure in realizing the economic and ecological benefits of enterprises, aids enterprises in meeting stakeholders’ needs for environmental protection, conveys to the market a positive image of actively assuming environmental responsibility, and is conducive to enterprises gaining an advantageous position in MC and enhancing their core competitiveness. With the development of Internet technology, online media, as a platform for people to obtain information and communication, plays an increasingly significant role in CGI.
By gathering information about the corporate environment and leading public opinion, the media, which serves as a vital hub linking the public and companies, can affect the attitude of companies towards green innovation [20]. Positive coverage exerts a different influence on the public than negative coverage, which to some extent represents problem identification [21], and the public is more inclined to accept negative reports than positive ones. Meanwhile, due to the interest-driven nature of news media, they prioritize reporting negative news that can attract positive public attention and response. The legitimacy theory believes that organizational legitimacy is a key factor and primary prerequisite for the operational and productive activities of companies in the social system, which depends on the public’s overall understanding of the enterprise and the appropriateness of its behavior [22]. With the deepening of ecological civilization construction, the supervision of corporate environmental behavior by the media and the public is gradually strengthening, and EPOP on companies is increasing; in order to obtain and enhance organizational legitimacy, companies will be actively involved in green innovation.
The effect of EPOP on CGI is mainly reflected in three aspects: Firstly, as a non-formal system, EPOP can influence CGI by prompting governments to strengthen environmental regulations [23]. Public opinion is a low-level, bottom-up pressure on the government [24], carrying the demands of citizens’ interests. Through his research, Dyck et al. found that media reports have compelled companies to fix their infractions, while also strengthening the supervision of corporate behavior by regulatory authorities [25]. EPOP directly affects corporate pollution behavior, and official media reports urge the government to take action [13]. Secondly, media reports influence CGI by influencing investor behavior. As the media collect and disseminate information, there is a reduction in the level of information asymmetry among firms and outside investors [26,27]. Barber and Odean found through research that media coverage is closely related to investor behavior, and investor interest in a company may increase as a result of favorable or neutral media coverage [28]. When a company is exposed to negative news, investors will make negative evaluations of the company and give up investing. Thirdly, along with the growing consciousness of environmental protection among consumers and the transformation of consumption concepts, consumers’ preference for green consumption has increased. At the time of making the purchase decision, consumers care about the ethical performance of firms and will gladly pay more for firms whose ethical performance meets expectations [29]. At the time the company is exposed to environmental pollution scandals, its legitimacy will decrease, and the consumers’ recognition of the company will be reduced [30]. Under this pressure, enterprises will actively engage in green innovation to reduce environmental pollution, gain public favor, and gain core competitive advantages [31]. The following hypotheses have been proposed on the basis of the theoretical analysis:
H1: 
EPOP is positively related to CGI.

2.2. The Mediating Role of Green Corporate Image

There is an interaction and mutual influence between corporate reputation and corporate behavior, and corporate reputation can affect the future behavior of enterprises [32]. Corporate reputation is the cognitive and emotional response of stakeholders to a company based on its behavior or other characteristics [33], it consists of two elements: corporate identity characteristics and corporate image [34]. A good green corporate image can help improve public green satisfaction, help companies win the advantage of green development, promote corporate green innovation, and enable companies to stand out in competitive incentives [23]. Green company image reflects the inherent temperament of the enterprise and is also the overall impression and evaluation of consumers and stakeholders on the environmental care presented by the enterprise [35]. A good GCI helps to increase public green satisfaction, alleviate public concerns about corporate environmental behavior, and enable enterprises to stand out in fierce competition.
Media supervision places enterprises and executives under the siege of public opinion. A favorable GCI is a signal of environmental commitment to the enterprise’s main stakeholders and a means of enhancing the legitimacy of the enterprise’s environment [17]. Legitimacy theory holds that in a regime of standards, values, convictions, and definitions constructed by society, legitimacy is the public’s evaluation of enterprises. Media coverage of enterprises can trigger widespread public discussion and bring strong legal pressure on enterprises [36]. Therefore, when facing higher media attention, enterprises will pay more attention to shaping GCI [37], the enhancement of corporate green image will reduce legitimacy pressure, then, enterprises will strengthen their green innovation actions and gain recognition from the public in terms of green environmental protection. In addition, EPOP generated by media attention contributes to the fulfillment of corporate social responsibility [38]. Based on signal transmission theory, the fulfillment of social responsibility as a signal can convey to the outside world positive information such as a strong awareness of corporate social responsibility, which helps the enterprise to shape a good reputation in the public’s mind in favor of the enhancement of the GCI [39]. The enhancement of corporate green image sends a signal to the outside to fulfill social responsibility, and companies will take green actions to promote green innovation.
According to reputation theory, enterprises will actively maintain and enhance their reputation out of consideration for future profits, and their behaviors will also be constrained by reputation. On the one hand, enterprises pursuing a higher green image will be more concerned about ecological issues and social benefits and will prefer to make investments in green innovation-related activities to secure sustainable development for their enterprises [40]. On the other hand, enterprises with a higher green image have stronger green innovation capabilities. A favorable green image helps enterprises attract more quality resources such as capital investment, external technical knowledge, and senior talents [41], which provides resource support for the development of green innovation activities.
In summary, EPOP promotes green innovation by enhancing the GCI. Facing EPOP brought about by media attention, the enterprise through the green enterprise image shaping and promotion alleviates the public’s concern about the enterprise’s environmental protection behavior, reduces the legitimacy pressure, and provides necessary resources for green innovation to enhance their willingness to green innovate. The following hypotheses have been proposed on the basis of the theoretical analysis:
H2: 
GCI mediates the positive relationship between EPOP and CGI.

2.3. The Moderating Role of Market Competition

EPOP and MC are the same as the external environmental factors affecting CGI, which may have a substitution effect on the influence of CGI. MC is the struggle between enterprises to compete against each other in the market and realize the process of survival of the fittest. According to resource dependency theory, there is a tight relationship between the existence and growth of an organization and the outside environment in which it is situated [42]. MC, which is the greatest significant external environment for company operations, significantly affects stockholder interests and corporate performance.
Facing the fierce MC, it is difficult for enterprises to compete from the increasingly close product, price, and service level, more and more enterprises seek to build a highly distinguishable green image in the industry. At this time, the green image created by enterprises is manifested as a lasting competitive advantage and strategic resource. Favorable GCI will gain the recognition of stakeholders, consolidate the collaborative relationship between the enterprise and its stakeholders [43], and contribute to the establishment of a competitive advantage for the enterprise [41]. In a fiercely competitive market environment, enterprises will actively fulfill their corporate social responsibility [44], satisfy the interests of stakeholders, and establish a good GCI [45], which reduces the EPOP from the public on the enterprise. On the contrary, when the MC in which the enterprise is located is relatively mild, the enterprise is subject to weaker market constraints and faces less pressure for survival, which leads to insufficient motivation for the enterprise to fulfill its social responsibility, so that the GCI has not been greatly improved, and at this time, the EPOP from public supervision plays a greater role in enhancing the CGI. Therefore, the more intense the MC, the weaker the promoting effect of EPOP on the GCI.
In addition, stakeholder theory holds that stakeholders, including the government, competitors, and shareholders, influence CGI behavior [46]. The fiercer the MC, the more enterprises rely on the support of stakeholders; meanwhile, they will be more actively concerned about the innovation strategies of competitors, resulting in a more noticeable peer effect of CGI [47,48]. In this situation, companies will voluntarily undertake social responsibility, devote themselves positively to green innovation, strengthen self-restraint, and reduce the incidence of environmental pollution incidents, which can effectively prevent public opinion crises. Thus, under the fierce MC, the promotion effect of EPOP on CGI is weakened. The following hypotheses have been proposed on the basis of the theoretical analysis:
H3a: 
MC plays a negative regulatory role (substitution effect) in the relationship between EPOP and GCI.
H3b: 
MC plays a negative regulatory role (substitution effect) in the relationship between EPOP and CGI.
Figure 1 presents the theoretical model for this essay.

3. Research Methods

3.1. Designing of the Questionnaire and Variable Measurement

The Likert 5-point scale was used to score the survey questionnaire during this investigation (1 = “fiercely disapprove”, 5 = “fiercely approve”). The relevant variable measurement scale drew on mature scales in China and beyond to form an initial questionnaire. The items’ semantic composition and presentation logic were defined through discussions with members of the research group, and communication and interviews were conducted with some enterprise personnel. After modifying and improving the initial questionnaire, the final scale was determined. Pre-testing of the questionnaire was carried out, which initially verified that the validity and reliability of the research scale were well.

3.1.1. CGI

The existing research on measuring corporate green innovation mainly includes two types. One type focuses on the enterprise level and designs questionnaires and items based on the perspective of green product innovation or green technology innovation; this method is relatively systematic. Another type is to focus on the industry level and select the current number of green patent applications or green patent authorizations of enterprises as the measurement method, which has strong subjectivity. The measurement of CGI draws on the research of Utterback and Abernathy [49], focuses on the enterprise level, and designs questionnaires and items based on the perspective of green product innovation or green technology innovation, the content focuses on the characteristics of CGI in the construction industry. The two components of building materials and construction processes were used to develop the scale, which was divided into five items. Scoring higher in each item, the higher the degree of CGI. The reliability of the scale was 0.737.

3.1.2. EPOP

Regarding the measurement of EPOP, most of the current research used the number of news reports to determine the EPOP faced by a company, but this measurement method has some limitations and is only applicable to listed companies. This article drew on the scales of Jørgensen et al. [50,51,52], and combined the characteristics of EPOP in construction enterprises to adapt the scale. The three components of public opinion regulation pressure, public opinion normative pressure, and public opinion imitation pressure were used to develop the scale, which was divided into five question items. Scoring higher in each item, the greater the EPOP the company faced. The reliability of the scale was 0.791.

3.1.3. GCI

GCI is stakeholder perception and evaluation of corporate environmental behavior, which can be measured based on corporate green reputation and corporate trust. Referring to the studies of Keller and Aaker [53], and Weiss et al. [54], the two components of green reputation and green credibility were used to develop the scale, which were divided into eight items. Scoring higher in each item, the better the GCI. The reliability of the scale was 0.853.

3.1.4. MC

Drew on Fang and Zou [55], Jansen et al. [56] regarding the measurement scale of competition intensity, combined with the characteristics of the MC. Designed the scale from three aspects: MC situation, price competition intensity, and competitors in the market, and divided it into four items. Scoring higher in each item, the more intense the MC the company was in. The scale reliability was 0.643.

3.1.5. Control Variables

Based on the previous literature, for control variables, this article selected corporate age, corporate size, and property rights. Corporate age was calculated as the interval between the year the questionnaire was returned (2023) and the year the company was founded, and the natural logarithm was taken for processing. Corporate size was calculated using the natural logarithm of its total number of enterprise employees. The research sample divided property rights into two groups: state-owned and non-state-owned companies (state-owned companies = 1; non-state-owned companies = 0).

3.2. Sample Selection and Data Collection

3.2.1. Sample Selection

In recent years, the construction sector has gradually developed into a pillar industry of the Chinese national economy as a result of the fast economic growth. However, the construction industry is characterized by low productivity, high energy consumption, a wide range of production, and serious pollution. The production activities of construction enterprises were once considered to be the main “contributor” to environmental pollution and became the forefront of public opinion. Facing the internationalization of the market and the increasingly fierce competition in the industry, it is highly significant to strengthen the green innovation capabilities of construction enterprises and to research and apply green innovation achievements. Therefore, the paper selected construction enterprises that cause significant environmental pollution as survey samples and collected data through a questionnaire survey. The survey subjects are employees of construction enterprises in the CCEC.

3.2.2. Data Collection

There were two phases of data collection in the study: the first phase was a pretest, which lasted from October 2022 to November 2022. With the help of the social relationships established by the authors’ research team members, there were a total of 60 questionnaires distributed to friends working in construction enterprises, and 52 valid questionnaires were obtained. Using SPSS24.0 software for independent sample t-test, reliability analysis, and exploratory factor analysis, the data results are consistent with the expected questionnaire design and can be officially distributed.
The second phase is formal testing, which began in December 2022 and ended in February 2023. In this stage, the survey was conducted on employees of construction enterprises in the CCEC. The questionnaire used a combination of online and offline survey methods. During the questionnaire distribution process, we received recommendations and assistance from MBA graduate students, alumni, and relevant experts from the author’s institution, with a total of 412 people distributed and a total of 412 questionnaires. After removing invalid questionnaires, 328 valid questionnaires were obtained, and their recovery rate was 79.6%. Table 1 displays the fundamental information regarding the companies that were surveyed.

3.3. Steps and Methods of the Study

The data from the scales and the research hypotheses will be tested in the study using the following steps and methods: First, the homology bias test. This study drew on the viewpoints of Podsakoff and Organ [57] to examine homoscedastic bias through the Harman single-factor test method. Second, reliability and validity analysis. Using SPSS 24.0 software and AMOS 27 software, the study conducted reliability tests on the scale to ensure high stability and consistency. Then, exploratory factor analysis, confirmatory factor analysis, and model fit analysis were used to analyze the scale’s validity to ensure high reliability and validity. Third, descriptive statistics and correlation analysis. Each variable was first tested for descriptive statistics and Pearson correlation coefficient tests, and then the variance inflation factor (VIF) was utilized to test for multicollinearity in all explanatory variables. Fourth, regression analysis and hypothesis testing. The main, mediating, and moderating effects were examined using the Baron and Kenny [58] method of three-step testing as a reference. Then used the Bootstrap method of the PROCESS plugin to perform robustness tests on the research model.

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. (χ2/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.

Author Contributions

Conceptualization, methodology, software, validation, writing—original draft preparation, X.H. and B.W.; investigation, resources, writing—review and editing, visualization, supervision, H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 16 07286 g001
Figure 1. Theoretical model.
Figure 1. Theoretical model.
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Sustainability 16 07286 g002
Figure 2. The regulatory effect of market competition.
Figure 2. The regulatory effect of market competition.
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Table 1. Fundamental information about the surveyed companies.
Table 1. Fundamental information about the surveyed companies.
ItemCategoryFrequency%
Corporate ageLess than 3 years4714.3%
4–5 years6118.6%
6–10 years8726.5%
11–20 years6419.5%
Over 20 years6921.1%
Corporate sizeLess than 300 employees7823.8%
300 < Number of employees < 4998124.6%
500 < Number of employees < 9996218.9%
1000 < Number of employees < 19995516.8%
2000 < Number of employees < 49993611.0%
More than 5000 employees164.9%
Enterprise total assets Less than 40 million7623.2%
40–100 million9428.7%
100–400 million6018.3%
More than 400 million9829.8%
Property rightsstate-owned enterprises12839.0%
non-state-owned enterprises20061.0%
Table 2. Testing for reliability and validity.
Table 2. Testing for reliability and validity.
VariableItemCranach’s AlphaFactor LoadingAVECR
EPOPA10.7910.6140.5480.856
A20.618
A30.813
A40.818
A50.807
GCIB10.8530.6450.4940.886
B20.714
B30.733
B40.763
B50.677
B60.706
B70.703
B80.678
MCC10.6430.7420.4880.792
C20.692
C30.653
C40.705
CGID10.7370.7290.4900.827
D20.746
D30.618
D40.734
D50.663
Notes: EPOP = environmental public opinion pressure; GCI = green corporate image; MC = market competition; CGI = corporate green innovation.
Table 3. Analysis of correlation and descriptive statistics (N = 328).
Table 3. Analysis of correlation and descriptive statistics (N = 328).
VariableMeanSDAGESTATESIZEEPOPGCIMCCGI
AGE2.1670.6531
STATE0.3900.4890.111 *1
SIZE6.6281.0010.389 **0.323 **1
EPOP3.6200.766−0.105−0.019−0.043(0.740)
GCI3.8570.6480.178 **−0.048−0.0020.326 **(0.703)
MC3.9250.6360.113 *−0.113 *−0.0550.408 **0.441 **(0.699)
CGI3.9210.6340.152 **−0.078−0.0330.294 **0.668 **0.578 **(0.700)
Notes: * p < 0.05; ** p < 0.01; EPOP = environmental public opinion pressure; GCI = green corporate image; MC = market competition; CGI = corporate green innovation. The diagonal data are the square root of the variable AVE.
Table 4. The results of the model fit.
Table 4. The results of the model fit.
Modelχ2/dfRMSEASRMRCFINFITLIIFI
Four-factor model1.3440.0320.0480.9730.9040.9660.974
Three-factor model2.2840.0630.0650.8860.8160.8720.887
Two-factor model3.7590.0920.0850.7530.6940.7250.755
One-factor model4.3560.1010.0920.6980.6430.6660.701
Notes: four-factor model = EPOP, GCI, MC, CGI; three-factor model = EPOP + MC, GCI, CGI; two-factor model = EPOP + MC +CGI, GCI; one-factor model = EPOP + MC + CGI + GCI.
Table 5. Results of the test for main and mediating effects.
Table 5. Results of the test for main and mediating effects.
VariableGCICGI
Model 1Model 2Model 3Model 4Model 5
AGE0.208 ***0.243 ***0.219 ***0.0540.071
STATE−0.065−0.062−0.091−0.052−0.053
SIZE−0.044−0.043−0.054−0.026−0.027
EPOP 0.294 ***0.259 *** 0.080 *
GCI 0.643 ***0.609 ***
R20.0400.1590.1350.4520.460
△R20.0310.1490.1240.4450.451
F4.464 **15.316 ***12.560 ***66.537 ***54.810 ***
Notes: * p < 0.05; ** p < 0.01; *** p < 0.001; EPOP = environmental public opinion pressure; GCI = green corporate image; CGI = corporate green innovation.
Table 6. Results of moderating effect test.
Table 6. Results of moderating effect test.
VariableGCICGI
Model 1Model 2Model 3Model 4Model 5Model 6Model 7
AGE0.208 ***0.243 ***0.177 **0.178 **0.219 ***0.117 *0.118 *
STATE−0.065−0.062−0.018−0.019−0.091−0.023−0.024
SIZE−0.044−0.043−0.026−0.020−0.054−0.026−0.019
EPOP0.294 ***0.175 ***0.653 **0.259 ***0.076 **0.649 **
MC0.339 ***0.737 ***0.522 ***1.001 ***
EPOP×MC−0.119 *−0.142 **
R20.0400.1590.2470.2560.1350.3510.365
ΔR20.0310.1490.2350.2420.1240.3410.353
F4.464 **15.316 ***21.089 ***18.435 ***12.560 ***34.776 ***30.761 ***
Notes: * p < 0.05; ** p < 0.01; *** p < 0.001; EPOP = environmental public opinion pressure; GCI = green corporate image; MC = market competition; CGI = corporate green innovation.
Table 7. Results of robustness tests.
Table 7. Results of robustness tests.
Variable RelationshipEffect ValueStandard DeviationBootstrapping 95%CI
LLCIULCI
EPOP→GCI→CGI 0.080 0.036 0.008 0.151
EPOP×MC→GCI −0.119 0.058 −0.233 −0.004
EPOP×MC→CGI −0.142 0.053 −0.246 −0.039
EPOP→GCI 0.187 0.046 0.097 0.277
EPOP→CGI 0.090 0.041 0.009 0.171
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Wang, H.; Huang, X.; Wang, B. The Impact of Environmental Public Opinion Pressure on Green Innovation in Construction Enterprises: The Mediating Role of Green Corporate Image and the Regulatory Effect of Market Competition. Sustainability 2024, 16, 7286. https://doi.org/10.3390/su16177286

AMA Style

Wang H, Huang X, Wang B. The Impact of Environmental Public Opinion Pressure on Green Innovation in Construction Enterprises: The Mediating Role of Green Corporate Image and the Regulatory Effect of Market Competition. Sustainability. 2024; 16(17):7286. https://doi.org/10.3390/su16177286

Chicago/Turabian Style

Wang, Huaming, Xing Huang, and Bo Wang. 2024. "The Impact of Environmental Public Opinion Pressure on Green Innovation in Construction Enterprises: The Mediating Role of Green Corporate Image and the Regulatory Effect of Market Competition" Sustainability 16, no. 17: 7286. https://doi.org/10.3390/su16177286

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