1. Introduction
In the new phase of world economic development, achieving high-quality, sustainable development has become a corporate goal. ESG is a sustainable development concept on how to reconcile environmental, social, and corporate governance, conveying a perspective that pursues the integration of economic and social values [1]. Therefore, ESG is an important influencing factor for achieving sustainable economic development and high-quality corporate development.
Chinese economy has gradually changed from a high-growth model to a high-quality development model. Achieving high-quality development is a major strategy to lead the construction of a modern power [2]. The development of micro firms is a fundamental condition for macroeconomic growth. Ultimately, the high-quality development of the economy depends on the high-quality development of firms at the micro level. State-owned enterprises (SOEs) are an important pillar of Chinese economy [3,4], its high-quality development implementation has a direct impact on the process of SOEs reform and high-quality development at the macroeconomic level [5,6,7]. Therefore, how to help SOEs in the achievement of high-quality development is an important proposition in the current academic and practical circles.
The connotation of high-quality is efficient, equitable, and sustainable development [8], coinciding with the ESG concept of firms. ESG is a comprehensive evaluation index, integrating the three dimensions of environment, social responsibility, and governance. All of these embody the development concept of firms pursuing the combination of economic value and social value [9,10,11]. Scholars have conducted theoretical and empirical studies on the relationship between ESG and firm development [12,13,14,15]. However, at present, there is no consensus on whether ESG can boost firm development and how to boost firm development, in particular, the research on the relationship between the internal dimensions of ESG is very scarce.
Existing studies on ESG behavior of firms usually sum up the performance scores of firms in environmental, social, and governance aspects into a single variable for investigation. These studies ignore the interaction among the three elements of ESG [16]. In terms of the proposition of SOEs high-quality development, whether E, S, and G have an equivalent boosting effect, or whether there is a complex combination of complementarity, mutual replacement, and even mutual exclusivism in different firm contexts, are still key problems to be solved in theoretical research. In addition, the inclusion of firm context can distinguish the heterogeneity of SOEs in terms of resources, structure, and strategy. Moreover, it can help in the clarification of the applicable boundary of ESG in different contexts.
We aimed to explore what configurations of ESG dimensions and firm contextual factors can contribute to the high-quality development of SOEs. To address these issues, we adopted the qualitative comparative analysis (QCA) approach [17,18,19,20], a configuration analysis method based on set theory. The QCA approach deconstructs the complex interactions among the dimensions within ESG while matching relevant firm contextual factors. Therefore, using the QCA approach helps in the deconstruction of how ESG contributes to the high-quality development of SOEs from a more comprehensive perspective.
Our article is structured as follows: Section 2 is our theoretical basis and analytical framework. Section 3 describes our methods. Section 4 reports our empirical analysis and results. Finally, Section 5 provides the conclusions and contributions.
2. Theoretical Basis and Analytical Framework
2.1. High-Quality Development of State-Owned Enterprises (SOEs)
High-quality development is the general trend of China’s economic development at this stage, with the goal of promoting the establishment of a modern economic system as well as meeting the growing material and cultural needs of the people. The high-quality development of economy ultimately depends on the high-quality development of firms at the micro level. SOE is one of the most important pillars of China’s economic system [3,4], with the responsibility of promoting national development, improving China’s competitiveness in the international arena, and maintaining social fairness. As an important pillar of socialist economy with Chinese characteristics, the realization of SOEs high-quality development has a direct impact on the process of SOEs reform and the high-quality development at the macroeconomic level. The 2018 government work report emphasized that SOEs should actively reform and innovate to lead high-quality development, suggesting that high-quality development is the key to deepening the reform of SOEs and a clear goal for China’s economic transformation at the present stage. In-depth and systematic studies on issues of great practical significance, such as the summary of the regularity of high-quality SOEs development, the internal relationship between SOEs reform and high-quality development are urgently needed to be conducted [5,6,7].
SOEs high-quality development has become an important way to realize Chinese economy high-quality development in the new era, but it has received less attention from the academic community. Previous studies have demonstrated that the active inspection and supervision behavior of party organizations are important means to participate in governance [21], SOEs high-quality development can be promoted by improving their independent innovation. Financing constraints can inhibit SOEs high-quality development by weakening their flexibility in dealing with external markets and reducing the effective investment in technological innovation, and this inhibition effect is stronger in commercial and non-provincial SOEs.
In general, previous studies have focused on using traditional methods, such as multiple regression analysis and econometrics, to study the impact of a single factor on the high-quality development of SOEs, while ignoring the synergistic effect of configuration of multiple factors [22] in promoting high-quality development of firms. However, high-quality development of SOEs is a comprehensive and complex concept in the real society, and it should be a non-linear result affected by various factors. It is difficult to explain the multiple concurrent causal relationships of high-quality development by considering only the role of single factors [23]. In terms of the industries to which SOEs belong, the factors influencing high-quality development, the paths to achieve it, and the outcomes of high-quality development also differ significantly among SOEs in different industries. Therefore, the research findings will be more systematic and holistic if we study the impact of the linkage of different factors on the SOEs high-quality development based on the configurational perspective.
2.2. ESG
ESG is a sustainable development concept reflecting the integration of environment, society, and governance by firms [24,25,26], and it also coincides with the intension requirements of high-quality development of firms. Environmental score is reflected in a firm’s ability to effectively address and continuously optimize the results of its environmental-related governance, such as its sewage system and resource allocation, which can help firms develop a competitive advantage. Social score is mainly reflected by the relationship maintenance between firms and stakeholders while operating the firms, which creates multi-dimensional and sustainable values for firm stakeholders to the greatest extent possible. Firm governance score can be divided into two aspects: Internal governance and external governance, including but not limited to the comprehensive evaluation of corporate governance performance from external supervision and internal board characteristics. Therefore, the high score of ESG firms may help in the promotion of firm high-quality development.
The research on the economic consequences of firm ESG behavior can be roughly divided into two aspects: The impact of ESG behavior on firm risk and firm value. ESG behavior can reduce various types of risks through various channels, including systemic risk [27], credit default risk [28], etc. However, no consistent conclusions have been drawn on the effect of ESG behavior on idiosyncrasy risk. Among all types of ESG literature, one of the most controversial issues is whether a firm’s ESG behavior will affect firm value. Theoretically, firms’ ESG activities may have both positive and negative effects on firm value. On the one hand, firms’ ESG behavior may increase shareholders’ wealth [24] by increasing the cash flow (e.g., customers may prefer to purchase products from firms with high ESG score, employees may work more efficiently in firms with high ESG score, etc.), and lowering the discount rate (e.g., firms with high ESG score have lower cost of capital). On the other hand, managers may engage in ESG behaviors aimed at enhancing their own reputation rather than shareholder value. Stakeholders benefit from ESG activities at the direct cost of the firm’s value. Therefore, increasing ESG behavior may directly lead to poor future stock performance and long-term deterioration of ROA [29]. It is not difficult to infer that the positive ESG behavior of firms may inhibit the high-quality development of firms.
This contradictory mix of findings suggests that there are limitations to the study of ESG as a single variable for high-quality development of firms. In addition, SOEs of different types and industries have their own unique characteristic boundaries, and do not need the same ESG configuration to achieve high-quality development. Therefore, it is of great practical significance to study how to combine ESG with other firm context factors to form different realization paths of high-quality development.
2.3. Firm Situational Factors
Considering contextual factors in a configuration analysis framework is a useful practice for combining the configuration and contingency perspectives [30]. From the resource-based view, unique strategic resources and outstanding dynamic capabilities are required for high-quality development of SOEs, and the availability of resources also affects firms’ investment capabilities in different ESG dimensions [16]. Therefore, we examined three important contextual conditions based on resource view: Innovation intensity, capital structure, and firm size.
Capital structure. Differences in the resources owned by firms lead to the differences in the capital structure of firms, causing the difference in the ESG configurations. What kind of capital structure can promote the high-quality development of firms is a topic of practical significance and quite controversial. On the one hand, the highly leveraged capital structure of firms essentially reflects its advantage in the availability of external resources and provides resource guarantee for its high-quality development [31]. In addition, the high leveraged capital structure characteristics can be used as an external signal of high profitability, thus easing the financing constraints of firms and contributing to high-quality development.
On the other hand, the high interest payments required by a highly leveraged capital structure may also compress the firm’s internal resource allocation space, thus increasing the probability of firms falling into financial difficulties [32]. The financial pressure exerted by a highly leveraged capital structure may force firms to place more emphasis on short-term profits and less on sustainable growth. In summary, the optimal capital structure for high-quality development should be examined under a configurational perspective.
Innovation intensity. Firm innovation is the key factor determining the development of firms, as well as an important driving force for the high-quality development of macro-economy and micro-firms. Firms can promote the research of competitive high-quality products by increasing attention and investment in innovation, thus driving their own high-quality development. Meanwhile, state ownership enables firms to obtain more resources to invest in R&D activities [33]. In addition, China’s political conferences further emphasized the promotion of high-quality development with innovation capacity as the main goal, placing higher expectations on SOEs to improve their technology innovation capacity. Existing studies have explored the positive relationship between a number of variables related to innovation intensity and environmental performance, such as R&D intensity [34], R&D expenditure [35], and innovation capability [36]. Moreover, Yuan et al. [37] found that firms following innovation-oriented strategies (visionaries) perform better CSR than firms following efficiency-oriented strategies (defenders).
Firm size. Large firms have more redundant resources to invest in sustainability initiatives [13,38] and have more structured, institutionalized reporting structures to disclose rich ESG-related information [16]. Therefore, firm size is an internal determinant continuously and positively influencing high-quality reporting [39,40]. For individual dimensions of ESG, firm size has different complementary effects. The popularity of large firms will generate greater publicity and exposure, thus leading to higher public pressure driving firms to practice social responsibility activities [41]. In addition, large firms tend to have higher levels of governance since they have stronger resource utilization and integration capabilities, as well as more rational and scientific structures. Moreover, large firms tend to have stronger risk resistance and financing capacity, and they are more likely to receive support from the government and social institutions. These advantages facilitate large firms’ access to resources for high-quality development.
The architecture analysis framework combined with ESG and contextual factors is shown in Figure 1.
3. Methods
3.1. Research Design
We used the fuzzy set qualitative comparative analysis (fsQCA) method [17] for configuration analysis to investigate “what combination of ESG can help achieve SOEs high-quality development”. Contrary to quantitative methods based on correlation, fsQCA uses set operational logic and truth table tools to generalize the different configurations leading to a given result, mapping causal relationships of necessity and sufficiency based on set relationships. In addition, a multi-stage qualitative comparative analysis design [42] is performed for the panel data set, which helps in the identification of stably emerging and dynamically changing configurations, thus overcoming the static limitations of QCA cross-sectional analysis. Our research time window is from 2017 to 2019 for the following reasons. (1) The concept of high-quality development was first proposed at the 19th National Congress of China in 2017, provides a policy starting point for this research. (2) Considering the complex impact of COVID-19 in 2020, we set the research deadline in 2019. (3) The 3-year window period provides a good time interval for identifying stable and changing configurations.
3.2. Samples and Data
We collected data from multiple sources. First, total factor productivity (TFP) of firms was used as the outcome variable, and the calculation data came from Wind database and CSMAR. Bloomberg ESG Disclosure Index, firm size (Size), R&D intensity (RD), and capital structure (CS) were used as conditional variables. Among all the data above, ESG data came from Bloomberg database, R&D data came from Wind database, and the rest of the data came from CSMAR. We used A-share data excluding ST, *ST, PT, financial, and real estate industry data. The final sample included 692 annual observation samples of SOEs in China. The 2017 sample size is 216, the 2018 sample size is 236, and the 2019 sample size is 240. It is worth noting that the conditional variables were lagged for 1 year to match the outcome variables. Finally, we collected materials, such as annual reports of listed firms, public media reports, and academic research materials, providing supplementary explanations for typical case firms obtained from the results of configuration analysis.
3.3. Variable Measurement
The outcome variable of our research is high-quality development. We referred to some research results and used firm total factor productivity (TFP) as an agent of firm high-quality development [7,43,44,45,46,47]. This is due to the fact that the steady improvement in total factor productivity is the most critical connotation of achieving firm high-quality development. The word “high-quality” indicates that the governance mode and output mode of a firm can meet the basic requirements of actual operation. In addition, these modes should meet the requirements of quality and competitiveness in the development process of the firms with higher cost performance. Therefore, high-quality development is ultimately reflected in the efficiency of firms in exploiting and utilizing resources, and this is the meaning of firm total factor productivity. We used LP semi-parametric method [48] to calculate firm total factor productivity.
The main conditional variables included ESG, firm size, innovation intensity, and capital structure. More specifically, we used the environmental index E, social responsibility index S, firms governance index G, the logarithm of total assets of firms, the ratio of R&D expenses to total assets of firms, and the financial leverage of firms as the main conditional variables. Among the above indexes, the environment index E is embodied in firm environment-related governance outcomes, such as effectively handling and continuously optimizing the drainage system and allocating resources. The construction of the index is from Bloomberg’s rating of firm disclosure of air quality, climate change, ecology and biodiversity, energy, raw materials and waste, the environment of supply chain management and water resources. The social index S mainly emphasizes that a firm should strengthen the management of stakeholder relations in the process of production and operation, the construction of this index is based on the comprehensive scoring of Bloomberg after referring to the disclosure information of firms on communities and customers, diversity (mainly reflected in gender equality, etc.), ethics and compliance, human resources and supply chain, etc. The corporate governance index G includes the internal and external governance of the firms, which is a comprehensive evaluation conducted by Bloomberg after referring to the disclosure of the audit risk and supervision of the firms, the characteristics of the board of directors, the salary system, the nomination committee, and sustainable governance. In addition, we used the logarithm of total assets of firms to represent the firm size, and we used the ratio R&D expenses to total assets to represent the innovation intensity to make the innovation of firms of different sizes comparable. Finally, we used leverage ratio (total liabilities/total assets) to measure the capital structure of firms.
We illustrated the descriptive statistics and covariance matrix in Table 1, and we also listed the distribution and correlation of outcome variables and condition variables. From the above research results, on average, the variation range of outcome variable TFP and conditional variables E, S, and G is clear, indicating that TFP and ESG show different characteristics among different firms. In addition, conditional variables are not independent of each other, and many variables are significantly correlated, reflecting the interaction between different evaluation indicators of the same firms in the real world, also indicating the necessity of using QCA rather than traditional analysis techniques.
3.4. Calibration
For set analysis, the original scalar needs to be transformed into a set membership through a calibration procedure, where “1 value” represents complete membership in the condition and “0 value” represents complete non-membership. For continuous variables, it is appropriate to use the “direct calibration method” [18], which specifies three anchors for each condition and then calibrates through the calibration program of fsQCA3.0 software. After looking at the distribution of data, we found fewer extreme values. Therefore, referring to the practices of Greckhamer [49] and Witt, Fainshmidt and Aguilera [42], the 90th, 50th, and 10th percentiles of the original numerical distribution were used as full membership point (N1), intersection point (N2), and full non-membership point (N3), respectively. In subsequent analyses, the cases containing a membership value of 0.5 will be automatically discarded in the software, thus we referred to the practices of Crilly et al. [50] and manually changed the calibrated 0.5 value to 0.499. In addition, we examined previous documents and found the similar calibration basis for partial conditions to avoid unified calibration anchors’ mechanical defects. For example, Campbell et al. [51] used the same anchor points as this paper when calibrating “capital structure” variables. Furthermore, the sample data of each year were independently calibrated, and the calibration points are shown in Table 2.
4. Empirical Analysis and Results
4.1. Analysis of Necessary Conditions
We first conducted a necessity analysis to examine whether a single antecedent condition can be used as a necessary condition for high-quality development. A necessary condition is a condition that must exist to promote the result, but its existence does not guarantee the inevitable occurrence of the result. Required consistency is an indicator to judge the degree of subset relations. In necessity analysis, if the consistency of a certain condition as a result of the superset is higher than 0.9 [52], it can be considered as a necessary condition leading to the result. The analysis results of necessary conditions are shown in Table 3, and the consistency of all conditions is less than 0.9, indicating that no condition can be regarded as a necessary condition for high-quality development of SOEs.
4.2. Configuration Analysis
Configurational analysis involves the construction and screening of truth tables to obtain minimal configurational solutions, which are filtered by the following criteria. First, the minimum case frequency was set to three to exclude relatively rare configurations, while retaining more than 85% of cases. Second, 0.8 was used as the threshold value for original consistency to ensure the adequacy of subset relationships, and 0.75 was used as the threshold value for PRI consistency to avoid possible simultaneous subset relationships [53]. Then, standardized analysis was conducted. Given that there is no clear theoretical expectation between various conditional variables and high-quality development in existing studies, this paper does not make any clear directional assumptions [54]. In this paper, intermediate solutions were mainly reported, and simplified solutions were matched one by one to distinguish the core conditions and edge conditions in each configuration [18]. Configurations were grouped and named according to the core conditions.
The results are shown in Table 4. We conducted the configuration analysis year after year in this paper, obtaining three configurations in the year 2017, four in 2018, and four in 2019. The consistency of each configuration and the global solution is higher than the threshold value of 0.8. In addition, the coverage rate of the global solution is 0.55, 0.61, and 0.6, respectively. Since the coverage is affected by the total number of cases, there is no consensus on the evaluation standard of coverage in current studies, but the coverage in this paper is at a high level compared with studies with similar case sizes [18]. Moreover, the empirical correlation of each configuration is assessed by original coverage and unique coverage. Original coverage refers to the extent to which each configuration covers the resulting cases; namely, the proportion of cases that have membership in their respective paths. While unique coverage explains the part of the configuration that does not overlap with other configurations in interpreting the results.
4.3. Configuration Patterns
By comparing the configuration analysis of each year, configurations with similar core conditions can be merged into the following three configuration patterns.
Pattern 1: Resource and capability prominent. The first pattern is characterized by core conditions of high innovation intensity, high leveraged capital structure, and large firm size, and we named it “Resource and capability prominent pattern”. This pattern includes C1a and C1b in 2017, C3 (containing C3a and C3b) in 2018, and C6 in 2019. Configuration C1a, C3a, and C6 have the same peripheral conditions, but the coverage is slightly different, with low social score and low governance score as the peripheral conditions. C1b is a subset of C3a, with the former containing environmental and social scores as peripheral conditions, and the latter containing only environmental scores. The core competence combination of high innovation intensity, high leveraged capital structure, and large firm size explains how large SOEs achieve high-quality development by constructing unique resource combination and cultivating dynamic capability advantages. The unique combination of resources and capabilities has always been an important basis to explain the competitive advantage of firms [55,56]. For SOEs, the continuous process of restructuring and transformation has intensified the market competition as well as improved the investment and operation optimization of state-owned capital, which is conducive to the layout optimization, structural adjustment, and strategic reorganization of state-owned economy [5,6,7]. The optimization of state-owned capital is embodied in two aspects: Resource acquisition and resource utilization. First, as the agent of the government, SOEs in emerging markets play an important role in allocating capital, land, technological infrastructure, and other key elements of resources [57]. SOEs can obtain loans from capital markets at low cost, thus achieving a highly leveraged capital structure. Second, high innovation intensity effectively links the firms’ innovation activities with market-based advantages, which helps firms develop dynamic capabilities to flexibly adapt to complex market changes [56,58]. In addition, capital structure and innovation intensity are complementary and interdependent in this pattern. Highly leveraged capital structure helps SOEs deploy more resources in R&D investment, thus supporting more innovation activities [33].
Typical firms in the first category include Changhong (C1a) (
Pattern 2: Sustainability driven. The second pattern has completely different characteristics from the first, characterized by the combination of core conditions of high ESG dimension, and we named it “Sustainability-driven”. This pattern includes C2 in 2017, C4 in 2018, and C7 (C7a and C7b) in 2019. The core conditions in C2 are high social score, high governance score, and low innovation intensity, while the core conditions in C4 and C7 are high environmental score and high governance score. The high ESG dimensions, as the core conditions, explain how SOEs achieve high-quality development by strengthening sustainability. High-quality development refers to a development model that satisfies current needs without compromising future needs [13]. Organizational sustainability lies at the intersection of firms economic, environmental, and social performance [59], which is reflected in the complementary intersection of governance, environmental, and social scores in Pattern 2. The sustainability model of SOEs is a useful solution to solve the problem of “unbalanced, uncoordinated, inadequate and unsustainable” macro-economic development. Based on different core conditions, the sustainability model has two modes. One is the combination of core conditions of society and governance (C2), while the other is the combination of core conditions of environment and governance (C4/C7). Both contain governance scores, while society and environment play a surrogate role in the core conditions. This suggests that firms can formulate measures that contribute to value creation in some of these three dimensions [12], without having to demand perfection. In addition, the innovation intensity in this pattern is usually not high. One possible explanation is that the investment in ESG-related activities of SOEs occupies the R&D expenditure. Nevertheless, excellent governance capabilities compensate for low innovation by improving the operational efficiency of SOEs [60].
Typical firms in the second category include China Shenhua Energy Company Limited (C2/C4) (
Pattern 3: Responsibility and growth balance. The third pattern is characterized by the combination of high social score and high leveraged capital structure (C5) or the combination of high innovation intensity and large firm size (C8), and we named it “Responsibility and growth balanced pattern” in this paper. High social scores reflect the effective management of SOEs on stakeholder relationships, especially in the aspects of community and customer, diversity, ethics and compliance, health and safety, human resources, supply chain, etc. [16]. Moreover, SOEs complement their social responsibilities by optimizing their capital structure or investing in innovative activities to meet the growth needs while safeguarding the interests of different stakeholders. Furthermore, this complementarity reflects the strategic corporate social responsibility view that firms can promote social welfare while achieving strategic business objectives at the same time [61,62]. Significant amounts of time and resources are required to fulfill strategic social responsibility, which also affect organizational structure and strategy formulation [63]. Therefore, the optimization of capital structure or the improvement in innovation ability can help firms fulfill social responsibility more efficiently. At the same time, close relationships with external stakeholders, such as local communities, customers, and environmental groups can provide new financing sources or knowledge bases [64], which facilitates capital structure optimization and innovation capacity improvement. Social responsibility and strategic social responsibility practices promote the interdependence between firms and their stakeholders, which is conducive to promoting organizational resilience with both stability and flexibility [65]. Therefore, SOEs, which take both responsibility and growth into account, can construct a flexible and efficient adaptive firm management mode, thus achieving management innovation and high-quality development [5,6,7].
Typical firms of the two sub-patterns include China Datang Corporation Ltd. (i.e., CDT; C5) (
4.4. Comparison between Configurations across Stage
We compared the configurations of high-quality development in different times, aiming to find the commonalities and differences across the stages. First, the 2018 analysis showed only small configuration changes compared to the 2017 analysis. Specifically, C1a and C3a are the same configuration, C1b is a subset of C3b, C2 is a subset of C4, and a new configuration C5 emerged in 2018. However, a more noticeable change came in 2019, in which a new configuration C7b showing a significantly different structure from the previous configuration, and another new configuration C8 emerged.
Second, our results suggest two progressive stages for high-quality development, i.e., the shift from high-quality economic production to high-quality social leadership. The resource and capability prominent pattern as well as sustainability driven pattern always exist, but the proportion of the former is decreasing. Instead, the proportion of the sustainability driven pattern as well as the responsibility and growth balanced pattern have increased in the last 2 years.
Third, we can find changes and invariances in the two sub-paths of the sustainability driven pattern. The environmental score in C4 (2018) and C7 (2019) replace the social score in C2 (2017) as the core condition, indicating that more emphasis on ecological environment construction is the necessary path for SOEs to achieve high-quality development. Moreover, Rajesh and Rajendran [13] confirmed that corporate environmental performance can negatively moderate the relationship between social performance and ESG total score. Specifically, when corporate environmental performance improves, the impact of governance on ESG performance will be weakened, indicating that environmental and social dimension can potentially replace each other. Behind this change lies the core complementary role, which is the high governance score. SOE reform relies on diversifying the shareholding structure, thus improving the corporate governance mechanism. The sustainable construction of governance mechanism can effectively support the SOEs in the aspects of stakeholder responsibility management and ecological environment governance.
Fourth, as the relationship between environmental, social, and governance factors changes in different configurations, the relationship between innovation intensity and capital structure also changes from complementarity (C1 (2017)/C3 (2018)/C6 (2019)) to mutual replacement (C5 (2018)/C8 (2019)), but the large firm size is always a necessary element. In the analysis of necessary conditions, although the necessary consistency of firm size is lower than the threshold value of 0.9, it is still significantly higher than those of other conditions. Firm size usually represents the resources and capabilities of firms. For large SOEs, political resources and capabilities play a more important role [66]. Therefore, combining market-based and politically-based resources as well as capabilities provides large SOEs with a greater advantage in shifting their development focus from scale expansion to structural upgrading [5,6,7].
4.5. Robustness Test
To ensure the internal and external validity of our results, we referred to existing research to conduct several robustness tests [20,52], as shown in Table 5 (more detailed information can be found in Appendix A). First, we processed the data of 3 years from 2017 to 2019 on average, and conducted a configurational analysis using the same threshold as the main analysis, obtaining four configurations. The consistency and coverage of solution were both improved compared to the original analysis. In the robustness test, the three patterns C2 (2017), C4 (2018), and C8 (2019) disappeared. Second, the minimum case frequency threshold was raised to four, which retained approximately 79% of the cases number, yielding three configurations in 2017, three configurations in 2018, and four configurations in 2019. The coverage of the solution decreased slightly due to the higher frequency, while C5 (2018) disappeared. Third, row consistency and PRI consistency were adjusted to 0.9 and 0.8, respectively, and two configurations are obtained in 2017, three configurations in 2018, three configurations in 2019. Our results suggested the decrease in solution coverage, and the disappearance of C1a, C3a, C7a, and C8. Finally, the upper and lower quartiles (i.e., 75% and 25%) were used as full membership points and completely independent points, respectively for recalibration, and the analysis thresholds consistent with the main analysis are used to obtain three configurations in the year 2017, three configurations in 2018, four configurations in 2019. The solution coverage decreased and C5 (2018) disappeared. The number of solutions varied for different samples, calibration anchors, and analysis thresholds. However, the resulting configurations all showed a clear subset relationship with the main analysis results and met the robustness criteria for the configuration analysis results [52,67].
5. Conclusions and Contributions
5.1. Conclusions and Discussion
How to achieve the high-quality development of SOEs is a hot topic in academia and practice. We explored how ESG matches with innovation intensity, capital structure, and firm size, thus working together to realize the high-quality development of SOEs. Based on the configuration analysis of 692 annual observation samples of SOEs from 2017 to 2019, we obtained the following three configuration patterns. (1) Resource and capability prominent pattern. Firms accelerate business expansion through a highly leveraged capital structure and rely on a high-growth innovation-driven model to support high R&D investments. Ultimately, firms build sustainable independent innovation systems to increase their long-term competitive advantage. (2) Sustainability driven pattern, which aims to create long-term social value by relying on the excellent core governance ability of firms and taking positive measures in social or environmental aspects. (3) Responsibility and growth balanced pattern, where the firm’s social value realization and growth needs are comprehensively considered, and a flexible and efficient adaptive firm’s management model is constructed with organizational toughness that is both stable and flexible. Furthermore, comparing the configuration results across years, we found a decrease in the number of resource and capacity prominent pattern and an increase in the proportion of sustainability driven pattern, with the environment score making a significant contribution. Finally, different ESG dimensions and firm contextual elements can complement and substitute each other compared to the overall allocation, and firm size is a universal condition in all configurations.
The results of our configuration analysis are unique compared to existing research on the impact of ESG on firms. The widespread use of ESG scores in practice and academia has led to a better understanding of the combination of different disclosure measures that firms should consider. However, research still struggles to reach consensus on whether individual E, S, and G have a positive [68], negative [16], or no impact [69] on firms. In focusing on the interactions between ESG elements, we found complementary and substitutive relationships between elements, which resonates with the findings of several studies. For example, Rajesh and Rajendran (2019) found that when a firm’s environmental performance improves, the impact of the governance score on ESG performance decreases [13]. Furthermore, the impact of governance score on ESG performance decreases when a firm’s social performance improves, while the determined impact of environmental score on ESG performance decreases when a firm’s governance performance improves. This demonstrates the substitution effect between the three elements. Our configurations C1b, C3b, C5, C7b, and C8 all reflect this substitution relationship, i.e., when one or two of these elements are present, the other elements can be absent or “do not care”.
In addition, we further specify the boundary contexts in which these ESG combinations work, i.e., firm conditions. For example, Duque-Grisales and Aguilera-Caracuel (2021) found that financial redundancy (defined as current assets/current liabilities) is beneficial in moderating the negative relationship between ESG and firm financial performance [12]. Our C1, C3, C5, and C6 similarly found complementary effects between similar conditional capital structures and different ESG elements. Similarly, firm size is used as a general condition in all our configurations, which corroborates the positive effect between size and ESG scores found by Drempetic et al. (2020) [16].
5.2. Theoretical and Practical Contributions
Based on the architecture analysis method, we may make the following theoretical contributions and practical enlightenment to the research on the high-quality development of SOEs and ESG. First, we empirically investigated the realization patterns of high-quality development of SOEs, making a supplement to the existing research on high-quality development. Studies have been conducted to point out the importance of microscoping the topic of high-quality development from the macroeconomic level to the enterprise level. SOEs, as the backbone of the national economy [5,6,7], are even more important in achieving high-quality development. However, extant studies mainly used theoretical deduction or simulation experiment, and it is still a puzzle whether the high-quality development patterns of SOEs in reality are consistent with the theoretical expectation. We used qualitative comparative analysis (i.e., a method of inductive reasoning) to obtain abstract configuration patterns inductively from the experience of real cases, thus confirming and complementing the existing research findings.
Second, we developed a configuration theory for the high-quality development of SOEs, which combined the configuration perspective and contingency perspective, and integrated the influencing factors discussed separately in the existing research. We found that no condition can be regarded as a necessary condition for high-quality development of SOEs, including the internal sub-dimensions of ESG, innovation intensity, capital structure, and firm size. It is the interaction between these key factors that promotes SOEs to achieve the goal of high-quality development, echoing the high-quality development holism. In addition, we found that patterns of the high-quality development of SOEs are equivalent [18]. There are several ways for SOEs to achieve the goal of high-quality development, reflecting the diversified core [5,6,7].
Third, the diversified configurations we obtained in this paper revealed the complex interaction between the internal dimensions of ESG, promoting related research. The study of Rajesh and Rajendran [13] examined the cross-moderating effects of environmental, social, and governance performance, finding that the improvement in performance in each single dimension will weaken the effects of others. Based on the configurational approach, we also found the alternative relationship among environment, society, and governance, which is cross-verified with the moderating analysis based on the correlation approach. In addition, the introduction of contingency perspective and related firm context factors can further answer the boundary applicability of ESG.
Finally, our main practical insight is to provide specific configurational reference suggestions for SOEs to achieve high-quality development. The problem of SOEs being large but not strong is negatively affecting the evolution of SOEs [5,6,7]. We provided advice to managers of SOEs on how to allocate resources across different aspects of ESG based on the firm contextual factors to facilitate the growth and development of large SOEs.
5.3. Limitations and Prospects
First, although we are able to observe changes over time through periodic analysis, there are problems due to the limitations of secondary data. Specifically, it is difficult to further discuss how each of the high-quality development patterns emerged and how the different patterns have changed over time. Future research can use qualitative investigation and longitudinal cases to enrich our study results. Second, we used Bloomberg’s ESG disclosure score as a measure for corporate ESG behavior, in which potential limitations may exist. Future research can perhaps examine other measures of ESG performance. Third, the configuration patterns in this paper are only applicable to SOEs, and their applicability to private firms remains to be discussed. Future studies can expand the sample to enrich our findings. Fourth, future research could further expand the firm context factors. For example, considering the important role of female executives in corporate governance, including an indicator of executive gender ratio in the comparative analysis may yield new configurations. As another example, the academic experience of executives may also have an important impact on corporate governance, including executive academic experience in the comparative analysis may shed new light on corporate practice.
Conceptualization, G.S. and C.G.; methodology, G.S. and C.G.; software, G.S. and C.G.; validation, G.S., C.G., J.Y., C.J. and N.X.; formal analysis, G.S., C.G., J.Y. and C.J.; investigation, G.S., C.G., J.Y., C.J., N.X. and H.L.; data curation, G.S. and C.G.; writing—original draft preparation, G.S. and C.G.; writing—review and editing, G.S., C.G., J.Y., C.J. and H.L.; project administration, G.S., C.G., J.Y. and C.J. All authors have read and agreed to the published version of the manuscript.
Ethical review and approval were waived for this study, as it did not involve personally identifiable or sensitive data.
This study did not involve human or sensitive data.
The data presented in this study are available on request from the corresponding author.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Descriptive statistics and covariance matrices.
| Variables | Mean | SD | TFP | E | S | G | RD | CS |
|---|---|---|---|---|---|---|---|---|
| TFP | 9.331 | 1.041 | ||||||
| E | 15.307 | 9.558 | 0.445 *** | |||||
| S | 28.922 | 10.790 | 0.308 *** | 0.590 *** | ||||
| G | 48.158 | 4.880 | 0.365 *** | 0.430 *** | 0.334 *** | |||
| RD | 0.017 | 0.021 | −0.030 | −0.032 | −0.073 ** | −0.134 *** | ||
| CS | 0.524 | 0.190 | 0.506 *** | 0.176 *** | 0.114 *** | 0.232 *** | −0.149 *** | |
| Size | 24.015 | 1.499 | 0.817 *** | 0.491 *** | 0.356 *** | 0.466 *** | −0.197 *** | 0.535 *** |
Note: Data came from Bloomberg, wind, and CSMAR. *** p <0.01, ** p <0.05.
Calibration anchors.
| Conditions and Results | 2017 | 2018 | 2019 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| n1 | n2 | n3 | n1 | n2 | n3 | n1 | n2 | n3 | |
| High-quality development | 10.758 | 9.219 | 7.887 | 10.801 | 9.345 | 7.951 | 10.827 | 9.342 | 7.989 |
| Environmental score | 26.357 | 11.628 | 4.651 | 30.233 | 12.791 | 6.977 | 32.636 | 13.178 | 6.977 |
| Social score | 42.146 | 26.316 | 17.544 | 47.368 | 28.070 | 17.544 | 47.368 | 28.070 | 17.544 |
| Governance score | 53.571 | 48.214 | 41.071 | 57.143 | 48.214 | 42.857 | 53.929 | 48.214 | 42.857 |
| Innovation intensity | 0.034 | 0.012 | 0.000 | 0.042 | 0.013 | 0.000 | 0.039 | 0.012 | 0.001 |
| Capital structure | 0.756 | 0.570 | 0.253 | 0.750 | 0.546 | 0.246 | 0.739 | 0.525 | 0.232 |
| Firm size | 25.944 | 23.793 | 22.034 | 26.042 | 23.887 | 22.125 | 26.123 | 23.972 | 22.057 |
Note: n1 refers to full membership, n2 refers to intersection, and n3 refers to full non-membership.
Analysis results of necessary conditions.
| Antecedent Conditions | Result: High-Quality Development | |||||
|---|---|---|---|---|---|---|
| 2017 | 2018 | 2019 | ||||
| Consistency | Coverage | Consistency | Coverage | Consistency | Coverage | |
| Environmental score | 0.70 | 0.75 | 0.70 | 0.75 | 0.71 | 0.76 |
| ~Environment score | 0.56 | 0.54 | 0.54 | 0.50 | 0.53 | 0.52 |
| Social score | 0.65 | 0.72 | 0.64 | 0.73 | 0.65 | 0.72 |
| ~Social score | 0.60 | 0.56 | 0.60 | 0.53 | 0.58 | 0.54 |
| Governance score | 0.67 | 0.71 | 0.67 | 0.69 | 0.67 | 0.69 |
| ~Governance score | 0.58 | 0.56 | 0.59 | 0.56 | 0.56 | 0.55 |
| Innovation intensity | 0.55 | 0.60 | 0.56 | 0.60 | 0.57 | 0.61 |
| ~Innovation intensity | 0.65 | 0.62 | 0.65 | 0.61 | 0.63 | 0.61 |
| Capital structure | 0.75 | 0.75 | 0.76 | 0.73 | 0.75 | 0.74 |
| ~Capital structure | 0.48 | 0.50 | 0.48 | 0.49 | 0.49 | 0.51 |
| Firm size | 0.84 | 0.85 | 0.85 | 0.84 | 0.85 | 0.85 |
| ~Firm size | 0.46 | 0.47 | 0.45 | 0.45 | 0.45 | 0.46 |
Note: The symbol “~” indicates the absent of a condition.
Configuration analysis results.
| Antecedent Conditions | 2017 | 2018 | 2019 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| C1a | C1b | C2 | C3a | C3b | C4 | C5 | C6 | C7a | C7b | C8 | |
| Environmental score | • | • | • | ● | • | ● | ● | • | |||
| Social score | ᴜ | • | ● | ᴜ | • | ● | ᴜ | • | ● | ||
| Governance score | ᴜ | ● | ᴜ | ● | ᴜ | ● | ● | ᴜ | |||
| Innovation intensity | ● | ● | U | ● | ● | ● | ᴜ | ● | |||
| Capital structure | ● | ● | ● | ● | ● | ● | • | ||||
| Firm size | ● | ● | • | ● | ● | • | • | ● | • | • | ● |
| Raw coverage | 0.24 | 0.27 | 0.34 | 0.26 | 0.33 | 0.43 | 0.42 | 0.25 | 0.33 | 0.43 | 0.21 |
| Unique coverage | 0.09 | 0.05 | 0.17 | 0.06 | 0.02 | 0.07 | 0.02 | 0.07 | 0.03 | 0.09 | 0.02 |
| Consistency | 0.94 | 0.97 | 0.94 | 0.94 | 0.96 | 0.93 | 0.94 | 0.94 | 0.93 | 0.93 | 0.95 |
| Solution coverage | 0.53 | 0.61 | 0.60 | ||||||||
| Solution consistency | 0.93 | 0.92 | 0.92 | ||||||||
Note: “●” indicates that the condition is present, “ᴜ” indicates that the condition is absent, and the symbol size indicates the core and edge conditions, respectively.
Summary of robustness analyses.
| Type of Change Made To The Analysis | |||||
|---|---|---|---|---|---|
| Original Analysis Reported in Results | Pooled |
Higher |
Higher |
Changing |
|
| 2017 years (C1a, C1b, C2) | |||||
| Frequency/Consistency thresholds/PRI consistency | 3/0.8/0.75 | 3/0.8/0.75 | 4/0.8/0.75 | 3/0.9/0.8 | 3/0.8/0.75 |
| Configuration differences | C2 eliminated | none | C1a |
none | |
| 2018 years (C3a, C3b, C4, C5) | |||||
| Frequency/Consistency thresholds/PRI consistency | 3/0.8/0.75 | 3/0.8/0.75 | 4/0.8/0.75 | 3/0.9/0.8 | 3/0.8/0.75 |
| Configuration differences | C4 eliminated | C5 eliminated | C3a |
C5 eliminated | |
| 2019 years (C6, C7a, C7b, C8) | |||||
| Frequency/Consistency thresholds/PRI consistency | 3/0.8/0.75 | ||||
| Configuration differences | C8 eliminated | none | C7a, C8 |
none | |
Appendix A
Summary of robustness analyses (detailed).
| Type of Change Made To The Analysis | |||||
|---|---|---|---|---|---|
| Original Analysis Reported in Results | Pooled |
Higher |
Higher |
Changing |
|
| 2017 years (C1a, C1b, C2) | |||||
| Frequency/Consistency thresholds/ PRI consistency | 3/0.8/0.75 | 3/0.8/0.75 | 4/0.8/0.75 | 3/0.9/0.8 | 3/0.8/0.75 |
| Number of configurations | 3 | 4 | 3 | 2 | 3 |
| Solution consistency | 0.93 | 0.93 | 0.93 | 0.94 | 0.91 |
| Solution coverage | 0.53 | 0.62 | 0.53 | 0.44 | 0.47 |
| Configuration differences | C2 eliminated | none | C1a |
none | |
| 2018 years (C3a, C3b, C4, C5) | |||||
| Frequency/Consistency thresholds/PRI consistency | 3/0.8/0.75 | 3/0.8/0.75 | 4/0.8/0.75 | 3/0.9/0.8 | 3/0.8/0.75 |
| Number of configurations | 4 | 4 | 3 | 3 | 3 |
| Solution consistency | 0.92 | 0.93 | 0.93 | 0.94 | 0.90 |
| Solution coverage | 0.61 | 0.62 | 0.56 | 0.48 | 0.53 |
| Configuration differences | C4 eliminated | C5 eliminated | C3a |
C5 eliminated | |
| 2019 years (C6, C7a, C7b, C8) | |||||
| Frequency/Consistency thresholds/PRI consistency | 3/0.8/0.75 | 3/0.8/0.75 | 4/0.8/0.75 | 3/0.9/0.8 | 3/0.8/0.75 |
| Number of configurations | 4 | 4 | 4 | 3 | 4 |
| Solution consistency | 0.92 | 0.93 | 0.92 | 0.94 | 0.90 |
| Solution coverage | 0.60 | 0.62 | 0.59 | 0.53 | 0.52 |
| Configuration differences | C8 eliminated | none | C7a, C8 |
none | |
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; Guo, Changwei 1 ; Ye, Junchen 1 ; Ji, Chaoran 2 ; Xu, Nuo 3 ; Li, Hanqi 4 1 School of Business, Renmin University of China, Beijing 100872, China
2 School of Finance, Jilin University of Finance and Economics, Changchun 130117, China
3 School of Literature and Law, Zhengzhou Technology and Business University, Zhengzhou 451400, China
4 Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA; School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China