Corporate Decision on Digital Transformation: The Impact of Non-Market Factors

Abstract

This multiple regression study investigated the relationship between the political network and the adoption of digital transformation strategy and if moderated by perceived corruption and political ideology of top managers among diverse enterprises in China. The aim of this study is to contribute to strategic management research on non-market factors of digital transformation. The instruments used for the study were political network relationships, digital transformation strategies, perceived corruption, economic conservatism, cultural conservatism, social conservatism, and meritocracy of top managers. A basic regression model is a causality test between the political network and digital transformation. In addition, moderated multiple regression analysis with factor analysis to analyze moderator variables perceived corruption and political ideology and their interaction with the political network and effect on digital transformation. The study included 214 firms as a convenience sample. Practical significance indicates that the firm with a higher level of political network relationships is more likely to conduct digital transformation and the higher level of top manager’s perceived corruption and economic conservatism mitigates the positive impact of the political network on digital transformation, while the higher level of top manager’s meritocracy tendency strengthens the positive impact of the political network on digital transformation.

1. Introduction

As Industry 4.0 is reshaping the manufacturing process, competition rules, and structure of industries in recent years, the term digital transformation has garnered much attention in management and organization research and practice [1]. It has been proposed that digital transformation as a sustainable development strategy can help to design a response that maximizes enterprises’ chances of survival in complex environments earlier [2,3]. Views of the urgency of initiating digital transformation have been further influenced by the stressful experience of the global pandemic of COVID-19. Mura [4] informed that overall business activities have not been successful in recent years, and the COVID-19 pandemic has exacerbated the problems. The vast majority of enterprises are not able to respond immediately and need more time to restructure their activities. Due to insufficient reserves to cover several months without income, many of them are forced to lay off their employees. Digital transformation is suggested to be the breakthrough point that can allow an enterprise to surpass its growth limit and ensure sustained growth capacity [2,3,5]. Therefore, firms have to adjust their actions and strategies in the current digital revolution to stay competitive and achieve the goal of sustainability.

In various industries, such as agriculture, mining, as well as education, the digital transformation strategy is regarded as a function-level strategy and a business-level strategy [6,7]. It is not only the application of the Internet of Things, social media, cloud computing, social media, data analytics, and other digital technologies, but also the transformation of enterprise strategy and organization [2,3], including organizational structure [8], operational process [9] and corporate culture [10].

In today’s digital era, it may be difficult for most enterprises to complete digital transformation by themselves. In this case, enterprises are prone to adopt flexible collaboration, cooperation, and coopetition strategies to achieve this goal. For example, compared with large enterprises, small and medium-sized enterprises (SMEs) that are constrained by their resource need to form coopetition alliances to share information, resources, as well as risks with other entities, which helps to spread costs and improve efficiency, and thus promote enterprise growth [11]. The prior literature shows that the partners of enterprises in digital transformation are usually peers, suppliers, and complements [10,11]. Recently, however, there has been an increasing number of enterprises establishing various forms of coopetition with the government during digital transformation in some countries [12,13]. Non-market factors have to be taken into account when studying the influencing factors of digital transformation decisions. Taking the political network as an example, this non-market factor has been proven to play a unique role in the reform of enterprises in some developing countries [14]. By taking advantage of political network relations, the optimum portfolio of coopetition is no longer limited to interfirm but can be chosen between interfirm and public–private partnerships.

With the significant adoption of public–private paradigm during digital transformation, researchers are increasingly contributing to this research area [15,16]. Zhang et al. [17] explored the bottleneck problems encountered in enterprise digital transformation and mentioned China’s government launched a series of policy documents related to the ‘Internet’, further promoting the digital transformation of traditional enterprises. Parakhina et al. [12] carried out a retrospective analysis of the use of public–private partnerships in the Russian Federation and the EU countries, revealing the essential role of public–private partnerships in the implementation of major infrastructure innovation projects in the context of digital transformation. With regard to the above articles, the intervention of the government in enterprise digital transformation is beneficial. Although political networks have been recognized as a powerful force that potentially affects corporate decision making [18], strategies [19], and organizational transformation [20], our understanding of the impact of non-market factors on digital transformation is far from complete. Findings by scholars show that the extent of the political network that entrepreneurs establish is a factor that increases the chances of firms receiving more support from the government in emerging economies [21]. Political connectedness can help firms obtain more business transformation options [22]. In addition, firms’ decision-making processes do not occur in isolation but are significantly traceable to the external environment and the top management team’s psychological attributes [20]. Accordingly, we suggest that perceived corruption and the ideology of top managers may be a boundary condition of this correlation. The need to discuss non-market elements such as political networks, perceived corruption, political ideology, etc., reflects a gap in the literature related to antecedents of corporate digital transformation.

Second, the circumstances under which the political network influences digital transformation strategy lack theoretical clarity. Entrepreneurs and managers making use of political connections in digital transformation represent an opportunity for further discussion around resource and institution constraints, and digital transformation strategy. The recent evolution of theories of cooperation and competition has implications as a sound theory driving managers’ search for dynamic capabilities and resources across political networks in the digital economy.

In light of these gaps, the purpose of this study is to reveal the role of non-market factors in the digital transformation of enterprises by analyzing the relationship between the political network and corporate decision of digital transformation, as well as the impact of the external political environment and individual ideology on corporate digital transformation. This study addressed three research questions: (1) How do political networks affect corporate decision-making of digital transformation? (2) How does perceived corruption affect the relationship between the political network and digital transformation strategy? (3) How does ideological conservatism (e.g., economic conservatism, cultural conservatism, social conservatism, meritocracy) affect the relationship between the political network and digital transformation strategy?

The research may contribute to the existing literature in two ways. First, by testing a model that suggests that political networks drive decision making in digital transformation, it adds to studies that used the coopetition theory [23], the institutions-based view [24,25], and upper echelons theory, and explains the organizational transformation from the non-market perspective. Specifically, this study shows that political network relationships drive the adoption of digital transformation strategies. In doing so, it explains the antecedents of digital transformation by extending previous research. Second, this study reveals the essence of the impact of political networks on the digital transformation strategy of enterprises is the development of a new coopetition mode, namely public–private alliance. It is likely to supplement the literature on coopetition and strategic alliance and make contributions to the development of coopetition theory.

2. Theoretical Framework and Hypotheses

2.1. Integrating Coopetition Theory and Political Networks on Digital Transformation Strategy

2.1.1. Coopetition Theory

Coopetition was first introduced by Brandenburger and Nalebuff [23] from a game-theoretic lens for interpreting inter-firm behaviors. The coopetition strategy is an amalgamation of the two strategies of “competition” and “cooperation” [26,27,28], which align different interests toward a common objective and help to create opportunities for competitive advantage by removing external obstacles and neutralizing threats. For decades, enterprises from various industries have been employing this practice to achieve different strategic objectives [29]. Coopetition theory suggests that in addition to buffering companies from external threats, political network relationships can also achieve performance through successful cooperation. Prior studies report that information on complex public policy processes is costly to obtain from other sources. However, political networks can effectively reduce these costs and provide access to decision makers [26,30]. In addition, the privileged information of existing and emerging regulations can guide enterprises, thereby reducing uncertainty generated by external organizations [31]. In turn, greater certainty and confidence in strategic initiatives and investments can improve firm performance.

Prior research shows that the government may play an important role in promoting the development of digital transformation of enterprises [17]. Xie et al. [13] argue that the government should not be considered the main attacker but should be considered as a guide in the field of digital economy development. The government can effectively integrate resources and provide financial support to support the digital transformation of enterprises. It is valid to consider that firms with good political network relationships are likely to conduct coopetition with the government to obtain resources in digital transformation, and these external resources are needed for successful digital transformation.

2.1.2. Public–Private Alliance (PPA) and Digital Transformation

Recent studies advance the research of competition alliance by revealing the role of public–private partnerships in competition and cooperation. Public–private partnership has been actively used over the past decades to create and implement various types of innovations that require significant long-term investments, such as developments in the field of robotics and artificial intelligence [12]. The study from Xie et al. [13] provides a theoretical basis for the government and enterprises in developing countries to form alliances to jointly promote digital transformation. Research results of Monticelli et al. [32] show the role of governmental intervention in the interplay of competition and cooperation in highly regulated industries in developing economies. Li and Ding [33] focused on the government subsidy mechanism in the process of digital transformation of enterprises, and Casprini and Palumbo [16] suggested this mode of sharing the necessary material conditions is actually attributed to the formation of public–private alliance (PPA).

Digital transformation is actually a risk-taking strategy for firms, which may not always be successful [34]. In a recent study, the role of political networks as a “buffer” is highlighted [35]. Good political network relationships will largely mitigate the risks that firms may face in the digital transformation. Furthermore, as digital transformation is on the ascendant, problems such as uneven distribution of market resources and asymmetric information are very prominent in some emerging economies [36]. The greater administrative, economic, and political proximity brought about by political networks allows greater and more specific interaction and information exchange between enterprises and governments [37], which is likely to contribute to the success of enterprises’ digital transformation. Under the encouragement of local government policies, enterprises are likely to be directly influenced and embedded in relevant rules, regulations, policies, and activities [31,38]. Governments can also directly experience the benefits and costs of operating businesses within their jurisdiction [39]. Since the emergence of PPA relies heavily on the political network relationship of entrepreneurs and managers of enterprises [40], the researcher considers that the ultimate form of utilizing political networks in strategic transformation by enterprises is to establish coopetition alliances with the government to achieve the transformation goals.

The biggest obstacle in the process of enterprise digital transformation is insufficient funds [13,41]. The resources that political relations may bring include not only direct cash or resource transfers [22] and low-cost bank loans [42], but also government contracts [43], and tax exemptions and subsidies [44]. In addition to tax incentives and more financial support, political network relationships are proven to play an important role in talent introduction [45] during corporate digital transformation. A high level of political network relationships may also provide intangible resource benefits, such as influencing policies, regulations, rules, and their implementation [46], enhancing legitimacy [30], and promoting market entry [47]. According to the literature above, the researchers propose the primary hypothesis of this study:

Hypothesis 1.

A firm with a higher level of political network relationships is more likely to conduct digital transformation.

2.2. Engagement in Perceived Corruption as a Moderator of Political Networks and Digital Transformation Relationships: An Institution-Based View

The early history of the institution-based view provides a specific version of change and stagnation, where organizations appear more and more like each other due to the power of legitimacy and socio-cultural pressure [24]. Institutional theory extended to the field of organization in the 1970s, as an extension of the knowledge revolution of the previous decade, which introduced the concept of open systems into organizational research, emphasizing the importance of context or environment because it constrains, shapes, penetrates, and renews the organization [48]. Since institutions shape firms’ strategic choices [49,50,51], the pressure exerted by the environment on the system may limit corporate decision making.

One of the fundamental causes of corruption is the nature of the political system and government behavior [52]. When managers perceive the environment as burdensome, they may turn to rent seeking, such as lobbying and corruption [53]. The supplier of corruption (i.e., bribery) originates from companies that actively utilize corporate political activities, such as lobbying, to gain the support of politicians [54]. In this situation, companies rely on establishing network connections with government officials and making greater use of non-market strategies to address corruption issues in specific markets [55]. In light of firm heterogeneity, a firm’s perceived corruption environment may have different impacts on strategic decision making [56]. For instance, firms with poor political connections may consider digital transformation as a preferred strategy for enterprise survival and sustainable development, while the abundant profits brought by political relations make it difficult to motivate decision makers of enterprises with high levels of political network relationships to abandon the status quo and participate in digital transformation. For politically connected firms, the pressure from perceived corruption is significantly lower than the uncertainty and risk that digital transformation may bring. Thus, the researchers propose Hypothesis 2:

Hypothesis 2.

The higher level of a top manager’s perceived corruption mitigates the positive impact of political networks on digital transformation.

2.3. Engagement in Political Ideology as Moderators of Political Networks and Digital Transformation Relationships: Leveraging Upper Echelons Theory

The characteristics of managers determine their behavior and strategic choices, which in turn affect the goals, behaviors, and outcomes of the enterprise. Previous studies have found a significant relationship between the commitment of top management and the current state of the enterprise [57] and strategic change in the enterprise [58]. In addition, scholars believe that top managers play a crucial role in the innovation efforts of enterprises [59,60]. All these aspects provide initial indications pointing toward the leading position that top managers assume in firms’ digital transformation processes. Thus, it is necessary to theoretically understand the role and practice of the top management team (TMT) in digital transformation. Prior research has shown that top management team (TMT) decision-making processes do not occur in isolation but are significantly traceable to the chief executive officer’s (CEO) psychological attributes [61]. Therefore, the political ideology of top management as a reflection of personal values may also affect organizational strategies. And it should be seen as an important factor in understanding corporate decision making.

Political ideology refers to an individual’s values towards social ideal goals and the best means to achieve these goals. Political ideology reflects enduring and higher-order values that guide behavior under uncertainty [62]. Managing uncertainty is considered the core of political conservatism [63]. Based on this premise, an increasing number of management studies have shown that the political ideology of CEOs can influence various firms’ strategic outcomes [64,65]. Chin et al. [66] argue that it is necessary to examine two distinct constructs: social ideology and economic ideology to better understand the behavioral implications of political ideology.

The key to economic ideology reflects an individual’s stance on competition and collaborative interaction with others [67]. Economic conservative people tend to view competition as a means of achieving positive outcomes, while economic liberals value the benefits of cooperation based on shared and interdependent goals and rewards [68]. In addition, economic conservatism is not equivalent to risk aversion. For instance, individuals may not be willing to accept new technologies that change their lives, even if they reduce uncertainty or risk [69]. The researcher addresses previous theoretical gaps by theorizing how the social and economic ideologies of CEOs independently shape the strategic decision-making process of top management teams and influence strategic outcomes at the company level. This study combines political psychology with high-level research, especially in terms of the interface between CEO and TMT. Specifically, the assumptions followed emphasize the impact of political networks on corporate digital transformation through mediating CEO economic conservatism. Relatively speaking, most economically conservative managers tend to use high-level political network relationships to maintain the existing business of the enterprise rather than carry out a digital transformation strategy, since digital transformation is actually a risky strategy. Thus, this study proposes Hypothesis 3:

Hypothesis 3.

The higher level of top manager’s economic conservatism mitigates the positive impact of political networks on digital transformation.

Social conservatism had been considered as “social–cultural conservatism” in earlier research [67,68]. Malka et al. [70] extracted the definition of cultural conservatism from the concept of social conservatism. To ensure the integrity of the conceptual construct of political ideology, this study discusses the two terminologies of social conservatism and cultural conservatism separately. Social conservatives have resistance to change and need to “act cautiously” and conform to the crowd. Scholars noted that social conservatives tend to avoid risks and uncertainty, fear change, and follow traditional social ideologies [60].

Since a great distrust of modern trends in the field of digital technology of cultural conservatives [71], the researchers assume that cultural conservatism of top management may affect the role of political network relationships in the decision making of digital transformation. Scholars have ascribed elements of digital transformation strategies to several essential dimensions and one of the important aspects is culture [72,73]. Since corporate culture that encourages employees to share knowledge and make innovations plays a significant part in digital transformation, the cultural ideology of top managers tends to affect digital transformation strategy. Cultural conservative enterprises are more inclined to comply with existing norms and systems in their corporate activities by using political network relationships, rather than disruptive innovations such as digital transformation. Therefore, this study proposes:

Hypothesis 4.

The higher level of top managers’ cultural conservatism mitigates the positive impact of political networks on digital transformation.

Research in political psychology suggests that individuals can hold conservative economic ideologies and liberal social ideologies, and vice versa. Although political psychologists acknowledge the difference between social and economic ideologies, management research has not incorporated this more subtle approach into political ideology. Therefore, previous studies have overlooked how these ideologies shape the decision-making process in different ways, resulting in the current implementation of ideology theory being unable to explain the results of some corporate strategic decisions or behaviors. This study divides political ideology into economic ideology, social ideology, and cultural ideology in a more detailed manner.

Social ideology refers to the degree to which individuals respect and adhere to traditional norms and practices [67,68]. Social conservatives tend to make decisions more intuitively, relying on past practices or their own experiences. In contrast, social liberals rely less on traditional norms or their own intuition based on experience, and instead extensively search for information [74]. Flexibility is considered an essential dimension of corporate digital transformation [73], and it reflects the degree to which enterprises respond to complex and changing market conditions. In fact, this flexibility is often reflected in inclusiveness in business activities and social behavior. Top managers with weaker inclusiveness often hold socially conservative values, which makes them particularly cautious when making decisions about risk-taking strategies. Social conservative enterprises rely more on past experience and rarely engage in innovative or speculative behavior when using political network relationships for business activities. Considering social conservatives against risk taking and situatedness in the modern era of public systems’ digital transformation [75], this study proposes:

Hypothesis 5.

The higher level of top manager’s social conservatism mitigates the positive impact of political networks on digital transformation.

Considering the particularity of the research question, that is, the demand for the enterprise’s digital transformation strategy for professionals, whether the values in the enterprise’s human resources management are meritocratic or not, may also have a moderating effect on our primary hypothesis. Meritocratic ideology as a significant dimension of psychological attributes, reflecting a CEO’s preferences and biases, has significant effects on the strategic decision making of firms [76]. As human resource management is expected to become the driving force of digital transformation, meritocracy is likely to motivate top managers and provide opportunities for qualified personnel who may accelerate corporate digital transformation. According to that, this study proposes:

Hypothesis 6.

The higher level of top manager’s meritocracy tendency strengthens the positive impact of political networks on digital transformation.

This study represented managers as the unit of observation and the political networks and digital transformation of firms as the unit of analysis. The research framework is shown in Figure 1.

3. Research Methodology

This section may be divided into subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

3.1. Research Models

This quantitative study methodology was focused on determining the relationships between the independent (political network), moderating (perceived corruption, political ideology), and dependent (digital transformation) variables.

Political network: The key independent variable of interest is a political network, a metaphor for ties with government officials. To elucidate, senior managers often exploit their contacts with government officials to improve the likelihood of tangible and intangible resource access. It is measured using a scale developed by Peng and Luo [77]. Respondents were asked to indicate the degree to which a manager at their firm has utilized networks and connections with (1) political leaders in various levels of the government, (2) officials in industrial bureaus, and (3) officials in regulatory and supporting organizations, such as tax bureaus, state banks, commercial administration bureaus, etc. The scale ranges from 1—very little to 5—very extensively.

Digital transformation: Digital transformation is the key dependent variable. The researchers measured corporate digital transformation strategy by using 8 items considered as necessary foundations for conducting digital transformation, with insights drawn from prior researchers [73]. Items of digital transformation in the study of Imgrund et al. [73] include expertise, organization flexibility, association involvement, digital strategy, IT strategy, security, collaboration, and culture. Considering the concept of the research object in this study, the researcher made slight adjustments to the 8 items as follows. (1) Professionals: whether there are any employees with IT and data-related skills and abilities in the enterprise and whether there are knowledge management mechanisms that enable the enterprise to benefit from employee specialization and facilitate co-creation and collaboration. (2) Flexibility: whether there is an adequate degree of organizational flexibility, which includes organizational agility, scalability, and adaptability, to adapt to continuously changing market conditions and whether the cross-departmental coordination and communication within the enterprise are efficient and can benefit the enterprise. (3) Association: whether customers participate in the design and development of new products and services and whether the enterprise benefits from the integration of data and processes within their supplier network. (4) Digitization: whether the enterprise establishes Chief Digital Officer (CDO) positions or related departments to support the digital transformation. (5) Restructuring: whether there is a consistent IT strategy implemented aligning enterprises’ business operations to new technologies. (6) Security: whether the management specifications for data and information security have been formulated and complied with, and whether there is a data security emergency plan and the information system is maintained regularly. (7) Collaboration: whether the enterprise is committed to strengthening interaction and relationship building, such as adopting horizontal coordination mechanisms to eliminate the obstacles of cross-department cooperation. (8) Culture: whether there is an open-minded environment that facilitates creativity and risk-taking and whether the enterprise offers training and education opportunities and adapts management structures by introducing new organizational roles and/or responsibilities.

The respondents were presented with a list of factors that constituted an eight-dimensional scale entailing professionals, flexibility, association, digitization, restructuring, security, collaboration, and culture—and asked whether each met the requirements of digital management. One or two items are used to measure each of the eight dimensions on a five-point Likert scale ranging from 1 = “strongly disagree” to 5 = “strongly agree”. The mean value of the eight dimensions constituted the digital transformation measure.

Perceived Corruption: Perceived corruption is one of the moderating variables. It is the response to the question “What is the degree to which a manager perceives corruption as pervasive within the home market?” The extent to which CEOs/entrepreneurs perceive corruption is on a five-point scale, ranging from 1 = “very little” to 5 = “very extensive” [78].

Political ideology is also the moderator in the research model. According to previous research, political ideology encompasses multiple dimensions [66,67,68,70]. Considering the special research topic, the researchers divided political ideology into four sub-variables, economic conservatism, cultural conservatism, social conservatism, and meritocracy.

Economic conservatism: Managers’ economic ideology is on a five-point scale, ranging from 1 = “strongly agree with distribution orientation” to 5 = “strongly agree with growth orientation”. The tendency of conservatism increases from 1 to 5.

Cultural conservatism: CEOs/entrepreneurs’ cultural ideology is on a five-point scale, ranging from 1 = “strongly agree with multiculturalism” to 5 = “strongly disagree with multiculturalism”. The tendency of conservatism increases from 1 to 5.

Social conservatism: Social ideology reflected in CEOs/entrepreneurs’ attitudes toward LGBTQ groups is on a five-point scale, ranging from 1 = “strongly LGBT friendly” to 5 = “strongly disagree with LGBT friendliness”. The tendency of conservatism increases from 1 to 5.

Meritocracy: Meritocracy is reflected in CEOs/entrepreneurs’ attitudes on human resource management on a five-point scale, ranging from 1 = “credentialism” to 5 = “meritocracy”. The tendency of conservatism decreases from 1 to 5.

Control variables: Control variables included a 9-item demographic and firmographic questionnaire capturing firm size, firm age, place of business, operating income, listing condition, entrepreneur owners’ gender, education level, and industry sector. The study’s sample and target populations were CEOs/entrepreneurs/managers firms in China. The researcher added various certifications as a solid foundation to reflect and capture data related to the research background.

Because we have cross-sectional data, we estimate our regression models through Generalized Least Squares (GLS) analysis. The clustering problem has long been recognized in the econometric literature. The most common approach to address this problem is to estimate a linear model using OLS (ordinary least squares) and then correct the standard errors for the intracluster correlation. In this study, we use a GLS estimation, which may be performed easily and will asymptotically result in a more efficient estimator and more powerful tests than OLS. The general form of the estimation equation is given as follows:

$$\begin{gathered} {Digital transformation}_i=\mathsf{\alpha }+{\mathsf{\beta }}_1{Political network}_i+ \\ {\mathsf{\beta }}_2{Perceived corruption}_i+{\mathsf{\beta }}_3{Economic conservatism}_i+ \\ {\mathsf{\beta }}_4{Cultural conservatism}_i+{\mathsf{\beta }}_5{Social conservatism}_i+ \\ {\mathsf{\beta }}_6{Meritocracy}_i{+\mathsf{\beta }}_7{Firm age}_i+{\mathsf{\beta }}_8{Firm size}_{it}{+\mathsf{\beta }}_9{Place}_i+ \\ {\mathsf{\beta }}_{10}{Operating income}_{it}+{\mathsf{\beta }}_{11}{Listing conditions}_{it}+{\mathsf{\beta }}_{12}{Industry}_i+ \\ {\mathsf{\beta }}_{13}{CEO gender}_i+{\mathsf{\beta }}_{14}{CEO degree}_i+{\mathsf{\epsilon }}_{it} \end{gathered}$$

(1)

The subscript i and t denote firm i at time t, respectively. Then, to test the second hypothesis, we extended Equation (1) by including an interaction term of Political network * Perceived corruption:

$$\begin{gathered} {Digital transformation}_i=\mathsf{\alpha }+{\mathsf{\beta }}_1{Political network}_i+ \\ {\mathsf{\beta }}_2{Political network}_i\ast {Perceived corruption}_i+{\mathsf{\beta }}_3{Perceived corruption}_i+ \\ {\mathsf{\beta }}_4{Economic conservatism}_i+{\mathsf{\beta }}_5{Cultural conservatism}_i+ \\ {\mathsf{\beta }}_6{Social conservatism}_i+{\mathsf{\beta }}_7{Meritocracy}_i{+\mathsf{\beta }}_8{Firm age}_i+ \\ {\mathsf{\beta }}_9{Firm size}_{it}{+\mathsf{\beta }}_{10}{Place}_i+{\mathsf{\beta }}_{11}{Operating income}_{it}+ \\ {\mathsf{\beta }}_{12}{Listing conditions}_{it}+{\mathsf{\beta }}_{13}{Industry}_i+{\mathsf{\beta }}_{14}{CEO gender}_i+ \\ {\mathsf{\beta }}_{15}{CEO degree}_i+{\mathsf{\epsilon }}_{it} \end{gathered}$$

(2)

To test the third hypothesis, the researcher extended Equation (1) by including an interaction term of Marketing orientation * Economic conservatism:

$$\begin{gathered} {Digital transformation}_i=\mathsf{\alpha }+{\mathsf{\beta }}_1{Political network}_i+ \\ {\mathsf{\beta }}_2{Political network}_i\ast {Economic conservatism}_i+ \\ {\mathsf{\beta }}_3{Perceived corruption}_i+{\mathsf{\beta }}_4{Economic conservatism}_i+ \\ {\mathsf{\beta }}_5{Cultural conservatism}_i+{\mathsf{\beta }}_6{Social conservatism}_i+ \\ {\mathsf{\beta }}_7{Meritocracy}_i{+\mathsf{\beta }}_8{Firm age}_i+{\mathsf{\beta }}_9{Firm size}_{it}{+\mathsf{\beta }}_{10}{Place}_i+ \\ {\mathsf{\beta }}_{11}{Operating income}_{it}+{\mathsf{\beta }}_{12}{Listing conditions}_{it}+{\mathsf{\beta }}_{13}{Industry}_i+ \\ {\mathsf{\beta }}_{14}{CEO gender}_i+{\mathsf{\beta }}_{15}{CEO degree}_i+{\mathsf{\epsilon }}_{it} \end{gathered}$$

(3)

To test the fourth hypothesis, the researcher extended Equation (1) by including an interaction term of Marketing orientation * Cultural conservatism:

$$\begin{gathered} {Digital transformation}_i=\mathsf{\alpha }+{\mathsf{\beta }}_1{Political network}_i+ \\ {\mathsf{\beta }}_2{Political network}_i\ast {Cultural conservatism}_i+ \\ {\mathsf{\beta }}_3{Perceived corruption}_i+{\mathsf{\beta }}_4{Economic conservatism}_i+ \\ {\mathsf{\beta }}_5{Cultural conservatism}_i+{\mathsf{\beta }}_6{Social conservatism}_i+ \\ {\mathsf{\beta }}_7{Meritocracy}_i{+\mathsf{\beta }}_8{Firm age}_i+{\mathsf{\beta }}_9{Firm size}_{it}{+\mathsf{\beta }}_{10}{Place}_i+ \\ {\mathsf{\beta }}_{11}{Operating income}_{it}+{\mathsf{\beta }}_{12}{Listing conditions}_{it}+{\mathsf{\beta }}_{13}{Industry}_i+ \\ {\mathsf{\beta }}_{14}{CEO gender}_i+{\mathsf{\beta }}_{15}{CEOd egree}_i+{\mathsf{\epsilon }}_{it} \end{gathered}$$

(4)

To test the fifth hypothesis, the researcher extended Equation (1) by including an interaction term of Marketing orientation * Social conservatism:

$$\begin{gathered} {Digital transformation}_i=\mathsf{\alpha }+{\mathsf{\beta }}_1{Political network}_i+ \\ {\mathsf{\beta }}_2{Political network}_i\ast {Social conservatism}_i+{\mathsf{\beta }}_3{Perceived corruption}_i+ \\ {\mathsf{\beta }}_4{Economic conservatism}_i+{\mathsf{\beta }}_5{Cultural conservatism}_i+ \\ {\mathsf{\beta }}_6{Social conservatism}_i+{\mathsf{\beta }}_7{Meritocracy}_i{+\mathsf{\beta }}_8{Firm age}_i+ \\ {\mathsf{\beta }}_9{Firm size}_{it}{+\mathsf{\beta }}_{10}{Place}_i+{\mathsf{\beta }}_{11}{Operating income}_{it}+ \\ {\mathsf{\beta }}_{12}{Listing conditions}_{it}+{\mathsf{\beta }}_{13}{Industry}_i+{\mathsf{\beta }}_{14}{CEO gender}_i+ \\ {\mathsf{\beta }}_{15}{CEO degree}_i+{\mathsf{\epsilon }}_{it} \end{gathered}$$

(5)

To test the sixth hypothesis, the researcher extended Equation (1) by including an interaction term of Marketing orientation * Meritocracy:

$$\begin{gathered} {Digital transformation}_i=\mathsf{\alpha }+{\mathsf{\beta }}_1{Political network}_i+ \\ {\mathsf{\beta }}_2{Political network}_i\ast {Meritocracy}_i+{\mathsf{\beta }}_3{Perceived corruption}_i+ \\ {\mathsf{\beta }}_4{Economic conservatism}_i+{\mathsf{\beta }}_5{Cultural conservatism}_i+ \\ {\mathsf{\beta }}_6{Social conservatism}_i+{\mathsf{\beta }}_7{Meritocracy}_i{+\mathsf{\beta }}_8{Firm age}_i+ \\ {\mathsf{\beta }}_9{Firm size}_{it}{+\mathsf{\beta }}_{10}{Place}_i+{\mathsf{\beta }}_{11}{Operating income}_{it}+ \\ {\mathsf{\beta }}_{12}{Listing conditions}_{it}+{\mathsf{\beta }}_{13}{Industry}_i+{\mathsf{\beta }}_{14}{CEO gender}_i+ \\ {\mathsf{\beta }}_{15}{CEO degree}_i+{\mathsf{\epsilon }}_{it} \end{gathered}$$

(6)

In Equations (1)–(6), $\mathsf{\alpha }$ represents the constant, $\mathsf{\beta }$ represents the coefficient of variables, and ${\mathsf{\epsilon }}_{it}$ represents possible errors. All variables are previously defined.

3.2. Description of the Population and Sample

Bulleted lists look like this: The populations of interest in this study were CEOs, top managers, and entrepreneurs from 214 large companies and SMEs from various regions with different levels of economic development in China. The survey data collection was completed in mid-March 2023. The study’s descriptive findings encompass the measure of frequencies and percentages. Firmographic data in the

Table 1. Participant firmographics: firm size, age, place, operations, listing conditions, industry.

Firmographic Category	Frequency	Valid %
Firm size (employees)
SMEs	113	52.8%
Large companies	101	47.2%
Firm age (operating history)
Strat-ups (less than 42 months)	29	13.6%
Firms operating more than 42 months	185	86.4%
Place
Yangtze River Delta region (Shanghai, Jiangsu, Zhejiang, Anhui)	42	19.6%
The Pearl River Delta (Guangdong, Hong Kong, Macao)	26	12.1%
The Bohai Rim region (Beijing, Tianjin, Hebei, Shandong, Liaoning)	48	22.4%
West Triangle Region (Chongqing, Shaanxi, Sichuan)	25	11.7%
Middle Yangtze River region (Hubei, Hunan, Jiangxi)	29	13.6%
Middle Yellow River region (Shanxi, Henan, Inner Mongolia)	27	12.6%
Other regions	17	7.9%
Listing conditions
Yes	21	9.8%
No	193	90.2%
Industry sector
Manufacturing	72	33.6%
Service	105	49.1%
Other industries	37	17.3%

Note: Total n = 214.

concluded that half of the Chinese businesses in the sample were SMEs that employed no more than 300 people (52.8%). A small number of Chinese businesses in the sample were startups (13.6%) whose operating history was no more than 42 months. Most businesses were most commonly performing work in the manufacturing (33.6%) and service industries (49.1%).

Table 2. Participant firmographics: CEO gender, CEO education level.

Firmographic Category	Frequency	Valid %
CEO gender
Male	157	73.4%
Female	57	26.6%
CEO education level (degree)
Junior high school	9	4.2%
Senior high school	25	11.7%
Junior college education	26	12.1%
Bachelor’s	87	40.7%
Master’s	47	22.0%
Doctorate	20	9.3%

Note: Total n = 214.

firmographics represent the Characteristics of CEOs/entrepreneurs of Chinese businesses in the sample. Male managers and female managers accounted for 73.4% and 26.6%, respectively. Most top managers held bachelor’s (40.7%) and master’s (22.0%) degrees, while only 20 individuals held doctorate (9.3%) degrees. In addition, top managers with lower educational backgrounds account for 28%, and junior college education (12.1%), senior high school (11.7%), and junior high school (4.2%), respectively.

Table 3. Descriptive statistics of variables measures.

Variable	Mean	SD	Minimum	Maximum
Digital transformation	−2.71	1.00	−2.65	2.09
Political network	3.27	1.22	1	5
Perceived corruption	3.46	1.24	1	5
Economic conservatism	3.32	1.16	1	5
Cultural conservatism	2.32	1.11	1	5
Social conservatism	3.21	0.97	1	5
Meritocracy	2.48	1.19	1	5

Note: Total n = 214.

reports the means, standard deviations, maximums, and minimums of the main variables in this study.

4. Results

4.1. Details of the Analysis

In order to validate the measurement scales of all theoretical structures, a confirmatory factor analysis was conducted [79], as shown in

Table 4. Confirmatory factor analysis for dimensions of digital transformation.

Factors	Description of Factors	Factor Loading	Reliability
Professionals	Enterprise benefits from employee’s specialization in IT and digital knowledge	0.315
Flexibility	An adequate degree of organizational flexibility to adapt to changing market conditions	0.499
Association	Customers participate in the design and development of new products and services	0.68
Digitization	To use IT for process automation, digitization, and data integration	0.28
Restructuring	To align technological and business structures	0.259
Security	To formulate rules and guidelines for data and information security	0.531
Collaboration	To eliminate the obstacles to cross-department cooperation	0.455
Culture	Our culture encourages employees to innovate and share knowledge	0.321	a = 0.83

Note: n = 214.

.

The factor loadings for each of the items in Table 4 were above +0.5. Hair et al. [79] believe that these values are very important. The results show that the measurement model for evaluating digital transformation fits the data acceptably. Cronbach’s alpha value was used to determine the reliability of the factor digital transformation. The alpha value is 0.83 for digital transformation. The bivariate correlations were also used to determine the reliability of the factors underlying political network, perceived corruption, economic conservatism, cultural conservatism, social conservatism, meritocracy, and digital transformation amongst the respondent Chinese firms. As shown in

Table 5. VIF.

Variables	VIF
Political network	1.38
Perceived corruption	1.23
Economic conservatism	1.12
Cultural conservatism	1.21
Social conservatism	1.29
Meritocracy	1.43
Firm age (startup or not)	1.30
Industry	1.17
Place	1.24
CEO gender	1.08
CEO degree	1.36
Listing conditions	1.68
Operating income	2.54
Firm size	2.79

, the variance inflation factor (VIF) value of all variables is less than 3. These results excluded the possibility of multicollinearity.

4.2. Details of the Results

The researchers present the results from estimating Equations (1) to (6) in

Table 6. Econometric results of the analysis.

Variables	Model 1 (Controls)	Model 2 (H1)	Model 3 (H2)	Model 4 ((H3))	Model 5 (H4)	Model 6 (H5)	Model 7 (H6)	Model 8 (Full Model)
Political network		0.189 ***	0.520 ***	0.456 ***	0.250 ***	0.281 ***	0.394 ***	0.917 ***
		(0.000)	(0.000)	(0.000)	(0.000)	(0.005)	(0.000)	(0.000)
Political network X Perceived corruption			−0.094 ***					−0.070 ***
			(0.000)					(0.002)
Political network X Economic conservatism				−0.081 ***				−0.067 ***
				(0.001)				(0.006)
Political network X Cultural conservatism					−0.027			−0.032
					(0.266)			(0.192)
Political network X Social conservatism						−0.03		−0.018
						(0.332)		(0.564)
Political network X Meritocracy							0.085 ***	0.056 **
							(0.001)	(0.027)
Perceived corruption	0.098 ***	0.083 ***	0.378 ***	0.100 ***	0.086 ***	0.090 ***	0.076 ***	0.323 ***
	(0.001)	(0.005)	(0.000)	(0.001)	(0.003)	(0.003)	(0.009)	(0.000)
Economic conservatism	−0.143 ***	−0.171 ***	−0.152 ***	0.087	−0.170 ***	−0.168 ***	−0.157 ***	0.069
	(0.000)	(0.000)	(0.000)	(0.289)	(0.000)	(0.000)	(0.000)	(0.400)
Cultural conservatism	−0.024	−0.02	0.002	−0.022	0.065	−0.019	−0.006	0.104
	(0.462)	(0.534)	(0.955)	(0.495)	(0.434)	(0.566)	(0.850)	(0.205)
Social conservatism	0.187 ***	0.103 ***	0.128 ***	0.109 ***	0.105 ***	0.200 *	0.101 ***	0.185 *
	(0.000)	(0.007)	(0.001)	(0.004)	(0.006)	(0.062)	(0.008)	(0.078)
Meritocracy	−0.208 ***	−0.174 ***	−0.164 ***	−0.181 ***	−0.178 ***	−0.173 ***	−0.468 ***	−0.370 ***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Firm age (startups or not)	0.021	−0.087	−0.127	−0.109	−0.089	−0.071	−0.108	−0.144
	(0.845)	(0.419)	(0.233)	(0.307)	(0.404)	(0.510)	(0.312)	(0.178)
Industry	−0.207 ***	−0.232 ***	−0.241 ***	−0.215 ***	−0.244 ***	−0.225 ***	−0.239 ***	−0.239 ***
	(0.007)	(0.002)	(0.001)	(0.004)	(0.001)	(0.003)	(0.001)	(0.001)
Place	0.013	0.029	0.023	0.032*	0.027	0.03	0.026	0.023
	(0.514)	(0.131)	(0.222)	(0.090)	(0.161)	(0.118)	(0.174)	(0.213)
CEO gender	−0.039	−0.025	0.034	0.019	−0.034	−0.033	−0.022	0.043
	(0.623)	(0.742)	(0.660)	(0.811)	(0.657)	(0.668)	(0.775)	(0.587)
CEO degree	0.195 ***	0.178 ***	0.161 ***	0.179 ***	0.174 ***	0.181 ***	0.177 ***	0.163 ***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Listing conditions	−0.083	−0.123	−0.175	−0.174	−0.13	−0.121	−0.123	−0.212
	(0.540)	(0.352)	(0.182)	(0.186)	(0.324)	(0.359)	(0.347)	(0.105)
Operate income	0.116 ***	0.111 ***	0.092 ***	0.101 ***	0.108 ***	0.107 ***	0.098 ***	0.075 ***
	(0.000)	(0.000)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.006)
Firm size	0.024	0.027	0.026	0.029 *	0.028 *	0.027	0.034 **	0.035 **
	(0.176)	(0.116)	(0.119)	(0.088)	(0.098)	(0.109)	(0.044)	(0.039)

* p < 0.1, ** p < 0.05, *** p < 0.01.

. This model does not use the principle of ordinary least squares but instead uses the principle of maximum likelihood or general least squares. The dependent variable in the model is digital transformation, and the independent variable of interest is the political network.

In model 2, the coefficient for the political network main effect (β = 0.19) was positive and significant (p < 0.01), indicating that firms with higher levels of political networks are more likely to adopt digital transformation. Among the control variables, coefficients for firm size main effect (β = −0.03), firm age (β = −0.09), and CEO gender (β = −0.03) were nonsignificant. In models 1–8, the coefficient for the CEO degree main effect was partially positive and significant, indicating that a positive and marginal relationship exists between top managers’ education level and corporate digital transformation adoption.

In model 3, the coefficient for political network (β = 0.52) was positive and significant (p < 0.01), while the coefficient for the interaction between political network and perceived corruption (β = −0.09) was negative and significant (p < 0.01). According to Figure 2, the marginal effect of perceived corruption on political networks was negative with a high perceived corruption score. These results suggest that political network relationships do not drive firms to conduct digital transformation in the presence of perceived corruption.

In model 4, the coefficient for political network (β = 0.46) was positive and significant (p < 0.01), while the coefficient for the interaction between political network and economic conservatism (β = −0.08) was negative and significant (p < 0.01). According to Figure 3, the marginal effect of economic conservatism on political network was negative with a high economic conservative score. These results suggest that political network relationships do not drive firms to conduct digital transformation when top managers are economic conservatives.

In model 5, the coefficient for political network (β = 0.25) was positive and significant (p < 0.01), while the coefficient for the interaction between political network and cultural conservatism (β = −0.03) was negative but nonsignificant. Thus, null Hypothesis 4 was accepted, which means that the culturally conservative ideology of top managers was less likely to impact the relationship between the political networks of firms and the adoption of digital transformation.

In model 6, the coefficient for political network (β = 0.28) was positive and significant (p < 0.01), while the coefficient for the interaction between political network and social conservatism (β = −0.03) was negative but nonsignificant. Thus, null Hypothesis 5 was accepted, which means that the socially conservative ideology of top managers was less likely to impact the relationship between the political networks of firms and the adoption of digital transformation.

In model 7, the coefficient for political network (β = 0.39) was positive and significant (p < 0.01), while the coefficient for the interaction between political network and economic conservatism (β = 0.09) was positive and significant (p < 0.01). According to Figure 4, the marginal effect of meritocracy on political networks was positive with a high meritocratic score. These results suggest that political network relationships drive firms to conduct digital transformation when top managers hold meritocratic ideology.

Thus, we provide evidence consistent with hypotheses 1–3 and 6 that (1) firms with higher levels of political network relationships are more likely to adopt digital transformation; (2) the impact of the political network on digital transformation is negatively moderated by perceived corruption; (3) the impact of the political network on digital transformation is negatively moderated by economic conservative ideology of top managers; and (6) the impact of the political network on digital transformation is positively moderated by meritocratic ideology of top managers. These results reflect that enterprise heterogeneity affects corporate equality policies and that state-level conditions influence corporations choosing LGBT friendliness.

As a robustness test, we ran a model that included the regional GDP to control for possible economic differences across the regions. The results were largely consistent with those reported in the main models and the regional GDP variable was not significant.

5. Discussion

The current study followed a theoretical framework that was bounded by coopetition theory, institutional theory, and upper-echelon theory. This study’s results have contributed to the advancement of non-market factors, such as political networks, affecting corporate digital transformation as may or may not be moderated by perceived corruption and political ideology of top managers.

5.1. Implications for Theory

The development of the digital economy has encouraged enterprises to implement digital transformation, which is actually a risk strategy. Because a large number of enterprises are unable to bear the huge risks and obstacles that digital transformation may bring, they tend to choose partners to share the risks. Coopetition theory as a management orientation helps align different interests toward a common objective. For enterprises with high-level political networks, the role of a “buffer” [35] of government and other public departments has become the cooperative object of many enterprises to implement digital transformation. The resources that political relationships may bring include direct cash or resource transfers [22], government contracts [43], tax exemptions and subsidies [44], and low-cost bank loans [40], which will largely mitigate the risks firms may face in the digital transformation. The significant research finding of research question 1 is that good political network relationships significantly (p < 0.01) motivate firms to conduct digital transformation strategy (β = 0.189).

Institutional theory emphasizes the role of context or environment that may constrain, penetrate, and renew the organization [48]. The pressure exerted by the environment on the system may shape firms’ strategic decision making [49,50,51]. In the context of weak and corrupt systems, companies rely on establishing network connections with government officials and making greater use of non-market strategies to address corruption issues in specific markets [55]. When corruption is widespread, digital transformation is considered to be the preferred strategy for enterprise survival and sustainable development for firms with poor political connections [20]. But for companies with good political networks, the pressure to deal with corruption is significantly lower than the uncertainty and risk that digital transformation may bring. The finding of research question 2 recognizes that there was a negative (β = −0.094) and significant (p < 0.01) effect of perceived corruption on the relationship between the political network and digital transformation. It is valid to consider that the lucrative profits brought by political ties make it difficult to motivate decision makers to abandon the status quo and engage in organizational transformation in the context of corruption.

The mechanism of non-market factors in the strategic decision making of enterprise digital transformation can be explained theoretically not only from the organizational level and institutional level, but also from the individual level. According to upper echelons theory, top managers play a crucial role in corporate strategic decision making and innovation efforts of enterprises [57,59,60]. And a significant relationship has been found between the characteristics of managers and strategic change in the enterprise [58]. The finding of research question 3 recognizes that there was a negative (β = −0.081) and significant (p < 0.01) effect of top managers’ economic conservatism on the relationship between the political network and digital transformation and there was a positive (β = 0.085) and significant (p < 0.01) effect of top managers’ meritocracy on the relationship between the political network and digital transformation. These aspects provide initial indications pointing toward the leading position that the political ideology of top managers is a vital non-market factor that needs to be considered in corporate digital transformation study.

5.2. Implications for Practice

This study provides a framework to examine the impact of research findings on professional practice. Despite the risks, the digital transformation strategy is a well-designed plan aimed at managing the transformation caused by the integration of digital technology in a sustainable way [80]. Digital transformation can bring sustainable competitive advantages to enterprises in the long run, but the high cost of transformation also exposes enterprises to certain risks. In that case, strategic alliance becomes an ideal paradigm for implementing digital transformation for most firms, especially SMEs. During this transformation process, strategic alliances can provide firms with new products and services, including digitally integrated ones [81]. Recently, strategic alliances aimed at digital transformation have taken on more diverse forms [44], such as inter-firm alliances, public–private alliances, international alliances, etc. Specially forming a public–private alliance with the government is worth considering by top executives of companies undergoing or preparing for digital transformation. Furthermore, from a more general macroeconomic perspective, digital transformation may further increase the demand for human capital, which has been shown to play a key role in modern economic growth [8,82].

Actually, the public sector, such as the government, is an ideal partner for firms’ innovation activities. With respect to digital transformation, it enables firms to access exclusive resources and opportunities. Insufficient funds are considered to be the biggest obstacle in the process of digital transformation [13,41]. The government can provide firms not only direct cash transfers [22], low-cost bank loans [42], tax incentives, and more financial support [44], but also some intangible resource benefits, such as influencing policies, regulations, rules, and their implementation [40], enhancing legitimacy [30], and promoting market entry [47]. In addition, a public–private alliance established based on political network relationships may buffer firms from pressures that threaten their survival during innovation activities. A buffer is an intervention factor that protects organizations from environmental stress [83].

Drawing from findings of strategic management research, both resource and institutional buffering benefits can mitigate obstacles and increase the likelihood of enterprises’ success in innovative strategies. It is valid to believe that the use of public–private alliances for digital transformation will obtain twice the result with half the effort for firms with a high level of political network relationships. Additionally, the strategic choice of firms with poor political network relationships in digital transformation will be a point of our future research.

5.3. Theoretical Results Relate to Practice

The combination of coopetition theory, institutional theory, and upper echelons theory provides a theoretical basis for the formation of strategic alliances during digital transformation in the new situation of public–private alliances for firms with good political network relationships. In existing strategic alliances research, scholars generally view strategic alliances as a form of strategic cooperation between enterprises. A theoretical differentiation for this strategic management study’s findings is that it proposes a new form of strategic alliance, namely public–private alliance, to summarize the strategic decisions and behaviors of enterprises using political network relationships for digital transformation. For strategic decisions on digital transformation, enterprises should fully consider the favorable conditions that the government and other public sectors may bring to their transformation, and also have sufficient estimates of the costs that may arise from utilizing the resources brought by the public sector for transformation rather than maintaining existing business. This requires enterprises to clarify the role of the public sector in business operations and its function in the sustainable development of the enterprise.

Rai and Surana [84] divided coopetition alliances into two types: contract-based governance and relation-based governance when studying alliance governance issues. When establishing a digital transformation strategic alliance based on political connections, executives should choose the most favorable form of coopetition for the sustainable development of the enterprise based on the level of perceived corruption and the depth of binding between TMT and public sector officials. This may improve the efficiency and success rate of digital transformation.

5.4. Limitations

There may be many non-market factors that affect the decision making of enterprises’ digital transformation. However, this study only examines whether the political network is the motivation for enterprises’ digital transformation, which is the first limitation. Additionally, this study explores the moderating effects of perceived corruption and entrepreneurs’ ideology on the implementation of digital transformation strategy by enterprises with political network relationships. However, the research results are of little value to enterprises without political networks at all. Additionally, due to data unavailability, this study does not control for the exporting firms versus the firms that serve only the local market or the CEO/entrepreneurs’ membership in the Communist Party. Future studies could control whether this factor influences the relationship between political networks and corporate digital transformation strategy.

Another potential insightful avenue for further research would be to undertake a quantitative study of other non-market factors that impact the adoption of digital transformation. While it may be difficult to find all motivations of corporate digital transformation, the value of being able to understand as many non-market factors as possible that may impact corporate digital transformation would be extremely valuable to the field of strategic management. Finally, future research could also try to confirm and extend our findings with non-convenience/probability sampling approaches, in order to ensure the generalizability of the results. Similarly, future studies can also focus their design on the study of potential confounding variables that might affect the observed relationships of our research.

6. Conclusions

This study determined if and to what extent a relationship exists between the political network of firms and the adoption of digital transformation and if it is moderated by perceived corruption and political ideology composed of economic conservatism, cultural conservatism, social conservatism, and meritocracy in China. The population of interest in this study was CEOs, top managers, and entrepreneurs operating firms in China. The target population comprised 214 large companies and SMEs from various regions with different levels of economic development in China. Our results show that the firm with a higher level of political network relationships is more likely to conduct digital transformation and the higher level of top manager’s perceived corruption and economic conservatism mitigates the positive impact of the political network on digital transformation, while the higher level of top manager’s meritocracy tendency strengthens the positive impact of the political network on digital transformation.