Social Strategies for Business Success: The Key Role of Social Networks in SMEs

Abstract

This study aims to explore the relationship between a company manager’s activities and their impact on business performance. Networking is considered a worthy factor in professional and organizational success, providing access to important research, industry insights and future partnerships. Through the analysis of the data used in the study, this paper adopts a methodological approach to examine how managerial networking influences business results, with a particular focus on French small and medium-sized enterprises (SMEs). The findings indicate a strong and positive correlation between the manager’s ability to build and maintain professional relationships and the entire performance of their business. In fact, managers who actively engage in networking often gain access to better business opportunities, funding sources and strategic collaborations that increase growth and competitiveness. Additionally, strong networks facilitate the exchange of knowledge, best practices and innovative ideas, thereby improving decision making and operational efficiency. The review further highlights that networking is not just about expanding contacts, but also about attending meaningful and beneficial affairs that contribute to long-term success. These results underline its importance as a strategic tool for business leaders, sustaining the idea that well-connected managers are better equipped to navigate challenges, catch opportunities and drive sustainable business prosperity in an increasingly competitive market.

1. Introduction

The modern business environment is constantly evolving, and so are the relationships between stakeholders. Within this dynamic landscape, corporate behavior plays an important role in creating business interactions. Building on the research carried on by Gazzola (2005), this study explores how ethical values and mutual respect influence corporate conduct.

In a progressively interconnected world, corporate social responsibility (CSR) and social networks are becoming essential drivers for these business interactions (Furlani & Lutman, 2012). The digital revolution, accelerated by the Internet, has significantly shaped business operations. It has reduced psychological and geographical barriers between companies, while facilitating access to global information and international collaboration. This research empirically examines how social networks and other factors influence business performance (Fatima & Bilal, 2020). In this context, digital social networks—such as LinkedIn, but also other industry-specific digital platforms—have become key enablers of sustainable innovation, resource efficiency, and inclusive growth for SMEs (small and medium-sized enterprises). By reducing barriers to knowledge access and enabling scalable collaboration, these tools actively support the integration of sustainability principles into SMEs strategies (Raihan, 2024). This evolution reflects a broader shift in the understanding of entrepreneurship itself, which is increasingly framed as a process aimed at creating not only economic outcomes, but also environmental responsibility and social cohesion.

In this multidimensional vision, sustainable entrepreneurship aligns with ESG principles by embedding long-term engagement and ethical practices at the core of value generation (Konys, 2019).

Drawing from Burt’s (1992) work on network structures, we analyze how corporate decisions are shaped by the connections within these structures and their impact on business outcomes. CSR, a spot for individual participation as well as knowledge-sharing, will be examined in relation to evolving societal expectations. This study will explore how companies integrate CSR into their strategies, focusing on its influence on public perception and stakeholders’ inclusion. The objective of this research is to highlight the critical role of networking in the operations of SMEs. The paper follows a structured methodology: it begins with the analysis of theoretical approaches by scholars’ theories—providing an overview of SMEs, networks and their contribution to SMEs development—and business performance. It then shifts to a case study that considers a rich database for French SMEs. Supported by concrete data, the research concludes with a discussion on the findings, which offer valuable insights into the interplay between corporate networks, CSR and business success.

2. Literature Review

2.1. The Nature of a Social Network: A Conceptual Analysis

The INSEE (Institut National de la Statistique et des Études Économiques) defines SMEs as “companies with fewer than 250 people, an annual turnover of less than 50 million euros and a balance sheet total not over 43 million euros”. In today’s business landscape, social networks on the other hand, characterized by individuals connected through interpersonal relationships, serve as platforms for discussions and evaluations on a larger scale than smaller groups. These networks are fundamental for business expansion by providing essential resources for development and facilitating information exchange among participants. Nowadays, this concept extends beyond traditional interpersonal relations to include digital social platforms, which act as dynamic environments for interaction and exchange indeed. Digital tools—such as the aforementioned LinkedIn, sector-specific platforms, and online collaborative forums—not only facilitate access to information, but also enhance innovation capacity, promote resource efficiency, and contribute to sustainable practices within SMEs (Raihan, 2024), as previously illustrated. Recent studies also confirm that digital transformation fosters sustainable value creation when accompanied by entrepreneurial orientation, which allows SMEs to strategically align technological adoption with long-term ecological and social objectives (Vrontis et al., 2022).

Building on this, Bruce et al. (2023) synthesized the recent literature to underline how the use of digital social networks promotes communication between firms and their stakeholders, including customers and potential investors, regarding sustainability ideas and projects. Moreover, the study claims that improvements in efficiency and innovation driven by social media also lead to better sustainability performance.

At the same time, not all works agree on the positive use that firms make of these instruments. For example, the research developed by Russo et al. (2022) highlights how 115 EU companies of the utilities and energy sectors took advantage of Twitter (now X) to increase their legitimacy in the environmental, social and governance matter, rather than actually improving their sustainability. Quoting directly from the paper at issue: “Therefore, social media contribute more to the construction of companies’ CSR identity than the management of analytic aspects of sustainability performance” (Russo et al., 2022).

These tools however allow small firms to engage in strategic networking beyond geographical constraints, reinforcing their adaptability and long-term viability in complex markets. Additionally, a social network is formed to share views and ideas about products or services, allowing both small and medium-sized enterprises and their customers to connect through bilateral communication. This method particularly enables buyers to exchange opinions regarding specific services, providing enterprises with a wider audience. As a result, the company’s image is shaped by customers’ experiences, whether positive or negative. Consequently, social networks are a fundamental tool for the corporate ecosystem’s functioning (Muldoon et al., 2018).

Within this relational context, internal managerial choices also play a central role, as each company adopts a unique management approach, which is a critical aspect affecting business success. Said approaches impact on factors such as operational efficiency, innovation and strategic decision making, which are essential for the company’s long-term profitability. Nevertheless, social rooting can be both an asset and a liability, since relationships may either promote or obstruct business operations, with social expectations potentially imposing limitations.

Another determining factor in a company’s success is the role of its Board of Directors. Conflicts of interest between management and shareholders can occasionally arise, leading to instability, particularly in financial matters, which often undergo significant fluctuations. As a consequence, the Board of Directors plays a major role in making the best decisions to preserve the company’s financial stability, with a particular focus on the economic aspects of SMEs. Moreover, corporate governance mitigates risks and agrees with the company’s strategies and its long-term objectives. The Board’s diverse expertise and strategic leadership are essential for navigating complex financial landscapes, driving sustainable growth, and promoting transparency and accountability within the organization.

2.2. Influence of Social Networks on Performance Measurement

Social networks contribute enormously to business operations, from optimizing logistics and work planning, to easing access to financing. They also help boost technological advancement and production innovation. Furthermore, as previously anticipated, social networks enable SMEs to acquire additional knowledge regarding customer needs and opinions, as well as competitors’ conduct. With the help of the fourth industrial revolution, the increasing development of technology allows SMEs to garner new tools to evolve in a sustainable way and retain customers. A company’s sustainability, quantified through business development and profitability, is indeed a fundamental criterion to evaluate its true potential (Ali Qalati et al., 2021).

Networking is an intentional process of building, supporting and pulling relationships within social and professional circles. It is influenced by personal traits, job-related factors and workplace conditions, leading to benefits such as increased visibility, through customers’ interactions and feedback, as well as advertising campaigns. Further derived assets are power—gained by monitoring competitors’ moves or collaborating with them, namely through alliances or exchange of information—career success for individuals, job-related factors and strategic insight for organizations.

Hence, SMEs can benefit from social networks through both internal and external use. Internal use involves activities such as communication and knowledge sharing, which support SMEs’ growth. In contrast, external use aims to reach a wider audience—that is, more customers and partners—through platforms such as X, TikTok and YouTube (Ghazwani & Alzahrani, 2024). The concept of “social media capital” further expands the understanding of digital networking, as online ties on platforms can significantly enhance SMEs’ business network strength and export performance (Mahmoud et al., 2023).

External networking may also contribute to employee turnover (Gibson et al., 2014). Recognizing the impact of social networks is necessary for future research, which should focus on refining measurement techniques and gaining a deeper understanding of the mechanisms driving the effects of social networking itself. Overall, leveraging both internal and external social networks, including social media capital, can significantly enhance SMEs’ performance while shaping organizational outcomes and strategic decision making.

The economic operations of emerging firms are shaped by network connections. In their early stages, businesses rely on trust-based relational ties, personal connections and informal mechanisms, such as relational contracting, to establish a strong foundation. These bonds are crucial to get initial success, providing credibility and access to resources and strategic partnerships. As businesses grow, they shift toward market-driven network ties that require more formal control mechanisms and structured agreements.

Relational embeddedness is a complex concept that includes personal relationships, economic interactions, and social capital. This framework examines the nature of ties and suggests that said relational embeddedness depends on the specific type of social relationship underlying those ties. Furthermore, the dynamic nature of the latter underscores the evolving interaction between formal and informal mechanisms as firms grow, highlighting the need for adaptability in maintaining competitiveness and leading long-term development. Consequently, this adaptability enables firms to change market conditions, leverage new opportunities, and maintain robust stakeholders’ relationships, ultimately driving resilience in a competitive environment. Network ties among firms are shaped by social, historical, and emotional factors that evolve over time. This advancement encompasses various forms of embeddedness—including personal, affective, structural, market-based, institutional, and social dimensions—that collectively influence firms’ strategic interactions and developmental trajectories. The capacity to adapt to these shifting relational dynamics is essential for accessing resources, responding to changing market conditions and, as previously anticipated, sustaining long-term growth and competitiveness (Hite, 2005). To summarize the crucial aspects influencing emerging firms’ success, Figure 1 serves as a useful representation: it merges concepts from both this section and Section 1, providing an overall overview of the matter.

Two main theoretical perspectives explain how social connections generate economic and social benefits. The weak-tie approach (Granovetter, 1983; Uzzi & Gillespie, 1999) suggests that maintaining a broad and diverse set of distant relationships facilitates access to publicly available information and promotes openness to new opportunities. In contrast, the strong-tie approach (Sandefur & Laumann, 2009) argues that close, dense relationships enable trust-based cooperation and the exchange of exclusive knowledge. Reconciling these two views—weak versus strong ties, or sparse versus dense network structures—can be complex, as both provide complementary advantages.

Uzzi and Gillespie (1999) empirically confirmed this duality, demonstrating that distant ties enhance access to general information, while close and dense connections facilitate the transfer of exclusive assets and foster collaboration within network clusters. Different types of social links therefore shape how knowledge is shared and absorbed within organizations. Moreover, weak ties are less socially intertwined than strong ties, leading to networks with lower density that connect clusters among close friends. They serve as essential bridges facilitating the exchange of information across different social groups. People with few weak ties indeed do not have access to information from different sources and may, therefore, be disadvantaged in the labor market. Without weak ties, social systems would become fragmented and less cohesive, hindering new ideas and scientific progress. These themes are explored in greater detail in Granovetter’s (1983) study The Strength of Weak Ties. In addition, according to the theory of embeddedness, Uzzi and Lancaster (2003) later explained how weak ties promote exploitative learning, that is a short-term approach focused on maximizing existing resources, improving efficiency, as well as reducing costs. On the other side, strong ties enable the transfer of private knowledge, fostering exploratory learning, which emphasizes new knowledge, innovation and long-term opportunities. By utilizing both types of ties, organizations can enrich their overall learning abilities. Indeed, through the balance of weak and strong ties, businesses can improve agility, innovation and sustainable growth. Consequently, entrepreneurs should actively build and maintain diverse networks to enhance their competitive edge in an ever-evolving market. Building on the discussion of social ties and network structures, knowledge and human capital become crucial resources that organizations can leverage to improve performance.

As a matter of fact, establishing strong networks among SMEs’ executives is crucial, as collaboration and knowledge sharing can drive business efficiency, optimize resources, and reduce costs (H. Wang et al., 2019). Bartoloni (2022) emphasizes that a company’s reputation is closely tied to its social network, making engagement with potential customers a key priority. Internal social networks also foster development and innovation by building on past achievements. Similarly, Donckels and Lambrecht (1995) pointed out that combining weak and strong ties is crucial for small business growth, as it improves access to valuable knowledge and resources. Birley et al. (1990) also underline that networking strategies must be adapted to different cultural and economic contexts, and associate business success with the size and diversity of its network. Szarka (1990) also suggested that networking enhances information and facilitates the creation of strategic alliances.

Finally, building on this discussion, Cho et al. (2022) conceptualized entrepreneurs as embedded agents who continuously interact with and shape their entrepreneurial ecosystems. Their evolutionary approach emphasizes that such ecosystems are dynamic and path-dependent, requiring entrepreneurs to continuously interpret institutional, cultural, and economic signals to adapt and influence their surroundings. This co-evolutionary process reinforces resilience and fosters innovation-driven trajectories. Figure 2 visually reflects these dynamics within an evolutionary ecosystem framework, showing how entrepreneurs connect national and international networks while actively engaging with various institutions, actors, and environmental dimensions that affect firm growth and learning.

The dynamics of collaborative network communities influence an organization’s exploratory innovation. We can generally observe two points of view: the ego-network perspective (focusing on direct/indirect connections) and the whole network perspective (concerning overall network properties). Both offer different knowledge resources, and the findings of Donckels and Lambrecht (1995) show that greater global cohesion strengthens the effects of moderate network community dynamics, while local cohesion is more effective in networks with less activity. The research conducted by J. Wang and Yang (2019) addresses gaps in understanding the relationship between network communities and exploratory innovation, while also acknowledging industry specific limitations and measurement challenges.

Various research studies have highlighted that network structures are important in innovation, efficiency, and overall business performance. Thus, collaborating with a diverse set of partners in an open network is linked to superior outcomes, as it provides access to a wide range of information and perspectives, promoting creativity and adaptability. Operating in different industries broadens business growth opportunities but makes it harder to maintain a firm culture and specialization, underlining the relative dependence of different network structures on operational success. Other studies also show that close ties to strong professional support networks and tightly knit closed networks are associated with greater stability and long-term partnerships, implying that trust is essential for enabling knowledge transfer and promoting effective collaboration. They also suggest that open networks enhance information acquisition, while closed ones support deeper integration and application of expertise, optimizing overall business innovation and strategic decision making. These findings confirm that network structures crucially affect adaptability, growth, and competitive advantage.

One of the main challenges for small firms is that effective search behavior in limited and fragmented environments depends on their network position. Densely connected clusters often provide information redundancy rather than new insights. To strategically address these cluster constraints, individuals must overcome them by both using technological tools and “structural holes” (which the paper will illustrate in the next paragraphs), and intentionally seeking social positions in and around structural gaps. Such positions increase the likelihood of recognizing opportunities and accessing unique information. When entrepreneurs need to explore beyond their direct connections, they can leverage their understanding of social identities as a starting point rather than searching randomly (Aldrich & Kim, 2007).

Social networks influence research productivity and quality in management. Open networks stimulate creativity and high-impact publications by providing several perspectives; while closed networks, built on trust, enhance knowledge adoption and citation counts (Brassett, 2013). The former facilitates access to information, whereas the latter ensures its transfer, making each of them essential for optimizing research outcomes. Moreover, the previously mentioned study of Donckels and Lambrecht (1995) states that in fragmented environments, entrepreneurs benefit from strategically nesting within networks.

The literature review also highlights the importance of human capital in SMEs, emphasizing that it reflects the organizational framework. This capital includes resources derived from an individual’s experience, professionalism and abilities, transforming inputs into outputs. Small firms have limited social networks, with entrepreneurs who are rarely engaged in formal or rational decision making. Information sources are primarily verbal (Martin-Rios & Erhardt, 2017), whereas meetings tend to be informal. However, both formal and informal network contacts are crucial in foreign market selection and entry initiatives (Coviello & Munro, 1995).

Castellano (2011) underlines the importance of a well-established social network in the improvement of business performance. These influences affect every phase, from developing innovative solutions to reinforcing previously achieved results (Baum et al., 2000).

The implementation and maintenance of a strong social network enhance the company’s innovation and provide crucial resources for its survival. This is a vital step toward realizing social capital, a concept that refers to systems of interpersonal relationships that engender trust, cooperation, as well as collective action (Nahapiet & Ghoshal, 1998). It includes both the structure of networks and the opportunities accessible through them. This idea encompasses structural, relational, and cognitive dimensions, which are interrelated and form the bedrock for intellectual capital. Traditionally, economists have viewed physical and human capital as key resources for businesses. As a matter of fact, knowledge has been recognized as a valuable resource, and “intellectual capital” refers precisely to the knowledge within organizations or intellectual communities, serving as a crucial resource for knowledge-based actions indeed. The economy of intellectual capital relies on exchanging explicit collective knowledge and experiences which promote innovation along with social interactions. This view further confirms Penrose’s (2009) ideas on the crucial role that teamwork plays in business growth. Organizational advantage is perceived as a social matter, rooted in social relationships and their structure. Unlike individualistic perspectives, this outlook attests to the importance of social capital in transaction costs and efficiency through dynamic growth. Consistent with resource-based theory, unique social or intellectual resources are basic for competitive advantage. Building up and maintaining it requires significant assets and a careful balancing of opposing forces, involving large investments in relationships and personal trust (Nahapiet & Ghoshal, 1998).

Moreover, organizations use strategic planning to design supply chains that optimize economic performance over increasing periods, typically between three and ten years. This strategic planning includes decisions on market and product zones, production stages, and opening or closing facilities. Social networks within the organization play an important role in sharing information, experiences, and best practices among executives. This allows them to facilitate decision making, improving efficiency as well as strengthening the supply chain, in line with adapting to evolving conditions (Goetschalckx & Fleischmann, 2005).

2.3. The Manager

In today’s technological landscape, SMEs are undergoing a digital transformation (Hönigsberg & Dinter, 2019), leading entrepreneurs to fully leverage the Internet’s potential and benefits. Research on social networks and business success shows that managers play an important role, depending on how they interact with the former. In their article Interfirm Alliances in the Small Business: The Role of Social Networks, Barnir and Smith (2002) identify four factors explaining managers’ behavior: the leader’s networking inclination, the extent of their networking activities, the degree of interaction with others, and their managerial status. These are the typical traits of extroverted individuals who are open to both dialogue and building relationships.

In networking activities, the more heterogeneous the social network, the easier it is to access financial resources. Furthermore, larger social networks and more frequent communication among participants can also influence the company’s financial capital, as Gharsalli (2013) highlights in Social ties, trust and bank financing of SMEs: an exploratory study. Thanks to a wider network of contacts, managers of large companies rely on a position that allows them to establish privileged relationships with financial institutions and gain from lower loan rates. As a result, they can access a larger market: entrepreneurs (or managers) are closely linked to both their businesses and the external environment, effectively orchestrating the network elements. On the other hand, in the SMEs field, networking responsibilities are handled by small business owners, who rarely delegate this task. Previous studies suggest that education significantly influences the nature of networks, with highly educated owners tending to cultivate more diverse ones. The previously mentioned Figure 2 further illustrates the interdependence between SMEs and their external environment, as well as the causal relationship between networks and growth, affected by company-related factors (Donckels & Lambrecht, 1995).

A study by He (2022) that analyzes data from 2001 to 2012 on executives of publicly traded companies in the United States, illustrates the concept of “network centrality”. This notion refers to a manager’s social ties with other executives through work, shared professional experiences, or education. In particular, “degree centrality” measures an executive’s standing in terms of direct connections. Directors with a high value of said degree, such as CEOs and CFOs, hold a privileged position in the network. However, the research has also shown that well-connected CEOs and CFOs are associated with a higher incidence of financial irregularities, with the second ones having a more significant impact than the former. Traditional governance mechanisms and the managerial labor market are ineffective when it comes to high centrality executives, especially CFOs. Thus, additional monitoring mechanisms should be implemented to discipline them.

Building a strong social network starts with identifying and capitalizing economic opportunities (Amit & Zott, 2001). Currently, the most effective tool for creating and expanding connections is social networking. This medium offers many opportunities. However, it must be used carefully, as it can also be harmful, as noted by Cuomo et al. (2011). An example is the so-called “amplifying effect” of social media, according to which positive feedback is often exaggerated, while criticism becomes particularly harsh. The first step in identifying entrepreneurial opportunities is recognizing one to pursue, which necessitates a thorough knowledge of complementary domains. Consequently, aspiring entrepreneurs must recognize and integrate diverse knowledge bases before others capitalize on that opportunity. The position that individuals hold in various networks affects their access to private information, which is crucial for recognizing said opportunities. Furthermore, the broader the social networks of a young firm’s founders, the more likely it is that the firm will succeed in attracting highly qualified individuals. Social networks facilitate this through multiple mechanisms, underscoring their crucial role in the entrepreneurial journey (Stuart & Sorenson, 2005).

Regarding corporate social responsibility, Prior et al. (2008) detect the negative financial impact of combining accounting manipulation with CSR improvements to secure stakeholders’ support. On the one hand, strategic CSR, which involves social practices to enhance relationships with stakeholders, positively affects financial performance, whereas on the other hand discretionary CSR, which includes social practices that do not directly impact profits, has a negative consequence due to costs outweighing benefits. Thus, managers should escape using it as a mask for earnings management activities, as they harm financial performance over time. The main recommendation for them is to avoid making concessions that merely solidify stakeholders’ position. Projecting a socially friendly image to disguise earnings management practices may reduce the likelihood of being fired in the short term, due to stakeholders’ support, but it cannot be sustained over time because of the damage done to financial performance.

Another crucial aspect of a firm is its absorptive capacity (ACAP), which refers to its ability to maintain a competitive advantage over time. Among the most relevant components of ACAP there is dynamic capability, which encompasses strategies aimed at developing organizational elements, such as marketing, management, and distribution. Social networks play a fundamental role in this context, particularly in the sharing of knowledge. Zahra and George (2002) describe social networks as “informal social integration”, which is useful, but not sufficiently systematic. Their critical role in achieving absorptive capacity, which would otherwise remain untapped, is well recognized.

2.4. The Theory of Structural Holes

A manager’s network connections are uniquely structured, with values changing from person to person. The diversity of ties within these social networks helps shape distinct dynamics. These variations are essential to the formation of structural holes, which paradoxically act as catalysts for corporate success. As suggested by Adams et al. (2014), a greater proliferation of said structural holes within social networks provides broader opportunities for critical knowledge acquisition. The theory regarding it, developed by Burt (1992), shows that such gaps emerge between non-redundant contacts. In practice, as social networks become more interconnected, additional structural holes are created, thereby enhancing access to information. His model outlines the advantages of a privileged position and consists of three components. The third topic (subject C) acts as a “bridge” or “mediator,” facilitating communication between subject A and subject B. In this context, said preferential position confers influential power to the first subject, highlighting how the configuration of social networks can determine the degree of influence and success in the corporate environment. In this regard, Burt (2004) analyses how brokerage between social groups creates social capital. Mediators who connect different groups can uncover opportunities that would otherwise remain hidden. In small firms, effective search behavior depends on one’s network position. Breaking out of dense clusters to engage with diverse contacts increases the chances of recognizing these opportunities: innovative ideas emerge from the intersection of social worlds. Latest empirical evidence shows that individuals with strong capabilities in acquiring external knowledge proactively establish extensive connections with diverse peers, creating structural holes that enhance access to unique information and support innovative outcomes (Y. Wang et al., 2025).

In a similar vein, brokers (or boundary spanners) play a crucial role by connecting otherwise disconnected groups, facilitating the exchange of information and resources across organizational boundaries. Recent empirical studies demonstrate significant advantages for individuals or organizations that bridge these gaps, including promotions and performance improvements. Three incentives have been identified: creating surpluses, earning intermediation rents, and avoiding intermediaries. When individuals form connections, they strategically invest in increasing their structural importance, while others may circumvent these efforts. Under the assumptions of this model, networks are either fully connected or completely empty in equilibrium. The desire to create surpluses drives connections, intermediation incentivizes the formation of star structures, and avoiding intermediaries leads to cyclical patterns. In the end, star structures become the only equilibrium network, where all individuals are connected without capacity constraints, as shown in Figure 3 (Ahuja, 2000).

3. Results—The Conceptual Model and Research Hypothesis

This study focuses exclusively on French SMEs and analyzes 12,341 companies using a cross-sectional dataset, with all observations referring to a single year “2024”. The following is an explanation of the procedure employed to develop the research (each step will be described in greater detail in the subsequent paragraphs). To begin with, data was collected and eventually organized to conduct the study in a more efficient way, facilitating for instance the selection of the necessary variables (in this regard, a brief description is provided in Section 3.1). With the help of relevant literature sources, the hypothesis was developed through an analytical method. Finally, the results were processed and interpreted with the help of R (version 4.4.3) and Python (version 3.13) software tools.

Data extraction was conducted using a web scraping procedure, with specific parameters designed to efficiently collect relevant information. In greater detail, web scraping consists of obtaining data from websites, using two parts, called “crawler” and “scraper”: while the former searches for data, the latter eventually secures it from the web. Throughout the process, data is converted from an initial HTML format to a final worksheet or database, which researchers will eventually work on. This method enabled the structured acquisition of data related to company characteristics, performance, and management.

Following the collection phase, all relevant information was systematically compiled to allow a comprehensive analysis of the selected variables. Grounded in previous academic literature and existing theories, two hypotheses were developed to guide this research and address existing gaps:

• Managers’ Social Networks: defined as both direct and indirect ties with other individuals and companies, they are hypothesized to positively influence corporate stability and performance. Managers with a greater number of social connections are expected to gain access to critical resources and information, enhancing the company’s resilience and effectiveness.
• Internal Company Variables: factors such as the number of employees and the labor productivity significantly impact corporate performance. As a matter of fact, this analysis explores how company size and internal productivity contribute to value generation and sustainable growth.

Finally, multiple linear regression serves as a fundamental analytical tool to examine the relationships between a dependent variable and multiple independent variables. Additionally, Principal Component Analysis (PCA) is employed to assess data in a multidimensional context, ensuring a comprehensive evaluation without sacrificing essential information.

3.1. Explanation of Variables

The databases with legal and financial information on managers are Société.com and Dirigeant.com: they provided the data from which all variables were collected or calculated.

Before proceeding with the analysis, thereby ensuring a clearer comprehension, it is crucial to recall that the research, as well as the results deriving from it, specifically refer to France. Thus, the outcome of the analysis cannot be taken as reference in other countries, as every nation has specific rules to follow, along with different social, economic and political layouts.

For instance, a study by H. Wang et al. (2019) exposes how network centrality, together with structural holes, supplies wider benefits to SMEs in countries that chiefly focus on the individual needs in comparison with collectivist ones. Moreover, nations characterised by a higher flexibility regarding rules and cultural influences are proved to gain an edge on tighter cultures.

Having said that, among the several variables collected in the database, three main groups stand out. The first one regards the identification aspects of both the firm and its leader, such as the SIREN number (indeed a unique identification for the company) and the number of employees, for the former; and name, gender, age, etc., for the latter. The largest proportion of indicators consists of economic and financial measures for every business sampled. To name a few: debt, fixed capital, EBITDA, revenue, but most importantly added value rate,1 cash, labor productivity rate,2 ROA (Return of Assets), ROE (Return on Equity),3 accounts receivable turnover ratio and turnover for ROE.4 The remaining variables aim to explain the network structure of managers, further illustrated in Figure 4. They comprise the number of direct and indirect ties with both individuals and firms—for the manager himself—and the number of total links with individuals—for the company. The latter measure, as the paper will explain in the following paragraphs, is crucial in our analysis. Overall, the richness and diversity of the dataset ensure a comprehensive understanding of each SME sampled, also paving the way for numerous potential future research patterns (for a more detailed description of the variables, see

Table A1. Full list of the variables collected from the data, including their names, brief descriptions, and the sources from which they were retrieved (either one of the two websites consulted, or derived mathematically from other quantities).

Variable	Description	Source
Number of employees	A number between 5 and 50 for all companies.	Société.com
Company seniority	The number of years the company has been active since its founding.	Société.com
Revenues	The total revenue from the sale of goods or services during a fiscal year, excluding taxes. Revenue is a good indicator of a company’s activity and size.	Société.com
Added value	The difference between the value of produced goods and services and the costs incurred in their production. It measures the value created by the company in its production process.	Société.com
EBITDA	Earnings Before Interest, Taxes, Depreciation and Amortization: the company’s profit before deducting taxes, interest, depreciation and amortization.	Société.com
Net profit	The difference between a company’s total revenues and expenses. It represents the actual profit made after all production costs and expenses have been deducted.	Société.com
Number of indirect connections with other companies (for the company)	The number of indirect links the company has with other companies.	Société.com
Number of direct connections with individuals (for the company)	The number of direct links the company has with managers.	Société.com
Added value rate	The percentage efficiency of the company’s production tool, representing its contribution to the value of the production.	Société.com
Number of terms	The total number of mandates held by members of the Board of Directors. In case of multiple Board members, the number indicated will be the average for their mandates.	Dirigeant.com
Leader’s age	The age of the company’s manager.	Dirigeant.com
Number of direct connections with other companies (for the manager)	The number of direct links the manager has with other companies (manager-company connections). In the case of multiple board members, we considered shared activities only once.	Dirigeant.com
Number of indirect connections with individuals (for the manager)	The number of indirect links the manager has with individuals (manager-company-individual connections). These individuals are co-agents of the manager for companies they share. In the case of multiple board members, shared connections are counted only once.	Dirigeant.com
Number of total connections with other companies	The sum of all direct and indirect connections with companies.	Elaboration
Number of total (business) connections with individuals	The sum of all direct and indirect connections with individuals.	Elaboration
Number of total connections	The total sum of connections between companies and individuals.	Elaboration
ROA (Return on Asset)	The percentage ratio of net profit income to total assets. It is an indicator of the company’s profitability, showing its ability to generate profit using its total assets.	Elaboration
ROE (Return on Equity)	A measure of the company’s profitability, calculated as the ratio of net income to shareholder equity.	Elaboration

in the Appendix A).

3.2. Introduction to the Regression Model: The Dependent Variable

To examine the influence that managerial and operational variables play on a company’s performance, several OLS regression models were developed to analyze these dynamics. This approach facilitated identifying key factors in business success. All the models incorporate company performance as a common indicator to provide empirical support for the research. The comparison revealed that neither ROA nor ROE performed well as response variables for predictive modeling. Although the overall F-tests were statistically significant (p-value < 0.05), the individual regression coefficients were not, indicating that no single predictor had a strong explanatory power. Therefore, ROA and ROE do not appear to be reliable dependent variables.

The financial analysis highlights a positive correlation between managerial networking and immediate liquidity, suggesting that companies with well-connected executives tend to maintain higher cash reserves. This phenomenon may result from a greater ability to access short-term financing, more efficient working capital management, or more favorable payment terms with customers and suppliers. These factors gather the cash collection and payment cycle, reducing the risk of liquidity imbalances. The same companies, on the other hand, with a high level of networking exhibit a lower self-financing capacity relative to acquisition, indicating a greater dependence on external funding sources. However, such companies also show a shorter cash conversion cycle, indicating more effective working capital management. The debt service coverage ratio is generally higher for firms with well-connected executives, reflecting a greater ability to meet short-term financial obligations.

Another model was then developed: it used “log_turnover” as the dependent variable, since it is widely recognized in the literature for measuring company performance. The logarithmic transformation was adopted to mitigate potential distortions caused by the non-linear distribution and possible skewness of the data, and to enhance the model’s robustness and accuracy in predicting business performance.

Independent variables, which will be described and analyzed in Section 3.3, were added to indicate the unique traits of the company, and Bayesian Information Criterion (BIC) was used to identify the model that best fits the data: among multiple options, the one with the lowest BIC was selected.

3.3. Choice of the Independent Variables

The independent variables selected therefore include “added_value”, “employees_number”, “labour_productivity_ratio” and “total_links_w_individuals”.5 These regressors were strategically chosen based on their theoretical relevance and support from existing literature, and each represents a crucial element in understanding business operations. Donckels and Lambrecht (1995) highlight the role of value creation in the growth of a small business, while Birley et al. (1990) and Szarka (1990) underscore the importance of networking in securing resources and fostering business opportunities. The number of employees serves as a proxy for company scale, and labor productivity reflects operational efficiency in converting inputs into outputs, consistent with prior studies on firm dynamics (Birley et al., 1990). The inclusion of executive social ties follows the argument that well-connected managers can leverage networks to enhance strategic decision making and resource accessibility (Szarka, 1990).

As previously said, after selecting the independent factors that can reliably predict the dependent variable, we identified the final OLS regression model, developed to assess how managerial and operational factors influence company performance. It is defined as follows:

$$\begin{gathered} log\text{\_}turnover={\beta }_0+{\beta }_{1}added\text{\_}value+{\beta }_{2}employees\text{\_}number \\ +{\beta }_{3}labour\text{\_}productivity\text{\_}ratio+{\beta }_{4}total\text{\_}links\text{\_}w\text{\_}individuals+\epsilon \end{gathered}$$

3.4. Regression Results and Considerations

The R² index can be used to certify the model and assess the quality of fit. With at least two explanatory factors, the Adjusted R² in the multiple regression model represents the percentage of variability elucidated by the independent variables. Consequently, the results of our analysis are statistically significant, as shown in

Table 1. Summary Table of the key characteristics of the Model.

Number of observations	12,341
Residual standard error	0.176 on 12,335 degrees of freedom
Multiple R2	0.736
Adjusted R2	0.736
F-statistic	8596.7 on 4 and 12,335 degrees of freedom
p-value (F-test)	<0.001

: with an Adjusted R² of 0.736, the model explains 73.6% of the variability of the dependent variable. This finding underscores the fundamental role of these regressors in explaining company performance. Moreover, the independent variables were statistically significant overall (p-value < 0.001), confirming the model’s robustness and relevance.

It is also possible to highlight the absence of multicollinearity factors based on the observations collected in the case of variable correlation. The study of VIF (Variance Inflation Factor), which indicates how much an independent variable can be explained by the other regressors in the equation, was used to diagnose it. Generally speaking, multicollinearity is high if VIF > 10: in our situation, the low VIF level for each variable supports the idea that said situation is absent.

The results of the linear regression model, presented in

Table 2. Model’s Coefficients Summary.

Variable	Coefficient	Std. Error	t Statistic	Pr (>|t|)
Added value	2.49 × 10−8	7.38 × 10−10	33.695	3.49 × 10−22
Number of employees	4.91 × 10−3	6.04 × 10−5	81.213	2.35 × 10−34
Labor productivity rate	8.11 × 10−7	6.65 × 10−9	121.915	2.47 × 10−21
Number of links with individuals	1.26 × 10−3	2.94 × 10−4	4.278	1.90 × 10−5

, show that all the variables considered are significantly associated with economic performance, measured in terms of turnover. Among these, the number of direct links between managers and individuals stands out as a relevant variable, with a positive coefficient and a high level of statistical significance (p-value < 0.001). Although its impact is less pronounced compared to certain structural characteristics of the firm—such as the number of employees—this finding suggests that managerial networking plays an important role in explaining business success. The contribution of social ties lies in their ability to activate indirect mechanisms: this is the reason for the low value of the coefficient. To name a few of these mechanisms: access to strategic information, trust-building, reputation enhancement, facilitation of new opportunities. As a matter of fact, while not immediately translating into measurable economic outcomes, they positively influence firm performance over the medium to long term. Therefore, even in the presence of strong structural determinants, the relational social capital of management emerges as a significant factor for the competitiveness of the SMEs under analysis.

In conclusion, empirical findings support the hypothesis that companies with professional networks have higher levels of immediate liquidity, compared to the sample average. Firms with well-connected executives are able to negotiate better credit conditions and optimize the management of short-term financial resources. By improving receivable turnover and optimizing working capital management, these companies can relieve the risk of cash imbalances and maintain short-term financial stability. However, an effective networking strategy should be pursued selectively. Accounting and financial analysis suggests that companies must carefully evaluate the impact of their connections on management. Networking should complement financial efficiency as a part of broader corporate planning, rather than being treated as an independent success factor. A selective and strategic approach allows companies to maximize the benefits of their connections without settling financial stability and operational profitability in the long term. To explore the benefits of well-developed networking, it is crucial to understand the role of digital tools and platforms in enhancing these connections. Through social media, industry forums, and professional networks—which can facilitate real-time information exchange, collaborative opportunities and strategic partnerships—there will be an improvement of financial outcomes and competitive advantage.

3.5. Introduction to the Principal Component Analysis

To identify the most critical aspects of the company’s characteristics, we used Principal Component Analysis (PCA), a statistical method designed to combine a group of variables into a smaller number of latent dimensions, which effectively capture a substantial portion of the information derived from the variables themselves. Firstly introduced by Pearson (1901) and later developed by Hotelling (1933), it is one of the Factor analysis methods and can be defined more precisely as a multidimensional technique that identifies key elements, forming the fundamental framework of interactions—observed in the covariance matrix or, given that the data have been standardized, in the correlation matrix—based on a wide range of variables. Therefore, it reduces the number of indicators, along with erasing redundant data. The correlation matrix serves as the starting point of the procedure, as the objective of PCA is to create new constructs from mathematically correlated variables. Thus, principal components are identified and used to examine potential relationships between the original variables and the observations. The crucial stage of the analysis involves evaluating the suitability of the chosen method.

3.6. Selection of the Principal Components

Each principal component explains a portion of the total variance, which is calculated by dividing its corresponding eigenvalue by the sum of all eigenvalues. These eigenvalues are obtained from the correlation matrix, and their number equals the number of original variables. The variance of the first principal component will be the highest eigenvalue, as it represents the dimension with the greatest variance. At the same time, the variance of the second principal component will be the second-highest eigenvalue, and so on for the remaining eigenvalues in descending order.

Table 3. Eigenvalues Matrix.

	Eigenvalue	Variance Percentage	Cumulative Variance Percentage
Dim. 1	1.655	41.374	41.374
Dim. 2	1.076	26.890	68.264
Dim. 3	0.975	24.367	92.631
Dim. 4	0.295	7.369	100.0

displays the eigenvalues, the percentage of variance explained, and the cumulative percentage of variance explained for the four principal components in the analysis. The first eigenvalue is 1.655, which represents 41.37% of the total data variance and confirms that the first principal component analyzes the largest portion of the variability. The second component has an eigenvalue of 1.076, representing 26.89% of the variance. The third principal component has an eigenvalue of 0.975, which explains 24.37% of the variability. Finally, the fourth component has an eigenvalue of 0.295, representing 7.37% of the variance. The cumulative variance explained by these components is 100%, meaning that the latter are sufficient to account for the entire data variance without significant information loss.

As previously illustrated, in PCA the objective is to reduce the dimension of the dataset, with the aim to facilitate data interpretation and simplify the analysis. Several criteria exist to determine how many principal components should be retained. One of the most common is the eigenvalue-one criterion (or Kaiser criterion), which recommends selecting all components with eigenvalues greater than 1 (given that the data have been standardized). In this case, the selection represents most of the data’s variability (precisely, the first two components explain 68.26% of it), providing a substantial explanation of the data structure. Another useful technique for identifying the number of significant factors is the “Scree plot.” This graph compares the number of components to the percentage of explained variance, ordered from highest to lowest. The goal is to identify a turning point, or “elbow,” where said percentage decreases rapidly. The components before this “elbow” are considered relevant, as they explain a significant portion of the variability.

In Figure 5, the inflection point (“elbow”) occurring after the second principal component confirms that two main factors are sufficient to describe most of the financial variability among the analyzed companies. From an accounting perspective, SMEs’ economic performance is largely influenced by profitability and financial structure. This type of segmentation can be used to profile SMEs, based on their financial solidity and dependence on external financing (providing valuable insights for investors and financial institutions). It is essential to keep in mind that their interpretation can grow more challenging from a “methodological” perspective. In fact, since their explained variance is lower than that of the first factors, their interpretation often relies on experience.

3.7. Factor Coordinates of Variables

The factor coordinates of the variables are presented in

Table 4. Table of Variable Contribution in Percentage. Source: Personal Analysis.

	Dim. 1	Dim. 2	Dim. 3	Dim. 4
Added value	0.971	0.095	0.045	−0.209
Number of Employees	0.806	−0.326	−0.092	0.485
Labour productivity rate	0.039	0.917	0.331	0.218
Number of links with individuals	0.752	0.551	0.436	0.469

. Since the analysis considers the correlation among the same variables, this data can be used to deduce the relationships between each variable and its corresponding factor. The first principal component can combine the data most effectively and undergo minimal loss during spatial reduction. The positioning angles of the variables on the new axes correspond to the correlations between the old and new variables. It should be noted that these new axes are determined by the eigenvalue: the descending number that, when multiplied by an angle, produces its value (i.e., an axis with intensity and direction). The largest one will determine the primary direction and will be followed by all the others with smaller values. Moreover, the higher eigenvalue corresponds to radius 1 of the factor circle 1–2 (the general theorem of Huygens).

The variable “total_links_w_individuals” is fundamental to the model’s structure, significantly contributing to all four dimensions of the PCA. This balance demonstrates that the variable is transversal and relevant to various aspects of the enterprise, reflecting its capacity to connect with the economic environment and its influence on business performance.

The analyses of company locations (or observations) and of variable locations can both be used to find the dimension that effectively synthesizes the essential structure of the original data matrix. It is important to note that, although there is a noticeable difference between the two locations (observations and variables), the eigenvalues produced by the two analyses correspond to each other. This demonstrates that the individual principal components are equivalent. The significance of each variable can be interpreted by examining the locations of the companies, hereafter referred to as observations. On the other hand, studying variable locations allows us to determine the order in which the variables of each unit appear.

The process of factor search is often facilitated by factor coordinate graphs. The variables within a specific arc of the circle all point in the same direction, if they are positively correlated with each other: this means that when one of them takes on a high value, the others will follow. A negative correlation exists between two indicators that move in opposite directions. Two variables will also be independent of each other if placed perpendicularly.

In this case, as shown in Figure 6, the output strategy produces a circle with a radius of one unit. As indicated, the upper coordinate of the factor represents its correlation with the variable and will be close to 1 because the current analysis is based on correlations. The coordinates’ meaning is interpreted by reading the correlation circle: the closer a point is to the edge, the more representative its coordinates are. All points will remain within the circle, as the sum of all factors coordinates (i.e., the squared correlations between them and the factors) cannot exceed 1. The arrows for “log_turnover” and “total_links_w_individuals” point in the same direction and create a minimal angle between them, suggesting that these two variables behave similarly in relation to the principal components, and consequently indicating a positive and strong correlation.

3.8. Observations and Outliers: A Graphical Representation

Here, the distribution of points (12,341 observations) for the proposed 1–2 factorial design is analyzed, since the position of the selected companies varies under certain conditions. The analysis of the graph shows that the higher a point is on the graph, the more it will be characterized by better respected values for the given factor and the further it will deviate from the averages. Conversely, the lowest values will be visualized if they are in the background of the graph.

Figure 7 illustrates the relationship between various business variables in the principal component space of the analysis. Notably, as anticipated in the factor coordinates graph, it reveals a positive correlation between “log_turnover” and “total_links_w_individuals”. The arrows representing these two variables point in the same direction, providing a clear visual indicator of a positive relationship: as the number of links a company has increases, so does its turnover. This connection is further supported by the clustering of data points representing companies along the shared direction of the two variables. This suggests that companies with higher turnover tend to invest more in personal relationships and networking, which could be a key factor that may positively influence their economic performance. The relation highlighted by the graph is not random, but significant, and it underscores how personal networking is a crucial lever for the financial success of companies. This also suggests that firms with a broader managerial network tend to generate higher business volumes, consistent with the hypothesis that a larger network can facilitate access to new business opportunities and improve the ability to secure financing.

According to the studies conducted by Fafchamps and Quinn (2013), companies with highly connected executives tend to have greater cash reserves, which help reduce the risk of short-term financial stress, due to their ability to negotiate more flexible payment terms with suppliers and customers. The empirical results confirm this hypothesis: firms with more extensive managerial networks exhibit higher immediate liquidity levels than the industry average. Particularly, their cash conversion cycle indicates a greater ability to optimize cash flow and reduce working capital requirements. These findings highlight that managerial networking not only influences business performance but also represents a key factor in the financial stability of SMEs, enhancing resource management and reducing the risk of financial imbalances. A lack of essential resources, legitimacy and innovation, on the other hand can lead to high uncertainty and competition. This applies to new ventures in emerging economies: networking is crucial for them, especially in less developed markets with scarce institutional services. Research shows in fact that networking plays a significant role in BMI (Business Model Innovation), providing firms with valuable resources, information, and innovative ideas. As a consequence, by building broad network ties, companies can improve performance and survive in turbulent markets.

We also aim to use a different model to reduce information loss and improve our PCA analysis. To do so, we tried to exclude some “outliers”, i.e., firms whose coordinates in the new space suggest a point that is highly different from the other units analyzed. Using the pca.outlier() function from the mt library in R (version 3.13), we assessed the Mahalanobis distance, which differs from the Euclidean distance as it accounts for the correlation between variables.

Figure 8 illustrates the outcomes of the outlier’s analysis. Given the size of our dataset (12,431 companies), only 251 outliers were detected, represented as red points in the graph. The visual distribution may give the impression that they outnumber the blue points (observations within the confidence ellipse). However, this is a perceptual distortion caused by the high density of overlapping blue points in the central region, where most observations cluster. Actually, they dominate the dataset, whereas the red points, though more conspicuous at the periphery, constitute only a minor fraction of the total data.

This finding aligns with prior research emphasizing that small firms and entrepreneurial ventures benefit from strong network ties, rather than being significantly impacted by extreme, outlying data points (Birley et al., 1990; Donckels & Lambrecht, 1995). The presence of a stable core network reinforces the robustness of the dataset and ensures that outliers do not skew overall conclusions (Birley et al., 1990). As Szarka (1990) suggests, the structure of business networks plays a critical role in resource accessibility and firm performance, a pattern also reflected in our analysis, where the central cluster represents the dominant business trends. Due to their limited proportion, these outliers do not meaningfully influence the final results, further validating the reliability of our interpretations.

3.9. Limitations and Future Research

Before moving to the conclusions, it is important to highlight the scope and nature of this research and, even more importantly, the potential opportunities to extend the topic. Our study specifically employs a cross-sectional design, focusing on the year 2024 across a large sample of 12,341 companies located in France. Such a sample size naturally ensures that our results are representative and robust but limits the analysis to a short-term perspective. It is not designed to capture long-term trends and does not provide insights into how variables evolve over time. Future research could broaden this perspective, incorporating temporal dynamics to gain even deeper insights.

Regarding the geographical dispersion of the observations, the aim of the study was to focus on the French context, using data from across the country to provide a well-structured, nation-specific analysis. As anticipated, the authors believe that a broader study could, again, provide insightful results about the relationship between social networks and company performance, including the different effects of cultural and economic environments on the subject.

Finally, our study focused on the effect of managers’ social networks on company performance, without distinguishing the nature or type of these connections. Once the existence, robustness, and effectiveness of this relationship are established, future studies could investigate how different types of network connections may influence the results.

4. Conclusions

The role of managers within a company has become significant, as they currently bear greater responsibility to ensure that businesses remain competitive in the market. Specifically, there is a growing demand for individuals capable of managing the financial aspects of the business, given the reduced capital held by large companies. The latter are seeking leaders with extensive social networks to promote growth and ensure long-term success. In this research, we have highlighted the importance of social networks in a firm and how social interactions among all participants can influence its performance. Well-connected executives enable their companies to maintain greater cash reserves and mitigate short-term financial stress, primarily through negotiating more flexible payment terms with suppliers and customers. Firstly, we have observed how leaders and managers can leverage these networks to advance their careers, since more extensive ones potentially lead to higher compensation. Empirical findings confirm that companies with well-developed managerial networks tend to achieve higher immediate liquidity and improved short-term financial stability through better resource allocation and negotiation power. These firms, led by well-connected executives, benefit–as just mentioned–from negotiating favorable credit conditions, optimizing cash flow, as well as enhancing working capital management. As a result, they mitigate the risk of cash imbalance and ensure financial efficiency. However, companies must carefully assess the impact of their connections on financial management. As previously anticipated, an effective networking strategy should complement financial efficiency, rather than be pursued as an independent factor of success, ensuring long-term financial solidity and operational profitability.

Illustrating the concept of network was necessary to clarify its key peculiarities. We started with a basic definition and eventually developed a more detailed explanation as the study progressed, using arguments from the leading figures in the literature on the subject for support. The foundation of the notion of structural holes was laid by Burt (1992), who showed how social networks are particularly influential in a company and in its outcomes. Revenue growth, cost reduction, net profit increase, improved executive compensation, and several other factors all depend on it. On the other hand, corporate performance, determined by financial measures such as ROA and ROE, has shown inconsistent results: our study found no actual link between the characteristics of networks and such measures. Statistical analysis indicates a positive correlation between executives’ resources and “economic outcome and turnover”, especially when transformed using the logarithm. The way in which the private resources of CEOs can increase sales and reduce costs is an aspect that is easy to understand, but it is challenging to apply in improving corporate performance. Therefore, CEOs’ networks primarily influence the firm’s solidity (e.g., turnover) and only marginally impact corporate performance (e.g., financial indices) for the sampled companies.

The analysis indicates that managers with a greater number of social connections have access to critical resources and information, which positively impacts revenue and immediate liquidity. This supports the hypothesis that managerial social networks enhance corporate stability and performance. In this regard, variables such as the number of employees and added value also emerge as key determinants: the model shows that these variables explain 73.6% of the variation in revenue. This confirms that company size and internal productivity significantly influence firms’ performance.

Data analysis using PCA validated our initial hypotheses, showing that although the network does affect corporate performance, its impact is less significant than expected. Nevertheless, social networks show a positive effect on some aspects of corporate stability, such as turnover and self-financing capacity. This suggests that networking, a powerful tool, must be integrated with targeted business strategies and managed proactively to maximize benefits.