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Essay

Analysis of the Paths Affecting Corporate Green Innovation in Resource-Based Cities: A Fuzzy-Set QCA Approach

by
Yunhui Zhao
1,*,
Xinyue Wu
2 and
Jian Zhang
1
1
School of Business Administration, Inner Mongolia University of Finance and Economics, Hohhot 010051, China
2
School of Business Administration, Capital University of Economics and Business, Beijing 100070, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(1), 337; https://doi.org/10.3390/su15010337
Submission received: 30 November 2022 / Revised: 20 December 2022 / Accepted: 20 December 2022 / Published: 26 December 2022
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Abstract

:
Green innovation is essential for companies to achieve their dual carbon goals. However, academics have been researching how to encourage enterprises in resource-based cities to take the initiative to implement green innovations. In contrast, we construct a configurational framework based on organizational ecology theory and propose that corporate green innovation does not depend on a single condition, but on the interaction of spatial agglomeration, digital economy, and institutional environment. We identify and explore six conditions that influence corporate green innovation in this study based on a fuzzy-set qualitative comparative analysis of firms in resource-based cities. We show that high-level corporate green innovation can be achieved through different combinations of antecedent conditions. There is also a clear influence of synergy between spatial agglomeration, digital economy, and institutional environment, which can jointly promote corporate green innovation. This study provides a more systematic explanation of how companies can raise their levels of green innovation, as well as valuable insights for companies seeking to improve their green innovation awareness proactively.

1. Introduction

Chinese resource-based cities face the twin challenges of slowing economic growth and increasing ecological constraints [1,2]. At the United Nations General Assembly’s 75th session, China announced its goal of peaking carbon emissions by 2030 and reaching carbon neutrality by 2060 [3,4,5]. Resource-based cities rely heavily on resource consumption to drive economic development. What should enterprises in resource-based cities do to achieve carbon neutrality? Enterprises, as an important part of the innovation body, are an important part of providing low-carbon products [6]. Companies in resource-based cities do not have access to higher returns on innovation, so awareness of green innovation is low in the short term. Meanwhile, local governments in China are exerting institutional pressure to reduce carbon emissions, forcing resource-based companies to innovate green [7]. The government has proposed measures to accelerate the development and promotion of green technologies to help resource-based enterprises achieve their carbon neutrality targets as soon as possible [8,9]. This leads enterprises to pay more attention to attaching importance to the law of ecological development and abandon the goal orientation of pursuing short-term profit maximization. Green innovation helps create new products and technologies to reduce environmental risks such as pollution and the negative impacts of resource exploitation [10]. Thus, promoting green innovation not only improves energy efficiency but also recovers CO2 [6,11,12], which is an important way to effectively achieve the dual carbon target.
Most companies in resource-based regions are now reshaping their business models. This shift is not only from a resource-dependent to a technology-innovative approach but also from a traditional monolithic approach to a symbiotic and coexisting development model. This enables a growing number of companies to collaborate more closely, sharing, coordinating, and integrating capabilities in order to address the possibilities and challenges created by the external environment and achieve long-term sustainability [13]. Enterprises in ecosystem symbiosis operate within the symbiosis and are influenced by factors such as technological change and institutional environment, resulting in corporate green innovation as a complex endeavor. Most studies consider the high proportion of resource-based enterprises as the key to the poor green development of resource-based cities [14,15]. Some resource-based enterprises can be closely linked together to form a co-collaborative and mutually beneficial agglomeration network [16]. Therefore, for companies in resource-based cities to achieve the dual carbon goal, they must have a stable agglomeration relationship with the environment, which is key to reducing pollution. Currently, most scholars consider this agglomeration network relationship to be an important means of driving firm innovation [17,18]. Highly networking relationships between geospatially adjacent enterprises not only promote information interaction and exchange among local enterprises but also enable the openness of agglomeration. As green innovation has positive knowledge spillover externalities, agglomerative network relationships among firms reinforce the flow of knowledge. It allows firms in resource-based cities to leverage knowledge to respond to environmental changes [19]. This relationship is not limited to the realization of green innovation benefits in the short term, but promotes inter-firm partnerships with information sharing as a key tool [20], providing firms with the chance to develop low-carbon products and improve energy-intensive production processes through taking the initiative. Many scholars also believe that such symbiotic relationships need the guarantee of an institutional environment [21,22]. A good institutional environment enables inter-firm symbiotic relationships to share and integrate information effectively to achieve value co-creation. Firms in an ecosystem exist in a more complicated institutional context, driven by both internal and external system influences [23]. In turn, enterprises must not only obey the policy’s legitimacy but also improve the company’s social trust; otherwise, the company’s reputation would suffer related to a lack of internal legitimacy [21]. If firms have high pollution problems, they cannot gain consumer trust and this will weaken their sense of green innovation [24]. Therefore, considering the institutional environment can also help firms to improve their green innovation capabilities.
Considering the characteristics of enterprises in resource-based cities that are overly dependent on resources and lack an innovation base, the spatial agglomeration and institutional environment cannot be relied on alone. High-speed changes in the external environment make it hard for businesses to preserve their competitive advantages in the digital era. Academics have come to different conclusions from different perspectives on whether the digital economy promotes green innovation in enterprises. The digital economy can break the technological barriers between firms through disseminating information [23,25]. It can facilitate rapid knowledge spillover even if geographically distant regions do not create agglomeration effects. It has also been noted that a large amount of data in the digital economy does not improve innovation efficiency in firms [26], let alone green innovation. Therefore, corporate green innovation cannot be explained from a single perspective alone, and other factors need to be considered as well. However, these elements are rarely considered in the context of green innovation’s overall influence on enterprises. In terms of research methodology, most academics have studied the net effect of a single factor rather than the interaction of numerous circumstances using classic econometric approaches [19]. In this case, qualitative comparative analysis (QCA) has the potential to overcome traditional approaches’ shortcomings. It focuses on the cumulative effects of antecedent conditions and contributes to a better understanding of the causal mechanisms of green innovation. In most cases, analyzing the impact of antecedent conditions at only one level often yields different results. In an ecosystem with many participants and a more complex relationship structure, the antecedent conditions leading to corporate green innovation are not independent, and there are complex causal relationships. In turn, this study employs Necessary Conditions Analysis (NCA) and Fuzzy Set Qualitative Comparative Analysis (fsQCA) to investigate the impact of the digital economy, spatial agglomeration, and institutional environment on corporate green innovation from the perspective of ecosystem complexity.

2. Literature Review

2.1. Enterprise Green Innovation

Enterprises, as the most active innovation agents, decide the direction of industrial development [27]. Green innovation is vital to achieving sustainable economic development at many levels. The long-term development of China’s economy is closely related to its capacity for green innovation because of its limited resources and fragile environment. Moreover, a more important link of green innovation in resource-based cities is to reduce energy consumption. Thus, enterprises need help to increase quality products that meet ecological and environmental protection standards. Schumpeter said, “Innovation is the mixing of diverse factors of production into the present production system to produce a new production system” [28]. In contrast to traditional innovation, green innovation not only has the spillover effect of “knowledge externality” but also can generate “environmental externality” by reducing environmental costs [29]. Green and low-carbon have emerged as major topics in China’s economic and social development in China. The act of green innovation is critical for businesses in resource-rich areas. Due to pollution’s externality, businesses have a longer time recouping their original investment costs, which is counterproductive to the growth of short-term economic benefits [19]. Moreover, there are more risks associated with green innovation for businesses. Moreover, the uneconomic behavior of large capital investment will instead inhibit enterprises’ willingness to carry out green innovation [30]. As a result, corporate green innovation drivers often arise from various levels of linkage, and the drivers of corporate green innovation should not be purely explained in terms of demand–pull.
Organizational ecology theory emphasizes that ecosystems consist of different symbiotic units and are influenced by spatial agglomeration, institutional environment, and external influences of the digital economy. Meanwhile, the idea asserts that organizations and surroundings can interact to produce adaptive results [31], which provides a better analytical framework for studying the complexity of corporate green innovation. A lack of transparency and asymmetric information, inadequate regulation, and inefficient resource allocation made the previous green innovation system inefficient. The network structure established by the agglomeration of firms in resource-based cities is more conducive to resource interaction among resource-based enterprises to improve resource usage and reduce pollution. This network structure is more likely to stimulate corporate green innovation behavior and also reflects muscular mobility. Changes in information transmission carriers have substantially enlarged the channels of information sharing between businesses in the digital age. As a result, incorporating digital technology into the network structure can considerably improve information openness between companies and encourage the interchange of innovative ideas. It not only promotes more enterprises to form symbiotic relationships but also can enhance the awareness of green innovation among enterprises. On the other hand, green investments made by businesses in the process of green innovation are difficult to recoup quickly, making it difficult for them to take the initiative to produce environmentally friendly products. A sound institutional environment is more conducive to forcing enterprises to engage in green innovation behavior [32], probably because a sound institutional environment can promote resource-based enterprises to innovate obsolete production processes and drive green innovation development. In summary, the drivers of corporate green innovation are not unique and independent, and the combined effects of different conditions in the ecosystem should be further investigated [33,34]. As a result, this study contends that influencing corporate green innovation is a complicated process. It is not sufficient to investigate the drivers of corporate green innovation from a single perspective; Instead, they should be examined in conjunction with the ecosystem symbiotic relationship framework.

2.2. Spatial Agglomeration

Successfully stimulating green innovation among enterprises in resource-based cities is one of the primary challenges that must be overcome to establish a creative country. Domestic and foreign scholars have discovered that densely inhabited locations have a higher potential for beneficial innovation. Organizational ecology theory suggests that diverse participants in an ecosystem can provide more information [35], facilitating participants to create new opportunities and share valuable information. On the one hand, population clustering has a scale effect, allowing the region to have better infrastructure and to share a rich “information/resource pool” [36]. The interaction of firms based on the “information/resource pool” can stimulate a sense of green innovation [25]. On the other hand, geographical proximity allows for better matching of multiple players in the supply chain, reducing production costs, especially for resource-based firms. Green innovation benefits from knowledge spillovers that facilitate knowledge exchange among individuals, further creating population clustering. Likewise, population clustering will further promote green innovation among firms based on the network effect formed.
Organizational ecology theory suggests that complex interactions among enterprises in a certain region will lead to the formation of symbiotic relationships, a kind of industrial agglomeration. It has been shown that many participants in a symbiotic relationship lead to a more complex structure [37]. This complex network structure helps enterprises in resource-based cities obtain resources about green innovation to eliminate enterprises’ simple imitation type of green innovation. The clustering of diverse types of businesses lowers the industry’s overall cost, allowing businesses to reap greater benefits from green products and encouraging resource-based businesses to embrace green innovation. Meanwhile, the rationality, effectiveness, and economy of the industrial layout can improve regional firms’ competitive advantages while reducing pollution caused by resource-based enterprises. Overall, the symbiotic relationship formed by spatial agglomeration has an openness that strengthens the flow of information and attracts many participants to use the available resources to enhance their competitiveness.

2.3. Digital Economy

Enterprise digital development is critical to supporting the digital economy, backed by digital infrastructure as a foundation and big data technology as a crucial tool. The digital economy provides new tools for enterprises to innovate and guide demand, which may form new business models [38]. From the perspective of organizational ecology theory, digital infrastructure can strengthen the connectivity of information for each symbiotic subject in the ecosystem. Resource-based firms can gain spillover effects of green innovation through connected information networks [39]. However, the scope of digital infrastructure is too large, and there are problems such as waste of resources and redundancy of information, making information exchange more difficult. Therefore, promoting digital infrastructure according to the development of different regions will benefit resource-based enterprises to make reasonable use of digital information for green innovation.
In addition, big data technology can also change the behavior of symbiotic subjects [40]. On the supply side, big data technologies can be used by resource-based businesses to track the green product manufacturing process and reduce spending. It enhances resource-based firms’ production efficiency and ensures the production process’s safety [41]. Furthermore, big data technology facilitates the integration of resource-based businesses with other types of businesses, establishing symbiotic ties between businesses and the interchange of information across geographical boundaries. On the demand side, bettering big data development can provide insight into consumer demand for green products and assist businesses in better understanding the market situation. Big data can help companies in resource-based cities reduce the cost of information search and consumption of resources in the industry chain but also helps access innovation resources in a complex ecosystem [42]. Therefore, big data technology promotes green innovation in resource-based enterprises by breaking enterprises’ innovation boundaries, reducing transaction costs, and improving collaborative innovation. However, excessive application of big data technologies can also expose firms to “information overload” [26], further expanding green innovation risks. In general, the network economy effect of the digital economy can enhance resource-based companies’ information processing capacity, decrease information asymmetry between the two sides of the transaction, and allow enterprises to benefit from green innovation by relying on the ecosystem’s symbiotic relationship.

2.4. Institutional Environment

The institutional environment is primarily a governance mechanism constructed to sustain symbiotic relationships [22] and includes both formal and informal institutions. A good institutional environment implies a high level of governance and marketability and can facilitate resource-based firms to cooperate with other institutions to form a virtuous ecosystem [21]. However, enterprises in resource-based cities are forced by market pressure to choose green innovation, driving them away from their dependence on traditional resources by developing green products. Furthermore, the formal system promotes the marketization process in the region where the enterprise is located. If a lower level of marketization entails more government interference, resource-based businesses will have less access to external knowledge, making it more difficult to breach the information communication barrier between businesses. Additionally, information asymmetry can be detrimental to resource-based firms in gaining cost advantages and reducing firms’ initiatives to implement green innovation activities [7].
North [43] argued that the essential to tackling the difficulties of China’s transition phase is a match between informal and official institutions. As a crucial period of progress in the economic transformation, resource-based firms’ economic operations, which entail the cooperative behavior of many companies, are important to all parts of society. Because the informal system is part of a trust mechanism, it can improve the public’s capacity to forecast business conduct [44]. As a result, the informal system will assist resource-based businesses in implementing green innovation initiatives. For example, a high degree of trust in a company’s location will boost the willingness to exchange knowledge and the likelihood of proactive green innovation. From the standpoint of organizational ecology theory, some researchers see the symbiotic connection governance mechanism as a constraint developed to ensure the symbiotic relationship’s effective operation [45]. The number of symbiotic subjects in an ecosystem is large, and mutual trust must be increased to ensure that all of the symbiotic subjects work toward the same goal [46]. Mutual trust among enterprises in resource-based cities must be strengthened if innovation advantages are to be achieved in a complex ecosystem [31]. As a result, corporate green innovation can be successfully driven by the institutional environment, and it is vital to investigate the impact of various antecedent conditions on corporate green innovation from an ecosystem viewpoint.

2.5. Configurational Framework

There is a mutual, two-way linkage between the three antecedent conditions of the digital economy, institutional environment, and spatial agglomeration. Organizational ecology theory suggests that organizations must respond to the external environment to develop better, and the sustainable development of organizations can be improved by establishing symbiotic relationships. Loosely coupled symbiotic units within a symbiotic relationship form diverse network relationships and promote information exchange between symbiotic units through symbiotic behavior to achieve symbiotic efficiency. Enterprises in resource-based cities form an agglomerative network relationship through resources, and resource-based agglomerative enterprises generate high energy consumption and pollution. Therefore, enterprises, as symbiotic ecosystem units, should pay more attention to environmental pollution emission issues. Existing studies show that most resource-based cities fail to achieve effective information sharing, which creates the problem of poor access to innovation information channels among firms and obvious information lag. Symbiotic network relationships not only help organizations exchange information but also increase their access to information. The loosely coupled relationship structure within the ecosystem is more conducive to combining different knowledge resources, but this symbiotic relationship also needs to consider whether rules need to be formulated to ensure that it can operate effectively. The coordination of formal/informal institutions leads firms to jointly lead the symbiotic relationship [45], thus largely weakening the impact of poor communication between the government and the market. The formal system is a way to promote green innovation of enterprises by protecting intellectual property rights and constraining subjects’ behavior. In addition, the informal system works well in conjunction with the formal system. Positive or negative media stories can help the public understand the true conduct of businesses, minimize information asymmetry, decrease credit risk, and improve the trust mechanism between the two parties to a transaction. For example, through effective monitoring by the media, some resource-based enterprises need to consider satisfying the public’s right to know to avoid public opinion crises caused by excessive resource consumption or environmental pollution, to achieve open and transparent information, and to strengthen the public’s trust in the enterprises.
Organizational ecology theory suggests that ecosystem symbiosis is influenced by technological change, especially the rapid development of digital technology, which has brought many innovation opportunities. The digital economy has built effective digital platforms for breaking information barriers [40]. Investing in relevant information resources on digital platforms not only increases access to information between companies but also increases the possibility of profitability for platform participants. Along with embedding digital technologies, inter-firm relationships gradually evolve from traditional linear relationships to symbiotic network relationships among multiple firms, and a shift from closed to open system structures occurs [47]. The digital economy also creates a sense of urgency for resource-based enterprises to be phased out, causing resource-based firms to forge mutually beneficial symbiotic connections. This close communication enables resource-based enterprises to change their original crude development model. Through the above two-way linkage, a symbiotic and self-consistent system is formed between the three antecedent conditions to create a complex problem of corporate green innovation (as in Figure 1). The ecosystem’s multiple enterprises can collect a plethora of data [36], enabling resource-based companies to interact based on the data and continuously improve their green innovation awareness. This can enhance the symbiotic relationship among enterprises and significantly improve the efficiency of enterprise green innovation and realize value co-creation.

3. Materials and Methods

3.1. Fuzzy-Set Qualitative Comparative Analysis

Most academics believe that some factors affect company green innovation and that combining such factors determines the research outcome. However, traditional measurement methods based on organizational ecology theory have limitations. Traditional econometric models treat elements as independent, making them unsuitable for studying complex causal interactions with many links [48]. QCA is a modeling approach based on a configuration perspective [49,50] for solving configuration problems among multiple factors. In this study, a QCA approach based on set theory is used to analyze the prerequisites affecting green innovation in firms from the perspective of organizational ecology theory. The following main points are considered.
Firstly, QCA reveals different path analyses. The traditional measurement methods in which factors such as digital economy, spatial agglomeration, and institutional environment act independently or interact between two cannot comprehensively analyze the impact on corporate green innovation. Compared with traditional measures, the QCA approach is based on “additivity” in which conditions interact with each other [51]. QCA analyzes the causal relationship between different combinations of antecedent conditions to understand the differentiated paths of green innovation in Chinese enterprises. Second, QCA is outcome oriented. Chinese firms’ main concerns may impact firm decisions differently when facing different external environments. The QCA approach not only identifies whether specific conditions are required to achieve an outcome [52] but also enables examination of the asymmetry between high and non-high levels of numerical maturity of causality [48,49]. Compared to csQCA and mvQCA, fsQCA utilizes affiliation calibration, thereby improving the study and better explaining complex causal relationships. As a result, from the standpoint of organizational ecology, the QCA approach is more appropriate for analyzing the complex mechanisms of various determinants for green innovation in Chinese enterprises.

3.2. Data Sources

Based on the National Plan for “National Plan for Sustainable Development of Resource-based Cities (2013–2020)” [53], this study combines the green patent standards of the World Intellectual Property Office and the CSMAR database to screen the sample data of 156 listed companies. Resource-based cities are cities whose mining and processing of minerals, forests, and other natural resources are the leading industries. On this basis, according to the geographical location of the parent companies of Shanghai and Shenzhen A-share listed companies in resource-based cities, the final selection comprises 156 enterprises. At the same time, the fsQCA method integrates qualitative research and quantitative research. The ideal conditions for the study of medium samples (15 to 50 cases) are generally between 4 and 7 [50]. The number of conditions for studies with large samples (more than 100 cases) can be more [54]. This study has six antecedent conditions, and the sample number is sufficient. To better reflect the differentiated paths affecting the green innovation of enterprises, this study uses the CSMAR database, the 2019 China Provincial Big Data Development Index Analysis Report, the Marketization Index of Chinese provinces [55], the China City Business Credit Environment Index, and the 2019 Statistical Yearbook of each municipality to match with the enterprises mentioned above and obtain data related to enterprises, including digital infrastructure, the level of development of big data, the formal system, informal system, population agglomeration, and industrial agglomeration.

3.3. Measures

3.3.1. Corporate Green Innovation

The outcome is corporate green innovation. In existing studies, corporate green innovation has been measured using indicators such as green R&D investment [56], patents [57], and ISO14001 [58]. Considering data availability in China, this study analyzes the ratio of the number of green patents granted by listed companies to the number of green patents applied for that year to measure corporate green innovation [59]. Some scholars believe that choosing the percentage of patent applications can reflect the impact of patents in the application process and will be more stable and timely compared to the percentage of grants [60]. However, another view is that the application percentage fails to reflect the actual innovation ability of enterprises in the current period. Consequently, evaluating green innovation by comparing the proportion of granted green invention patents to all of the enterprise’s invention patents in the current year takes into account the application procedure’s influence on innovation. It represents the current period’s actual innovation capability.

3.3.2. Spatial Agglomeration

Spatial agglomeration is generally analyzed from the perspective of population and industry, specifically population agglomeration and industry agglomeration. The degree of regional urbanization reflects population agglomeration directly. This research uses Liu and Dong’s [61] calculation method for the level of population agglomeration by dividing the urban built-up area by the overall urban area in 2019. Because the agglomeration of employed persons is a portion of the population agglomeration, the Herfindahl index reflects the entire industrial agglomeration [62]. The following is the exact formula.
H H I = ∑ i n ( X i / X ) 2
where   X i represents a single company’s main business revenue, X represents the industry’s total main business revenue, and ( X i / X ) represents the company’s industry market share. That is to say, it is the sum of the squares of the ratio of each company’s main business revenue to the industry’s total main business revenue.

3.3.3. Digital Economy

As a new engine for economic expansion that improves quality and efficiency, the digital economy is critical to meeting the double-carbon target. Among these, the digital economy is primarily built on the development of digital infrastructure and the use of big data technology as the primary way of stimulating organizational innovation benefits. Therefore, this study analyzes the digital economy from two aspects: digital infrastructure construction and big data development. This study measures the level of digital infrastructure in each region using the number of Internet accesses per million people as a measurement indicator, based on the study by Doong and Ho [63]. The overall index in the 2019 China Provincial Big Data Development Index Analysis Report, which is a complete assessment of 31 provinces in China in three dimensions includes big data for government, commercial, and private usage, is used to determine the big data development. Accordingly, each province’s big data development index is assigned to cities as a measure of the regional big data development level.

3.3.4. Institutional Environment

The institutional environment is analyzed from both formal and informal perspectives, specifically formal and informal institutions. Formal institutions refer to the degree of perfection of regional institutions and are comprehensive measures involving various aspects such as government preferences and market environment [64]. In this study, based on the China Marketization Index Report compiled by Fan et al. [55], the marketization index of each province matched to cities is used as a measure of the quality of regional institutions. The informal system complements the formal system and refers mainly to the trust mechanisms established between the parties to a transaction. This study refers to the report of China’s Urban Business Credit Environment Index (2019), which uses each city’s composite credit environment index to measure the informal system. Specifically, it provides a comprehensive evaluation of a city’s credit environment based on seven aspects: credit placement, corporate credit management, credit collection system, breach of trust and violation, integrity education, and business perception.

3.4. Calibration

Variable calibration is transforming a variable into an ensemble and assigning an ensemble affiliation score to the sample [49], which is the key to the fsQCA method. According to published research, there are three fundamental ways for calibrating the sampled ensemble affiliation value. The first method is from pre-set scale anchor points as thresholds [51]. The main representatives are Likert scales, which provide these qualitative anchor points (e.g., “strongly agree,” “neither agree nor disagree,” and “strongly disagree”) that can be directly conceptualized for calibration. The second calibration method is based on the sample’s maximum, median, and minimum values (valid data place) [48]. The third method is the direct method and is the dominant approach in most of the literature. Calibration is performed at the percentile of the sample data [60,61]. For example, Fiss [48] calibrated the variable data with tracing points using quartiles. In addition, different scholars have to determine the anchor points according to the actual distribution of the sample data [49]. Since the data in this study are not questionnaire data and lack theoretical and practical knowledge to guide the set to select the calibration threshold value, for which the reader can refer to previous studies [48], it is more appropriate for us to use the third method based on the calibration performed on the sample data in this study. Therefore, the qualitative anchor points for each variable with 95%, 50%, and 5% quantile values were used as fully affiliated, crossover, and fully unaffiliated anchor points. Table 1 shows the calibration data for each antecedent variable and the outcome variable.

4. Results

4.1. Necessity Conditions Analysis

Before analyzing the condition sets, we must first determine the “necessity” of each one. Table 2 illustrates the findings of the fsQCA 3.0 software’s analytical assessments of the circumstances of green innovation capability requirements for high and low-level enterprises. Since the consistency level of all conditions is less than 0.9 [48], it indicates that all conditions do not constitute necessary conditions for high and non-high levels of green innovation.

4.2. Sufficient Solutions

We analyzed the calibrated data using fsQCA 3.0 software. Based on existing studies, we used a minimum case frequency benchmark for adequacy analysis ≥ 1 [65,66,67] and a raw consistency benchmark ≥ 0.75 [68,69,70]. In addition, we should observe PRI values to filter the truth table rows further reliably associated with the results [71,72]. As PRI scores below 0.5 may show inconsistencies [71], we adjusted the fraction less than 0.5 to 0 based on a comprehensive analysis of case details and data distribution.
Using these combined criteria, we obtained confirming truth table rows and obtained configuration paths by running the data. Table 3 displays the results. Five paths were found that lead to high levels of corporate green innovation. The overall solution consistency was 0.729, which explains the importance level of all configurations. The results indicate that the five configurations capture 56% of high levels of corporate green innovation. We further identified three paths that may lead to low levels of corporate green innovation. The overall solution consistency was 0.830, and the coverage was 0.381. We then used Ragin’s [49] logistic scheme to further summarize the eight pathways from a theoretical perspective. We propose three high-level corporate green innovation types with different core characteristics: agglomeration oriented, digital technology trust oriented, and digital foundation industry-oriented.
Configurations 1a and 1b show that when the spatial agglomeration effect is strong, firms will have a high level of green innovation capability. This means that the spatial agglomeration effect is the basic condition to improve enterprises’ green innovation level. In both configurations 1a and 1b, population and industrial agglomeration are critical. Configuration 1b then encompasses the supporting role of big data development. According to organizational ecology theory, ecosystems are diversified and networked, and the network structure facilitates resource exchange among symbiotic actors [73]. This open structure is more likely to undergo inter-firm variation due to the resource-dependent nature of firms in resource-based regions. On the one hand, diversified company participation can result in a rich and heterogeneous mix of resources, which is favorable to growing the network structure’s scope. Strengthening network relationships, on the other hand, can speed up the flow of information by allowing diverse participants to use the information better to communicate. Since other conditions are irrelevant for high-level corporate green innovation when population clustering and industrial agglomeration exist under these two paths, we name these two paths as agglomeration-oriented paths. This configuration indicates that diverse industry participants encourage the construction of new network structures, which continue to attract new people and firms, making the symbiotic association among firms further open and flexible. In addition, the sum of this maturity coverage is only 0.052.
Configuration 2 shows that corporate green innovation can take place without population clustering when both big data technologies and informal institutions are present. In this environment, where big data technologies and informal institutions dominate, firms may reduce overcrowding. Although population clustering aids in the faster flow of information resources, companies in resource-based cities can use big data technologies to improve information transparency and continuously improve their green innovation capabilities to respond to environmental changes and consumer demand. With the explosive growth of digital information, companies can not only use big data technology to integrate information to improve their reputation to win the trust of the public but also improve the trust mechanism of the company’s location and provide the media with effective information for analysis. Since big data development and informal systems need to be mutually linked and adapted to function, we call it digital technology trust oriented. This path has the highest coverage rate of about 0.167.
Firms with a well-developed digital infrastructure have a higher degree of green innovation when there is a more significant industrial agglomeration, as seen in configurations 3a and 3b. In configuration 3a, digital infrastructure, industrial agglomeration, and population agglomeration play a central role, and informal institutional conditions play a supporting role. In configuration 3b, the presence of digital infrastructure and industrial agglomeration plays a central role, while formal institutional conditions play a supporting role. In both environments, if a resource-based city has a better digital infrastructure, it can attract more firms to join, providing more resources and reducing the firms’ dependence on natural resources. At the same time, multiple participants within the ecosystem can reduce the cost of acquiring information, improve the efficiency of knowledge resource allocation, and jointly enjoy the benefits brought by green innovation. Since the realization of green innovation in high-level enterprises still requires the synergistic effect of digital technology and industrial agglomeration, we name it as digital-based industry oriented. Among them, about 0.02 can be explained by these two paths.
We can further determine the probable substitution relationship of distinct conditions by analyzing the similarities and differences of configurations 3a and 3b. For resource-based firms in regions with well-developed digital infrastructure, the conditions of the informal regime and population agglomeration can substitute with the formal regime to promote a higher level of green innovation in firms when the industrial agglomeration level is high (as in Figure 2). Firms can help reduce information asymmetries through the agglomeration effect of the public and the trust it generates. In an ecosystem, a larger number of participants makes it more costly for participants to commit opportunistic behaviors, enhancing mutual trust [74]. As a result, frequent interaction between the public and businesses can narrow the gap in trust between both, enhance the perception of firms in resource-based cities, and assist them in making the transition to green growth.

4.3. Horizontal Analysis of Antecedent Conditions

We conducted cross-sectional comparisons to check whether the preconditions influence each other rather than being independent of each other [75]. The combined comparison of the variable combinations indicated that industrial agglomeration was present in most of the high-firm green innovation groupings (e.g., configurations 1a, 1b, 3a, and 3b), suggesting that industrial agglomeration significantly affects firms’ green innovation capabilities. However, firms can achieve high levels of green innovation regardless of the presence or absence of population agglomeration. Therefore, population agglomeration must be effectively combined with other conditions affecting firms’ green innovation.
Regarding the population agglomeration effect, in high levels of firm green innovation, firms either have excessive population mobility (e.g., configurations 1a, 1b, and 3a) or lack it (e.g., configuration 2), suggesting that a single population agglomeration condition does not significantly affect firm green innovation outcomes. However, it must be combined with other conditions, confirming the interaction between preconditions.

4.4. Robustness Analysis

We used standard methods to perform a robustness analysis of QCA results. Common methods include adjusting calibration thresholds, changing consistency thresholds, adding or removing cases, and adding other conditions [74]. We referred to the above methods and used the ensemble relationship and poor fit of the configurations proposed by Schneider and Wagemann [67] as the judging criteria. First, we increased the consistency threshold from 0.75 to 0.8, resulting in four types, and the resulting groupings were largely consistent with the previous study. The overall consistency increased slightly, and the overall coverage decreased slightly. The adjusted results are shown in Table 4.

5. Discussion and Implications

5.1. Research Conclusions

Spatial agglomeration, digital economy, and institutional environment cannot be used as necessary conditions to influence corporate green innovation alone. The study’s findings suggest that (1) a higher level of corporate green innovation can be attained by integrating multiple antecedent conditions. Based on the combination of spatial agglomeration, digital economy, and institutional environment, we identified three high-level corporate green innovation types: agglomeration oriented, digital technology trust oriented, and digital foundation industry oriented. (2) We observed the collaboration between spatial agglomeration, digital economy, and institutional environment. In the digital foundation industry-oriented type, there is a replacement between formal institutions and combinations of informal institutions and population agglomeration. This suggests that different paths can lead to a high level of corporate green innovation, i.e., “different routes to the same goal”. (3) According to the cross-sectional analysis based on the antecedent conditions, industrial agglomeration is necessary and fundamental in the green innovation process of enterprises. The digital economy, as well as the institutional environment, plays a supporting role in improving the ability of firms to innovate green. However, it is difficult to achieve green innovation for firms in resource-based cities through the digital economy or institutional environment alone. These factors must be sensibly integrated, validating the ecosystem’s intricate cause-and-effect interactions.

5.2. Theoretical Contributions

(a) Based on the analysis of organizational ecology theory, the influence mechanism of corporate green innovation is revealed from three aspects: spatial agglomeration, digital economy, and institutional environment. Currently, most of the literature focuses on the influence of individual elements or two–two combinations on corporate green innovation [7,57]. Many scholars analyze how firms’ internal and external distribution can improve the efficiency of green innovation in terms of organizational factors [76]. We add to the existing body of knowledge by looking at the mechanisms of corporate green innovation via the lens of organizational ecology. In the ecosystem, there are symbiotic relationships formed by numerous participants through complex interactions, and the sustainability of enterprises can be improved by establishing symbiotic relationships. That is, firms form interdependent relationships by exchanging information resources that are extremely important to enhance their innovation capabilities. In addition, ecosystem symbiotic relationships are more complex and are not driven by any single condition. Therefore, this study reveals the collaboration and matching of three aspects: spatial agglomeration, digital economy, and the institutional environment by explaining the complex mechanisms that influence this phenomenon.
(b) Multiple linkages influence ecosystem symbiosis, with a particular focus on the impact of technological progress. A prominent topic in the contemporary study is how the digital economy influences company performance [77] and green innovation [78]. As a result, we examine how two dimensions of the digital economy, namely, digital infrastructure and the level of big data development, affect corporate green innovation in the context of the existing economic and social development pattern to reveal the interaction mechanism between the digital economy and other conditions. The emergence of the digital economy has disrupted the structure of the original ecosystem and pushed more participants to establish symbiotic relationships for value co-creation.
(c) To widen the research methodologies for studying green innovation, we used the QCA approach to examine corporate green innovation. The empirical analysis of the mechanisms of corporate green innovation in linear regression is independent. By introducing the fsQCA method into the study of corporate green innovation, we reveal not only the driving mechanisms and conditional substitution relationships of high-level corporate green innovation but also the combinatorial paths leading to high-level corporate green innovation from the perspective of causal asymmetry. Thus, the QCA method breaks the limitations of traditional methods and provides a new way of thinking for analyzing corporate green innovation.

5.3. Practical Significance

This study also provides practical management suggestions for corporate green innovation. First, enterprises in resource-based regions strengthen symbiotic relationships through spatial agglomeration by including more enterprises from different industries in the original structural network to cope with changes in the external environment (ecological issues) of enterprises. Specifically, this symbiotic relationship is reflected in the use of spatial agglomeration to significantly increase the opportunities for face-to-face communication among personnel, which is the basic condition for knowledge spillover to promote enterprises’ independent innovation. However, with the deterioration of the ecological environment, enterprise innovation gradually turns to the direction of green and low-carbon development. This suggests that enterprises in resource-based regions should build open network relationships to promote the flow of information resources that can facilitate interpersonal communication (see configurations 1a and 1b). For resource-based regional enterprises, producing green products is more complicated and requires higher costs based on the original industrial chain. The greater the spatial scale of various businesses, the easier it is to lower the degree of difficulty in the manufacturing process. As a result, managers should consider the market environment and the level of human capital in the region and use agglomeration externalities to address the excessive energy consumption in the development of enterprises in resource-based regions, thereby encouraging green innovation in enterprises.
Second, based on spatial agglomeration, enterprises should seize the new opportunities brought by the digital economy. The necessary digital infrastructure must be continuously improved in resource-based regions to provide new tools for green innovation in enterprises (see configurations 3a and 3b). In the context of the digital era, managers must actively establish information networking relationships as a basis for accelerating the dissemination of digital information, which facilitates the use of information for exploration by researchers and developers, as well as promotes the timely development of the digital economy strategies by managers to ensure competitive advantage. In addition, the institutional environment as a supporting condition has a facilitating effect on corporate green innovation. Enterprises should reasonably improve the industrial chain according to the relevant policies formulated by resource-based cities, shorten the wasteful behavior of resources along the industrial chain, and avoid excessive redundancy of digital information (see configuration 3b). To achieve sustainable development, resource-based enterprises should fully understand the actual situation of their regions and find an effective path that suits their needs and achieves green and low-carbon goals.
Third, digital technology and social trust are particularly important in corporate green innovation (see configuration 2). Considering inter-firm network structures can drive new applications of digital technologies and reduce technological uncertainty. Therefore, managers should adopt advanced digital technologies for resource sharing to achieve a high level of corporate green innovation. With digital technology change, the inter-firm network relationship also changes, which causes more high-tech firms to join. At the same time, when resource-based enterprises are in areas with high social trust, managers should build a timely feedback mechanism for consumers and focus on information dissemination, sharing, and integration to enhance their core competitiveness. Therefore, the linkage of spatial agglomeration, digital economy, and institutional environment reveals enterprises’ complex mechanism of green innovation, which should choose the appropriate development path according to their external conditions.

5.4. Limitations and Future Prospects

This study has the following limitations. First, we have chosen cases from different industries in resource-based cities, which may have different conditional environments and economic bases. For example, the heavy metal industry is penalized for damaging the environment, while the chemical material manufacturing industry is less penalized. In the future, we can analyze and compare the differences in corporate green innovation based on the same industry characteristics to refine our conclusions further. Second, the study sample was selected from resource-based cities in China, and these regions may be more concerned about green and low-carbon development. Including other city samples in QCA can further enrich our findings and improve generalizability. Third, this study only focused on the ecosystem’s core elements and major external conditions. Future research could form a more comprehensive research model from different perspectives and explore the mechanisms of multiple paths affecting corporate green innovation.

6. Simple Summary

In resource-based cities, it has become essential for companies to reduce energy consumption at this stage. When the government promotes green innovation, the impact of the complex environment on the green innovation of enterprises is uncertain. We found that although firms can be interdependent through network relationships, the symbiotic relationship of this ecosystem is very complex and not driven by any single condition. In response, we constructed a configurational framework based on organizational ecology theory and explored different pathways affecting corporate green innovation using a fuzzy set qualitative comparative analysis approach (fsQCA). Moreover, while multiple conditions influence the network structure of ecosystems, a particular focus on the digital economy is required. We argue that the emergence of the digital economy has disrupted the original structure, prompting more participants to establish symbiotic relationships and co-create value.

Author Contributions

Y.Z.: conceived and supervised the study and had substantial inputs into the methodology and all ideas. X.W.: conducted the study and created a first draft of the paper. J.Z.: co-supervised the study and had substantial inputs into the analysis and drafts of the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partially supported by National Social Science Foundation Project (21XMZ063), Natural Science Foundation of Inner Mongolia (2021MS07001), Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (NJYT-20-A02), and Supported by Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region (NMGIRT2202).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 00337 g001 550
Figure 1. Conceptual mode (compiled by the authors).
Figure 1. Conceptual mode (compiled by the authors).
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Figure 2. The substitution effect between institutional and agglomeration (compiled by the authors).
Figure 2. The substitution effect between institutional and agglomeration (compiled by the authors).
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Table
Table 1. Calibration of antecedent and outcome variables.
Table 1. Calibration of antecedent and outcome variables.
ConditionCalibration
Fully inCrossover PointFully Out
Spatial agglomerationPopulation
agglomeration		0.858 0.388 0.133
Industrial
agglomeration		0.397 0.147 0.057
Digital economyDigital infrastructure52.970 41.690 25.460
Big data development0.763 0.293 0.170
Institutional environmentFormal institution10.960 7.620 4.880
Informal institution74.665 70.340 65.536
Corporate Green InnovationCorporate green patent0.878 0.500 0.111
Note: Calibration options 95%, 50%, and 5%.
Table
Table 2. Necessity analysis of single conditions.
Table 2. Necessity analysis of single conditions.
ConditionHigh Levels of
Corporate Green Innovation		Not-High Levels of
Corporate Green Innovation
ConsistencyCoverageConsistencyCoverage
Population agglomeration0.5830.6480.5810.630
~Population agglomeration0.6670.6200.6760.613
Industrial agglomeration0.5740.6810.5570.645
~Industrial agglomeration0.7010.6190.7250.624
Digital infrastructure0.6590.6630.6360.625
~Digital infrastructure0.6270.6390.6570.653
Big data development0.6370.6980.6320.676
~Big data development0.7050.6630.7180.658
Formal institution0.6540.6700.6500.649
~Formal institution0.6580.6580.6700.654
Informal institution0.6150.6390.6150.623
~Informal institution0.6370.6290.6440.620
Note: The symbol (~) represents the negation of the characteristic.
Table
Table 3. Enterprise green innovation (high level) configuration.
Table 3. Enterprise green innovation (high level) configuration.
Antecedent Condition1a1b23a3b
Spatial
agglomeration		Population agglomeration⚫⚫⊗⚫
Industrial agglomeration⚫⚫ ⚫⚫
Digital economyDigital infrastructure ⊗ ⊗ ●⚫⚫
Big data development ●⚫ ⊗ ⊗
Institutional
environment		Formal institution ● ●
Informal institution ⊗ ⚫●
Raw coverage0.2850.2690.3670.2270.251
Unique coverage0.0430.0090.1670.0020.018
Consistency0.8070.8690.7580.8620.855
Overall solution coverage0.562
Overall solution consistency0.729
Note: ⚫ = core casual condition (present). ● = peripheral casual condition (present). ⊗ = core casual condition (absent). ⊗ = peripheral casual condition (absent). Blank spaces indicate “do not care”.
Table
Table 4. Robustness test results.
Table 4. Robustness test results.
Antecedent Condition1234
Spatial agglomerationPopulation agglomeration⚫⚫ ⊗ ⚫
Industrial agglomeration⚫⚫⚫⚫
Digital economyDigital infrastructure ⊗ ⊗ ⚫⚫
Big data development ● ⊗
Institutional environmentFormal institution ●
Informal institution ⊗ ●●
Raw coverage0.2850.2690.2920.227
Unique coverage0.0430.0090.1020.005
Consistency0.8070.8690.8370.862
Overall solution coverage 0.772
Overall solution consistency0.456
Note: ⚫ = core casual condition (present). ● = peripheral casual condition (present). ⊗ = core casual condition (absent). ⊗ = peripheral casual condition (absent). Blank spaces indicate “do not care”.
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Zhao, Y.; Wu, X.; Zhang, J. Analysis of the Paths Affecting Corporate Green Innovation in Resource-Based Cities: A Fuzzy-Set QCA Approach. Sustainability 2023, 15, 337. https://doi.org/10.3390/su15010337

AMA Style

Zhao Y, Wu X, Zhang J. Analysis of the Paths Affecting Corporate Green Innovation in Resource-Based Cities: A Fuzzy-Set QCA Approach. Sustainability. 2023; 15(1):337. https://doi.org/10.3390/su15010337

Chicago/Turabian Style

Zhao, Yunhui, Xinyue Wu, and Jian Zhang. 2023. "Analysis of the Paths Affecting Corporate Green Innovation in Resource-Based Cities: A Fuzzy-Set QCA Approach" Sustainability 15, no. 1: 337. https://doi.org/10.3390/su15010337

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