The Impact of Supply Chain Network Centrality on Sustainable Mergers and Acquisitions: Evidence from China

Abstract

Mergers and acquisitions (M&As) are key drivers for resource integration and the operational efficiency of enterprises. Companies that hold central positions within supply chains may leverage their strategic location to reduce information asymmetry, enhancing their ability to engage in sustainable activities. However, research on the role of supply chain network centrality in M&A decisions remains underexplored. This study aims to empirically examine whether and how centrality in supply chain networks enhances the likelihood and success of M&A activities, contributing to both theory and practice in corporate strategy. In particular, we construct a novel measure of firm centrality within the supply chain and utilize panel data from Chinese A-share listed companies spanning 2008 to 2021. Our findings reveal that the central position of supply chain networks promotes the probability and frequency of mergers and acquisitions. Mechanism analysis reveals that gaining information advantages and relieving financial constraints are two key channels through which the supply chain network promotes mergers and acquisitions. Furthermore, the effects are more pronounced for firms with non-state ownership, with closer proximity to customers or suppliers, with higher operational risk, and with growth and decline phases. A series of robustness tests support these results, including alternative measures, alternative estimating methods, and sub-sample tests. Moreover, central supply chain companies exhibit better long-term financial performance following mergers and acquisitions. This paper enriches our understanding of the roles of supply chain networks in firms’ mergers and acquisitions and holds important practical implications for companies seeking to achieve sustainable and long-term development.

1. Introduction

In the global context, sustainable development has emerged as a crucial issue in the 21st century. Countries and corporations alike are grappling with common challenges such as resource depletion, environmental degradation, and social inequality while striving for economic growth. As the world’s second-largest economy and largest CO2 emitter, accounting for 27% of global emissions in 2019, China faces severe environmental issues. In 2020, 98% of Chinese cities exceeded WHO air quality guidelines. Water scarcity is critical, with 28% of key rivers unfit for human contact. Rapid urbanization, increasing from 26% in 1990 to 64% in 2020, has strained resources and infrastructure. In response to these pressing issues, there is a growing need for a transition from high-speed to sustainable, high-quality development. Mergers and acquisitions (M&As) have emerged as a strategic tool for businesses to drive this transition. In 2023, the total value of M&A transactions in China was 346.3 billion dollars, accounting for 12.1% of the global value. This trend aligns closely with the growing corporate emphasis on sustainable development strategies. A 2022 Deloitte survey revealed that 51% of executives consider ESG factors “very important” in M&A decision-making, a dramatic increase from just 1% in 2016. This trend aligns with an increasing corporate focus on sustainable development strategies and directly contributes to broader sustainable development goals.

M&As can significantly contribute to sustainable development by facilitating resource integration, technological innovation, and market expansion. First, acquiring companies with advanced environmental technologies can help the parent company reduce its ecological footprint and enhance its sustainability performance [1,2]. Companies use M&As to rapidly adopt eco-friendly technologies and practices, such as renewable energy firms buying clean energy companies or manufacturers acquiring recycling startups [3,4]. For example, French oil giant Total acquired a majority stake in SunPower, a U.S. solar panel manufacturer, to expand its renewable energy portfolio. The trend creates a “green synergy” where sustainable practices spread through corporate networks, amplifying positive environmental impacts [5]. Moreover, M&As can foster collaboration and coordination among firms, minimizing resource wastage and promoting a circular economy [6]. Therefore, the interrelation between sustainable development and M&As not only enables companies to thrive in competitive markets [7] but also supports long-term societal progress [8]. As a result, M&A strategies are becoming a key tool for businesses to address global sustainability challenges while creating long-term value. Therefore, this paper explores this dynamic relationship, highlighting the theoretical and practical implications of integrating sustainability into M&A strategies.

Mergers and acquisitions can maximize resources and promote operating efficiency. Meanwhile, prior researchers have categorized internal and external factors influencing M&As. Scholars have found that potential synergy is a key motive for M&As, where the entity is more valuable than the sum of its parts [9]. This can include cost savings, enhanced market reach, and improved operational efficiency. Diversifying product lines or geographic presence and reducing risk is one of the motives for engaging M&As. Regarding financial motives, valuation [10] and the cost of capital [11] are crucial to M&As. Specifically, stock market performance, interest rates, the availability of financing options, and a lower cost of capital, which can decrease the cost, tend to spur M&A activity. For management, the vision of executives [12], board connections [13], young executives with financial experience [14], and promotion motives [15] play vital roles in M&As, indicating that effective leadership and management are important in navigating the complexities of M&As. From the perspective of equity, research has found that institutional investors as corporate shareholders can effectively reduce information asymmetry, improve corporate internal governance, and thus enhance M&A performance [16]. Additionally, market conditions, including industry trends, technological advancements, and competitive dynamics can also affect M&As [17]. Meanwhile, the existing literature focuses on the relationship between the supply chain network and M&As from the perspective of managerial characteristics. For example, Fich and Nauyen (2020) [18] assert that a CEO of the acquirer with work experience in supply chains can generate significant gains for shareholders of acquirer firms. However, few studies concentrate on the perspective of firms’ networks. Therefore, we conducted empirical study to investigate how centrality in the supply chain network affects M&As. By methodically investigating the impact of supply chain network centrality on M&As, this study aims to close this gap.

In contrast to the traditional linear structure, enterprises are no longer isolated entities but part of a complex and intricate business network. This is defined as a supply chain network, where enterprises can acquire more resources and data through interaction, improve their performance, and survive in a competitive business environment. Thus, enterprises increasingly recognize the significance of exploring supply chain networks, including outcomes, strategies, and challenges. The supply chain network plays a critical role by contributing to environmental benefits, promoting fair labor practices, and utilizing resources efficiently, thereby helping to reduce emissions and conserve resources. However, few studies explore the relationship between supply chain networks and M&As. This study attempts to bridge this gap by exploring how network centrality in supply chains can enhance M&As.

Mergers and acquisitions benefit enterprises in various dimensions, especially in improving the quality of M&As through enhanced informational availability. Information relevant to the other party in mergers and acquisitions is an advantage that can be utilized to extract the maximum benefit [19]. The supply chain network combines numerous pieces of private information from customers and suppliers, which are invaluable in M&As. Specifically if an enterprise’s location is more central in the supply chain network, the information advantage is more pronounced, reducing the uncertainty in an M&A. Despite the importance of this subject in economic activities, few studies discuss how to utilize supply chain networks to improve M&As and enhance business performance effectively.

Based on these identified gaps in existing studies, this research focuses on the following question: Can network centrality in supply chains influence enterprises’ M&A behaviors? To address this question, we construct an indicator to measure the position of an enterprise in the supply chain, reflecting its distance from the center. Our hypothesis is that a company’s ability to support M&As increases with its proximity to the central location of the supply chain network. The position of enterprises within the supply chain network and their M&A activities are experimentally examined in our research. We also investigate two additional hypotheses: firms at the center of the supply chain network promote M&As by effectively mitigating information asymmetry and financial constraints. These hypotheses aim to analyze how information advantage and financial constraints modulate the relationship between supply chain network positions and M&As, exploring the underlying mechanisms. Specifically, this study investigates whether a firm’s position in the supply chain network influences the probability and frequency of M&As, the mechanisms behind this influence, any heterogeneity in these effects, and the impact on M&A performance.

The following are major contributions to existing literature: First, this paper offers new insights into the impact of supply chain network centrality on M&As. Previous research in corporate finance has extensively studied the direct effects of supply chain dynamics on firm performance, including customer loyalty [20], technology integration [21], organizational resilience [22], profitability [23], and operational efficiency [24]. However, our study uniquely focuses on how the centrality of firms within supply chain networks influences their M&A behavior, utilizing social network analysis to evaluate supply chain centrality. This approach builds on the work of Ahuja [25] and Borgatti and Li [26], who highlight the importance of network positions in determining firm outcomes. By empirically examining the influence of supply chain network positions on M&As, we enrich the understanding of how informational advantages and financial constraints mediate this relationship.

Secondly, although numerous scholars have explored the effect of supply chain relationships on firm decisions [27], our study is one of the first to empirically examine the mechanisms of how supply chain network centrality impact M&As from the viewpoints of information advantage and financial constraint, helping to clarify the financing and information paths. We extend the literature by analyzing the mediating mechanisms of information advantages and financial constraints, which are crucial for understanding the nuances of M&A success. This provides a nuanced view of how firms leverage their network positions in M&A activities.

Third, this study provides valuable practical references for enterprises seeking to enhance their long-term performance. By improving their central position in supply chains, firms can more effectively manage their operations, access critical information, and increase their M&A success rates. Our findings suggest that centrality in supply chain networks can better mitigate information asymmetry and financial constraints, thereby facilitating more successful M&A transactions. This practical contribution supports the work of Barney [28,29], offering actionable insights for managers.

The structure of this paper is as follows: Section 2 proposes the research hypotheses on the impact of supply chain network centrality on corporate M&As. Section 3 describes the research methods and empirical data. Section 4 presents the main results of the empirical regression and discusses robustness, heterogeneity, and endogeneity. Section 5 analyzes the economic consequences of the main results. Section 6 concludes the empirical research findings and provides suggestions for enterprises implementing M&As for sustainable future development.

2. Literature Review and Hypotheses Development

2.1. Supply Chain Network and M&As

Supply chain network centrality, defined as the strategic positioning and influence of a firm within a supply chain network, plays a critical role in enhancing merger and acquisition (M&A) outcomes. Research shows that enterprises at the center of a network benefit from social prestige, information, and strategic resources provided by their social connections [30,31]. These advantages positively influence M&A activities. In the case of the continuous refinement of market division of labor, there are not only direct transactions between upstream and downstream enterprises in the supply chain, but also indirect connections through the same customers or suppliers. The supply chain network between enterprises is formed by sharing customers or sharing supply chain links [32]. As an important part of the social network in which the enterprise is located, it will also have an impact on the merger and acquisition behavior of the enterprise. Enterprises with high network centrality are better positioned to leverage information, reduce costs, and enhance financial performance, thus facilitating successful M&A activities. Network centrality in the supply chain also can be viewed as a source of motive for mergers and acquisitions (M&As) for several reasons: First, firms with high network centrality have improved access to and control over information flows, enabling them to reduce information asymmetry [33,34]. Enhanced communication and knowledge sharing about operations and corporate culture between firms can lead to more informed and effective M&A transactions [35]. Meanwhile, firms can mitigate potential cost synergy and risk through M&As via supply chain integration [36]. This information advantage can also influence takeover premiums and transaction prices, contributing to better deal outcomes.

Second, high centrality in the supply chain network can improve internal financing capabilities and reduce transaction costs. Central firms can increase bargaining power, manage cash flows more efficiently, and achieve better coordination and communication in the supply chain [26,37]. These advantages help alleviate financial constraints, allowing firms to pursue M&A opportunities more effectively [38,39]. Third, central firms can develop stronger relationships with supply chain partners, fostering trust and long-term collaborations that reduce transaction costs associated with opportunistic behaviors and contract enforcement [27]. Additionally, centrality allows firms to benefit from economies of scale and scope, optimizing production processes and lowering overall transaction costs [40].

Centrality in the supply chain network can be a double-edged sword. Although the central firms can alleviate financial constraints, it can also lead to higher operating risk. Some scholars consider that the central firms are often highly dependent on a limited number of upstream and downstream firms, which increases their exposure to disruptions in these relationships [41]. In addition, central firms and upstream suppliers may receive higher operating risk coming from major customers [42,43,44], increase hold-up cost [29] and the cost of equity capital, and have higher cash holding to protect firms from exposure [45]. According to previous analysis, we conjecture that the positive effects on M&As outweigh the negative effects such as operating risk.

In summary, supply chain network centrality, which refers to a firm’s strategic positioning and influence within a supply chain, is crucial for enhancing M&A outcomes. Firms at the center of a network benefit from social prestige, information, and strategic resources, positively impacting their M&A activities. These firms can leverage their network position to reduce costs, improve financial performance, and facilitate successful M&A transactions. Additionally, stronger relationships with supply chain partners, economies of scale, and reduced transaction costs further support their M&A strategies. Hence, this study proposes the following hypothesis:

Hypothesis 1:

Firms at the central position of the supply chain network improve M&A activities.

2.2. Supply Chain Network, Information Advantage, and M&As

Firms with high network centrality are better positioned to reduce information asymmetry due to their enhanced access to and control over information flows. Constructing a connection between firms increases the probability of communication and deepens their knowledge and understanding about the operations and corporate culture of their partners [35]. This enhanced knowledge and information advantage can lead to more successful M&A transactions. Specifically, the information advantage may affect the takeover premium and, as a consequence, the transaction price of the deal. Enterprises closer to the central position in the supply chain can facilitate more effective information sharing and communication across the supply chain. This improved information exchange helps align expectations and actions among supply chain partners, thereby reducing the risks associated with information asymmetry [46].

Moreover, centrality in the supply chain network allows enterprises to implement advanced information systems and technologies to gather and disseminate critical data more efficiently. It not only enhances transparency [47] but also enables real-time decision-making and responsiveness to market changes. Consequently, network centrality in the supply chain can manage demand variability, inventory levels, and production schedules, resulting in more synchronized for supply chain operations. By utilizing the information advantage, enterprises can negotiate better terms with upstream and downstream enterprises, promote the service level, enhance the efficiency of the supply chain [48], and sustainable development [49].

In summary, network centrality in a supply chain can facilitate effective information sharing, align expectations across the supply chain, and reduce risks to leverage information advantage, which mitigates information asymmetry. Meanwhile, this centrality also allows for better management of demand, inventory, and production, leading to more synchronized operations. Ultimately, firms can leverage this information advantage to negotiate better terms with partners, enhance service levels, and improve M&A activities. Hence, this study proposes the following hypothesis:

Hypothesis 2:

Firms at the central position of the supply chain network improve M&As by effectively utilizing information advantages.

2.3. Supply Chain Network, Financial Constraints, and M&As

Financial constraint measures the limitations firms face in acquiring capital both internally and externally. Higher financial constraints indicate greater difficulties in obtaining the necessary funds for operations, investments, and growth opportunities. Enterprises facing intense financial constraints tend to be more prudent with investment activities [50]. Financial constraints significantly impact strategic decisions such as mergers and acquisitions (M&As), capital expenditures, and development activities. Since M&As can improve external financial conditions, enterprises with lower financial constraints tend to perform better in these activities [38,39]. The existing literature indicates that financial constraints greatly affect M&A success. Studies suggest that firms central to supply chain networks can gain private information, increase scale economies [51], and access external finance more easily [52]. This centrality reduces financial constraints and enhances M&A activities.

Supply chain network centrality refers to the position and influence an enterprise holds within a supply chain network. High centrality enterprises frequently serve as important hubs, connecting a variety of suppliers and consumers, which can improve internal financing capacities and lower transaction costs. In terms of internal finance, high centrality can increase bargaining power and improve cash flow management, aligning payments better in financial cycles [37]. According to existing studies [26], central firms can achieve better coordination and communication in the supply chain, leading to reduced transaction costs. Central firms often have better access to information and resources, enabling them to negotiate better terms with suppliers and reduce inefficiencies [41]. Additionally, centrality in the supply chain network allows firms to develop stronger relationships with their partners. These relationships can lead to trust and long-term collaborations [27], further reducing transaction costs associated with opportunistic behaviors and contract enforcement. Furthermore, central firms can benefit from economies of scale and scope, as their position allows them to aggregate demand and optimize production processes, as their position lowers overall transaction costs [40].

In summary, network centrality in the supply chain can improve M&As by alleviating financial constraints through enhanced internal financing and reduced transaction costs. And it can also enhance financial constraints by the bargaining effect, liquidity constraints, and its opposition to risk. Although the positive and negative effects of supply chain network centrality on financial constraints have been mentioned, the mediating effect of financial constraints is not clear. Our study determines that the positive effects can outweigh the negatives. It is helpful for enterprises to recognize the impact of supply chain network centrality on M&As by releasing costs, reducing inefficiencies, and optimizing the production process. Hence, this study proposes the following hypothesis.

Hypothesis 3:

Firms at the central position of the supply chain network improve M&As by effectively alleviating financial constraints.

3. Research Design

3.1. Model Specification

According to previous studies [18,44], we utilize the following model to examine the impact of centrality in supply chain networks on M&As:

$${MA}_{i,t}\left({MA\_N}_{i,t}\right)={\beta }_0+{\beta }_1{lndegree}_{i,t}+{\beta }_2{Controls}_{i,t}+\delta {\mu }_j+\phi {\nu }_t+{\epsilon }_{i,t}$$

(1)

The behavior of firm i’s mergers and acquisitions at time t is represented by MA_i,t and MA_N_i,t in the equation above, which we refer to as El-Khatib [53]. The year fixed effect is Vt, while the industry fixed effect is μ_j. The random disturbance term is indicated by ε_i,t. Controls represent a set of control variables including firm size (${Size}_{i,t}$), leverage (${Lev}_{i,t}$), return on assets (${ROA}_{i,t}$), firm liquidity (${Cashflow}_{i,t}$), growth opportunities (${Grow}_{i,t}$), board scale (Board), independent directors’ ratio (${Indep}_{i,t}$), the largest shareholders’ proportion ${(Top1}_{i,t})$, ownership $\left({SOE}_{i,t}\right),$ proportion of other receivables ${(Occupy}_{i,t}),$ administrative expense ratio${(Mfee}_{i,t}),$ the institutional investors’ proportion (${INST}_{i,t}$), firm age (${Firmage}_{i,t}$), the second-largest shareholders’ ratio (${Balance1}_{i,t}$). Appendix A has a detailed presentation of all variable definitions. β₁ is a measurement of the direct impact on the behavior of mergers and acquisitions of firms. If the coefficient is significantly positive, it demonstrates that the network centrality can influence the behavior of mergers and acquisitions of firms.

Furthermore, we analyze the two mechanisms through two externalities which firms are facing: information advantage and financial constraint. We established a two-way fixed-effect model to explore the effect:

$${KVIndex}_{i,t}\left({\mathrm{S}\mathrm{A}}_{i,t}\right)={\chi }_0+{\chi }_1{lndegree}_{i,t}+{\chi }_3{Controls}_{i,t}+\delta {\mu }_j+\phi {\nu }_t+{\epsilon }_{i,t}$$

(2)

$${MA}_{i,t}/{M{A}_N}_{i,t}={\gamma }_0+{\gamma }_1{lndegree}_{i,t}+{\gamma }_2{KVIndex}_{i,t}\left({SA}_{i,t}\right)+{\gamma }_3{Controls}_{i,t}+\delta {\mu }_j+\phi {\nu }_t+{\epsilon }_{i,t}$$

(3)

where ${\chi }_0$ and ${\gamma }_0$ represent intercepts; the remaining variables are configured similarly to Model (1). Subsequently, the analysis examines the results of the next stage in Model (2) to explore the extent to which supply chain network centrality significantly affects a company’s informational advantages and financial constraints. In the final step, the KV index and SA are included in the regression of supply chain network centrality on M&As in Model (3).

3.2. Variable Selection

3.2.1. Dependent Variable

According to previous studies [54,55,56], the behavior of mergers and acquisitions in this study is a combination of probability (MA) and frequency (MA_N). The probability of mergers and acquisitions is defined as a dummy variable, which is attributed a value of 1 when the mergers and acquisitions happen in reality; otherwise, it is 0. The frequency of mergers and acquisitions initiated by listed companies during the year is measured by adding 1 to take the natural logarithm of the frequency of mergers and acquisitions initiated by listed companies during the year. The frequency of mergers and acquisitions (MA_N) initiated by listed companies during the year is measured by adding 1 to take the natural logarithm of the number of mergers and acquisitions initiated by listed companies during the year.

3.2.2. Explanatory Variables

Network centrality is a crucial indicator of network structure, which indicates the positions of individuals within the network. The previous literature provides several metrics for measuring network centrality, including degree, betweenness, closeness, and eigenvector [19,53,57]. Closeness cannot be applied in this study because the supply network is not fully connected, and it shows a strong correlation with other indicators. Additionally, given that the supply chain network in this paper is primarily based on preexisting nodes and linked paths of firms, eigenvector centrality is also deemed unsuitable for this study. Degree centrality, which measures the direct connections between firms in the network by counting the number of direct links to a node, is widely used in academic research [19,53]. Therefore, we employ the nature logarithm of degree as the network centrality (lndegree), while closeness centrality and eigenvector centrality have been chosen as alternative variables for robustness tests. The higher index means that the location is closer to the centrality of the network.

3.2.3. Mechanism Variables

According to previous studies [58], we employ the KV index to measure the information advantage, implying that the KV index is lower, and the quality of information disclosure is higher. Therefore, it leads to less asymmetry of information and a greater information advantage.

In the aforementioned literature [17], enterprises which are located in the centrality of a supply chain network enhance the availability of endogenous and exogenous financing to alleviate the financing constraints faced by enterprises, thereby increasing the probability and number of mergers and acquisitions by enterprises. This study utilizes the SA index, which is defined as the scale of a firm, scale squared, and firm age, to measure the financial constraint in the capital market. The higher index stands for more financial constraint.

3.2.4. Control Variables

Referring to previous studies [59], we also selected some factors which influence the merger and acquisition behavior of firms, including the firm scale (Size), the asset-to-debt ratio (Lev), return of asset (ROA), growth of firms (Growth), ration of cashflow (Cashflow), the scale of board (Board), the shareholding ratio of the largest shareholder (Top1), age of firms (FirmAge), ownership of firm (SOE), ratio of independent board (Indep), the ratio of management fee (Mfee), percentage of institutional investors’ shareholding (INST), equity checks (Occupy) and balances (Balance1), and ratio of fixed assets (FIXED).

3.3. Data and Sample

Our research examines data from listed companies in the China Stock Market and Accounting Research (CSMAR) database to focus on how status in the supply chain network affects merger and acquisition behavior.

Step 1. We identify all of the top five suppliers and top five customers of listed companies from 2008 to 2021 and collect information and data based on the unified social credit code and its name.

Step 2. Using the name and corresponding unified social credit code for the network matching of enterprises, we obtain the required data and information manually from the official website in case the misidentification is caused by mixing abbreviations and full names.

Step 3. We construct a supply chain network relationships table, convert the text of the relationship list into a net file in the form of a network each year by txt2pajek, and import the table into Pajek 5.19 software to calculate the centrality of the supply chain network.

Step 4. We match the unified social credit code with the stock code of the listed companies, and the listed companies are screened as the object of inquiry for this study.

To be more specific, we select specific samples based on the following criteria: (1) Exclude financial enterprises and firms suffering losses for more than two years (ST and ST* companies). This helps us avoid bias from distressed companies, allowing us to focus on healthier firms that exhibit more typical financial behaviors. We omit financial enterprises due to their unique industry characteristics, including distinct regulatory environments, financial structures, and risk profiles, which is consistent with prior studies [15,60]. These factors often result in financial metrics that are incomparable to those of non-financial firms and could potentially skew our analysis results. (2) We winsorize all variables at 1% and 99%. This process helps to reduce the impact of extreme values on our analysis while preserving the overall structure of the data. By applying this method, we aim to improve the robustness of our statistical results and ensure that our findings are not disproportionately influenced by a small number of unusual or potentially erroneous data points. (3) Companies that are missing major data are also deleted.

4. Empirical Results

4.1. Descriptive Statistics

Table 1. Descriptive statistics. The descriptive statistics of the regression findings, along with the informational advantages, financial constraints, and control variables, are laid out in this table. A total of 11,285 enterprises that were listed during 2008 and 2016 on the Shenzhen and Shanghai stock exchanges were our selection. The information and the attributes of the listed companies were gathered from the CSMAR databases. Appendix A contains the definitions for these variables.

Variable	Obs	Mean	Std.Dev.	Min	Max
MA	11,285	0.3000	0.4583	0	1
MA_N	11,285	0.2784	0.4753	0	4.8828
lndegree	11,285	1.4511	0.7952	0	2.7726
Size	11,285	22.2210	1.4070	19.4058	26.4297
Lev	11,285	0.4617	0.2131	0.0274	0.9246
ROA	11,285	0.0386	0.0609	−0.3982	0.2539
Cashflow	11,285	0.0438	0.0730	−0.2244	0.2825
Growth	11,285	0.1856	0.4429	−0.6597	4.3304
Board	11,285	2.1659	0.2002	1.6094	2.7081
Indep	11,285	0.3710	0.0527	0.2500	0.6000
Top1	11,285	0.3538	0.1532	0.0813	0.7584
SOE	11,285	0.4518	0.4977	0	1
Occupy	11,285	0.0167	0.0829	0.0001	0.2020
Mfee	11,285	0.0916	0.0829	0.0070	0.7660
INST	11,285	0.4022	0.2320	0	0.8867
FirmAge	11,285	2.8195	0.3679	0.6931	3.6109
Balance1	11,285	0.3353	0.2898	0.0059	1
FIXED	11,285	0.2340	0.1769	0.0015	0.7694

reports the descriptive statistics for the main variables in our study. These statistics are consistent with theoretical expectations. The means of dependent variables (MA, MA_N) are 0.3 and 0.2784. The mean value of the independent variable (lndegree) is 1.4511, which ranges from 0 to 2.7726. Similarly, the size of enterprise (Size) ranges between 19.4058 and 26.4297, with a mean value of 22.221. The average number of ROA is 3.89%, suggesting that the profitability of samples is significantly different. The average leverage (Lev) is 46.17%. The average growth rate is 18.56%, but its variance is 44.29%, suggesting that the samples of firms have a deep difference. The distribution of board ranges from 1.6094 to 2.7081, with an average value of 2.1659. The value of Indep ranges from 0.25 to 0.6, with a mean value of 0.371. The mean value of SOE is 0.4518, while the max and min values are 1 and 0. The mean values of Occupy and Mfee are 0.0167 and 0.0916. With a range from 0 to 0.8867, the mean value of INST is 0.4022.

4.2. Benchmark Statistics

In baseline regression,

Table 2. Benchmark regression. The supply chain’s centrality network’s baseline results for mergers and acquisitions are shown in this table. The benchmark results are shown in regressions (1) and (2) without fixed effects, while the results in (3) and (4) include fixed effects for the industry and control year. Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

	(1)	(2)	(3)	(4)
Variable	MA	MA_N	MA	MA_N
lndegree	0.1347 ***	0.0249 ***	0.1020 ***	0.0183 ***
	(4.9020)	(4.4172)	(3.5137)	(3.1088)
Size	0.1119 ***	0.0331 ***	0.1479 ***	0.0428 ***
	(5.4161)	(6.8798)	(6.3342)	(8.2234)
Lev	0.7694 ***	0.1657 ***	0.5985 ***	0.1133 ***
	(5.5952)	(6.1296)	(3.9879)	(3.9795)
ROA	4.0064 ***	0.8680 ***	3.5932 ***	0.7255 ***
	(8.2577)	(9.9938)	(6.9816)	(8.0950)
Cashflow	−0.5446 *	−0.1309 *	−0.6373 *	−0.1434 **
	(−1.6476)	(−1.8627)	(−1.8502)	(−2.0060)
Growth	0.2527 ***	0.0563 ***	0.2324 ***	0.0508 ***
	(5.3296)	(5.3289)	(4.6666)	(4.6648)
Board	0.1777	0.0181	0.1375	0.0018
	(1.3957)	(0.6685)	(1.0366)	(0.0660)
Indep	−0.1813	−0.1193	−0.1182	−0.1093
	(−0.3916)	(−1.2051)	(−0.2492)	(−1.1004)
Top1	−0.5524 ***	−0.1288 ***	−0.5552 ***	−0.1294 ***
	(−2.8997)	(−3.1011)	(−2.7536)	(−2.9720)
SOE	−0.2266 ***	−0.0472 ***	−0.3094 ***	−0.0648 ***
	(−4.5236)	(−4.4773)	(−5.6766)	(−5.6999)
Occupy	2.8290 ***	1.0639 ***	2.0179 **	0.8568 ***
	(3.2575)	(4.5100)	(2.1816)	(3.5973)
Mfee	1.1987 ***	0.3043 ***	0.3615	0.1154 *
	(4.2276)	(5.0690)	(1.1476)	(1.7721)
INST	−0.0234	0.0189	−0.0484	0.0163
	(−0.2119)	(0.7949)	(−0.4239)	(0.6736)
FirmAge	−0.5014 ***	−0.1354 ***	−0.3699 ***	−0.0965 ***
	(−8.4197)	(−9.7096)	(−5.0860)	(−5.7408)
Balance1	−0.2805 ***	−0.0695 ***	−0.2631 ***	−0.0607 ***
	(−3.0773)	(−3.7635)	(−2.7566)	(−3.1841)
FIXED	−0.0254	−0.0461 *	−0.1859	−0.0541
	(−0.1880)	(−1.7152)	(−1.0522)	(−1.6135)
Constant	−2.7442 ***	−0.1736	−3.1935 ***	−0.3200 **
	(−5.2097)	(−1.5518)	(−4.5027)	(−2.2090)
Year	NO	NO	YES	YES
Industry	NO	NO	YES	YES
N	11,285	11,285	11,273	11,285
Pseudu_R2/Adj_R2	0.0236	0.0381	0.0407	0.0541

depicts the correlation between the supply chain network’s location and the company’s merger and acquisition activities. In Columns (1) and (2), the coefficients of the independent variable (lndegree) are positive at the 1% significance level without the fixed effects. Columns (3) and (4) exhibit that by controlling the year and industry fixed effects, the coefficients of the location of the supply chain in China are also significantly positive at 1% level, indicating that the location of the supply chain network in China is significant with respect to the merger and acquisition behavior of the enterprise. In other words, as the location of the supply chain network in China increases for each standard deviation, the merger and acquisition behavior of enterprises tends to improve by 10.2% and 1.83%. The above result implies that Hypothesis 1 is validated, which is similar to the previous study [61].

This result also validates the association between control variables and M&As. The coefficients of firm size (Size), leverage (Lev), growing ability (Growth), other-receivables-to-total assets ratio (Occupy), management expense ratio (Mfee), and ROA are positive at the 1% level, which implies that larger firms can utilize more resource and funding to take part in merger and acquisition and a better ROA leads to the higher merger and acquisition performance of a firm. The coefficient of leverage (Lev) is positive at a 1% significance level, which indicates that the leverage of a firm is associated with the merger and acquisition behavior of the enterprise; the coefficients of cashflow rate (Cashflow), the rate of top1(Top1), age of firms (FirmAge), ownership (SOE), and balances (Balance1) are negatively significant at the 1% level, implying that state-owned enterprises and enterprises with a higher balance, meaning a larger ratio of equity held by the second-largest shareholder compared to the largest shareholder, do not prefer merger and acquisition behaviors.

4.3. Mechanism Analysis

4.3.1. The Impact of Information Advantage

Firms with high network centrality have better access to and control over information flows, reducing information asymmetry [33,34,46]. This advantage fosters deeper communication and understanding of partner operations and corporate culture, leading to more informed and successful M&A transactions [35]. It can also influence the takeover premium and transaction price of deals. Moreover, central firms can also negotiate better terms with partners, optimize supply chain operations, and contribute to sustainable development [49].

The test of the information advantage mediation mechanism is shown in

Table 3. The impact of information advantage. The regressions of the mediation mechanism study, which was used to determine the mechanisms behind supply chain networks’ influence on mergers and acquisitions. Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

Variable	(1)	(2)	(3)
	KV Index	MA	MA_N
lndegree	−0.0099 ***	0.0937 ***	0.0140 **
	(−4.6351)	(3.2005)	(2.3720)
KV Index		−0.3948 ***	−0.0614 **
		(−2.9265)	(−2.1172)
Control	YES	YES	YES
Constant	−0.3346 ***	−2.6366 ***	−0.2017
	(−6.6547)	(−3.6379)	(−1.3555)
Year	YES	YES	YES
Industry	YES	YES	YES
N	10,916	10,905	10,916
Pseudu_R2/Adj_R2	0.3097	0.0405	0.0525

. The first step of the mediation mechanism test has been demonstrated in the baseline regression, showing that the supply chain network position significantly promotes corporate behaviors of mergers and acquisitions. Information advantage (KV index) is defined as the dependent variable in Column (1). Regression analysis reveals that, at the 1% level, the supply chain network centrality (lndegree) is significantly negative, suggesting that central enterprises are capable of effectively mitigating information asymmetry. Meanwhile, Columns (2) and (3) use the probability and frequency of mergers and acquisitions as dependent variables. Adding the information advantage (KV index) to the independent variables, the results indicate that information advantage is negatively correlated with the probability of mergers and acquisitions at the 1% significance and with merger and acquisition frequency at the 5% significance, indicating that lower information asymmetry better facilitates merger and acquisition activities. After that, to determine the mediation effect, we observed the coefficient signs and the significance of the independent variables. The value of the coefficient of lndegree in Column (1) is −0.0099 (a1), the coefficient of the mediation variable (KV index) in Column (2) is −0.3948 (b1), and the coefficient of Indegree in Column (2) is 0.0937 (c1). The signs of a1 multiplied by b1 are the same as c1, and all coefficients are significant, indicating that information advantage partially mediates the effect. Similarly, the mediation effect on mergers and acquisitions frequency in Column (3) is also significant, implying that firms in central positions of the supply chain network can effectively reduce information asymmetry, promoting merger and acquisition activities.

4.3.2. The Impact of Financial Constraint

In agreement with the aforementioned literature, we find that a firm which is closer to centrality in a supply chain network can leverage the nexus between upstream and downstream enterprises to attain more valuable information, mitigate information asymmetry, and improve efficiencies when negotiating [41]. Meanwhile, central firms can construct strong relationships with partners and develop long-term collaborations [27], leading to decrease the transaction costs [40], as well as optimize the production process. Therefore, centrality in a supply chain network can decrease financial constraints. If the financial constraints decrease, such as transaction costs, the merger and acquisition activities of enterprises become easier.

The test of the financing constraint mechanism is shown in

Table 4. The impact of financial constraint. The mechanism analysis results of the regression used to determine the distinct mechanisms underlying supply chain networks’ influence on mergers and acquisitions are presented in this table. Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

Variable	(1)	(2)	(3)
	SA	MA	MA_N
lndegree	−0.0113 ***	0.0933 ***	0.0173 ***
	(−6.0382)	(3.2235)	(2.9665)
SA		−0.6789 ***	−0.0918 ***
		(−4.5580)	(−2.1497)
Control	YES	YES	YES
Constant	−2.8453 ***	−5.1936 ***	−0.5811 ***
	(−48.7899)	(−6.2027)	(−3.3006)
Year	YES	YES	YES
Industry	YES	YES	YES
N	11,285	11,273	11,285
Pseudu_R2/Adj_R2	0.7413	0.0421	0.0548

. The first step of the mediation mechanism test has been demonstrated in the baseline regression, showing that the supply chain network position significantly promotes corporate mergers and acquisitions. Secondly, Column (1) uses financing constraint (SA) as the dependent variable. Regression analysis reveals a substantial negative correlation between Indegree and financing constraints at the 1% level, suggesting that central enterprises can effectively ease these constraints. Third, Columns (2) and (3) consider the frequency and probability rate of mergers and acquisitions as the outcomes of interest. When the financing constraint variable (SA) is introduced as an independent factor, it shows a strong and negative relationship with both the probability and frequency of M&A transactions in Columns 2 and 3, at a statistically significant level of 1%. This indicates that reduced financing constraints more effectively facilitate merger and acquisition activities. To ascertain the mediating effect, it is essential to pay attention to the direction of the coefficient signs and the significance of the independent variables. The value of the coefficient of lndegree in Column (1) is −0.0113, the coefficient of the mediation variable (SA) in Column (2) is −0.6789, and the coefficient of Indegree in Column (2) is 0.0933. The signs of the estimate of lndegree in Column (1) multiplied by the coefficient of SA are the same as the coefficient of lndegree in Column (2), and all coefficients are significant, indicating that financing constraints partially mediate the effect. Similarly, the mediation effect on merger and acquisition frequency in Column (3) is also significant. Table 4 demonstrates that firms in central positions within the supply chain network can significantly alleviate financing constraints, thereby promoting merger and acquisition activities, verifying Hypothesis 3.

4.4. Endogeneity Analysis

4.4.1. Instrumental Variable

Initially, we utilized an instrumental variable to confirm the validity of this study. Previous research [61] indicates that the one-year-delayed centrality of the supply chain network, connected to the network’s centrality, serves as the instrumental variable. The outcomes of a two-stage least-squares (2SLS) regression can be observed in

Table 5. Instrumental variable. The findings presented in Table 5 utilize the instrumental variable method. Our chosen instrumental variable is the one-year-lagged centrality of the supply chain network (lndegree_1). The results of the initial regression analysis, aligned with the F-statistic test, are outlined in Column (1). The second stage’s regression analysis is shown in (2) and (3). Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

Variable	(1)	(2)	(3)
	lndegree	MA	MA_N
lndegree_1	0.7774 ***
lndegree	(80.2100)	0.0292 ***	0.0247 **
		(3.0144)	(2.3823)
Control	YES	YES	YES
Constant	1.2371 ***	−0.0459	−0.2844
	(6.2400)	(−0.2425)	(−1.4699)
Year	YES	YES	YES
Industry	YES	YES	YES
N	7696	7696	7696
Adj-R2	0.6218	0.0361	0.0508

. In Column (1), the coefficients in the regression of the first stage validate the relevance condition and the F-statistic also indicates that the one-year-lagged centrality of the supply chain network (lndegree_1) passes the under-identification test. In the second stage, the estimations of instrumental variables are positively significant at 1% and 5% level with the fixed effects, implying that our regression results pass the endogeneity test.

4.4.2. Propensity Score Matching (PSM)

Based on previous studies [62], all control variables match the conditions to be utilized as propensity score matching method (PSM) covariates, leading to 4636 and 4659 valid sample observations after matching, which decline steeply. First, the network centrality indices are divided into four groups in ascending order. A value of 1 is designated for the primary group, also known as the treatment group, and a value of 0 is assigned to the remaining groups, identified as the control group. Next, the treatment group is connected with the control group through the application of kernel matching and one-to-one matching principles.

The table shows that all of the matched variables’ absolute values of the standardized deviations are less than 10% after matching, indicating the reliability of the chosen variables and methodology. Consequently,

Table 6. The propensity score matching (PSM). This table presents the propensity score matching (PSM) approach regression results. We divided samples into treatment groups and control groups, employed the nearest-neighbor match method in (1) and (2) and kernel-matching method in (3) and (4), and explored the endogeneity. Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

Variable	Nearest-Neighbor Matching (1:1)		Kernel Matching
	(1)	(2)	(3)	(4)
	MA	MA_N	MA	MA_N
lndegree	0.1501 ***	0.0244 ***	0.1030 ***	0.0184 ***
	(3.0098)	(2.6210)	(3.5483)	(3.1172)
Control	YES	YES	YES	YES
Constant	−4.1941 ***	−0.4177	−3.1667 ***	−0.3176 **
	(−3.5195)	(−1.6201)	(−4.4610)	(−2.1905)
Year	YES	YES	YES	YES
Industry	YES	YES	YES	YES
N	4636	4659	11,261	11,276
Pseudu_R2/Adj_R2	0.0510	0.0526	0.0403	0.0538

confirms the robustness of the regression results by showing that the explained variable’s coefficient is statistically significant at the 1% level.

4.5. Heterogeneity Test

4.5.1. State-Owned Enterprise vs. Non-State-Owned Enterprise

Willingness to communicate information is one of the factors that affects the relationship between the location of the supply chain network and the behaviors of M&As because the information obtained from the supply chain network influences the merger and acquisition behaviors of enterprises. Referring to existing studies, we found that SOEs have less efficient than non-SOEs [60] due to political interference and the lack of effective incentives [63]. Therefore, it is challenging for SOEs to utilize the information advantages of a supply chain network. We posit that compared with non-SOEs, SOEs are less likely to improve M&As through supply chain network centrality.

In China, enterprises can be divided into two groups according to marketization. Non-state-owned enterprises pay greater attention to information advantages during mergers and acquisitions than do state-owned enterprises. The samples of corporate entities are classified according to their ownership structure as either state-owned or non-state-owned enterprises in order to analyze the diversity of the regression findings. The value of SOE is 1 and the non-SOE is 0. The results for non-state-owned enterprises are presented in Columns (1) and (2) of

Table 7. Heterogeneity analysis based on ownership. The assessment of a firm’s ownership effect on the link between M&As and supply chain network centrality is shown in Table 7. Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

Variable	Non-State-Owned		State-Owned
	(1)	(2)	(3)	(4)
	MA	MA_N	MA	MA_N
lndegree	0.1073 ***	0.0253 ***	0.0703	0.0044
	(2.7531)	(3.2876)	(1.5408)	(0.4707)
Control	YES	YES	YES	YES
Constant	−2.7878 ***	−0.1924	−4.1578 ***	−0.6326 ***
	(−2.6859)	(−0.8516)	(−3.8463)	(−3.0771)
Year	YES	YES	YES	YES
Industry	YES	YES	YES	YES
N	6168	6186	5076	5099
Pseudu_R2/Adj_R2	0.0500	0.0616	0.0555	0.0656

. The Indegree coefficients hold significance at a rate of 1%, while the remaining coefficients do not carry the same weight. This implies that acquisitions and mergers within the non-state-owned sector hold greater significance compared to those involving state-owned enterprises.

4.5.2. Closer Proximity vs. Distant Proximity

Aligning with previous studies [19], as the efficiency of proximity in the supply chain network improved, the enterprises attained more information and resources to engage in mergers and acquisitions, which reduced the chances of information asymmetry and promoted collaborations between suppliers and customers. The method used to determine how close a listed firm is to its suppliers or customers is as follows: find the negative of the natural logarithm of the distance from its supplier or client, and then add one.

Table 8. The heterogeneity analysis based on proximity. Table 8 exhibits the results regarding the proximity of the association between centrality in supply chain networks and M&As. We classify the samples in closer proximity in (1) and (2) and distant proximity in (3) and (4) according to the distance to the suppliers. Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

Variable	Closer Proximity		Distant Proximity
	(1)	(2)	(3)	(4)
	MA	MA_N	MA	MA_N
lndegree	0.0935 ***	0.0152 **	0.1066	0.0204
	(2.7215)	(2.1790)	(1.3634)	(1.3928)
Control	YES	YES	YES	YES
Constant	−3.1288 ***	−0.3229 *	−4.1988 **	−0.4728 *
	(−3.8137)	(−1.9178)	(−2.5379)	(−1.6523)
Year	YES	YES	YES	YES
Industry	YES	YES	YES	YES
N	7724	7738	3514	3545
Pseudu_R2/Adj_R2	0.0446	0.0580	0.0550	0.0504

displays the regression findings. When the listed companies are located in close proximity to their suppliers or consumers, the regression findings of network centrality on firm mergers and acquisitions are presented in Columns (1) and (2). At the 5% and 10% levels, the regression coefficients of network centrality (lndegree) are 0.09 and 0.015, significant. Columns (3) and (4) show the results when the companies are geographically distant from their customers or suppliers, where the coefficients are not significant. This indicates that closer proximity to customers or suppliers enhances the impact of supply chain network centrality on promoting mergers and acquisitions activities.

4.5.3. High Risk vs. Low Risk

Corporate mergers are one-time transactions characterized by high risk, which can weaken the willingness to engage in such activities. Chen et al. [64] found that operational risk is closely related to investment behavior. High operational risk makes companies more cautious with investments, while low risk increases their willingness and capacity to merge. Central positions in the supply chain help firms manage risks better by establishing stable relationships with network members. Thus, high operational risk highlights the positive impact of central supply chain positions on mergers. This study select Z-score [65] as a proxy for business risk for the group regression, with the smaller index indicating a higher level of business risk. The regression results are exhibited in

Table 9. Heterogeneity analysis based on operating risk. The estimation findings for operating risk on the association between centrality in supply chain networks and M&As are shown in this table. We categorize firms as high-risk in (1) and (2) and low-risk in (3) and (4). Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

Variable	High Operating Risk		Low Operating Risk
	(1)	(2)	(3)	(4)
	MA	MA_N	MA	MA_N
lndegree	0.1296 ***	0.0173 **	0.0617	0.0158 **
	(3.1557)	(1.9632)	(1.4558)	(1.9631)
Control	YES	YES	YES	YES
Constant	−1.8183 *	−0.1319	−4.4817 ***	−0.4908 **
	(−1.8134)	(−0.6255)	(−3.9704)	(−2.2728)
Year	YES	YES	YES	YES
Industry	YES	YES	YES	YES
N	5568	5595	5679	5690
Pseudu_R2/Adj_R2	0.0515	0.0594	0.0494	0.0550

.

Columns (1) and (2) present the regression results regarding the impact of network centrality on corporate mergers and acquisitions when firms face high operational risk. The Indegree coefficients are 0.1296 and 0.0173, which are positive at the 1% and 5% significance, respectively. Columns (3) and (4) demonstrate the results for low operational risk firms. Specifically, the effect on merger and acquisition probability is not significant, while the effect on merger and acquisition frequency is significant at the 5% level. In sum, firms facing high operatingrisk more prominently benefit from network centrality in promoting merger and acquisition activities.

4.5.4. Growth and Decline Stage vs. Maturity Stage

Following previous studies [66], this study categorizes the corporate life cycle into three distinct stages: growth, maturity, and decline. Various limitations and obstacles confront businesses at various phases of their existence. During the growth phase, firms typically have business models that are still immature and cannot yield substantial profits because their development time is typically shorter than that of other businesses. When firms are in recession, it is also unfavorable for them to engage in mergers and acquisitions. When a firm is in a mature period, the constraints and challenges faced by other firms are greatly reduced and the development momentum is favorable. According to the aforementioned paper’s conclusion, supply chain network centrality has a greater positive influence on growing and declining businesses than it does on mature businesses. This helps firms mitigate the negative effects of life cycle changes and makes mergers and acquisitions easier.

The regression results are demonstrated in

Table 10. Heterogeneity analysis based on corporate life cycle stage. Table 10 reports the estimation results regarding corporate life cycle stage on the association between the centrality network of supply chains and M&As. We categorize the firms into two groups, with firms in the growth and decline stages in (1) and (2) and firms in the maturity stage in (3) and (4). Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

Variable	Growth and Decline		Maturity
	(1)	(2)	(3)	(4)
	MA	MA_N	MA	MA_N
lndegree	0.1419 ***	0.0296 ***	0.0407	0.0008
	(3.8152)	(3.9006)	(0.8484)	(0.0882)
Control	YES	YES	YES	YES
Constant	−3.5560 ***	−0.4802 ***	−2.4960 **	−0.0170
	(−3.7684)	(−2.6714)	(−2.1431)	(−0.0678)
Year	YES	YES	YES	YES
Industry	YES	YES	YES	YES
N	7085	7098	4131	4146
Pseudu_R2/Adj_R2	0.0474	0.0599	0.0450	0.0405

. In Columns (1) and (2), the coefficients of the dependent variable (lndegree) are 0.1419 and 0.0296, both significant at the 1% level. Columns (3) and (4) show the results for firms in the maturity phase. Here, the Indegree coefficients are not significant. This verifies the hypothesis that network centrality has a more pronounced impact on merger and acquisition activities during the growth and decline phases.

4.6. Robustness Tests

4.6.1. Alternative Measures of Mergers and Acquisitions Behaviors

According to a previous study [67], there are various measures of mergers and acquisitions, including probability, frequency, and total amount. In Column (1) in

Table 11. Alternative measures. Table 11 presents the result of the robustness test. We utilize different measures to reexamine the regression results, including the total amount of mergers and acquisitions (MA_V) in (1), and pagerank centrality as an independent variable in (2) and (3). Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

	(1)	(2)	(3)
Variable	MA_V	MA	MA_N
lndegree	0.3810 ***
	(3.5768)
pagerank		338.1519 ***	63.1941 ***
		(3.7707)	(3.5388)
Control	YES	YES	YES
Constant	−6.3411 **	−3.7256 ***	−0.4486 ***
	(−2.4098)	(−5.2160)	(−3.1082)
Year	YES	YES	YES
Industry	YES	YES	YES
N	11,285	10,973	10,985
Pseudu_R2/Adj_R2	0.0441	0.0401	0.0538

, we utilize the total amount of mergers and acquisitions (MA_V) to measure mergers and acquisitions, which is positive at a 1% significance.

For the independent variable, the pagerank centrality is also utilized for measuring the network centrality in some studies. We selected pagerank centrality (pagerank) as an independent variable in the robustness test. In Columns (2) and (3), the pagerank coefficients are positively significant at the 1% level, supporting the validity of Hypothesis 1 and the robustness of the regression.

4.6.2. Alternative Estimation Methods

Our research employs both the Probit model and Tobit model to assess the strength of regression based on earlier studies [68]. The results of the regression robustness test are displayed in

Table 12. Alternative estimation methods. This table displays the robustness test results regarding the impact of centrality in supply chain networks on mergers and acquisitions. Table 12 presents the alternative estimation methods to explore the correlation between M&As and the centrality of the supply chain network. Every regression analysis includes adjustments for industry-specific factors and the respective year. T-statistics are denoted by values in parentheses. Symbols *, **, and *** represent significance thresholds at 10%, 5%, and 1%, respectively.

	(1)	(2)
Variable	MA	MA_N
lndegree	0.0601 ***	0.0642 ***
	(3.4814)	(3.3573)
Control	YES	YES
Constant	−1.9336 ***	−2.4354 ***
	(−4.5323)	(−5.1827)
Year	YES	YES
Industry	YES	YES
N	11,273	11,285
Pseudu_R2/Adj_R2	0.0406	0.0352

. In Column (1), it is evident that our findings are indeed robust: in the Probit model, the estimate for network centrality is positively significant at the 1% level of significance. Similarly, the Tobit model reveals that the coefficient for the dependent variable is also positively significant at the 1% level. This confirms the validity of the results across both modeling approaches. This confirms that the results are robust across both modeling approaches.

4.6.3. Sub-Sample Regression Test

The State Council released a policy on “Opinions on Further Optimizing the Market Environment for Enterprise Mergers and Acquisitions” with the goal of increasing market efficiency in China in 2014. To exclude the impact of such policies, we selected samples after 2014 for regression. To verify the robustness of our study, we conducted sub-sample regression as a part of robustness checks. As a result, the sample was split into two groups according to the year.

In

Table 13. Sub-sample regression test. The outcome of the sub-sample test in the robustness test is shown in this table. We utilized the samples which subtracted the influence of policy to reexamine the regression results. All regression results take industry-fixed factors and year into account. T-statistics are indicated by the numbers in parenthesis. *, **, and *** denote significance levels, corresponding to 10%, 5%, and 1%, respectively.

	(1)	(2)
Variable	MA	MA_N
lndegree	0.1718 ***	0.0237 ***
	(4.1432)	(3.1423)
Control	YES	YES
Constant	−4.7403 ***	−0.3693 *
	(−3.8467)	(−1.9428)
Year	YES	YES
Industry	YES	YES
N	4826	4871
Pseudu_R2/Adj_R2	0.0514	0.0570

, the estimates of network centrality (lndegree) are both positive and significant at a 1% level, which is consistent with the above results of robustness tests.

5. Further Analysis

Existing studies assert that a high centrality network in the supply chain promotes mergers and acquisitions by mitigating uncertainty and making use of the information advantage. Furthermore, following the existing study [61], our study explores whether the supply chain network improves the future development of enterprises. To further explore whether network centrality can benefit development in the long term, we examined the performance of mergers and acquisitions in reference to the long-term corporate development by comparing it with short-term financial performance. We employed the Cumulative Abnormal Return (CAR) to assess the short-term financial performance of firms [69]. Specifically, with 150 to 30 trading days before the announcement, we selected one-day, three-day, and five-day cumulative abnormal returns (CAR [−1,1], CAR [−3,3], CAR [−5,5]) around the acquisition announcement. For long-term financial performance, we calculated the abnormal change in return on assets (ΔROA [−3,3]) over three years after announcing:

$${\displaystyle \frac{1}{3}}{\sum }_{t=1}^3\left({ROA}_{i,t}-{ROA}_{ind,t}\right)=\alpha +\beta {\displaystyle \frac{1}{3}}{\sum }_{t=-3}^{-1}({ROA}_{i,t}-{ROA}_{ind,t})+{\epsilon }_{i,t}$$

(4)

In this notation, i, ind, and t represent the acquirer, industry, and year, respectively. The merger year is designated as year 0. On the right-hand side, we have the three-year average ROA prior to the merger, while the left-hand side shows the three-year average ROA prior to the merger periods. Therefore, our second performance metric, the residual from this regression, represents the average change in operating performance that can be attributed to the merger event.

The empirical results in

Table 14. Further analysis. This table illustrates the economic effects of supply chain network centrality on mergers and acquisitions. Columns (1) and (2) demonstrate the short-term impact of supply chain network centrality on performance, while (3) and (4) reveal its influence on long-term performance. All regression results take industry-fixed factors and year into account. T-statistics are indicated by the numbers in parenthesis. *, **, and *** denote significance levels, corresponding to 10%, 5%, and 1%, respectively.

Variable	Short-Term Performance			Long-Term Performance
	(1)	(2)	(3)	(4)
	CAR [−1,1]	CAR [−3,3]	CAR [−5,5]	ΔROA [−3,3]
lndegree	−0.0029	−0.0026	−0.0062	0.0064 **
	(−0.7660)	(−0.4493)	(−0.8681)	(1.9683)
Control	YES	YES	YES	YES
Constant	0.1913 **	0.0993	0.0838	0.0589
	(2.0720)	(0.6931)	(0.4893)	(0.7476)
Year	YES	YES	YES	YES
Industry	YES	YES	YES	YES
N	1022	1022	1022	1028
Adj-R2	0.0848	0.1022	0.0968	0.2091

indicate that Columns (1) to (3) present short-term market responses which are not significant. Columns (4) show long-term financial performance results, indicating that supply chain network centrality positively affects the three-year financial performance difference. This suggests that, in contrast to short-term performance, supply chain network centrality plays a significant role in the long-term development of businesses. We summarize the reasons as follows: Firstly, because M&As require time for integration, their value only becomes apparent once a certain level of integration has been achieved. Second, corporations in central supply chain network positions tend to engage in larger mergers by prolonging the integration process, thus not significantly improving short-term market performance. However, as the integration progresses, network centrality significantly enhances long-term financial performance.

6. Conclusions, Discussions, and Policy Implications

6.1. Conclusions

China is moving from rapid economic expansion to high-quality, sustainable development, which is a crucial change for promoting long-term economic stability and resilience. In this context, engaging in mergers and acquisitions becomes critically important for achieving these objectives. Our paper utilizes panel data from Chinese A-share listed companies spanning 2008 to 2021 to explore how supply chain network centrality influences merger and acquisition activities.

Our findings indicate that enterprises which are closer to the centrality in supply chain networks are more inclined to engage in and frequently pursue acquisitions. Specifically, a closer position to the centrality of a supply chain network can construct more connections with upstream and downstream firms and allow a firm to attain more information about the market, leading to improvements in the efficiency and frequency of mergers and acquisitions. These results are supported by a series of robustness tests, confirming a supply chain network’s important role in promoting mergers and acquisitions. Furthermore, supply chain network centrality promotes mergers and acquisitions by conferring information advantages and alleviating financial constraints. When enterprises are closer to the centrality of supply chain networks, they can attain more accurate information about suppliers and customers, mitigate information asymmetry and uncertainty, and build up information advantages, which can improve mergers and acquisitions. Enterprises which have a more central position in supply chain networks can utilize their reputation, bargaining power, and social status to promote their own profitability, send positive signals for capital market, obtain more debt financing, and mitigate financial constraints, resulting in improvements regarding mergers and acquisitions. Heterogeneity analysis revealed that these positive effects are more significant in non-state-owned enterprises, firms close to customers or suppliers, those with higher operational risks, and companies belonging to the growth and decline phases. Finally, the economic consequence analysis indicated that companies with central positions in the supply chain network demonstrate improved long-term financial performance following mergers and acquisitions. Our study provides new insight into the relationship between supply chain network centrality and mergers and acquisitions and has important implications for the sustainable ability and long-term competitiveness of businesses.

6.2. Discussions

Our study found that centrality within the supply chain network significantly improves M&A outcomes, affirming our hypothesis that centrality has a substantial effect on corporate mergers and acquisitions. This finding builds upon previous studies that predominantly focused on the relationship between supply chain networks and M&As from the perspective of managerial characteristics. For instance, Fich and Nauyen (2020) [18] assert that CEOs with work experience in supply chains can generate significant gains for the shareholders of acquiring firms. Extending this line of inquiry, our research uses social network analysis at the firm level to explore how centrality in the supply chain network directly impacts M&A activities.

Consistent with our predictions, the results confirm the importance of considering network structural attributes when examining corporate behavior and performance outcomes. Specifically, when analyzing how information advantages and financial restrictions shape M&A outcomes, we observe that firms with a more central position in supply chain networks can acquire more resources and relevant information. This capability enhances communication efficiency, reduces the risks associated with information asymmetry, and leverages information advantages in M&As. These findings align with prior studies [45,47], which emphasize the benefits of information access and financial capabilities in strategic decision-making. Furthermore, firms with higher centrality in the supply chain are better positioned to reduce transaction costs by improving communication with upstream and downstream partners, ultimately facilitating more efficient M&A activities. These conclusions are further validated by studies [26,37,38,39].

Lastly, our study enhances the understanding of how supply chain networks influence the long-term development of firms by offering valuable insights into the role of network forces in shaping strategic decisions. Our findings indicate that firms positioned closer to the center of the supply chain network are more likely to engage in successful M&As, which in turn positively impact their long-term performance. This centrality provides firms with enhanced access to critical resources, information, and partnerships, allowing for improved decision-making and strategic flexibility.

These insights not only confirm the benefits of supply chain network centrality in fostering long-term development [61,69] but also advance the theoretical understanding of M&As within the context of network dynamics. By focusing on supply chain networks, our research extends the current literature, which has predominantly examined board member and executive networks, and sheds light on the significant role of supply chain structures in corporate strategy.

6.3. Policy Implications

Based on these findings, we can offer several reasoned recommendations.

First, enterprises need to assess their existing supply chain networks to identify potential synergies and opportunities for mergers and acquisitions. Improving positions within supply chain networks to access more crucial information on market trends and potential acquisition targets is recommended. This includes evaluating internal and external environments to ensure merger and acquisition activities bring lasting benefits. Moreover, enterprises should create strategic alliances and partnerships within their supply chain network to enhance collaboration, pool resources, share knowledge, and achieve economies of scale. Risk management strategies are essential to potential disruptions during mergers and acquisitions, including enhancing supply chain visibility and developing contingency plans. Additionally, enterprises should utilize the resources and opportunities provided by mergers and acquisitions to further develop and innovate within the supply chain. Enhanced capabilities can be used to explore new markets, support sustainability, and improve responsiveness to market demands.

Second, to support mergers and acquisitions, the government should implement comprehensive guidelines and regulations that promote collaboration and information sharing among firms within supply chain networks. The government should create an information-sharing platform and implement policies to lower financing costs. By offering incentives to companies that engage in and promote merger and acquisition activities, these businesses can better use their positions in supply chain networks. Furthermore, the government should allocate funds to essential infrastructure, such as logistics hubs and transportation networks, to enhance the effectiveness of supply chain networks. Simultaneously, the government should implement an evaluation and monitoring system to ensure that enterprises effectively boost their sustainable competitiveness through merger and acquisition activities. These approaches will foster enterprise innovation and ensure sustained growth.

Third, our heterogeneity analysis reveals that supply chain networks with greater centrality significantly enhance mergers and acquisitions, especially for non-state-owned companies, firms near customers and suppliers, enterprises with higher operational risks, and companies at the growth and decline stages of their lifecycle. Policymakers should tailor their strategies to meet the distinct needs of various corporate entities, optimizing resource allocation that supports state-owned companies, firms with lower operational risks, and those in mature stages of their lifecycle to enhance their awareness of mergers and acquisitions and improve their positions within supply chain networks. Enhancing logistical and infrastructural support for companies distant from customers and suppliers can significantly augment their merger and acquisition capabilities, thereby improving adaptability and securing long-term competitiveness.

6.4. Limitations and Future Research Directions

Finally, our study has some limitations. On the one hand, we selected samples focusing on publicly listed firms in a single country. This restricts the generalizability of our findings to other contexts. Future research can extend these findings to be broader and more diverse. On the other hand, we focus on the impact of supply chain network centrality from the perspective of firms’ networks. There exist some other potential factors that influence the nexus between supply chain network centrality and M&As. Future investigations could delve into these additional pathways, further enriching our understanding of how supply chain networks influence enterprise mergers and acquisitions.