Next Article in Journal
Towards Zero-Waste Cities: An Integrated and Circular Approach to Sustainable Solid Waste Management
Previous Article in Journal
How Does Industrial Intelligence Enhance Green Total Factor Productivity in China? The Substitution Effect of Environmental Regulation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 17
10.3390/su17177883
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Exploring the Roles of Corporate Social Responsibility and Artificial Intelligence Adoption in the Impact of Political Connections on Innovation Performance

by
Mingming Yao
and
Mengdan Xu
*
School of Marxism, Research Center for Socialism with Chinese Characteristics, Zhejiang University, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(17), 7883; https://doi.org/10.3390/su17177883
Submission received: 27 June 2025 / Revised: 17 August 2025 / Accepted: 22 August 2025 / Published: 1 September 2025
Browse Figure
Versions Notes

Abstract

With the rise of artificial intelligence (AI), how firms can better fulfill corporate social responsibility (CSR) in the context of AI and the role of CSR in firms have attracted increasing attention. This study explored the role of CSR and AI adoption in the impact of political connections on innovation performance. Using a sample of 269 Chinese private firms, it found that CSR mediated the positive relationship between political connections and innovation performance. AI adoption positively moderates the facilitating effects of political connections on CSR and CSR on innovation performance. The findings of the study not only investigate the mechanism through which unique political connections (the establishment of Party organizations in firms) in Chinese private firms, viewed from a CSR perspective, influence innovation but also explore the moderating role of AI in this mechanism, suggesting that AI can enhance the positive impact of political connections on innovation via CSR.

1. Introduction

Since the 1990s, corporate social responsibility (CSR) has emerged as a strategic tool in firm operations, serving to gain a competitive advantage. Recent studies have increasingly explored how CSR impacts innovation performance, with a focus on institutional and resource-based mechanisms. Scholars have considered CSR activities as an essential mechanism for enhancing firm performance and innovation outcomes [1]. The adoption of CSR practices can enhance innovation performance: socially recognized firms are more likely to attract valuable innovation resources, thereby boosting innovation outcomes [2]. Some recent studies have focused on the relationship between CSR, political connections, and innovation. Previous research suggests that the role of CSR in innovation may be influenced by the formation of political connections in fulfilling social responsibilities, which can impact innovation [3]. CSR is one of the important factors influencing firm innovation, but when firms have political connections in other areas, their expenditure on social responsibility activities may decrease [4]. There is a substitutive relationship between political connections and CSR [3].
Previous research on political connections has mostly focused on the individual level, which is considered a double-edged sword for firm innovation. Political connections enable firms to gain various benefits, such as tax incentives, government subsidies, government-backed credit, and financing channels [5], thereby having a positive impact on firm innovation. However, some studies suggest that political connections may increase firms’ perceived intervention by the governments and thus decrease flexibility in their innovation projects [6]. Most existing studies have explored the direct relationship between political connections and innovation. However, in the unique institutional context of China, political connections at the organizational level—specifically, the establishment of Party organizations in private firms—exhibit distinct characteristics that differ from individual-level political connections. Such political connections help firms gain legitimacy within the Party’s networks and influence the government’s resource allocation [7]. They neither stem from fulfilling social responsibilities nor conflict with them nor reduce a firm’s expenditure on social responsibilities. On the contrary, since the ruling party bears the responsibility for social development, firms also take on this mission after establishing Party organizations. This dynamic promotes firms’ assumption of social responsibilities through the Party organizations’ involvement in firm governance and the dissemination of political ideologies within the firms. The role of such organizational-level political connections in innovation has received less attention in previous research. With the advancement of artificial intelligence (AI) technology, a growing number of firms are integrating AI into their management practices. AI adoption not only significantly impacts firm innovation processes [8] but also revolutionizes firm governance, leading to greater transparency in managerial behavior and fostering more benevolent actions [9], thereby making firms more likely to consider stakeholders and engage in more CSR-related activities when making decisions. Therefore, against the backdrop of rising AI adoption, how do organizational-level political connections influence firm innovation? Do they affect firm innovation through CSR? Examining how political connections formed via Party organizations in Chinese firms influence innovation, while accounting for the moderating role of AI adoption, not only enriches prior research focusing solely on individual-level political connections and innovation [10] in a novel technological context but also effectively supplements studies that frame political connections and CSR as conflicting or substitutive by exploring the role of CSR herein.
Therefore, this paper focuses on Chinese private firms to explore the role of CSR in the relationship between political connections and innovation performance under AI adoption. The main research questions are as follows: To what extent does CSR mediate the relationship between organizational-level political connections (via Party organizations) and innovation performance? Does AI adoption by firms moderate this mediating relationship? To address these questions, this paper uses a sample of 269 Chinese private firms to identify a pathway where CSR mediates the relationship between political connections and firm innovation. It also explores variations across different levels of AI adoption, considers its moderating effect, and deepens understanding of how political connections impact firm innovation.
The theoretical contributions of this paper are as follows: (1) identifies the positive effect of unique political connections in the Chinese context on innovation; (2) examines the mediating role of CSR in the relationship between political connections and innovation; and (3) clarifies the boundary condition of AI adoption in the CSR-driven innovation pathway, i.e., how AI adoption moderates the mediating effect of CSR.

2. Literature Review and Hypotheses

2.1. Literature Review

2.1.1. Stakeholder Theory

Stakeholder theory is fundamental in organizational management, placing norms at its core [11]. Its primary view emphasizes that firms should strive to create value for all stakeholders, typically including customers, employees, financiers (such as shareholders, bondholders, and banks), suppliers, and communities. Furthermore, a firm can be regarded as a complex network of stakeholder relationships, necessitating the careful management of interactions and the pursuit of balanced trade-offs among stakeholders [12]. The establishment of robust and equitable relationships with stakeholders is crucial, as it places responsibility not only on firms and their executives for creating stakeholder value but also on stakeholders for nurturing enduring relationships [13]. Notably, the definition of CSR often relies on stakeholder theory. Stakeholder theory is particularly relevant to elucidating the concept of CSR, as it identifies which stakeholders firms are accountable to. The normative stakeholder theory greatly supports the ethical perspective of CSR, as it emphasizes the intrinsic value of stakeholder dignity, thereby emphasizing the demands of stakeholders [14]. Implementing stakeholder theory in practice involves integrating corporate responsibility into the firm’s core business activities rather than treating social responsibility and core business as separate entities. This entails producing products and services that align with stakeholder values and fostering a portfolio of stakeholder relationships that benefit all parties involved [15].
Previous studies have suggested that stakeholders can be an important source of innovation for businesses [16]. The government plays an essential role as a stakeholder when firms establish political connections. In doing so, firms not only gain access to timely and valuable information regarding innovation-related policies but may also influence the external institutional environment, including innovation policies [17]. The establishment of Party organizations creates a political connection that allows firms to meet institutional expectations, while CSR aligns with the government’s goal of creating public value. By transitioning from political connections to social responsibility, firms move from superficial engagement with the system to meaningful participation, thus generating tangible effects and promoting innovation.

2.1.2. Political Connections

Political connections refer to firms’ proactive or passive establishment of relationships with the government to seek their own development [18]. Political connections play a significant role in firms’ political strategies in advanced and emerging economies. These connections, which encompass institutional connections between firms and government authorities, have been extensively studied in the literature [19]. Previous research indicates that political connections can be established through various channels, including formal, informal, and interpersonal mechanisms. The nature of political connections can be categorized into ascribed bureaucratic connections and achieved political connections, depending on the specific institutional environment [3]. Different types of firms have distinct political connections. In China, traditional market-based resource structuring channels have largely been constrained by the immature market-supporting system; thus, political connections are important informal mechanisms for structuring firms’ innovation resource portfolios [20]. In China, research has found that private firms predominantly establish Party organizations as their primary form of political connection. Unlike individual-based political connections, this organizational linkage embeds firms into the institutional framework through formal governance participation. These connections serve a dual purpose: connecting with political resources externally and participating in the internal governance of the firm to guide, supervise, defend rights, coordinate activities, and cultivate firm culture [21]. This type of political connection results in stable, legitimate, and noticeable network effects. Consequently, this study aims to explore this type of political connection.
Once a firm establishes political connections, it is likely to reap the benefits of the associated Party’s political influence [22]. The existing literature holds inconsistent views on the role of political connections in innovation. Some studies have found that political connections contribute to firms’ innovative behavior. Political connections can reduce innovation risks for firms, help them better understand government and innovation-related policies, and promote innovation—for instance, by enabling access to government subsidies [23]. In China, research has suggested that political connections contribute to innovative activities for firms with innovative entrepreneurs but impede innovative activities for those without innovative entrepreneurs [10]. However, some scholars argue that political connections enhance firms’ market power and competitiveness, thereby reducing the pressure and motivation to engage in innovative activities [24], which may weaken their long-term technological innovation capabilities. The establishment of primary-level Party organizations within firms represents a unique form of political connection in the Chinese context. However, prior research has overlooked the impact of such political connections on innovation. The influence of this form of political connection on innovation, as opposed to conventional forms, warrants further empirical investigation.

2.1.3. Corporate Social Responsibility (CSR)

Since the inception of the CSR theory, it has been a topic of significant interest for firm management over the past century. Early research on this topic primarily focused on the relationship between economic and social responsibility, exploring whether they were parallel or subordinate concepts. Scholars such as Carroll have included corporate economic responsibility in CSR. For instance, Carroll [25] proposed a four-responsibility framework of CSR in his article A Three-dimensional Conceptual Model of Corporate Performance. However, regardless of the definition, they failed to break free from the limitations of the hypothesis of “profit maximization” in neoclassical economics until the emergence of stakeholder theory, which posits that CSR aims to positively impact various stakeholders and transcend their social and economic interests [26]. CSR has become a strategic imperative for firms to thrive in competitive business environments. Multiple stakeholder groups pressure firm managers to allocate resources to CSR [27].
CSR can impact firm innovation. CSR is not only a cost, constraint, or charitable act but also a powerful source of innovation and competitive advantage [28] and a driving mechanism for a more efficient and innovative firm. CSR has been found to positively impact innovation performance [29]. By engaging in CSR activities, firms can enhance their image and gain legitimacy from various stakeholders [30], which helps them strengthen their reputation and influence customers’ evaluations of their products [31]. Furthermore, CSR contributes positively to the long-term value of shareholders, thus serving as a motivator for innovation within firms. The synergistic effect of internal and external CSR activities provides a more decisive impetus for innovation [32]. Although there is a growing number of research studies on the CSR concept as a global phenomenon, there are some research gaps that leave room for further investigation in this field [33]. Few studies have examined how organizational-level political connections in developing economies interact with CSR to influence innovation—a gap this study addresses.
Moreover, few studies have focused on micro methods of CSR. Previous research on the relationship between CSR and innovation suggests that political connections play a significant role [10]. Notably, a significant reason firms engage in CSR initiatives is to cultivate political connections. However, the presence of other forms of political connections may decrease CSR [4]. In the practice of Chinese firms, we have observed that the relationship between CSR and the political connections derived from Party organization establishment within firms is not one of substitution but rather of mutual promotion. This relationship has been overlooked in previous studies. To further explore the impact of political connections formed through Party organization establishment in firms on CSR, we conducted empirical testing.

2.1.4. Artificial Intelligence (AI)

With the advent of big data and advanced computing, AI has been established as a discipline and made its way into human life and the social business environment, exerting a profound influence on businesses. Firms play a significant role in AI research, development, and deployment, with profound consequences for society [34]. “AI is commonly defined as ‘…activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment’ [35]”. Most literature outlines the phases of AI, its broad-scope definition, and its link to innovation [36]. AI fundamentally alters consumer behavior and impacts the cost structure of firms [37]. As a result, firms increasingly rely on AI, and managers are striving to identify and manage new AI-driven business models. AI is anticipated to serve as a new engine that propels the firms’ development and enhances innovation efficiency [38], thereby influencing not only the innovation process but also boosting the innovation capability of firms [39]. Moreover, AI technology alters the methodology and technological aspects of idea generation in firms’ innovation [40], prompting firms to revamp their innovation processes to enhance capabilities and achieve a competitive advantage. By utilizing panel data from 3185 Chinese listed firms spanning from 2008 to 2020, this study reveals that AI adoption significantly enhances the innovation efficiency of firms, with the impact being more significant in industries and regions where AI development is more advanced [38].
AI also exerts a range of impacts on CSR. Firms view social responsibility and AI innovation as key differentiation strategies to maximize their resources [41]. AI adoption serves as a critical contextual variable that moderates how CSR mediates the relationship between political connections and innovation performance. Furthermore, previous studies have not examined the relationship between political connections, CSR, and innovation performance within the specific context of AI adoption. To address this gap, we will incorporate AI adoption as a moderating variable to investigate its influence on the relationship between political connections, CSR, and innovation performance.

2.2. Hypotheses Development

Political connections and firm innovation performance
Scholars hold differing views on whether firms can leverage political connections to foster innovation. Many studies suggest that politically connected firms face lower risks [42] and are more likely to access government policies and innovation-related information, as well as more tangible resources such as government subsidies [23]. However, some scholars argue that political connections enhance market competitiveness, thereby reducing the pressure and motivation for firms to pursue innovation [24], which may ultimately weaken their technological innovation capabilities [43]. As a non-market mechanism, political connections take various forms, which can be categorized into individual-level and organizational-level political connections based on the parties involved. The political connections formed by private firms through Party organizations represent a uniquely Chinese form of organizational political connection. This connection possesses a distinct Chinese contextual specificity, differing from the impact of political connections on firm innovation in previous studies, with variations in its specific mechanisms, which promote firm innovation not only by assisting firms in acquiring resources but also through indirect influences such as guidance, supervision, coordination, and incentives.
In private firms, political connections formed through Party organizations can effectively avoid the political resource curse brought by political connections in previous studies and are conducive to exerting the promoting role of political connections on firm innovation. First, political connections help firms acquire the resources needed for innovation. By participating in firm governance, political connections can influence areas with weak resource allocation in the market, thereby promoting substantial and strategic innovation [44]. Private firms with political connections serve as a signal and can gain more trust from the government, thereby receiving more support in innovation-related resources. Firms with political connections can create a favorable environment to attract talent, not only acquiring excellent human resources but also setting an exemplary role for talent. Second, political connections can effectively mitigate various risks firms may encounter during innovation. Political connections participate in firm governance to control the direction of innovation, aligning the firm’s innovation with national development and reducing the risk of innovation getting out of control. Meanwhile, political connections exercise disciplinary supervision within firms, ensuring that all activities comply with laws and regulations, thereby reducing the risk of non-compliance during innovation. Third, political connections help build innovation networks for firms. Collaborative innovation networks among firms form the foundation for sharing innovation resources, transferring knowledge, and disseminating technologies, which are closely related to the improvement of a firm’s technological innovation performance [45]. The network of political connections established through Party organizations is extensive and includes various government administrative systems, public institutions, non-institutional firms, and social organizations. The social credit certification function of these networks enables firms to gain more social recognition in their external connections. Private firms, relying on political connections networks, can establish connections with more organizations in innovation networks and form mutually dependent relationships with organizations that complement each other in terms of resources, expertise, and business areas. Therefore, we propose the following hypothesis:
Hypothesis 1.
Political connections are positively related to innovation performance.
The mediating role of CSR in political connections and innovation performance
As part of the Chinese government’s political agenda, the government promotes CSR as a desired activity [46]. In a context like this, CSR is mainly a government-induced phenomenon. The concept and practice of CSR have cultural specificity or institutional constraints [47]. China, as the biggest emerging country in the world with vastly different social, cultural, and political structures from the West, and the current research on CSR in China needs further theoretical exploration. In China, once a firm establishes a Party organization, it establishes political connections. Firms are integrated into the Party’s organizational network system, and the government becomes an important stakeholder of the firm. The government has specific requirements for firms and influences their production and operations, which is mainly reflected in three aspects: First, political connections can play a leading role in political guidance. Through political connections, firms internalize political ideas into their own entity, guiding the development direction. Fulfilling social responsibility for firms, including safeguarding and promoting employee rights, paying taxes legally, making social donations, and pursuing green development, are the main contents of the government’s vision that firms should assume social responsibility. Second, political connections enable firms to obtain more resources related to social responsibility activities. The more politically connected a firm is, the more resources it will have for its social responsibility activities [48]. Thus, more CSR activities can be implemented. Third, fulfilling social responsibility is also one of the contents of political connections. The social behavior of an organization is crucial to its employees, who prefer working for socially responsible firms [49]. The government requires firms to fulfill their social responsibilities through political connections to promote the harmonious development of firm labor relations. Therefore, we propose the following hypotheses:
Hypothesis 2a.
Political connections are positively related to CSR.
CSR serves as the driving force for innovation [50]. Fulfilling social responsibility, which is the obligation that firms undertake towards them, including shareholders, employees, consumers, government, and the social environment [30], can help firms establish closer connections with stakeholders, obtain heterogeneous resources beneficial to innovation, and, at the same time, enhance a firm’s competitive advantage, attractiveness to institutional investors, and organizational reputation [51].
Firstly, firms actively fulfill their social responsibility to shareholders. While generating profits for shareholders, firms enhance shareholders’ investment confidence and provide financial support for technological innovation by issuing CSR reports, which contribute to a positive public image, public recognition, and maintaining relationships with the government [52]. Secondly, firms actively fulfill their social responsibility to employees. CSR activities create meaningful work for employees, reducing the likelihood of employee turnover driven by the pursuit of meaning [53] and providing talent support for firm innovation. Thirdly, firms actively fulfill their social responsibility to customers. Measures focusing on product safety and customer care enhance customer trust and loyalty, providing customer support for innovation. Lastly, firms actively fulfill their responsibility to the government and society. By paying taxes on time, complying with laws and regulations, adhering to sustainable development, supporting humanitarian causes, and engaging in charitable donations, firms can enhance social reputation, resolve information asymmetry issues between venture capital and firms, attract more individual capital investments, gain government support, establish trust relationships, and foster stable technological innovation networks [54]. Therefore, we propose the following hypotheses:
Hypothesis 2b.
CSR is positively related to innovation performance.
CSR serves as a crucial link between firms, government, and society, offering a significant lens for examining organizational sociology and institutionalism and understanding political connections. Based on institutional theory, firms can leverage CSR to obtain “rent-seeking” benefits, namely, favorable government policies that subsequently promote innovation. The transition from Party organization establishment in firms to CSR fulfillment represents a shift in the firm’s engagement with the institutional framework, from a formal to a substantive level, thereby producing tangible results and fostering innovation. Party organizations, through their participation in corporate governance and their internal political and ideological guidance, promote CSR. Stakeholders are a vital source of firm innovation; fulfilling CSR helps firms forge stronger ties with stakeholders, thereby gaining access to heterogeneous resources conducive to innovation [17]. Political connections elevate the firm’s commitment to CSR from a perspective of individual entrepreneurs’ moral philanthropy, consideration of social impact, and response to societal expectations to a comprehensive social contract based on stakeholders, becoming an integral part of the firm’s strategy and culture. This strategic focus on stakeholders facilitates more targeted and effective promotion of stakeholder-driven innovation, enabling firms to gain a more significant competitive advantage in innovation. Therefore, we propose the following hypothesis:
Hypothesis 2c.
CSR mediates the linkage between political connections and innovation performance.
The moderating role of AI adoption
AI refers to a system’s ability to correctly interpret external data, learn from it, and utilize this learning flexibly to achieve specific goals and tasks [55]. In recent years, AI technology has reshaped how businesses operate. AI holds immense potential in formulating modern marketing strategies, particularly in creating personalized solutions to improve predictions of individual customer group behavior [56]. AI enables firms to fundamentally reshape their innovation approaches [57]. The adoption of AI technology by firms influences the relevant effects of political connections, impacting firm innovation. AI will fundamentally alter how firms work—how they operate and compete [58]. AI has the potential to transform innovation management practices by enabling more effective and efficient innovation processes. When the application level of AI technology in firms is high, the political connections are more flexible, which helps improve the ability of private firm Party organizations to participate in firm governance and their policy sensitivity. This maintains internal stability in a changing competitive environment while simultaneously helping private firms acquire more innovative resources, identify innovative risks, and better expand their innovation networks. Therefore, we propose the following hypothesis:
Hypothesis 3a.
AI adoption plays a positive moderating role in the impact of political connections on innovation performance.
The adoption of AI can potentially change the influence of political connections on CSR. On the one hand, as a governance tool, AI can assist organizations in processing vast amounts of data and utilize the findings to gain a competitive edge [59]. Furthermore, establishing and implementing AI governance information systems can contribute to continuously enhancing firm governance quality. The political connections established through Party organizations will supervise and force firms to disclose information. When the adoption rate of AI within firms is high, it can improve resource management, accelerate information integration, enhance information transparency [9], and strengthen the supervisory role of political connections on CSR. These strengthened connections can promote the fulfillment of social responsibility by strengthening internal controls on information disclosure, reducing earnings management, and reducing opportunism. On the other hand, AI information dissemination methods can unify ideology, optimizing organizational structure and governance. AI can produce and output content with specific value orientations, thereby influencing users’ values. When AI adoption within a firm is high, it allows for greater organizational structure and governance flexibility, thereby enabling political connections to more effectively transmit the ruling party’s ideology and mission to the firm and encouraging the fulfillment of CSR commitments. Based on these considerations, we propose the following hypothesis:
Hypothesis 3b.
AI adoption plays a positive moderating role in the impact of political connections on CSR.
AI adoption has significant implications for the various actors involved in firm operations, many of whom have vested interests. Firms that extensively embrace AI technologies can establish stronger connections and interactions with shareholders, employees, consumers, and suppliers. On the one hand, adopting AI facilitates informed decision-making and effective control management, empowering firms to make prompt governance decisions that guarantee special rights for consumers and shareholders [60]. Additionally, AI is employed in firm governance to advocate for the interests of customers and shareholders within the organization, which enables firms to engage with stakeholders more effectively, bringing them closer together and fostering the development of innovation networks. On the other hand, adopting AI can promote greater openness and transparency across all facets of firm information, enabling stakeholders to access it more efficiently. This enhanced accessibility can contribute to disseminating a positive reputation and image for the firm [61]. Consequently, firms can garner increased support for innovation initiatives from their stakeholders. In light of this, we posit the following hypothesis:
Hypothesis 3c.
AI adoption positively moderates the impact of CSR on innovation performance.
Figure 1 illustrates the proposed conceptual model, highlighting the key components and their interrelationships. As shown in the Figure 1, the model emphasizes the interaction between political connections, CSR and firm innovation performance, the mediating role of CSR, and the moderating role of AI adoption. This illustration helps to clarify the theoretical foundation of our study and guides the subsequent analysis.

3. Materials and Methods

3.1. Sample and Data Collection

The requirements for establishing Party organizations in state-owned and private firms are different in China. This study only focuses on private firms, and the discovered mechanisms only apply to private firms. Our sample was selected through random sampling by industry associations, regulatory authorities, and third-party organizations based on a clear research scope. The scope requires private firms that apply AI, have established Party organizations, and carry out relevant activities. This study used a questionnaire survey to collect data. Through on-site interviews with three representative Chinese private firms, which are typical and representative, have outstanding performance in political connections, CSR, innovation, and AI. Relevant questionnaire measurement indicators were refined based on the survey feedback. The study targeted private firms in the manufacturing industry and used a snowball sampling method to distribute questionnaires to private firms of a certain level. While the snowball method may introduce potential biases, it has proven particularly effective in accessing hard-to-reach firm populations relevant to our study. This approach leverages existing networks within the industry to identify and recruit participants, significantly improving data accessibility that would otherwise remain difficult or impossible to obtain through alternative sampling techniques. The benefits of increased access to this specific population outweigh the inherent limitations of snowball sampling, ultimately enhancing the richness and depth of our empirical findings. Since the study involved political connections, CSR, and innovation performance, which are strategic aspects of firms and require expertise and awareness, senior managers filled out the questionnaire.
This study used a complete SEM framework to establish relationships and test hypotheses. Based on the characteristics of the research sample and the research content, we chose the CFA regression method. The data collection was divided into two stages: July 2023 and October 2023. After conducting variance analysis on the sample data from both stages, no significant differences were found, allowing the data to be merged for analysis. A total of 336 questionnaires were distributed in this study. To ensure the validity of responses, we included reverse items in the questionnaire and set a minimum completion time. After excluding invalid questionnaires with missing responses, excessive identical answers, and inconsistent responses to reverse items, 298 questionnaires and 269 valid questionnaires were obtained, with a questionnaire response rate of 80%. The basic characteristics of the sample are shown in Table 1.

3.2. Measures

The questionnaire mainly measured political connections, CSR, innovation performance, and AI adoption. The measurement of the main variables was based on established scales and combined with the practices of the surveyed firms to ensure the reliability and validity of the questionnaire. The measurement scales for CSR, innovation performance, and AI adoption were initially developed in English and were translated and back-translated into Chinese [62]. The measurement of political connections was based on institutional requirements. A Likert seven-point scale was used for all variables except for the control variables. The response options ranged from 1 = “Strongly Disagree” to 7 = “Strongly Agree”. Specific question items can be found in Appendix A. The present study incorporated innovation performance data from the questionnaire specifically designed for this year, while the data encompassing political connections, CSR, and AI adoption were obtained for the preceding three years. Establishing political connections within firms, implementing CSR, and adopting AI technology all require a certain amount of time to show changes. Therefore, to avoid endogenous issues and ensure the robustness of data, we have chosen three years as a cycle.
Political connections
Political connections focus on the organizational level, especially the implementation of party-building activities within the firm. The items were based on the relevant literature of Wang, Cao, and Liu [44] on organizational management, political connections, and party-building, and our specific items were developed based on the policies and institutional requirements related to party-building activities issued by the Chinese government. There are a total of four items measuring the normativity, activeness, richness, and innovativeness of party-building activities. The Cronbach’s alpha of the scale was 0.897.
CSR
Considering China’s national realities, CSR was measured using a 17-item scale of Turker [26] and 12 items adapted from Tian, Liu, and Fan [30]. The measurement covered four dimensions of CSR activities: employees, customers, communities, and laws. The Cronbach’s alpha for this scale was 0.936.
Innovation Performance
Innovation performance was measured by referring to the studies of Arundel and Kabla [63] and Hagedoorn and Cloodt [64]. The measurement included five items that assessed labor productivity, technological level, the proportion of new products, development speed, and success rate. The Cronbach’s alpha for this scale was 0.899.
AI Adoption
AI adoption was measured based on the research by Hess, Matt [65]; Chatterjee, Rana [66]; and Zeng, Li, and Yousaf [67]. It included six items that assessed the overall use of AI technologies and their relationship with business operations, workflow, and value creation. The Cronbach’s alpha for this scale was 0.905.
Control variables
In addition, this study included three control variables: firm age, firm size, and industry type. Firm age was calculated by subtracting the year of establishment from the year of the survey. The sales revenue measured firm size in the past three years. Due to the significant differences in scale between firms, the logarithm of sales revenue is used in the model. Industry type refers to the type of manufacturing industry to which a firm belongs. “1” indicates that the firm belongs to the high-tech manufacturing industry, and “0” indicates that the firm belongs to the traditional manufacturing industry. High-tech manufacturing relies on cutting-edge technological achievements and talents for manufacturing production, such as communication equipment manufacturing, intelligent equipment manufacturing, computer-related manufacturing, etc. [68]. Traditional manufacturing industries rely on traditional production methods and techniques, such as metallurgy, textiles, chemicals, and machinery manufacturing [69].

4. Results

4.1. Confirmatory Factor Analysis (CFA)

We examined our hypothesized model’s discrimination validity with a CFA series. The results of the CFA are as follows: χ2 = 887.561 (df = 381), χ2/df = 2.791, which is less than 3; IFI = 0.891, CFI = 0.890, and TLI= 0.879, all close to 0.9; RMSEA = 0.082. All the path coefficients in the model were found to be significant at p < 0.001. The results of CFA values demonstrate that all the models of this research were observed to fit the data well. The results of CFA are shown in Table 2.

4.2. Descriptive Statistics and Correlations

The means, standard deviations, and correlations among the study variables were reported in Table 3. Political connections are positively correlated with CSR (r = 0.541, p < 0.01) and innovation performance (r = 0.496, p < 0.01). CSR was positively correlated with innovation performance (r = 0.423, p < 0.01). AI adoption was positively correlated with CSR (r = 0.394, p < 0.01) and innovation performance (r = 0.451, p < 0.01). At the same time, to assess potential non-response bias in the collected dataset, a T-test was conducted on the data collected twice, revealing no statistically significant discrepancies. Thus, the non-response bias does not significantly affect the outcomes of this paper.

4.3. Hypothesis Testing

This study adopted hierarchical regression analysis to test the research hypotheses, and the regression models are presented in Table 4. The specific analysis is as follows:
Firstly, Model 1 represents the regression model of control variables on innovation performance. The study employed the causal step approach to examine the mediating effects [70]. From Model 2, we found that political connections have significantly positively affected innovation performance (β = 0.468, t = 8.911, p < 0.001), supporting Hypothesis 1. Regarding the influence of the independent variable on the mediator, Model 7 revealed a significant positive effect of political connections on CSR (β = 0.513, t = 10.124, p < 0.001), supporting Hypothesis 2a. Additionally, Model 3 showed that CSR significantly positively affects innovation performance (β = 0.379, t = 6.626, p < 0.001), supporting Hypothesis 2b. Comparing Model 3 and Model 4, we found that the coefficient of political connections on innovation performance becomes less (β = 0.380, t = 6.213, p < 0.001) after including CSR as a mediator variable. CSR also significantly influences innovation performance (β = 0.172, t = 2.725, p < 0.01), which indicates that CSR partly mediates the relationship between political connections and innovation performance, supporting Hypothesis 2c.
Next, the moderating effects of AI adoption were examined. The independent variable, mediator variable, moderator variable, and dependent variable were represented by the average values of their respective dimensions and were processed in a standardized manner. Comparing Model 2 and Model 5, the main effect remains significant (β = 0.383, t = 7.016, p < 0.001) after introducing the interaction item between AI adoption and political connections, and the coefficient of the interaction term is also significantly positive (β = 0.209, t = 3.870, p < 0.001), indicating that there is a moderating effect of AI adoption on the relationship between firm political connections and innovation performance, supporting Hypothesis 3a. Comparing Model 7 and Model 8, the main effect remains significant (β = 0.454, t = 8.277, p < 0.001). The interaction coefficient is also significant and positive (β = 0.125, t = 2.300, p < 0.05), indicating that AI adoption moderates the relationship between political connections and CSR, supporting Hypothesis 3b. Similarly, after including the interaction term between AI adoption and CSR, comparing Model 3 and Model 6, the main effect remains significant (β = 0.254, t = 4.415, p < 0.001). The interaction coefficient is also significant and positive (β = 0.147, t = 2.678, p < 0.01), suggesting that AI adoption moderates the relationship between CSR and innovation performance, supporting Hypothesis 3c.

5. Discussion

CSR mediates the relationship between political connections and innovation performance, following the path mechanism of “political connections-CSR-innovation performance.” AI plays a significant role in shaping and enhancing CSR mechanisms through political connections, thereby facilitating innovation. This conclusion underscores the notion that political connections enable firms to effectively address social obligations by forging robust relationships with stakeholders, mitigating the constraints on innovation inputs imposed by financial resources through increased resource access, and propelling innovation through diverse and heterogeneous resource availability. Furthermore, the incorporation of AI by firms assumes a moderating role in this process, wherein the advancement of AI capabilities corresponds to enhanced facilitation of innovation through CSR practices and increased influence of political connections on CSR endeavors.

5.1. Theoretical Implications

Firstly, this study introduces CSR to explore how political connections promote innovation. Previous studies have mainly focused on the direct relationship between political connections and innovation [71]. Discussions on the role of political connections in firm innovation often focus on individuals—for example, whether senior executives of the company are National People’s Congress deputies or members of the Chinese People’s Political Consultative Conference [72] or whether politicians join boards of directors or management teams [73]. This study introduces social responsibility and analyzes the mechanism by which political connections promote innovation. It is found that establishing political connections formed by Party organizations promotes innovation by prompting private firms to assume social responsibility. The introduction of CSR not only explains why this form of political connections can promote innovation but also more clearly depicts the mechanism of action: political connections improve the firm governance environment, link more resources to overcome capital constraints in promoting CSR, and thus promote firm innovation.
Second, this study explores the boundary conditions of how AI is politically associated with promoting innovation. The degree of AI adoption has different impacts on the internal operations of firms. Thus, the mechanism by which political connections via CSR act on innovation also differs. In order to seize the development opportunities brought by the new wave of industrial revolution, firms are accelerating the application of AI. AI has reshaped the organizational methods of firms and innovation management, and by utilizing AI to support the innovation process, it can bring actual value to firms by reducing the risks and costs of the innovation process. This study found that using AI can effectively promote a firm’s commitment to social responsibility and further positively impact innovation.
Third, this study provides a novel perspective on political connections for CSR. Previous studies have suggested that CSR plays a substitutive role with political connections in innovation. Previous discussions on the role of CSR in firms have primarily focused on individual-level perspectives, such as the relationship between CSR and customer loyalty and board diversity. This research, however, shifts the focus from individual-level “informal substitute mechanisms” to organizational-level “formal substitute mechanisms” in terms of political connections. These organizational-level political connections exhibit greater stability and sustainability, mitigating the negative impact of individual political connections on innovation. It provides a new theoretical basis for other countries to coordinate firm development and innovation initiatives from a macro perspective. While prior research in other countries suggests a substitute relationship between CSR and political connections in innovation, this study finds that political connections established through Party organizations do not substitute for CSR but rather promote CSR and further influence firm innovation performance. This finding offers implications for exploring similar issues in similar contexts in other countries, potentially identifying similar pathways and mechanisms.

5.2. Practical Implications

First, this study will provide references for CSR practices. CSR plays a crucial role in mediating the impact of political connections on innovative activities and should be fully leveraged in practice. On the one hand, political connections can impact firm innovation through direct involvement in fulfilling CSR. On the other hand, spreading political ideas throughout firms prompts firms to fulfill social responsibility and facilitate innovation. Given that CSR can effectively reduce employee turnover intentions through external reputation mechanisms and internal trust mechanisms, firms should balance internal and external social responsibility. Firms should adopt sound governance mechanisms to meet the needs of different stakeholders, including government stakeholders, and maintain political connections with the government. Business managers are encouraged to participate in long-term CSR investments and strategies, providing differentiated products and services to society and enhancing firms’ innovation capabilities.
Second, to provide valuable insights for AI in firms. Given the significant opportunities and challenges presented by AI innovation, firms should carefully consider the potential advantages and disadvantages associated with its implementation. By establishing political connections with Party organizations, firms can receive real-time supervision and guidance, effectively managing potential crises that may arise from AI technology. The impact of political connections on the fulfillment of CSR and innovation is particularly evident as the level of AI sophistication within the firm increases. Firms need to consider AI as part of their business strategy, such as establishing AI management departments within their internal organizational structure and using AI to transform their business processes to gain a competitive advantage, including strengthening competitiveness, increasing innovation, and reducing costs.
Finally, it provides references for the government to manage firms. This institutional arrangement, promoted by the ruling party, establishes Party branches within private firms to guide and support their development and encourage them to assume more social functions and become significant forces for innovation-driven development. This constitutes a key mechanism through which the government influences firms’ production and operations, analogous to a “visible hand”.

6. Limitations and Future Research

First, this study analyzes the relationship between political connections and innovation from the perspective of CSR. However, CSR is also influenced by various other factors. Future studies could explore mediating factors from other theoretical perspectives, such as firm culture and governance and organizational learning. The political connections established through Party organizations constitute a governance system with Chinese characteristics, which can impact various aspects of a firm’s governance process, and further exploration is needed to understand the content, direction, and mechanisms of these influences in future research. At the same time, whether there are fundamental differences between individual-level and organizational-level political connections in their impact on firm innovation through social responsibility, and whether there are interactive effects between the two levels of political connections, would be worth attention in the follow-up studies. Third, this study focuses only on AI; whether the technological characteristics of the AI era exert similar effects on the relationships between political connections, CSR, and innovation requires further investigation. At the same time, the depth of empirical analysis is limited. Future research exploring heterogeneity across subgroups, such as firm size and industry type, to assess whether the impacts of political connections and AI adoption vary across different contexts will significantly contribute to both theoretical refinement and practical relevance.

Author Contributions

Conceptualization, methodology, validation, data curation, writing—original draft preparation, M.Y.; investigation, writing—original draft preparation, writing—review and editing, M.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Fundamental Research Funds for the Central Universities.

Institutional Review Board Statement

This study is waived for ethical review due to the fact that the research is conducted at the organizational level of enterprises and does not involve individual human subjects.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Specific question items.
Table A1. Specific question items.
ConstructQuestionnaire ItemsReference
Political ConnectionsOur party organization meets the standards
Our party organization holds numerous activities
Our party organization holds various activities
Our party organization holds innovative activities		Wang, et al. (2023) [45]
CSROur firm makes investment to create a better life for future generations
Our firm implements special programs to minimize its negative impact on the natural environment
Our firm targets sustainable growth, which considers future generations
Our firm contributes to campaigns and projects that promote the well-being of society
The management of our firm is primarily concerned with employees’ needs and wants
Our firm implements flexible policies to provide a good work and life balance for its employees
The managerial decisions related with the employees are usually fair
Our firm respects consumer rights beyond the legal requirements
Our firm provides full and accurate information about its products to its customers
Customer satisfaction is highly important for our firm
Our firm always pays its taxes on a regular and continuing basis
Our firm complies with legal regulations completely and promptly		Turker (2009) [26]
Tian, et al., (2015) [30]
Innovation PerformanceOur labor productivity (product value added divided by capital investment) has improved
Our technical level has improved
Our new product output value has increased as a proportion of total sales
Our new product development speed has increased
Our new product success rate has increased		Arundel and Kabla (1998) [63]
Hagedoorn and Cloodt (2003) [64]
AI AdoptionWe are innovators, promoters, or followers of artificial intelligence technology
The artificial intelligence technology we adopt is crucial for the execution of firm strategies
We adopt diverse artificial intelligence methods
We have utilized artificial intelligence technology for business operations
We have integrated artificial intelligence technology to change business processes
We have utilized artificial intelligence technology to achieve value creation		Hess, et al. (2016) [65]
Chatterjee, et al. (2021) [66]
Zeng, Li, and Yousaf (2022) [67]

References

  1. Mo, X.; Boadu, F.; Liu, Y.; Chen, Z.; Ofori, A.S. Corporate Social Responsibility Activities and Green Innovation Performance in Organizations: Do Managerial Environmental Concerns and Green Absorptive Capacity Matter? Front. Psychol. 2022, 13, 938682. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  2. Ko, K.C.; Nie, J.; Ran, R.; Gu, Y. Corporate social responsibility, social identity, and innovation performance in China. Pac.-Basin Financ. J. 2020, 63, 101415. [Google Scholar] [CrossRef] [Scilit]
  3. Dang, V.Q.; Otchere, I.; So, E.P. Does the nature of political connection matter for corporate social responsibility engagement? Evidence from China. Emerg. Mark. Rev. 2022, 52, 100907. [Google Scholar] [CrossRef] [Scilit]
  4. Adomako, S.; Nguyen, N.P. Politically connected firms and corporate social responsibility implementation expenditure in sub-Saharan Africa: Evidence from Ghana. Corp. Soc. Responsib. Environ. Manag. 2020, 27, 2701–2711. [Google Scholar] [CrossRef] [Scilit]
  5. Li, Z.; Cheng, L.J. What do private firms do after losing political capital? Evidence from China. J. Corp. Financ. 2020, 60, 101551. [Google Scholar] [CrossRef] [Scilit]
  6. Shi, W.; Markóczy, L.; Stan, C.V. The continuing importance of political ties in China. Acad. Manag. Perspect. 2013, 28, 57–75. [Google Scholar] [CrossRef] [Scilit]
  7. Zhang, N.; Liang, Q.-Z.; Lei, H.; Wang, X. Are political ties only based on interpersonal relations? The organizational political tie and its role in firms’ innovations in China. Chin. Manag. Stud. 2016, 10, 417–434. [Google Scholar] [CrossRef] [Scilit]
  8. von Krogh, G. Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Acad. Manag. Discov. 2018, 4, 404–409. [Google Scholar] [CrossRef] [Scilit]
  9. Cui, X.; Xu, B.; Razzaq, A. Can application of artificial intelligence in enterprises promote the corporate governance? Front. Environ. Sci. 2022, 10, 944467. [Google Scholar] [CrossRef] [Scilit]
  10. Tsai, L.C.; Zhang, R.H.; Zhao, C.F. Political connections, network centrality and firm innovation. Financ. Res. Lett. 2019, 28, 180–184. [Google Scholar] [CrossRef] [Scilit]
  11. Parmar, B.L.; Freeman, R.E.; Harrison, J.S.; Wicks, A.C.; Purnell, L.; de Colle, S. Stakeholder theory: The state of the art. Acad. Manag. Ann. 2010, 4, 403–445. [Google Scholar] [CrossRef]
  12. Friedman, A.L.; Miles, S. Developing stakeholder theory. J. Manag. Stud. 2002, 39, 1–21. [Google Scholar] [CrossRef] [Scilit]
  13. Goodstein, J.D.; Wicks, A.C. Corporate and stakeholder responsibility: Making business ethics a two-way conversation. Bus. Ethics Q. 2007, 17, 375–398. [Google Scholar] [CrossRef] [Scilit]
  14. Fioravante, R. Beyond the business case for responsible artificial intelligence: Strategic CSR in light of digital washing and the moral human argument. Sustainability 2024, 16, 1232. [Google Scholar] [CrossRef] [Scilit]
  15. Dmytriyev, S.D.; Freeman, R.E.; Hörisch, J. The relationship between stakeholder theory and corporate social responsibility: Differences, similarities, and implications for social issues in management. J. Manag. Stud. 2021, 58, 1441–1470. [Google Scholar] [CrossRef] [Scilit]
  16. Leonidou, E.; Erasmia, M.; Vrontis, D.; Thrassou, A. An integrative framework of stakeholder engagement for innovation management and entrepreneurship development. J. Bus. Res. 2020, 119, 245–258. [Google Scholar] [CrossRef] [Scilit]
  17. Li, J.; Xia, J.; Zajac, E.J. On the duality of political and economic stakeholder influence on firm innovation performance: Theory and evidence from Chinese firms. Strateg. Manag. J. 2017, 39, 193–216. [Google Scholar] [CrossRef] [Scilit]
  18. Huang, H.; Zhao, Z. The influence of political connection on corporate social responsibility—Evidence from Listed private companies in China. Int. J. Corp. Soc. Responsib. 2016, 1, 9. [Google Scholar] [CrossRef] [Scilit]
  19. Hillman, A.J. Politicians on the board of directors: Do connections affect the bottom line? J. Manag. 2005, 31, 464–481. [Google Scholar] [CrossRef] [Scilit]
  20. Peng, M.W.; Luo, Y. Managerial Ties and Firm Performance in a Transition Economy: The Nature of a Micro-Macro Link. Acad. Manag. J. 2000, 43, 486–501. [Google Scholar] [CrossRef] [Scilit]
  21. He, X.; Ma, J. Does the party organization of private enterprise improve firm performance? Sociol. Stud. 2018, 3, 1–24. [Google Scholar]
  22. Park, S. Political connection and CSR: Evidence from Korea. Bus. Ethics Environ. Responsib. 2022, 31, 1102–1116. [Google Scholar] [CrossRef] [Scilit]
  23. Cheng, L.; Cheng, H.; Zhuang, Z. Political connections, corporate innovation and entrepreneurship: Evidence from the China Employer-Employee Survey (CEES). China Econ. Rev. 2019, 54, 286–305. [Google Scholar] [CrossRef] [Scilit]
  24. Hou, Q.S.; Hu, M.; Yuan, Y. Corporate innovation and political connections in Chinese listed firms. Pac.-Basin Financ. J. 2017, 46, 158–176. [Google Scholar] [CrossRef] [Scilit]
  25. Carroll, A.B. A three-dimensional conceptual model of corporate performance. Acad. Manag. Rev. 1979, 4, 497–505. [Google Scholar] [CrossRef] [Scilit]
  26. Turker, D. Measuring corporate social responsibility: A scale development study. J. Bus. Ethics 2009, 85, 411–427. [Google Scholar] [CrossRef] [Scilit]
  27. McWilliams, A. Corporate social responsibility: A theory of the firm perspective. Acad. Manag. Rev. 2001, 26, 117–127. [Google Scholar] [CrossRef] [Scilit]
  28. Sjödin, D.; Parida, V.; Palmié, M.; Wincent, J. How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. J. Bus. Res. 2021, 134, 574–587. [Google Scholar] [CrossRef] [Scilit]
  29. Martinez-Conesa, I.; Soto-Acosta, P.; Palacios-Manzano, M. Corporate social responsibility and its effect on innovation and firm performance: An empirical research in SMEs. J. Clean. Prod. 2017, 142, 2374–2383. [Google Scholar] [CrossRef] [Scilit]
  30. Tian, Q.; Liu, Y.; Fan, J.H. The effects of external stakeholder pressure and ethical leadership on corporate social responsibility in China. J. Manag. Organ. 2015, 21, 388–410. [Google Scholar] [CrossRef] [Scilit]
  31. Chernev, A.; Blair, S. Doing well by doing good: The benevolent halo of corporate ocial responsibility. J. Consum. Res. 2015, 41, 1412–1425. [Google Scholar] [CrossRef] [Scilit]
  32. Tuyen, B.Q.; Mai, N.P.; Long, T.Q. Does corporate engagement in social responsibility affect firm innovation? The mediating role of digital transformation. Int. Rev. Econ. Financ. 2023, 84, 292–303. [Google Scholar] [CrossRef] [Scilit]
  33. Stojanović, A.; Mihajlović, I.; Safronova, N.B.; Kunev, S.; Schulte, P. The multi-criteria analysis of corporate social responsibility: A comparative study of Russia, Bulgaria and Serbia. J. Manag. Organ. 2021, 27, 809–829. [Google Scholar] [CrossRef] [Scilit]
  34. Cihon, P.; Schuett, J.; Baum, S.D. Corporate governance of artificial intelligence in the public interest. Information 2021, 12, 275. [Google Scholar] [CrossRef] [Scilit]
  35. Nilsson, N.J. The Quest for Artificial Intelligence; Cambridge University Press: Cambridge, UK, 2009. [Google Scholar]
  36. Bahoo, S.; Cucculelli, M.; Qamar, D. Artificial intelligence and corporate innovation: A review and research agenda. Technol. Forecast. Soc. Change 2023, 188, 122264. [Google Scholar] [CrossRef] [Scilit]
  37. Verhoef, P.C.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Dong, J.Q.; Fabian, N.; Haenlein, M. Digital transformation: A multidisciplinary reflection and research agenda. J. Bus. Res. 2020, 122, 889–901. [Google Scholar] [CrossRef] [Scilit]
  38. Li, C.; Xu, Y.; Zheng, H.; Wang, Z.; Han, H.; Zeng, L. Artificial intelligence, resource reallocation, and corporate innovation efficiency: Evidence from China’s listed companies. Resour. Policy 2023, 81, 103324. [Google Scholar] [CrossRef] [Scilit]
  39. Haefner, N.; Wincent, J.; Parida, V.; Gassmann, O. Artificial intelligence and innovation management: A review, framework, and research agenda. Technol. Forecast. Soc. Change 2021, 162, 10. [Google Scholar] [CrossRef] [Scilit]
  40. Liu, J.; Chang, H.; Forrest, J.Y.-L.; Yang, B. Influence of artificial intelligence on technological innovation: Evidence from the panel data of China’s manufacturing sectors. Technol. Forecast. Soc. Change 2020, 158, 120142. [Google Scholar] [CrossRef] [Scilit]
  41. Li, G.; Li, N.; Sethi, S.P. Does CSR reduce idiosyncratic risk? Roles of operational efficiency and AI innovation. Prod. Oper. Manag. 2021, 30, 2027–2045. [Google Scholar] [CrossRef] [Scilit]
  42. Chen, Y.Y. Does political turnover affect corporate investment? Evidence from China. Emerg. Mark. Rev. 2022, 51, 21. [Google Scholar] [CrossRef] [Scilit]
  43. Shi, Y.; Zhu, P. The impact of organisational ageing and political connection on organisation technology innovation: An empirical study of IT industry and pharmaceutical industry in China. Asian J. Technol. Innov. 2014, 22, 234–251. [Google Scholar] [CrossRef] [Scilit]
  44. Wang, Y.F.; Cao, C.F.; Liu, D. Political party system and enterprise innovation: Is China different? Emerg. Mark. Financ. Trade 2023, 59, 376–390. [Google Scholar] [CrossRef] [Scilit]
  45. Wang, L.; Lang, Z.; Duan, J.; Zhang, H. Heterogeneous venture capital and technological innovation network evolution: Corporate reputation as mediating variable. Financ. Res. Lett. 2023, 51, 11. [Google Scholar] [CrossRef] [Scilit]
  46. Yin, J.; Zhang, Y. Institutional dynamics and corporate social responsibility (CSR) in an emerging country context: Evidence from China. J. Bus. Ethics 2012, 111, 301–316. [Google Scholar] [CrossRef] [Scilit]
  47. Campbell, J.L. Why would corporations behave in socially responsible ways? An institutional theory of corporate social responsibility. Acad. Manag. Rev. 2007, 32, 948–967. [Google Scholar] [CrossRef] [Scilit]
  48. de Andres, P.; Garcia-Rodriguez, I.; Romero-Merino, M.E.; Santamaria-Mariscal, M. Political directors and corporate social responsibility: Are political ideology and regional identity relevant? Rev. Manag. Sci. 2023, 17, 339–373. [Google Scholar] [CrossRef] [Scilit]
  49. Aguilera, R.V.; Rupp, D.E.; Williams, C.A.; Ganapathi, J. Putting the S back in corporate social responsibility: A multilevel theory of social change in organizations. Acad. Manag. Rev. 2007, 32, 836–863. [Google Scholar] [CrossRef] [Scilit]
  50. Nidumolu, R.; Prahalad, C.K.; Rangaswami, M.R. Why sustainability is now the key driver of innovation. Eng. Manag. Rev. 2013, 41, 30–37. [Google Scholar] [CrossRef] [Scilit]
  51. Ayoko, O.B. Leadership, ethics and corporate social responsibility. J. Manag. Organ. 2022, 28, 1–8. [Google Scholar] [CrossRef] [Scilit]
  52. Cao, F.; Peng, S.L.; Ye, K.T. Multiple large shareholders and corporate social responsibility reporting. Emerg. Mark. Rev. 2019, 38, 287–309. [Google Scholar] [CrossRef] [Scilit]
  53. Carnahan, S.; Kryscynski, D.; Olson, D. When does corporate social responsibility reduce employee turnover? Evidence from attorneys before and after 9/11. Acad. Manag. J. 2017, 60, 1932–1962. [Google Scholar] [CrossRef] [Scilit]
  54. Ou, Y.-C.; Hsu, L.-C. How does corporate reputation affect innovative performance. Int. Bus. Res. 2013, 6, 46. [Google Scholar] [CrossRef] [Scilit]
  55. Borges, A.F.; Laurindo, F.J.; Spínola, M.M.; Gonçalves, R.F.; Mattos, C.A. The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. Int. J. Inf. Manag. 2021, 57, 102225. [Google Scholar] [CrossRef] [Scilit]
  56. Haenlein, M.; Kaplan, A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. Calif. Manag. Rev. 2019, 61, 5–14. [Google Scholar] [CrossRef] [Scilit]
  57. Kravets, O.; Korolova, U.; Nosachenko, O.; Vasiltsova, O.; Koberniuk, S. AI-Powered digital marketing: Enhancing customer behaviour predictions. Eur. J. Sustain. Dev. 2025, 14, 84. [Google Scholar] [CrossRef] [Scilit]
  58. Füller, J.; Hutter, K.; Wahl, J.; Bilgram, V.; Tekic, A. How AI revolutionizes innovation management-perceptions and implementation preferences of AI-based innovators. Technol. Forecast. Soc. Change 2022, 178, 121598. [Google Scholar] [CrossRef] [Scilit]
  59. Marco, I.; Lakhani, K.R. Competing in the Age of AI: Strategy and Leadership when Algorithms and Networks Run the World; Harvard Business Review Press: Boston, MA, USA, 2020. [Google Scholar]
  60. Shen, W.J. Analysis of the application of artificial intelligence technology in the protection of corporate governance rights and interests. Front. Psychol. 2022, 13, 966689. [Google Scholar] [CrossRef] [Scilit]
  61. Felzmann, H.; Fosch-Villaronga, E.; Lutz, C.; Tamò-Larrieux, A. Towards transparency by design for artificial intelligence. Sci. Eng. Ethics 2020, 26, 3333–3361. [Google Scholar] [CrossRef] [Scilit]
  62. Brislin, R.W. Back-translation for cross-cultural research. J. Cross-Cult. Psychol. 1970, 1, 185–216. [Google Scholar] [CrossRef] [Scilit]
  63. Arundel, A.; Kabla, I. What percentage of innovations are patented? empirical estimates for European firms. Res. Policy 1998, 27, 127–141. [Google Scholar] [CrossRef] [Scilit]
  64. Hagedoorn, J.; Cloodt, M. Measuring innovative performance: Is there an advantage in using multiple indicators? Res. Policy 2003, 32, 1365–1379. [Google Scholar] [CrossRef] [Scilit]
  65. Hess, T.; Matt, C.; Benlian, A.; Wiesböck, F. Options for formulating a digital transformation strategy. MIS Q. Exec. 2016, 15, 123–139. [Google Scholar]
  66. Chatterjee, S.; Rana, N.P.; Dwivedi, Y.K.; Baabdullah, A.M. Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technol. Forecast. Soc. Change 2021, 170, 12880. [Google Scholar] [CrossRef] [Scilit]
  67. Zeng, X.C.; Li, S.C.; Yousaf, Z. Artificial intelligence adoption and digital innovation: How does digital resilience act as a mediator and training protocols as a moderator? Sustainability 2022, 14, 8286. [Google Scholar] [CrossRef] [Scilit]
  68. Tu, W.; Zhang, L.; Sun, D.; Mao, W. Evaluating high-tech industries’ technological innovation capability and spatial pattern evolution characteristics: Evidence from China. J. Innov. Knowl. 2023, 8, 100287. [Google Scholar] [CrossRef] [Scilit]
  69. Shen, S.; Zhu, C.; Fan, C.; Wu, C.; Huang, X.; Lin, Z. Research on the evolution and driving forces of the manufacturing industry during the “13th five-year plan” period in Jiangsu province of China based on natural language processing. PLoS ONE 2021, 16, e0256162. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  70. Baron, R.M.; Kenny, D.A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 1986, 51, 1173–1182. [Google Scholar] [CrossRef]
  71. Liu, S.E.; Du, J.; Zhang, W.; Tian, X.; Kou, G. Innovation quantity or quality? The role of political connections. Emerg. Mark. Rev. 2021, 48, 100819. [Google Scholar] [CrossRef] [Scilit]
  72. Nainggolan, Y.A.; Astuti, E.D.; Rahadi, R.A.; Afgani, K.F. Political connection and corporate social responsibility: Evidence from Indonesia. Int. J. Bus. Soc. 2021, 22, 922–940. [Google Scholar] [CrossRef] [Scilit]
  73. Ferris, S.P.; Houston, R.; Javakhadze, D. Friends in the right places: The effect of political connections on corporate merger activity. J. Corp. Financ. 2016, 41, 81–102. [Google Scholar] [CrossRef] [Scilit]
Sustainability 17 07883 g001
Figure 1. The theoretical model.
Figure 1. The theoretical model.
Sustainability 17 07883 g001
Table 1. Distribution of sample characteristics (n = 269).
Table 1. Distribution of sample characteristics (n = 269).
VariablesClassificationSample SizePercentageAccumulated Percentage
Firm AgeLess than 5 years145.2%5.2%
6–10 years7830.0%35.2%
11–15 years8330.9%66.1%
More than 15 years9434.9%100.0%
Firm Size
(Sales Volume: Million)		5–20 RMB5520.4%20.4%
21–50 RMB9635.7%56.1%
51–100 RMB4416.4%72.5%
More than 100 RMB7427.5%100.0%
Industry TypeTraditional industry12646.8%46.8%
High-tech industry14353.2%100.0%
Table 2. Result of CFA.
Table 2. Result of CFA.
No. of ItemsFactor LoadingCRAVE
Political Connections40.796~0.8790.9830.698
Corporate social responsibility120.703~0.7910.9930.567
Innovation performance50.727~0.8660.9870.656
Artificial intelligence adoption60.745~0.8450.9920.628
Note. CR = composite reliability, AVE = average variance extracted.
Table 3. Descriptive statistics and correlation matrix (n = 269).
Table 3. Descriptive statistics and correlation matrix (n = 269).
MeanS.D.MaxMinMed1234567VIF
1. Firm Age13.706.3541315— 1.111
2. Firm Size3.780.686.9033.900.252 **— 1.095
3. Industry Type0.530.501000.0830.091— 1.058
4. PC5.531.0371.755.750.121 *−0.037−0.060(0.897) 1.540
5. CSR5.850.7872.5860.169 **−0.0710.195 **0.541 **(0.936) 1.571
6. AI5.960.7271.25.80.0960.0220.1100.427 **0.394 **(0.905) 1.290
7. IP5.630.897360.200 **−0.0130.183 **0.496 **0.423 **0.451 **(0.899)
Note. Figures in parentheses are Cronbach’s a’s PC, political connections; CSR, corporate social responsibility; AI, artificial intelligence; IP, innovation performance. * p < 0.05; ** p < 0.01.
Table 4. Regression models and results (n = 269).
Table 4. Regression models and results (n = 269).
VariablesAI: IPAI: CSR
Model 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8
Coefficient
(t-Value)		Coefficient
(t-Value)		Coefficient
(t-Value)		Coefficient
(t-Value)		Coefficient
(t-Value)		Coefficient
(t-Value)		Coefficient
(t-Value)		Coefficient
(t-Value)
Control Variables
Firm Age0.206 ***
(3.359)		0.142 **
(2.619)		0.135 *
(2.333)		0.122 *
(2.254)		0.128 *
(2.525)		0.121 *
(2.248)		0.117 *
(2.240)		0.108 *
(2.122)
Firm Size−0.081
(−1.318)		−0.045
(−0.835)		−0.030
(−0.516)		−0.029
(−0.533)		−0.039
(−0.778)		−0.037
(−0.701)		−0.096
(−1.842)		−0.093
(−1.828)
Industry Type0.173 **
(2.915)		0.147 **
(2.818)		0.101 +
(1.791)		0.119 *
(2.265)		0.130 **
(2.658)		0.103+
(1.956)		0.163 **
(3.235)		0.152 **
(3.072)
Independent Variable
PC 0.468 ***
(8.911)		 0.380 ***
(6.213)		0.383 ***
(7.016)		 0.513 ***
(10.124)		0.454 ***
(8.277)
Mediator Variable
CSR 0.379 ***
(6.626)		0.172 **
(2.725)		 0.254 ***
(4.415)
Moderator Variable
AI 0.349 ***
(6.095)		0.381 ***
(6.477)		 0.228 ***
(3.950)
Interaction Item
PC × AI 0.209 ***
(3.870)		 0.125 *
(2.300)
CSR × AI 0.147 **
(2.678)
R20.0740.2880.2060.3080.3840.3160.3360.376
Adjusted R20.0630.2770.1940.2940.3700.3010.3260.362
F7.047 ***26.700 ***17.118 ***23.366 ***27.223 ***20.218 ***33.440 ***26.305 ***
Notes: PC, political connections; CSR, corporate social responsibility; AI, artificial intelligence adoption; IP, innovation performance; + p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yao, M.; Xu, M. Exploring the Roles of Corporate Social Responsibility and Artificial Intelligence Adoption in the Impact of Political Connections on Innovation Performance. Sustainability 2025, 17, 7883. https://doi.org/10.3390/su17177883

AMA Style

Yao M, Xu M. Exploring the Roles of Corporate Social Responsibility and Artificial Intelligence Adoption in the Impact of Political Connections on Innovation Performance. Sustainability. 2025; 17(17):7883. https://doi.org/10.3390/su17177883

Chicago/Turabian Style

Yao, Mingming, and Mengdan Xu. 2025. "Exploring the Roles of Corporate Social Responsibility and Artificial Intelligence Adoption in the Impact of Political Connections on Innovation Performance" Sustainability 17, no. 17: 7883. https://doi.org/10.3390/su17177883

APA Style

Yao, M., & Xu, M. (2025). Exploring the Roles of Corporate Social Responsibility and Artificial Intelligence Adoption in the Impact of Political Connections on Innovation Performance. Sustainability, 17(17), 7883. https://doi.org/10.3390/su17177883

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop