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Article

Research on Enterprises’ Intention to Adopt Green Technology Imposed by Environmental Regulations with Perspective of State Ownership

by
De Xia
1,
Wenhua Chen
1,
Qinglu Gao
1,*,
Rui Zhang
2,* and
Yundong Zhang
1,*
1
School of Management, Wuhan University of Technology, Wuhan 430070, China
2
Business School, Jianghan University, Wuhan 430056, China
*
Authors to whom correspondence should be addressed.
Sustainability 2021, 13(3), 1368; https://doi.org/10.3390/su13031368
Submission received: 15 December 2020 / Revised: 24 January 2021 / Accepted: 25 January 2021 / Published: 28 January 2021
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Abstract

:
Environmental regulations (ER) affect enterprise behaviors. Nevertheless, whether the state ownership influences the relationship between environmental regulations and enterprises’ green intentions and behaviors need to be explored further. In this paper, the effects of environmental regulations on enterprises’ intentions to adopt green technologies, especially the moderating role of state ownership between environmental regulations and green technologic adoption intentions (GTAI), are proposed. An empirical study is carried out with the questionnaire data collected from 207 Chinese managers and executives in order to explore the influence of environmental regulations. With the perspective of ownership, the results confirm that the three kinds of environmental regulations (command-and-control (CAC), market-based incentives (MBI) and voluntary environmental (VER) regulations) have positive effects on enterprise green technology adoption intention. Furthermore, the state ownership of enterprise plays a positive moderating role in the relationship between command-and-control environmental regulations and green technology adoption intentions, but plays a negative moderating role in the relationship between voluntary environmental regulations and green technology adoption intentions. It generates no significant moderate effect on the relationship between market-based incentives environmental regulations and green technology adoption intentions. The work verifies that the differences of ownership would lead to varying effects on the intentions of enterprise green technology adoption imposed by regulations. Managerial implications, as well as the limitation of the work, are concluded at the end of this paper.

1. Introduction

The pollution resulted from traditional operation of enterprises, raises serious challenges for human health and environment. With the increasingly urgent environmental problems, how to reduce air and water pollution caused by business activities of enterprises has triggered intensive attention from consumers to governments [1,2]. As a fundamental tactic for sustainable development, green technology adoption (GTA) has become an important means for enterprises to cope with the environmental pressures and improve their economic and environmental performance, which triggers the interest of practitioners as well as researchers [3,4]. In the traditional research on green technology adoption (GTA), many researchers focus on the green technology itself with its connotation, process, goals, and so on [5,6,7]. They prefer to pay attention to the impact of factors regarding the driver factors of GTA in macro and micro level. For instance, Hottenrott et al. [8] indicates that organizational innovations can be more efficient in adopting new green technologies that lead to improved productivity. Xia et al. [9] shows significant relationships between green technology selection and certain task-oriented circumstances and macro-circumstances. From the perspective of technology innovations. Weng and Lin [10] identify the factors that driver GTA consist of technological, organizational, and environmental dimensions. Horbach [11] finds that technological capabilities development by the highly qualified employees encourages the adoption intention of green technology. Pujari [12] and Triguero et al. [13] also observed that market is an important factor to promote enterprises’ GTAI when they providing environmental product. However, there is little systematic analysis of the effects of the environmental regulation on enterprise with the perspective of ownership feature regarding the green technology adoption intention (GTAI). The state ownership makes the owner–manager relation more complicated because the chain of principals and agents is expanded [14]. Theoretically, this gap hinders the understanding of the intention and propensity for green technology adoption, and effective design of environmental regulation to boost GTAI [15,16,17]. The examination of ownership influence can help better understand the enterprise green decision-making, especially in China, where there are considerable state-owned companies [18]. The work expounds the role of state ownership in the response mechanism of enterprises to external environment, will also be helpful to further supplement the theory of stakeholder. Therefore, the various operational influences of the specific environmental regulation (ER), resulting from differences of ownership right, on enterprises’ green technology adoption intention (GTAI) need to be explored further.
According to the institutional theory, regulatory intervention is a necessary supplement of the pricing mechanism to shape enterprise behavior in the market. In the late 1970s, command-and-control environmental regulations (CAC) became one of the main policy to regulate the corporate environmental behaviors in many countries [19]. The government motivates the corporate behaviors through laws and regulations. However, Pollution Haven Hypothesis argued that high environmental regulations may cause unemployment and disinvestment because of the additional cost incurred by environmental regulations [20]. Practical investment of GTA as well as additional operational cost always leads to lower productivity while enterprises follow CAC [8]. With the negative effects resulting from the action of CAC, many enterprises even lose the competitive margin and interesting innovation investment on GAT. Frankly, the green technology adoption is always delayed because of extensive externalities of effects, that is the full social benefits produced by green technology cannot be completely reflected by the financial statements [21]. As the awesome implementation cost, low flexibility and other shortcomings of CAC, more flexible market-based incentive environmental regulations (MBI) and voluntary environmental regulations (VER) arise and are necessary. As Porter hypothesis claims that properly designed environmental regulations can catalyze innovations, which to some extent offset the compliance costs [22,23]. Enterprises maybe actively reduce the compliance costs of environmental regulation through specific technical adoption according to their operational feature. Consequently, three kinds of environmental regulations have different functional influences on enterprises’ GTA behaviors because of the characteristics such as operational goals, measures, flexibility, etc. Previous literature and researches explore GTA with eyes on environmental regulation and the external conditions in order to explain their difference in enterprise operation [24,25]. Some literatures focus on discussing the environmental regulation on green technology adoption from the regional and industrial levels [26,27,28], which cannot directly display and explain the green technology intention of enterprises under the pressure of environmental regulations. Very few literatures focus on the enterprise level [29,30], with empirical research in the context of China. As the state-owned enterprises play a considerable role in Chinese and many other emerging economies, whether state ownership has influence on companies’ GTA is a valuable research question. Here, with the perspective of corporate ownership, the work attempts to explore the operational relationship between ER and GTAI as well as the influence mechanism.
Stakeholder theory and corporate governance theory indicate that enterprise environmental operations have been influenced not only by government and customers but also by a plenty of stakeholders such as NGO, competitors, shareholders, and employees [31,32]. The three types of environmental regulations represent the appeals of different stakeholder groups, while enterprises with different ownership are faced with different incentives and constraints [33]. The different attributes may affect the sensitivity of enterprises in GTAI originating from environment regulations [34,35]. Ownership is an important perspective to study the operational decision-making behavior of enterprises in green campaign, including green innovation [36,37]. Different ownership profile means difference rights arrangement in decision-making process during technology investment for Chinese enterprises [38,39]. As a key stakeholder, government has strong and intensive influence and intervention on state-owned enterprises. Stakeholder theory clarifies that the increasing proportion of state-owned ownership would lead to corporate governance power’s decentralization [40]. At the same time, the sharing and decentralization also makes the state-owned enterprises more accessible to authority resources [41]. As far as environmental regulation is concerned, the corresponding compulsory and different types of regulation instruments are built into the arrangement and allocation of corporate governance power. Currently literature shows that different profiles of ownership have an influence on enterprise’s response to environmental regulations [42], but it is still not clear what moderate effect is between specific environmental regulation instruments and green technology adoption. Therefore, this work explores the relationship between ER and GTAI, and analyzes how the relationship between them is affected by features of ownership. It would provide a new perspective to explain the enterprises’ intentions and behaviors of green technology adoption. The work may benefit the designer of enterprises’ greening plan and environmental regulations, motivating enterprises’ green behaviors. The research hypotheses are raised with theoretical basis in Section 2, while the empirical study is carried out to collect the data from filed in Section 3. Meanwhile result analysis is also followed to verify the research purposes. To explore details about the relations among factors, measurement items are designed for ER and GTAI and specific industries are selected as the sample for data collection. Therefore, result analysis is carried out with factor correlation analysis as well as hypothesis test in Section 3. Then the discussion and conclusion is drawn in the end of the work including managerial implications, contribution and limitation.

2. Theoretical Basis and Research Hypothesis

2.1. Environmental Regulations (ER) and Enterprises’ GTA

Environmental regulation consisted of tangible and intangible components, imposing influence on the individuals and organizations, leading to environmental friendly behaviors. There are many studies that divide ER into formal regulation and informal regulations [43,44]. Formal regulations come from all kinds of mechanisms implemented by the public authorities for regulating pollution emissions. Based on the stylistic difference in policies, formal regulation is more specifically classified into CAC and MBI [45]. CAC is a type of direct regulation, such as environmental licenses, emission standards. MBI characterized by economic incentives, is an embodiment of the practical application of Pigouvian tax theory, including pollution taxes, tradable permits and subsidies. Informal ER corresponds to all types of actions taken by the citizens, NGO, industrial guild aiming at modifying the behavior of pollution. Moreover, some studies divide ER into mandatory regulation and voluntary regulations [46,47]. The former is consistent with the CAC, the latter gives enterprises voluntarily choice in participation in environmental protection programs or agreements, which calls VER in the work.
Institutional theory indicates that the enterprise management decisions are strongly influenced by three kinds of pressure: compulsion, imitation, and standardization [48,49]. The green technology adoption behavior of enterprises is shaped by regulation pressure and the value system complied by a certain community. Sethi develops a framework that classifies corporate behavior into three stages, including social obligation, social responsibility, and social responsiveness [50]. Thus, it inspires our thought about a firm’s green behavior. When the behavior of enterprises deviates from the regulations requirement and social value orientation, the legitimacy of enterprises is threatened and weakened [51]. Siegel and Johnson [52] believe that environmental regulation would facilitate the adoption of environmental technology of enterprises to improve economic and environmental performance. Appropriate environmental regulations can also guide and push industry to develop green technology in operation [53]. Therefore legitimacy requirement for enterprises resulting from environmental regulation is a main driving force of environmental management behavior.
Many studies explore the concept and nature of green technology with different dimensions. Braun and Wield [54] consider that green technology is a series of measurements for environmental protection. It refers to the technology for reduction, reuse, and recycling of raw materials, natural resources, and energy to contribute to the performance of economy, environment, and society [55,56,57,58]. Green technology innovation and adoption is a responsive behavior and choice under specific circumstance, also a result of interaction among internal and external factors of enterprise operation system [59]. The adoption of green technology is influenced by the intensity of environmental supervision, capability of enterprises’ green technology innovation, expectation of environmental stakeholders, which involves complex decision of GTA [60]. It is a complex dynamic system while studies show environmental regulation promote the progress of green technology in industrial sectors [61].
There are different explorations and viewpoints on the relationship between environmental regulations and enterprise green innovations. Many scholars’ work shows that environmental regulation leads to the recession of corporate green innovation performance, and conclude that environmental regulation may prevent corporate green technology innovation [62]. The relationship between environmental regulations and green technology innovations may also be uncertain [63,64]. Some scholars indicate that the ER makes difference in enterprise’s operations due to different types of environmental regulations [19]. Wang’s empirical study [65] with data from high-end manufacturing enterprises confirms that CAC and MBI can significantly stimulate green operation. Company’s awareness of R&D may be enhanced further with strengthening environmental regulations [66]. Due to the asymmetric information quality of the environment, the “lemon market” effect of green technology adoption may also occur [67]. But voluntary environmental regulation measures, such as green environmental label, may enhance the competitive advantage of products, boost reputation and market share of enterprise [33]. Similarly, some research explain that it is possible for enterprises to get positive effect from green operation. GTA may facilitate enterprises to cope with environmental regulations pressure, remove green trade barriers, and catch market opportunities, shape a good reputation [68]. Consequently, there are obvious differences and gaps regarding the opinions about the relationship between environmental regulations and green technology innovations.
Exploration and measurement of behavior related to enterprises’ GTA is also an important issue. Theory of planned behavior (TPB) [69] indicates requirement of green technology adoption is that companies, especially senior managers, are willing to change and innovate, which may be interpreted and predicted by intention, attitude, subjective norms, and behavior control. Intention is the antecedent of behavior action, and helps in better understanding the process mechanism of enterprise response. GTA behavior acts in varieties of ways, also effect by factors like resource and capability, is harder to evaluate than intention. Zhang et al.’s study [70] explains the intention of enterprises in green campaign with senior managers’ attitude to the application of green technology and social pressure to develop green technology for sustainable operation. “Intention” may be looked at as a predictor of an enterprise behavior of green tech adoption. Therefore, the study of the relationship between environmental regulation and GTAI rather than GAT behavior in our work can provide more proactive feedback on managers’ intentions and decision-making, and reference for regulators, so as to timely adjust regulation strategies, and finally to trigger green technology adoption behavior.

2.2. State Ownership, Environmental Regulation and Enterprises’ GTA

Social norms, cognition and attitude about sustainability, as well as attributes of technology, may affect the enterprises’ adoption of green technology [71]. However, these dimensions are not the whole story about factors involving GTA. When it comes to enterprise attribute factors, especially the ownership attribute, whether and how the ownership plays a role in environmental regulation and enterprises’ intention of green technology adoption, the answer is still kept open.
Previous study and experience indicates that, for Chinese enterprises, the attribute of state ownership profile is a significant factor which influences the operation [72]. Regarding the profile of the internal and external stakeholders, participation paradigms, there are obvious differences between the state-owned enterprises and non-state-owned enterprises regarding the environmental pressure, motivation, and capability.
Some scholars believe that the ownership of state-owned enterprises is substantial in the charge of local and central government, therefore, the operational management is influenced by the government in some degree with eyes on Chinese economic, society condition [73]. With intensive pressure from the authorities, state-owned enterprises (or those with a high proportion of state-owned enterprises) may be more sensitive to compulsory environmental code. After comparative study about the behavior of GTA among state-owned, foreign capital owned, and private enterprises, Zhang and Zhao [74] found that the increasing intensity of environmental regulation will promote the technical progress of state-owned enterprises, but generate no significant impact on the foreign capital enterprises’ operation as well as negative impact to the private enterprises. Empirical study [70] confirms that the investment behavior of the company about the environmental protection is characterized by profile of ownership, while state-owned companies invested a huge money in environmental protection instead of private companies.
In contrary, some scholars hold that private enterprises are more sensitive to environmental regulations because of less tolerance to pressure from outsiders [72]. Cambini and Rondi’s study [75] shows that non-state-owned enterprises’ environmental investment is more sensitive to environmental regulations than state-owned enterprises. In order to obtain preferential policies from the government and more financial opportunities, as well as establishing a good corporate image, private enterprises may proactively implement the government environmental protection policies, thus invest more in environmental protection.
Regarding the interesting viewpoint at GTA of previous studies, the influences imposed by command-and-control, market-based incentive, and voluntary environmental regulations on enterprise’s green technology adoption intention need to be explored further. It is significant to examine the nature of state ownership’s moderating effects on the relationship between each environmental regulation and GTAI, which is shown in Figure 1.

2.2.1. Command-and-Control Environmental Regulation (CAC) and Green Technology Adoption Intention (GTAI)

Regarding the influence of command-and-control environmental regulation on corporate behavior in operation, it is generated through the compulsion and obligation, which requires enterprises to comply with environmental code and technical standards of CAC [6,19,65]. The US was an early adopter of stringent SO2 standards, which leads to a significant increase of patents. Similar trends are also observed in Japan and Germany following the implementation of stringent NOx regulations. It shows these CAC measures promote green technology development and adoption [8,76]. Jennings and Zandbergen [48] point out that compelling force is the main driver of sustainable operation and practice. Some institution sectors including environmental agents have sufficient power to impose structural forms or practices on organizational units [77]. State-owned enterprises are subject to more significant interventions and influences by authority and policy systems than non-state-owned ones. One of the main function of state-owned enterprises is to accomplish public policy objective [78]. Consequently, state-owned enterprises may bear more environmental and social responsibility from the policy-maker and biggest-share-holder and have to adopt green technology in advance [79]. Meanwhile, the enterprises also have more access to various resources as well as credits from financial institutions because they are state owned [80]. Therefore, state-owned enterprises are more likely to follow the voice and corresponding regulations from government. Under certain context of the environmental protection system, difference of internal factors (organization size, operational technology types, ownership profile, and financial information disclosure) of enterprises may have influence on their sensitivity to CAC, affecting the enterprise’s environmental protection behaviors. The state-owned enterprises’ legitimate sensitivity may be stronger than non-state-owned enterprises. As a result, state-owned enterprises are more likely to invest more in GTA for environmental protection. Therefore, here are the following proposes:
Hypothesis 1a (H1a).
CAC has a significant positive impact on the enterprise’s GTAI.
Hypothesis 1b (H1b).
The state ownership moderate the influence of CAC on the enterprise’s GTAI: CAC has a more significant positive impact on GTAI of state-owned enterprises than non-state-owned ones.

2.2.2. Market-Based Incentive Environmental Regulation (MBI) and Green Technology Adoption Intention (GTAI)

Regarding the influence of market-based incentive environmental regulations on enterprises, the green behavior may be cultivated by the market means such as pollution taxes, trading license, and environmental subsidies etc. Compared with other type of regulations, MBI has obvious advantages in the cost effectiveness to boost innovation and diffusion of green technology [81,82]. Zhou et al. suggests that government subsidies may alleviate the negative impact of compliance costs on firms, and enable the latter to adopt expensive green technology such as pollution abatement technologies [83]. Krass et al. [25] and Bertarelli and Lodi [84] report that green policies such as taxes and charges provide more effective incentive for enterprises to introduce green technologies than other kinds of policy. Some researches of scholars indicate that sensitivity of enterprises in tax burden may be different because of their operational attributes. Compared with private enterprises, incentive effect of tax on state-owned enterprises is very low, and is even less on central government-owned enterprises [85]. Meanwhile, subsidies lead to higher possibility to convince the private enterprises than state-owned ones to believe that it is the government’s recognition of GTA, which would cultivate their willingness to adopt green technology further [86,87]. Therefore, the differences in resource access channels may also lead to differences in the sensitivity of enterprises to subsidies. Generally, state-owned enterprises still have comparative advantages over private enterprises in business [88], while the latter are expecting MBI’s subsidies to boost their green campaign. For competitive advantage in market, enterprises have to get more support through green operation [89]. Appropriate green innovations and GTA would also help companies to reduce production cost, improve economic and environmental performances [90,91], while state-owned enterprises may be short of willingness to green innovation input, innovation efficiency, and production efficiency regarding the MBI [92]. Therefore, it is proposed that:
Hypothesis 2a (H2a).
MBI has a significant positive impact on the enterprise’s GTAI.
Hypothesis 2b (H2b).
The state ownership moderates the influence of MBI on the enterprise’s GTAI: MBI has a more significant positive impact on GTAI of non-state-owned enterprises than state-owned ones.

2.2.3. Voluntary Environmental Regulation (VER) and Green Technology Adoption intention (GTAI)

The implementation of voluntary environmental regulation consisted of environmental certification, voluntary agreements, etc., is mainly monitored informally by public. It encourages enterprises to be self-disciplined in voluntary environmental protection, to promote environmental behavior with more reasonable technologies, in order to obtain first-mover advantage [93,94,95]. Motivation to follow-up voluntary environmental regulation with GTA may be generated by cost saving, efficiency improvement, brand reputation, or external market pressure [96]. Normative pressure from industry associations can also prompt positive environmental behaviors in moral. Camisón [97] and Bu et al. [47] point out that compared with other types of environmental policies, voluntary regulations can trigger innovative activity. Woerter [98] indicates that voluntary agreements are one of the most important drivers for the adoption intention of green energy technologies. Jiang et al. [99] also confirms the positive effect of voluntary environmental information disclosure and environmental management system certification on enterprise innovation. Studies indicate that enterprises with various profile of ownership have differences in access to resource that leads to varying consciousness and sensitivity to VER [100]. In terms of corporate citizenship and fulfillment of social responsibilities, state-owned enterprises are traditionally trusted in the consciousness of Chinese people, and non-state-owned enterprises may have to take more effects to cultivate the circumstance for their business. According to the mimetic isomorphism mechanism in the new institution theory [101], when faced with uncertain factors in the business, enterprises would follow partners as the template to learn and regulate their behaviors [102]. For enterprises, green technology adoption behavior always have a positive demonstration effect in market, and the diffusion of positive information will further influence the decision-makers of other enterprises, ultimately boosting green campaign [103]. When the shareholder possess higher percentage of stock, they have more external control on managers and result in more voluntary CSR disclosure, thus fully reflected in private firms [104]. Additionally, other studies find that government support produces a negative moderating effect on the relationship between voluntary environmental regulations and economic benefits, social benefits of technological innovation [105]. The reason of this effect maybe resulted from the difference of ownership profile and resource access between state-owned enterprises and non-state-owned enterprises. Then ownership may be a moderator between voluntary environmental regulation and green technology adoption intention. This paper proposes:
Hypothesis 3a (H3a).
VER has a significant positive impact on the enterprise’s GTAI.
Hypothesis 3b (H3b).
The state ownership moderates the influence of VER on the enterprise’s GTAI: VER has a more significant positive impact on non-state-owned enterprises enterprise’s GTAI than state-owned ones.

3. Methodology

3.1. Sample and Data Collection

Regarding the environmental influence from business operation, industries such as manufacturing and civil engineering, which involve intensive consumption of resources and energy, cause a great deal of pollution. They are faced with the increasingly serious pressures from environmental regulations. Therefore, these energy-intensive industries may be the reasonable sample to explore the relationship between the green technology adoption intentions and environmental regulations. With the perspective of ownership, the sample structure should be extended, therefore to be consistent with the industrial context, other industries were not excluded from the survey to prevent missing some potential relationship. The questionnaire for data collection is mainly distributed through online and offline simultaneously. (1) Part-time MBA students of Wuhan University of Technology from different industries are selected to respond to the questionnaire offline; (2) With Yellow Page and social networking in related industries, other questionnaires are filled in by the enterprise managers and engineers through email. Finally, 250 questionnaires are collected, 207 of which were valid, with an effective recovery rate of 82.8%. More details about the data are shown in Table 1.
Our respondents include 90 executives, 102 managers, and the rest are mostly senior engineers. The focus enterprises are mainly from Hubei, Guangdong, and Shanghai of China. According to the statistical data, most of the participated enterprises are manufacturing enterprises (31.4%). Nearly half of the enterprises are state-owned (43.48%), whereas 56.52% are non-state-owned enterprise. Most of the enterprises have over 2000 employees (45.41%), and 30.92% of them are small-size with employees less than 300. 23.67% of them are medium-size with the employee number between 301 and 2000. The majority of the enterprises are in operation over ten years (53.14%). More details are shown in Table 1.

3.2. Variable and Items

All measures and items are derived from existing scales in previous research. Items are set up according to the characteristics of the three environmental regulations to measure, based on the regulatory theory [106]. There are 5 items for CAC, 5 items for MBI, and 5 items for VER. All of the items are derived from maturity scales [65,107,108]. Five-point scales for responses from 1 = completely disagree to 5 = completely agree. All items on three types of environmental regulations are shown in Table 2.
Technology adoption is the process in which organizations as well as relevant individuals recognize and implement new technologies. Here the green technology adoption intention means that enterprise is willing to budget for technology adoption, take the initiative to adopt green technology, and support the implement of policies, codes, and treaties on green technology introduced by the government, market, and relevant institutions. Therefore, organization’s behavioral intention in the whole process is measured and taken into account from the initial knowledge-awareness of green technology to consider the investment decision and the acceptance of green technology. Based on Meyer and Goes’s achievements in green technology adoption, the process of GATI is divided into three stages [109]. There are 10 items for GTAI, including 3 items of knowledge in awareness stage, 3 items of evaluation in choice stage, and 4 items of adoption in implementation stage (details shown in Table 2). The five-point measurement scale is applied for measuring GTAI from 1 (completely disagree) to 5 (completely agree).

3.3. Measurement and Results Analysis

3.3.1. Validity and Reliability Analysis

In order to identify the main factors of CAC, MBI, and VER as well as GTAI, an exploratory factor analysis (CFA) is carried out with application of SPSS20.0. Principal components method with a varimax rotation is introduced to extract the theoretical dimensions as well as initial eigenvalue test (the eigenvalue > 1) for the number of extracted factors. Here one factor is extracted for CAC with explanation 80.4% of the variations. One factor extracted of MBI explains 81.27% of the variations. One factor extracted of VER explains 78.73% of the variations. The Cronbach’s alpha is introduced to examine if those items grouped into particular factors are statistically reliable. The result indicates that the alpha value in this study is 0.906 for the factor of CAC, 0.859 for the factor of MBI, and 0.904 for the factor of VER. It shows a reasonable internal consistency.
The initial eigenvalue test indicates that the GTAI consists of three factors. According to the item meanings of each main factor, they may be labeled as knowledge-awareness stage, evaluation-choice stage, and adoption-implementation stage of GTAI. These three factors explain 83.59% of the inherent variations. Loading of these individual items on the three main factors is shown in Table 3. Similarly, check the reasonability of the item groping by reliability analysis. The alpha values for the items of those three main factors indicating fundamental stages of GTAI, are 0.912, 0.900, and 0.921, which means a reasonable internal consistency. Meanwhile, the KMO value of the items scale is 0.952, Bartlett-sphericity test is significant at the 0.001 level, indicating good structural validity.

3.3.2. Correlation among Variables

Correlation between major variables is analyzed with Pearson correlation coefficient test. The results are shown in the Table 4. The correlation coefficients between the three types of regulations and enterprise GTAI are 0.588, 0.659, and 0.733 respectively, all of which are significant at the significance level of 1%. It preliminarily verifies that there is a very close relationship among three types of regulations and enterprise GTAI, and all of them are positively correlated. While three types of regulations are the major categories for environmental protection, a certain correlation among them is there. According to Tsui et al. [110], the critical value of correlation level with multi-collinearity problems is generally more than 0.75, which means that there is no serious multi-collinearity problem in the sample data of this study. More details are shown in Table 4.

3.3.3. Results Analysis

Before hypothesis testing, independent variables and moderating variables are centrally processed to reduce the endogenous multi-collinearity. The following regression model is established and carried out with empirical data.
Model:
  GTAI = β 0 + β 1 I n d + β 2 S i z e + β 3 A g e + β 4 C A C + β 5 M B I + β 6 V E R + β 7 O w n s + β 8 CAC × Owns + β 9 M B I × O w n s + β 10 V E R × O w n s
The hierarchical regression is applied to examine those six hypotheses about the relationship between ER and GTAI, as well as the effect of ownership. Industry (Ind.), corporate size (Size), and corporate Age (Age) may influence that relationship. Thus, the three control variables are included in the model as the first step. As shown in Table 5, the ∆R2 = 0.018 indicates that the control variable explains an additional variation of 1.18% green technology adoption intention. To examine the effect of command-and-control environment regulation (CAC), market-based incentive environmental regulation (MBI), and voluntary environmental regulation (VER); these three factors are added at the second step. The results show that three types of environmental regulations on enterprise green technology adoption intention have significant positive influence. It means Hypothesis 1a, Hypothesis 2a, and Hypothesis 3a are verified and reasonable, while the independent variables and control variables together explained 58.7% of the GTAI variation. Then, the ownership is represented by 0–1 dummy variables, 1 represents state-owned enterprise, 0 represents non-state-owned enterprise, and are taken into the regression test in the third step.
Finally, three kinds of interaction terms between environmental regulations and state ownership are taken into account in regression. It can be seen that the regression coefficient of CAC × Owns is 0.327, which is significant at the significance level of 0.05, it indicates that the interaction between state ownership and command-and-control environmental regulation has a significant positive effect on green technology adoption intention. Meanwhile market-based incentive environmental regulation and state ownership interactive indicators (MBI × Owns) of the regression coefficient is not significant with value 0.069. It means the state ownership does not play a significant role as a moderator on the relationship. Therefore, Hypothesis 2b is rejected, but the results still have certain theoretical and practical significance. It shows that when it comes to market-based incentive environmental regulation, the sensitivity of state-owned enterprises and non-state-owned enterprises doesn’t differ a lot, so they have similar behavioral intensity in adopting green technology. In addition, the regression coefficient of the interaction between voluntary environmental regulation and state ownership (VER × Owns) is −0.496, which is significant at p = 0.001, indicating that the state ownership and voluntary environmental regulation has a significant interaction effect on the GTAI.
Regarding the moderating effect of state ownership on the relationship between certain regulations and GATI, more details are shown in Figure 2. It indicates that state ownership has a significant moderating effect on the relationship between CAC and GTAI. A further examination shows that, for state-owned enterprises, command-and-control environmental regulation has a higher positive impact on enterprises GTAI than non-state-owned enterprises. Hypothesis 1b is verified and acceptable. The details in Figure 3 shows that for the state-owned enterprises, the stronger voluntary environmental regulation would lead to lower GTAI. Meanwhile with stronger the voluntary environmental regulation for non-state-owned enterprises, the higher GTAI is over there. The result explains that state ownership has significant negative moderating effects on the relationship between VER and GTAI. For non-state-owned enterprises, VER has a more significant positive impact on the intention to adopt green technology, while for state-owned enterprises, VER has a negative impact on the GTAI. It shows that Hypothesis 3b is accepted and reasonable.
Based on the above result, the framework of moderating role of ownership in the relationship between ER and GTAI is restructured, as shown in Figure 4.

4. Discussion

4.1. The Effect of Environmental Regulations

Based on the regression analysis result, three type of environmental regulations including CAC, MBI, VER impose positive influence of enterprise GTAI. That is, they may promote the application of green technology in operation. First, CAC is positive related to GTAI, which means it is necessary to carry a certain intensity of mandatory norms on the enterprises’ operation, government should play a good regulatory role. Second, MBI has a positive impact on GTAI; the possible reason is that enterprises are aware of the future economic benefit in the market, which can be acquired by adopting green technologies. Moreover, VER has a very strong positive effect on GTAI. The result manifests that incentives on GTAI induced by the VER measures are much more effective than CAC and MBI. It indicates that the flexible voluntary environmental regulations provide enterprises more choice to adopt green technologies. These companies are more forward-looking in environmental management, with a stronger intention to adopt green technologies.

4.2. The Moderate Effect of State Ownership

On the one hand, state ownership plays a positive moderator role in the relationship between CAC and GTAI. Specifically, CAC imposes more positive influence on state-owned enterprise than non-state-owned enterprises regarding the GTAI. This reflects under different ownership, firms have different complying intentions but same regulations. The state ownership makes firm attach more importance to CAC from government regulators. Meanwhile, state-owned enterprises encountering VER are less sensitive; state ownership produces no or negative moderate effect in the relationship between VER and GTAI. On the contrary, non-state-owned firms show more enthusiasm in GTA when facing VER. The research results indicate that non-state-owned firms think that the public image has bigger impact on the operation and performance, while state-owned firms care resources and penalties from their owner and regulator more.
On the other hand, the moderate function of enterprise ownership is not verified regarding its effect on the relationship between MBI and GTAI. It means that state ownership does not play a significant role in the production of GTAI while facing market-based incentive environmental regulations. The result indicates that there are no difference regarding MBI (such as taxes, subsidies, compensation)’s effect on state-owned and non-state-owned enterprises GTAI. In other words, there is no significant difference regarding enterprise green behavior and operation when facing similar MBI.

4.3. Managerial Implication

The work may also benefit the practitioners in green campaign. For policy-makers, the attribute of regulations should be taken into account with the enterprises features, especially ownership profile. In addition to generally improve regulation strength to promote the enterprises’ green technology adoption, they should be tailored according to ownership attributes of the enterprises. That is, more CAC should be introduced into state-owned enterprises, such as stringent laws and technical standards, supplemented by MBI and VER, to promote corporate green technology adoption. Emphasis on VER for non-state-owned enterprises, may promote positive implementation of environmental management systems such as ISO14001, and guide and disclose environmental information about business operation. Thereafter a reasonable combination of three types of environmental regulations may be shaped and introduced into enterprise greening operation. For the senior managers, the conclusion may guide them to follow-up the rule of regulation mechanism, to achieve competitive advantage.

5. Conclusions

5.1. Contribution

Based on institution and stakeholder theories, the work explores the mechanism about whether and how the state ownership influences the response of enterprises to three types of environmental regulations. The work finds that state ownership poses a positively moderating effect on the influence of CAC to GTAI and negative moderating effect on the influence of VER to GTAI.
Our paper focused on the influence of state ownership on green innovation. There are already some research on the influencing factors of green innovation, which will help to find effective ways to promote green innovation and sustainability goal. The authors explored the influence of state ownership, which had not been addressed previously.
This article has discovered the different responses of state-owned and non-state-owned enterprises to environment regulations, which will help achieve a better understanding of enterprises GTA.
Different countries and regions have different market structures. In China and other emerging economies, state-owned enterprises are important market players. If the governments want to achieve sustainable goals, such as promoting green technologies, it must consider how to design effective environment regulations to achieve the desired outcome. According to the findings of this study, policy-makers should consider the state ownership. If there are more state ownership firms in certain industries, policy-makers should consider more CAC regulations. Otherwise, policy-makers should promote the publicity of VER regulations. The results of this study, along with previous studies, offer a better guide for policy-makers.

5.2. Limitations and Future Study

While we carry an innovation in the research perspective and get some novel viewpoints, the limitation should not be ignored. In terms of the research methodology, first, the data we collected are based heavily on respondents self-reporting, even though it is a popular method and acceptable widely [7,16,60], its reliability is also reminded by some researches [111]. Consequently, more reasonable measures to get data such as real field studies may be needed to be carried out in future to examine the study. Second, our samples are not all executives but also managers and senior engineers. Because many strategic thoughts and decisions of the firm are related to the management, it is better to focus on executives or leaders in the future. Regarding the environmental regulation, only three types are taken into account roughly: not covering environmental consciousness, value, and some other intangible code influencing enterprise green behavior. Ultimately, due to the limited resources and accessibility, the work only divides the ownership into state-owned and no-state-owned. As family and non-family firms play different roles in non-state-owned enterprises, family firms concerning reputation, pay more attention to sustainable operations and environmental behavior than their non-family counterparts [112,113]. That may also be taken into account and should be studied in the future.

Author Contributions

Conceptualization, D.X. and W.C.; methodology, W.C., Y.Z. and R.Z.; software, W.C. and Y.Z.; validation, D.X., W.C. and Q.G.; formal analysis, D.X. and Q.G.; investigation, W.C. and R.Z.; resources, D.X. and Q.G.; data curation, D.X. and Q.G.; writing—original draft preparation, D.X. and W.C.; writing—review and editing, D.X., W.C., R.Z. and Y.Z; visualization, D.X.; supervision, Q.G.; project administration, D.X., W.C., Q.G. and R.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The authors’ work was supported by the National Natural Science Foundation of China (Grant No. 71772143).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yao, X.; Huang, R.; Song, M. How to Reduce Carbon Emissions of Small and Medium Enterprises (SMEs) by Knowledge Sharing in China. Prod. Plan. Control. 2019, 30, 881–892. [Google Scholar] [CrossRef]
  2. Wu, J.; Xia, P.; Zhu, Q. Measuring Environmental Efficiency of Thermoelectric Power Plants: A Common Equilibrium Efficient Frontier DEA Approach with Fixed-Sum Undesirable Output. Ann. Oper. Res. 2019, 275, 731–749. [Google Scholar] [CrossRef]
  3. Song, M.; Wang, S.; Sun, J. Environmental Regulations, Staff Quality, Green Technology, R&D Efficiency, and Profit in Manufacturing. Technol. Forecast. Soc. Chang. 2018, 133, 1–14. [Google Scholar]
  4. Liu, H.; Wu, J.; Chu, J. Environmental Efficiency and Technological Progress of Transportation Industry-Based on Large Scale Data. Technol. Forecast. Soc. Chang. 2019, 144, 475–482. [Google Scholar] [CrossRef]
  5. Tornatzky, L.G.; Klein, K.J. Innovation Characteristics and Innovation Adoption-Implementation: A Meta-Analysis of Findings. IEEE Trans. Eng. Manag. 1982, 1, 28–45. [Google Scholar] [CrossRef]
  6. Jaffe, A.B.; Newell, R.G.; Stavins, R.N. Environmental Policy and Technological Change. Environ. Resour. Econ. 2002, 22, 41–70. [Google Scholar] [CrossRef]
  7. Darko, A.; Chan, A.P.C.; Yang, Y.; He, B.J.; Yang, J.; Shan, M.; Gou, Z. Influences of Barriers, Drivers, and Promotion Strategies on Green Building Technologies Adoption in Developing Countries: The Ghanaian Case. J. Clean. Prod. 2018, 200, 687–703. [Google Scholar] [CrossRef]
  8. Hottenrott, H.; Rexhäuser, S.; Veugelers, R. Organisational Change and the Productivity Effects of Green Technology Adoption. Resour. Energy Econ. 2016, 43, 172–194. [Google Scholar] [CrossRef] [Green Version]
  9. Xia, D.; Chen, B.; Zheng, Z. Relationships among Circumstance Pressure, Green Technology Selection and Firm Performance. J. Clean. Prod. 2015, 106, 487–496. [Google Scholar] [CrossRef]
  10. Weng, M.H.; Lin, C.Y. Determinants of Green Innovation Adoption for Small and Medium-Size Enterprises (SMES). Afr. J. Bus. Manag. 2011, 5, 9154–9163. [Google Scholar]
  11. Horbach, J. Determinants of Environmental Innovation—New Evidence from German Panel Data Sources. Res. Policy 2008, 37, 163–173. [Google Scholar] [CrossRef] [Green Version]
  12. Pujari, D. Eco-Innovation and New Product Development: Understanding the Influences on Market Performance. Technovation 2006, 26, 76–85. [Google Scholar] [CrossRef]
  13. Triguero, A.; Mondéjar, L.M.; Davia, M.A. Drivers of Different Types of Eco-Innovation in European SMEs. Ecol. Econ. 2013, 92, 25–33. [Google Scholar] [CrossRef]
  14. Estrin, S.; Perotin, V. Does Ownership Always Matter? Int. J. Ind. Organ. 1991, 9, 55–72. [Google Scholar] [CrossRef]
  15. Arvanitis, S.; Ley, M. Factors Determining the Adoption of Energy-Saving Technologies in Swiss Firms: An Analysis Based on Micro Data. Environ. Resour. Econ. 2013, 54, 389–417. [Google Scholar] [CrossRef]
  16. Bukchin, S.; Kerret, D. Once You Choose Hope: Early Adoption of Green Technology. Environ. Sci. Pollut. Res. 2020, 27, 3271–3280. [Google Scholar] [CrossRef]
  17. Stucki, T.; Woerter, M. Intrafirm Diffusion of Green Energy Technologies and the Choice of Policy Instruments. J. Clean. Prod. 2016, 131, 545–560. [Google Scholar] [CrossRef] [Green Version]
  18. Jefferson, G.; Albert, G.Z.; Guan, X.; Yu, X. Ownership, Performance, and Innovation in China’s Large-and Medium-Size Industrial Enterprise Sector. China Econ. Rev. 2003, 14, 89–113. [Google Scholar] [CrossRef] [Green Version]
  19. Xie, R.; Yuan, Y.; Huang, J. Different Types of Environmental Regulations and Heterogeneous Influence on “Green” Productivity: Evidence from China. Ecol. Econ. 2017, 132, 104–112. [Google Scholar] [CrossRef]
  20. Golombek, R.; Raknerud, A. Do Environmental Standards Harm Manufacturing Employment? Scand. J. Econ. 1997, 99, 29–44. [Google Scholar] [CrossRef]
  21. Bollinger, B. Green Technology Adoption: An Empirical Study of the Southern California Garment Cleaning Industry. Quant. Mark. Econ. 2015, 13, 319–358. [Google Scholar] [CrossRef]
  22. Porter, M.E. America’s Green Strategy. Sci. Am. 1991, 264, 193–246. [Google Scholar]
  23. Porter, M.E.; Van der Linde, C. Toward a New Conception of the Environment-Competitiveness Relationship. J. Econ. Perspect. 1995, 9, 97–118. [Google Scholar] [CrossRef]
  24. Requate, T.; Unold, W. Environmental Policy Incentives to Adopt Advanced Abatement Technology: Will the True Ranking Please Stand Up? Eur. Econ. Rev. 2003, 47, 125–146. [Google Scholar] [CrossRef]
  25. Krass, D.; Nedorezov, T.; Ovchinnikov, A. Environmental Taxes and the Choice of Green Technology. Prod. Oper. Manag. 2013, 22, 1035–1055. [Google Scholar] [CrossRef]
  26. Hattori, K. Optimal Combination of Innovation and Environmental Policies under Technology Licensing. Econ. Model. 2017, 64, 601–609. [Google Scholar] [CrossRef]
  27. Villegas-Palacio, C.; Coria, J. On the Interaction between Imperfect Compliance and Technology Adoption: Taxes versus Tradable Emissions Permits. J. Regul. Econ. 2010, 38, 274–291. [Google Scholar] [CrossRef]
  28. Zhen, L.; Wu, Y.; Wang, S.; Laporte, G. Green Technology Adoption for Fleet Deployment in a Shipping Network. Transp. Res. Part B Methodol. 2020, 139, 388–410. [Google Scholar] [CrossRef]
  29. Klemetsen, M.E.; Bye, B.; Raknerud, A. Can Direct Regulations Spur Innovations in Environmental Technologies? A Study on Firm-Level Patenting. Scand. J. Econ. 2018, 120, 338–371. [Google Scholar] [CrossRef]
  30. Ramanathan, R.; Ramanathan, U.; Bentley, Y. The Debate on Flexibility of Environmental Regulations, Innovation Capabilities and Financial Performance–A Novel Use of DEA. Omega 2018, 75, 131–138. [Google Scholar] [CrossRef] [Green Version]
  31. Sapra, H.; Subramanian, A.; Subramanian, K.V. Corporate Governance and Innovation: Theory and Evidence. J. Financ. Quant. Anal. 2014, 49, 957–1003. [Google Scholar] [CrossRef] [Green Version]
  32. Saengsupavanich, C.; Gallardo, W.G.; Sajor, E.; Murray, W.M. Constraints Influencing Stakeholder Participation in Collective Environmental Management. Environ. Earth Sci. 2012, 66, 1817–1829. [Google Scholar] [CrossRef]
  33. Ambec, S.; Lanoie, P. Does It Pay to Be Green? A Systematic Overview. Acad. Manag. Perspect. 2008, 22, 45–62. [Google Scholar]
  34. Donaldson, T.; Preston, L.E. The Stakeholder Theory of the Corporation: Concepts, Evidence and Implications. Acad. Manag. Rev. 1995, 20, 65–91. [Google Scholar] [CrossRef] [Green Version]
  35. Berrone, P.; Fosfuri, A.; Gelabert, L.; Mejia, L.R.G. Necessity as the Mother of ’Green’ Inventions: Institutional Pressures and Environmental Innovations. Strateg. Manag. J. 2013, 34, 891–909. [Google Scholar] [CrossRef]
  36. La Porta, R.; Silanes, F.d.L.; Shleifer, A. Corporate Ownership around the World. J. Financ. 1999, 54, 471–517. [Google Scholar] [CrossRef]
  37. Cronqvist, H.; Nilsson, M. Agency Costs of Controlling Minority Shareholders. J. Financ. Quant. Anal. 2003, 38, 695–719. [Google Scholar] [CrossRef]
  38. Child, J.; Tse, D.K. China’s Transition and Its Implications for International Business. J. Int. Bus. Stud. 2001, 32, 5–21. [Google Scholar] [CrossRef]
  39. Jiang, L.A.; Waller, D.S.; Cai, S. Does Ownership Type Matter for Innovation? Evidence from China. J. Bus. Res. 2013, 66, 2473–2478. [Google Scholar] [CrossRef]
  40. Xu, X.N.; Wang, Y. Ownership Structure and Corporate Governance in Chinese Stock Companies. China Econ. Rev. 1999, 10, 75–98. [Google Scholar] [CrossRef]
  41. Tan, J. Impact of Ownership Type on Environment–Strategy Linkage and Performance: Evidence from a Transitional Economy. J. Manag. Stud. 2002, 39, 333–354. [Google Scholar] [CrossRef]
  42. Sharma, S.; Pablo, A.L.; Vredenburg, H. Corporate Environmental Responsiveness Strategies: The Importance of Issue Interpretation and Organizational Context. J. Appl. Behav. Sci. 1999, 35, 87–108. [Google Scholar] [CrossRef]
  43. Féres, J.; Reynaud, A. Assessing the Impact of Formal and Informal Regulations on Environmental and Economic Performance of Brazilian Manu-Facturing Firms. Environ. Resour. Econ. 2012, 52, 65–85. [Google Scholar] [CrossRef]
  44. Brents, B.G.; Hausbeck, K. State-Sanctioned Sex: Negotiating Formal and Informal Regulatory Practices in Nevada Brothels. Sociol. Perspect. 2001, 44, 307–332. [Google Scholar] [CrossRef]
  45. Wu, G.C. Environmental Innovation Approaches and Business Performance: Effects of Environmental Regulations and Resource Com-Mitment. Innovation 2017, 19, 407–427. [Google Scholar] [CrossRef]
  46. Blackman, A.; Lahiri, B.; Pizer, W.; Planter, M.R.; Pina, C.M. Voluntary Environmental Regulation in Developing Countries: Mexico’s Clean Industry Program. J. Environ. Econ. Manag. 2010, 60, 182–192. [Google Scholar] [CrossRef]
  47. Bu, M.; Qiao, Z.; Liu, B. Voluntary Environmental Regulation and Firm Innovation in China. Econ. Model. 2020, 89, 10–18. [Google Scholar] [CrossRef]
  48. Jennings, P.D.; Zandbergen, P.A. Ecologically Sustainable Organizations: An Institutional Approach. Acad. Manag. Rev. 1995, 20, 1015–1052. [Google Scholar] [CrossRef]
  49. Henriques, I.; Sadorsky, P. The Determinants of an Environmentally Responsive Firm: An Empirical Approach. J. Environ. Econ. Manag. 1996, 30, 381–395. [Google Scholar] [CrossRef]
  50. Sethi, S.P. A Conceptual Framework for Environmental Analysis of Social Issues and Evaluation of Business Response Patterns. Acad. Manag. Rev. 1979, 4, 63–74. [Google Scholar] [CrossRef]
  51. Suchman, M.C. Managing Legitimacy: Strategic and Institutional Approaches. Acad. Manag. Rev. 1995, 20, 571–610. [Google Scholar] [CrossRef] [Green Version]
  52. Siegel, P.B.; Johnson, T.G. Measuring the Economic Impacts of Reducing Environmentally Damaging Production Activities. Rev. Reg. Stud. 1993, 23, 237–254. [Google Scholar]
  53. Acemoglu, D.; Aghion, P.; Bursztyn, L.; Hemous, D. The Environment and Directed Technical Change. Am. Econ. Rev. 2012, 102, 131–166. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Braun, E.; Wield, D. Regulation as a Means for the Social Control of Technology. Technol. Anal. Strateg. Manag. 1994, 6, 259–272. [Google Scholar] [CrossRef]
  55. Rennings, K.; Zwick, T. Employment Impact of Cleaner Production on the Firm Level: Empirical Evidence from a Survey in Five European Countries. Int. J. Innov. Manag. 2002, 6, 319–342. [Google Scholar] [CrossRef]
  56. Brunnermeier, S.B.; Cohen, M.A. Determinants of Environmental Innovation in US Manufacturing Industries. J. Environ. Econ. Manag. 2003, 45, 278–293. [Google Scholar] [CrossRef]
  57. Lesic, V.; De Bruin, W.B.; Davis, M.C. Consumers’ Perceptions of Energy Use and Energy Savings: A Literature Review. Environ. Res. Lett. 2018, 13, 033004. [Google Scholar] [CrossRef]
  58. Mills, B.; Schleich, J. Residential Energy-Efficient Technology Adoption, Energy Conservation, Knowledge, and Attitudes: An Analysis of European Countries. Energy Policy 2012, 49, 616–628. [Google Scholar] [CrossRef]
  59. Delmas, M.A.; Toffel, M.W. Organizational Responses to Environmental Demands: Opening the Black Box. Strateg. Manag. J. 2008, 29, 1027–1055. [Google Scholar] [CrossRef]
  60. Sharma, S. Managerial Interpretations and Organizational Context as Predictors of Corporate Choice of Environmental Strategy. Acad. Manag. J. 2000, 43, 681–697. [Google Scholar]
  61. Constantatos, C.; Herrmann, M. Market Inertia and the Introduction of Green Products: Can Strategic Effects Justify the Porter Hypothesis? Environ. Resour. Econ. 2011, 50, 267. [Google Scholar] [CrossRef]
  62. Brännlund, R.; Chung, Y.; Färe, R.; Grosskopf, S. Emissions Trading and Profitability: The Swedish Pulp and Paper Industry. Environ. Resour. Econ. 1998, 12, 345–356. [Google Scholar] [CrossRef]
  63. Conrad, K.; Wastl, D. The Impact of Environmental Regulation on Productivity in German Industries. Empir. Econ. 1995, 20, 615–633. [Google Scholar] [CrossRef]
  64. Lanoie, P.; Patry, M.; Lajeunesse, R. Environmental Regulation and Productivity: Testing the Porter Hypothesis. J. Product. Anal. 2008, 30, 121–128. [Google Scholar] [CrossRef]
  65. Liu, Y.; Zhu, J.; Li, E.Y.; Meng, Z.; Song, Y. Environmental Regulation, Green Technological Innovation, and Eco-Efficiency: The Case of Yangtze River Economic Belt in China. Technol. Forecast. Soc. Chang. 2020, 155, 119993. [Google Scholar] [CrossRef]
  66. Lanjouw, J.O.; Mody, A. Innovation and the International Diffusion of Environmentally Responsive Technology. Res. Policy 1996, 25, 549–571. [Google Scholar] [CrossRef]
  67. Akerlof, G.A. The Market for “Lemons”: Quality Uncertainty and the Market Mechanism [M]//Uncertainty in Economics; Academic Press: Cambridge, MA, USA, 1978; pp. 235–251. [Google Scholar]
  68. Chen, Y.S.; Lai, S.B.; Wen, C.T. The Influence of Green Innovation Performance on Corporate Advantage in Taiwan. J. Bus. Ethics 2006, 67, 331–339. [Google Scholar] [CrossRef]
  69. Ajzen, I. The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  70. Zhang, B.; Yang, S.; Bi, J. Enterprises’ Willingness to Adopt/Develop Cleaner Production Technologies: An Empirical Study in Changshu, China. J. Clean. Prod. 2013, 40, 62–70. [Google Scholar] [CrossRef]
  71. Corral, C.M. Sustainable Production and Consumption Systems—Cooperation for Change: Assessing and Simulating the Willingness of the Firm to Adopt/Develop Cleaner Technologies. The Case of the In-Bond Industry in Northern Mexico. J. Clean. Prod. 2003, 11, 411–426. [Google Scholar] [CrossRef]
  72. Zhang, B.; Bi, J.; Yuan, Z.; Ge, Y. Why Do Firms Engage in Environmental Management? An Empirical Study in China. J. Clean. Prod. 2008, 16, 1036–1045. [Google Scholar] [CrossRef]
  73. Sun, Q.; Tong, W.H.S.; Tong, J. How Does Government Ownership Affect Firm Performance? Evidence from China’s Privatization Experience. J. Bus. Financ. Account. 2002, 29, 1–27. [Google Scholar] [CrossRef]
  74. Hu, J.; Wang, Z.; Lian, Y. Environmental Regulation, Foreign Direct Investment and Green Technological Progress—Evidence from Chinese Manufacturing Industries. Int. J. Environ. Res. Public Health 2018, 15, 221. [Google Scholar]
  75. Cambini, C.; Rondi, L. Incentive Regulation and Investment: Evidence from European Energy Utilities. J. Regul. Econ. 2010, 38, 1–26. [Google Scholar] [CrossRef]
  76. Popp, D. International Innovation and Diffusion of Air Pollution Control Technologies: The Effects of NOX and SO2 Regulation in the US, Japan, and Germany. J. Environ. Econ. Manag. 2006, 51, 46–71. [Google Scholar] [CrossRef] [Green Version]
  77. Scott, W.R. The Adolescence of Institutional Theory. Adm. Sci. Q. 1987, 32, 493–511. [Google Scholar] [CrossRef]
  78. Sánchez, R.G.; Bolívar, M.P.R.; Hernández, A.M.L. Corporate and Managerial Characteristics as Drivers of Social Responsibility Disclosure by State-Owned Enterprises. Rev. Manag. Sci. 2017, 11, 633–659. [Google Scholar] [CrossRef]
  79. Cai, X.; Zhu, B.; Zhang, H.; Li, L.; Xie, M. Can Direct Environmental Regulation Promote Green Technology Innovation in Heavily Polluting Industries? Evidence from Chinese Listed Companies. Sci. Total Environ. 2020, 746, 140810. [Google Scholar] [CrossRef]
  80. Brandt, L.; Li, H. Bank Discrimination in Transition Economies: Ideology, Information, or Incentives? J. Comp. Econ. 2003, 31, 387–413. [Google Scholar] [CrossRef] [Green Version]
  81. McGartland, A. A Comparison of Two Marketable Discharge Permits Systems. J. Environ. Econ. Manag. 1988, 15, 35–44. [Google Scholar] [CrossRef]
  82. Malueg, D.A. Emission Credit Trading and the Incentive to Adopt New Pollution Abatement Technology. J. Environ. Econ. Manag. 1989, 16, 52–57. [Google Scholar] [CrossRef]
  83. Zhou, Y.; Zhu, S.; He, C. How Do Environmental Regulations Affect Industrial Dynamics? Evidence from China’s Pollution-Intensive Industries. Habitat Int. 2017, 60, 10–18. [Google Scholar] [CrossRef] [Green Version]
  84. Bertarelli, S.; Lodi, C. Heterogeneous Firms, Exports and Pigouvian Pollution Tax: Does the Abatement Technology Matter? J. Clean. Prod. 2019, 228, 1099–1110. [Google Scholar] [CrossRef]
  85. Tang, W.; Liang, X. Income tax incentives, property rights and corporation’s social responsibility performance. Commer. Account. 2015, 4, 50–52. (In Chinese) [Google Scholar]
  86. Feldman, M.P.; Kelley, M.R. The Ex Ante Assessment of Knowledge Spillovers: Government R&D Policy, Economic Incentives and Private Firm Behavior. Res. Policy 2006, 35, 1509–1521. [Google Scholar]
  87. Kleer, R. Government R&D Subsidies as a Signal for Private Investors. Res. Policy 2010, 39, 1361–1374. [Google Scholar]
  88. Lin, C.; Lin, P.; Song, F.M.; Li, C. Managerial Incentives, CEO Characteristics and Corporate Innovation in China’s Private Sector. J. Comp. Econ. 2011, 39, 176–190. [Google Scholar] [CrossRef] [Green Version]
  89. Choi, S.B.; Lee, S.H.; Williams, C. Ownership and Firm Innovation in a Transition Economy: Evidence from China. Res. Policy 2011, 40, 441–452. [Google Scholar] [CrossRef]
  90. Darnall, N.; Henriques, I.; Sadorsky, P. Adopting Proactive Environmental Strategy: The Influence of Stakeholders and Firm Size. J. Manag. Stud. 2010, 47, 1072–1094. [Google Scholar] [CrossRef]
  91. Abrell, J.; Rausch, S. Combining Price and Quantity Controls under Partitioned Environmental Regulation. J. Public Econ. 2017, 145, 226–242. [Google Scholar] [CrossRef] [Green Version]
  92. Kang, Y.S.; Kim, B.Y. Ownership Structure and Firm Performance: Evidence from the Chinese Corporate Reform. China Econ. Rev. 2012, 23, 471–481. [Google Scholar] [CrossRef]
  93. Amacher, G.S.; Malik, A.S. Instrument Choice When Regulators and Firms Bargain. J. Environ. Econ. Manag. 1998, 35, 225–241. [Google Scholar] [CrossRef]
  94. Taylor, C.M.; Pollard, S.J.T.; Rocks, S.A.; Angus, A.J. Better by Design: Business Preferences for Environmental Regulatory Reform. Sci. Total Environ. 2015, 512, 287–295. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Bowen, F.; Tang, S.; Panagiotopoulos, P. A Classification of Information-Based Environmental Regulation: Voluntariness, Compliance and Beyond. Sci. Total Environ. 2020, 712, 135571. [Google Scholar] [CrossRef]
  96. Welch, E.W.; Mori, Y.; Aoyagi-Usui, M. Voluntary Adoption of ISO 14001 in Japan: Mechanisms, Stages and Effects. Bus. Strategy Environ. 2002, 11, 43–62. [Google Scholar] [CrossRef]
  97. Camisón, C. Effects of Coercive Regulation versus Voluntary and Cooperative Auto-Regulation on Environmental Adaptation and Perfor-Mance: Empirical Evidence in Spain. Eur. Manag. J. 2010, 28, 346–361. [Google Scholar] [CrossRef]
  98. Woerter, M.; Stucki, T.; Arvanitis, S.; Ramer, C.; Peneder, M. The Adoption of Green Energy Technologies: The Role of Policies in Austria, Germany, and Switzerland. Int. J. Green Energy 2017, 14, 1192–1208. [Google Scholar] [CrossRef]
  99. Jiang, Z.; Wang, Z.; Zeng, Y. Can Voluntary Environmental Regulation Promote Corporate Technological Innovation? Bus. Strategy Environ. 2020, 29, 390–406. [Google Scholar] [CrossRef]
  100. Christmann, P.; Taylor, G. Globalization and the Environment: Determinants of Firm Self-Regulation in China. J. Int. Bus. Stud. 2001, 32, 439–458. [Google Scholar] [CrossRef]
  101. DiMaggio, P.J.; Powell, W.W. The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. Am. Sociol. Rev. 1983, 48, 147–160. [Google Scholar] [CrossRef] [Green Version]
  102. Oliver, C. Strategic Responses to Institutional Processes. Acad. Manag. Rev. 1991, 16, 145–179. [Google Scholar] [CrossRef] [Green Version]
  103. Kemp, R.; Volpi, M. The Diffusion of Clean Technologies: A Review with Suggestions for Future Diffusion Analysis. J. Clean. Prod. 2008, 16, S14–S21. [Google Scholar] [CrossRef]
  104. Fallah, M.A.; Mojarrad, F. Corporate Governance Effects on Corporate Social Responsibility Disclosure: Empirical Evidence from Heavy-Pollution Industries in Iran. Soc. Responsib. J. 2019, 15, 208–225. [Google Scholar] [CrossRef]
  105. Rooij, V.B.; Fryxell, G.E.; Lo, C.W.H.; Wang, W. From Support to Pressure: The Dynamics of Social and Governmental Influences on Environmental Law Enforcement in Guangzhou, C. Ty, China. Regul. Gov. 2013, 7, 321–347. [Google Scholar] [CrossRef] [Green Version]
  106. Gunningham, N.; Grabosky, P.; Sinclair, D. Smart Regulation. Regul. Theory 1998, 8, 133–148. [Google Scholar]
  107. Quinn, A.C. Smart Regulation: Designing Environmental Policy: Neil Gunningham and Peter Grabosky, Clarendon Press, Oxford, 1998. J. Gov. Inf. 2002, 29, 335–337. [Google Scholar]
  108. Ma, F.P.; Cha, N. The Impact of Environmental Regulation on Technological Innovation Performance—The Moderating Role of Institutional Environment. R&D Manag. 2012, 24, 60–66. [Google Scholar]
  109. Meyer, A.D.; Goes, J.B. Organizational Assimilation of Innovations: A Multilevel Contextual Analysis. Acad. Manag. J. 1988, 31, 897–923. [Google Scholar]
  110. Tsui, A.S.; Ashford, S.J.; Clair, L.S.; Xin, K.R. Dealing with Discrepant Expectations: Response Strategies and Managerial Effectiveness. Acad. Manag. J. 1995, 38, 1515–1543. [Google Scholar]
  111. Kormos, C.; Gifford, R. The Validity of Self-Report Measures of Pro-Environmental Behavior: A Meta-Analytic Review. J. Environ. Psychol. 2014, 40, 359–371. [Google Scholar] [CrossRef]
  112. Gavana, G.; Gottardo, P.; Moisello, A.M. Do Customers Value CSR Disclosure? Evidence from Italian Family and Non-Family Firms. Sustainability 2018, 10, 1642. [Google Scholar] [CrossRef] [Green Version]
  113. Berrone, P.; Cruz, C.; Gomez-Mejia, L.R.; Kintana, M.L. Socioemotional Wealth and Corporate Responses to Institutional Pressures: Do Family-Controlled Firms Pollute Less? Adm. Sci. Q. 2010, 55, 82–113. [Google Scholar] [CrossRef]
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Figure 1. Research framework of moderating role of state ownership in the relationship between environmental regulations (ER) and green technologic adoption intentions (GTAI).
Figure 1. Research framework of moderating role of state ownership in the relationship between environmental regulations (ER) and green technologic adoption intentions (GTAI).
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Figure 2. The moderate effect of state ownership on the relationship between CAC and GTAI.
Figure 2. The moderate effect of state ownership on the relationship between CAC and GTAI.
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Figure 3. The moderate effect of ownership on the relationship between VER and GTAI.
Figure 3. The moderate effect of ownership on the relationship between VER and GTAI.
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Figure 4. Results of hypotheses relationship and weights.
Figure 4. Results of hypotheses relationship and weights.
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Table
Table 1. Distribution of samples in terms of industries, ownership, and size.
Table 1. Distribution of samples in terms of industries, ownership, and size.
FeatureRangeNPercentage
Industry (Ind.)Agriculture, forestry, animal husbandry and fishery41.93%
Mining industry136.28%
Manufacturing6531.4%
Electricity, heat, gas and water production and supply188.7%
Civil engineering industry3114.98%
Transportation, warehousing and postal services157.25%
Accommodation and catering industry52.42%
Others5627.05%
Ownership (owns)State-owned enterprise9043.48%
Non-state-owned enterprise11756.52%
Number of employee (Size)<3006430.92%
<20004923.67%
>20009445.41%
No. of years establishment (Firm Age)≤23717.87%
3–52914.01%
6–103114.98%
>1011053.14%
Notes: in our work state-owned enterprise means state-owned assets investment or holding are more than 50% of the share, otherwise non-state-owned enterprise.
Table
Table 2. Variable measurement items.
Table 2. Variable measurement items.
VariableMeasurement Items
Command-and-control environmental regulations (CAC)Enterprise’s production follows strict technical standards from policy;
The products produced by the company meet the relevant environmental standards from regulatory department;
Enterprise control the concentration and total amount of waste according to emission performance standards;
Some production licenses of enterprise are determined by the regulatory department;
Enterprise violating the relevant regulations of environmental regulations will be severely punished.
Market-based incentive environmental regulations (MBI)Enterprise have to bear the corresponding taxes and fees when discharge pollutants;
Enterprise must pay a certain pollution deposit;
Enterprise can get financial subsidies for environmental pollution control;
Enterprise can get tax benefits for environmental pollution control;
Enterprise can get a certain deposit refund for recycling product waste.
Voluntary environmental regulations (VER)Enterprise publishes the business environment information in time and accurately;
Enterprise environmental management is ISO 14000 certified;
We will actively solicit opinions from relevant units, experts and the public on the enterprise’s environmental impact assessment report;
Companies are actively committed to achieving higher environmental performance than regulatory policies;
Enterprise implements clean production and whole process control.
Green technology adoption intention (GTAI)Knowledge-awareness stage
The employees of my company can know the existence of green technology in related business areas;
Corporate employees will actively consider the applicability of a certain green technology to the enterprise;
Corporate employees will actively participate in conference discussions about green technology adoption;
Evaluation-choice stage
Enterprise is willing to formally put forward strategic plans for green technology adoption practices;
Our company is willing to conduct technical and economic evaluations for the proposed green technology adoption ideas;
Our company is willing to evaluate the compatibility of alternative green technologies with the strategic requirements;
Adoption-implementation stage
Company is willing to adopt green technologies and try them out;
Company is willing to adopt green technologies and use them often;
Company is willing to continue to adopt this green technology in the next similar production activities;
Company is willing to adopt green technology and continue to expand, improve and innovate.
Resource: ER’s classify and items derived from [106]: Gunningham N., Grabosky P., Sinclair D. Smart regulation [J]. Regulatory Theory, 1998, 133. GTAI’s from [109]: Meyer A D, Goes J B. Organizational assimilation of innovations: A multilevel contextual analysis [J]. Academy of management journal, 1988, 31 (4): 897–923.
Table
Table 3. Factor analysis of CAC, MBI, VER, and GTAI.
Table 3. Factor analysis of CAC, MBI, VER, and GTAI.
VariablesItemFactor LoadingMeansCronbach’sα
CACCAC10.8823.9740.906
CAC20.895
CAC30.904
CAC40.902
CAC50.901
MBIMBI10.8664.1310.859
MBI20.901
MBI30.904
MBI40.927
MBI50.908
VERVER 10.7093.8980.904
VER 20.727
VER 30.775
VER 40.760
VER 50.688
GTAI
Knowledge-awareness stageGTAI10.8143.8340.912
GTAI20.845
GTAI30.815
Evaluation-choice stageGTAI40.7083.9730.900
GTAI50.770
GTAI60.652
Adoption-implementation stageGTAI70.7373.9540.921
GTAI80.831
GTAI90.865
GTAI100.737
Table
Table 4. Descriptive statistics and correlation matrix: dependent and explanatory variables.
Table 4. Descriptive statistics and correlation matrix: dependent and explanatory variables.
No. VariableMeanSD12345678
1 Ind.4.952.201
2 Owns0.430.500.15 *1
3 Size2.150.87−0.11−0.319 **1
4 Age3.041.180.057−0.0870.376 **1
5 CAC3.970.82−0.0030.253 **0.0230.0481
6 MBI4.130.92−0.059−0.0020.0770.0380.702 **1
7 VER3.900.90−0.075−0.0870.1120.1290.607 **0.712 **1
8 GTAI3.920.78−0.076−0.0180.0820.0990.588 **0.659 **0.733 **1
Notes: ** p < 5%, * p < 10%. TWO-TAILED. N = 207.
Table
Table 5. Descriptive statistics and correlation matrix: dependent and explanatory variables.
Table 5. Descriptive statistics and correlation matrix: dependent and explanatory variables.
Step1Step 2Step 3Step 4
Control variables
Ind.−0.027−0.010−0.010−0.019
Size0.036−0.004−0.006−0.018
Age0.0580.0150.015−0.015
Independent variables
CAC 0.134 *0.137 *0.133 *
MBI 0.172 **0.171 **0.154 *
VER 0.430 ***0.429 ***0.466 ***
Regulated variables
Owns −0.012−0.013
Interaction item
CAC × Owns 0.327 *
MBI × Owns 0.069
VER × Owns −0.496 ***
R20.0180.5860.5870.627
Δ R 2 0.0180.5690.0010.040
VIF<2<3<3<4
F1.22547.274 ***40.325 ***32.940 ***
Notes: *** p < 1%, ** p < 5%, * p < 10%.
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Xia, D.; Chen, W.; Gao, Q.; Zhang, R.; Zhang, Y. Research on Enterprises’ Intention to Adopt Green Technology Imposed by Environmental Regulations with Perspective of State Ownership. Sustainability 2021, 13, 1368. https://doi.org/10.3390/su13031368

AMA Style

Xia D, Chen W, Gao Q, Zhang R, Zhang Y. Research on Enterprises’ Intention to Adopt Green Technology Imposed by Environmental Regulations with Perspective of State Ownership. Sustainability. 2021; 13(3):1368. https://doi.org/10.3390/su13031368

Chicago/Turabian Style

Xia, De, Wenhua Chen, Qinglu Gao, Rui Zhang, and Yundong Zhang. 2021. "Research on Enterprises’ Intention to Adopt Green Technology Imposed by Environmental Regulations with Perspective of State Ownership" Sustainability 13, no. 3: 1368. https://doi.org/10.3390/su13031368

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