Cross-Firm Technological Linkages and Peer Effects on Corporate Governance

Abstract

This study extends the literature on peer effects by revealing that technological linkages drive cross-firm emulation of corporate governance practices, a core determinant of firms’ sustainable development capacity. Using a comprehensive sample of China’s A-share listed firms over the period 2004–2022, we document that R&D-intensive firms strategically extract governance insights from their technological peers. Our empirical analyses identify three distinct mechanisms underlying this governance emulation: information bridging, competitive isomorphism, and market feedback. Furthermore, this peer effect exhibits significant heterogeneity across firms with different corporate performance, R&D investment levels, and resource intensity. Notably, firms adopting peer-based governance practices experience a substantial improvement in financial performance, which reflects rational adaptation rather than blind herd behavior. Overall, this paper introduces technological peer relationship as a novel determinant of governance decisions and provides a micro-foundation for how firms optimize their governance arrangements to enhance long-term sustainable operation within technologically interdependent markets.

1. Introduction

To achieve sustainable growth under resource constraints and intense market competition, firms commonly leverage technological progress to cut production costs, develop innovative products, and reduce environmental pollution. Technological linkages are widely recognized as critical channels to obtain new technologies. Moreover, technological research and development involve long cycles, substantial investment, and high risks. Without long-term support of corporate governance, such initiatives are prone to being cut back under short-term performance pressure. Consequently, appropriate governance mechanisms are essential to align the interests of senior executives and technical experts with a firm’s long-term value creation.

However, designing effective governance mechanisms that align with technology-driven sustainable growth is a complex endeavor. It is challenging for firms to develop such a robust governance and decision-making system independently. Instead, learning from connected peers can mitigate shortcomings in firms’ governance decision-making capabilities.

Prior research on the peer effects of corporate governance has largely focused on traditional perspectives that conceptualize peer relationships in terms of industrial affiliation and geographic proximity. Based on imitation theory, organizations tend to prioritize imitating connected peers with similar key characteristics. From the perspective of technology-driven sustainable growth, the attribute of technological connection plays a more central role in guiding imitation than do industrial affiliation and geographic proximity. This study thus proposes a novel analytical lens by exploring governance peer effects from the perspective of technological interdependence, thereby addressing a critical research gap in the extant literature.

To establish cross-firm technological linkages, we utilize the Mahalanobis method [1,2] to quantify the cross-firm similarity in patent classifications. The relevant patent data are obtained from the Chinese Research Data Services (CNRDS). In contrast to conventional similarity measures, the Mahalanobis method takes advantage of capturing technological spillovers across different patent classes, thereby providing a more comprehensive understanding of the technological connectedness between firms.

Drawing on a comprehensive panel dataset of China’s A-share listed firms from 2004 to 2022, this paper investigates whether technological peer effects reshape firms’ corporate governance decision-making and, if so, how and to what extent this reshaping occurs.

Our empirical findings reveal that firms, particularly those with higher R&D efficiency, tend to imitate the corporate governance practices of their technological peers when making their own governance decisions.

On the underlying mechanism, information acquisition emerges as a key channel through which corporate governance peer effects operate. Specifically, the documented peer effect is more pronounced when the focal firm exhibits low governance quality or its peer firms have high governance quality. This suggests that firms are more likely to draw insights from their peers and revise their corporate governance strategies based on acquired information.

In addition, firms operating in highly competitive markets exhibit a significantly higher propensity to mimic governance policies of their peer firms. This finding underscores competitive imitation as a core driver of peer effects on corporate governance.

Moreover, the efficiency of integrating information from linked peers into the focal firm’s stock prices also plays a crucial role in shaping the peer effects. Firms with higher cross-firm price efficiency are more likely to emulate the governance strategies of their peer firms.

Furthermore, corporate governance peer effects exhibit significant heterogeneity across firm characteristics. Specifically, peer firms with superior performance become more attractive and are thus more likely to be imitated relative to underperforming peers. In addition, innovation-oriented firms display a stronger propensity to mimic the governance practices of their peer firms than their non-innovation-oriented counterparts. Moreover, production factor intensity acts as another key contingent factor: firms with different factor intensities exhibit notable differences in their governance imitation behavior, with technology-intensive firms in particular showing a more pronounced peer effect on corporate governance.

Importantly, the documented peer effect is not merely a manifestation of irrational herding. This is because the quality of peer firms’ governance positively predicts the focal firm’s future financial performance and ESG rating, underscoring that rational learning underpins these observed peer effects.

While prior research has mainly focused on how social networking interactions shape governance decisions, the primary contribution of this study is identifying the significant impact of technological linkages on corporate governance practices. It not only provides a novel analytical perspective on corporate governance peer effect, but also undertakes a comprehensive analysis of the underlying mechanisms that explain imitation behavior in corporate governance. By establishing a clear link between the imitation of peer firms’ governance practices and the focal firm’s subsequent financial performance, this study further provides actionable insights for firms aiming to optimize their corporate governance decision-making.

The remainder of the paper is structured as follows. Section 2 reviews the relevant literature. Section 3 presents the theoretical analysis and develops the research hypotheses. Section 4 outlines the empirical design, including the definitions of key variables and the empirical approaches adopted. Section 5 presents and discusses the empirical results. Section 6 offers the discussion and conclusion.

2. Literature Review

2.1. Research on Technological Linkage Networks

Traditional innovation theory posits that firms rely mainly on internal resources and independent R&D to drive innovation. As technological complexity rises, academia has increasingly shifted its focus to external connections and collaborations, with innovation networks emerging as a key analytical framework for explaining firms’ innovative behaviors. Lundwall [3] argues that the interactions among inventors exert a profound impact on the formation of innovation networks. Today, such networks have become a cutting-edge research topic in economics and management [4].

However, academia has yet to reach a consensus on its measurement methods. Constrained by the availability of collaboration data, constructing networks based on cross-firm patent collaborations has become the mainstream method [5,6]. For instance, many scholars use collaboration matrices and tools like Gephi to perform topological analysis [7,8], while others construct innovation networks from patent collaboration data by applying the “buzz-pipeline” model [8]. Such methods primarily capture explicit R&D collaborations, corresponding to narrow-sense innovation associations that often neglect key implicit innovative behaviors. To mitigate this limitation, recent studies have leveraged patent text and classification similarity [9,10], adopting methods including cosine similarity [11] and the Mahalanobis approach to measure generalized innovation associations [1,2].

Existing research confirms that innovation-related networks deliver four core benefits: First, they act as the main channel for firms to acquire external resources and knowledge [12,13]; second, they facilitate risk sharing in innovative activities [14]; third, they facilitate the integration of complementary technologies and collaborative innovation [15]; fourth, they offer intellectual property protection when contractual performance is unfeasible [16].

2.2. Corporate Governance and Peer Effects

Early studies on peer effects primarily focused on social behavior [17,18]. As research has advanced, peer effects have been increasingly extended to corporate behavior analysis, such as stock splits, corporate social responsibility [19,20], dividend policies [21], IPOs [22], corporate innovation [23,24], financial misconduct, information disclosure strategies, and charitable donations [25,26,27,28]. This provides an important perspective for understanding interactive patterns in corporate decision-making.

In the context of corporate governance, studies show that various social networks provide managers with channels to exchange governance-related information and experiences, enabling firms to learn and emulate connected peers in governance decision-making, thus leading to the homogenization of governance practices [29,30,31,32]. Related evidence exists in networks formed by common shareholders, which enhance monitoring effectiveness [33,34] and improve the quality of information disclosure [35,36]. Geographical proximity also serves as a basis for governance peer effects, particularly in the imitation of anti-takeover provisions [37] and convergence in information disclosure violations [38].

Furthermore, traditional corporate governance research has established a multi-level framework of driving factors at the micro, meso, and macro levels. At the micro level, the focus is on governance factors, including equity structure, major shareholder behavior [39], and institutional co-ownership [40,41]; at the meso level, emphasis is placed on governance mechanisms like board independence [42] and board size [43]; at the macro level, attention is paid to the role of legal systems, legal origins [44], and informal governance mechanisms [45]. Studies based on China’s transitional context have further identified local characteristics such as state-owned ownership and government intervention [46,47], laying the groundwork for subsequent localized research on governance peer effects.

2.3. Critical Assessment and Gap Analysis

Extensive research has documented corporate governance peer effects based on industry affiliation, geographic location, product markets, and social networks, providing ample empirical evidence for understanding cross-firm imitation behaviors. Meanwhile, as innovation activities become more critical to achieving sustainable growth, innovation-related networks have emerged as pivotal platforms for firms to engage in external collaboration and information exchange.

However, prior peer effect studies remain limited to traditional definitions of peer relationships and thus fail to adequately address the specific peer ties formed via innovation linkages and their actual impacts on corporate governance. Given the high risk and uncertainty inherent in innovation activities, an effective governance structure is essential to prevent R&D investment from being disrupted by short-term operational fluctuations. Since technologically linked firms typically share similar governance contexts, their governance practices may exhibit unique patterns that differ from those of traditional peer groups. It is therefore necessary to explore how the governance strategies of technological peer firms influence the focal firm’s governance decisions.

3. Theoretical Analysis and Research Hypotheses

3.1. Peer Effects and Corporate Governance

In social psychology, an individual’s behavior is influenced by others within the same group, a phenomenon known as “peer effects”. Peer effects have been documented not only in the realms of public security and social welfare [48,49] but also in corporate finance.

For instance, prior studies have revealed strong peer effects in stock splits [50], initial public offering [22], capital structure determination [51], and innovation [23,24]. Furthermore, peer effects have also been identified in corporate governance-related areas, such as the adoption of corporate social responsibility [19,20], corporate disclosure policies [52], and dividend policies [21,53].

While peer relationships are typically linked to industry groups, geographic neighbors, and social ties, little research has explored those derived from technological linkages. Unlike traditional peer relationships, technological peer relationships not only facilitate the dissemination of technological progress and breakthroughs but also provide unique opportunities for interaction, communication, and mutual learning of corporate governance experiences. Based on the above analysis, we propose the following hypothesis:

Hypothesis 1 (H1).

The quality of a firm’s corporate governance is positively associated with the average governance quality of its technological peers.

3.2. Underlying Mechanisms of Peer Effects on Corporate Governance

Drawing on social psychology and organizational behavior theory, Lieberman and Asaba [54] argue that two distinct mechanisms drive imitative behaviors: information acquisition-based imitation and competition-driven imitation. The former denotes organizational imitation motivated by acquiring valuable information for decision-making through active learning, while the latter refers to organizations imitating competitors to maintain their market position.

According to the law of learning, the propensity of cross-firm imitation depends on the relative gap in governance quality. Specifically, as the gap in governance quality between the focal firm and its technological peers widens, the focal firm’s managers become more motivated to engage in imitation. Subsequently, we propose the following hypotheses:

Hypothesis 2a (H2a).

The lower the quality of a firm’s own governance, the more pronounced the peer effect on corporate governance.

Hypothesis 2b (H2b).

The higher the quality of peer firms’ governance, the more pronounced the peer effect on corporate governance.

Under the competition-driven imitation mechanism, when a firm is confronted with external pressure from intensive market competition, a defensive strategy involves adopting homogeneous rather than pursuing aggressive governance polices. The rationale behind this mechanism is that market competition increases the risks of implementing heterogeneous governance policies. Indeed, substantial evidence of competitive imitation exists in capital structure decisions [55,56] and financing policies [57]. Thus, firms may imitate their technological peers’ governance decisions to address market competition pressures. We outline the corresponding research hypothesis below:

Hypothesis 2c (H2c).

The higher the degree of technological market competition, the more significant the peer effect on corporate governance.

Recently, it has been documented that stock prices reveal valuable information about firms’ future cash flows and decision-making on the real side [58]. Importantly, a firm’s business decisions are not only closely associated with its own stock prices, but also influenced by stock price movements of economically linked firms [59]. Therefore, when cross-firm price efficiency [2] improves, managers of the focal firm become more capable of correctly inferring governance-related information from its technological peers. This enhanced information dissemination boosts the focal firm’s confidence and willingness to mimic its technological peers’ governance decisions.

Hypothesis 2d (H2d).

The higher the cross-firm price efficiency, the more significant the peer effect on corporate governance.

3.3. Heterogeneity of Peer Effects on Corporate Governance

According to Chen et al. [30], cross-firm imitation follows the principle of logical imitation, whereby peer firms with superior performance are more likely to become imitation targets. More specifically, when the managers of the focal firm observe superior performance by their peers, they tend to associate these outcomes with an effective corporate governance system. This positive feedback urges focal firm managers to mimic their peers’ governance decisions, hoping to achieve comparable performance.

In contrast, underperforming peers are rarely considered as valid benchmarks for imitation. This performance-driven heterogeneity in corporate governance peer effects leads to the following hypothesis:

Hypothesis 3a (H3a).

Corporate governance peer effects are more pronounced when peer firms exhibit superior overall performance.

Given that the peer relationships are established based on technological similarity, firms with high R&D investment intensity and strong innovation orientation should pay more attention to technological linkages. Furthermore, the corporate governance of such firms often exhibits unique features, fostering a culture that encourages risk-taking for creativity and prioritizes long-term growth over short-term profits. Therefore, such innovative firms may demonstrate a stronger inclination to mimic their technological peers in corporate governance.

This study employs R&D investment intensity and R&D efficiency as core indicators of firm innovation capabilities, and develops the following hypotheses:

Hypothesis 3b (H3b).

For firms with higher R&D intensity, the peer effect on corporate governance is more prominent.

Hypothesis 3c (H3c).

For firms with higher R&D efficiency, the peer effect on corporate governance is more pronounced.

3.4. Peer Effect on Corporate Governance and Performance of the Focal Firm

While corporate governance peer effects exist among technologically linked firms, it remains unclear whether such imitation behavior can generate actual economic spillover effects. If this governance-related technological peer effect merely constitutes mindless mimicry, emulating the peers’ governance practices would not only fail to boost corporate value growth but also create potential risks that undermine future performance. To examine the economic implications of such governance peer effects, we conduct empirical analysis to test the following hypothesis:

Hypothesis 4 (H4).

The peer effects on governance exert a significantly positive impact on the focal firm’s financial performance and ESG rating.

4. Data and Empirical Methodology

4.1. Sample Selection and Data Sources

This study uses annual data of China’s A-share listed firms covering the period from 2004 to 2022. Specifically, the international invention patent classification data, essential for constructing technological linkages, is obtained from the China Research Data Services Platform (CNRDS). All financial and corporate governance-related data are obtained from the CSMAR database. After excluding firms that did not disclose their invention patent classifications, our sample consists of 4824 firms. To mitigate the impact of extreme values, we winsorize all continuous variables at the 1st and 99th percentiles. Following this screening procedure, the final sample for our empirical analysis consists of 19,846 firm-year observations.

4.2. Variable Definition and Construction

4.2.1. Dependent Variable

This paper follows Lin et al. [60] to construct a comprehensive measure of corporate governance quality ($CG$) using principal component analysis. This measure incorporates seven key corporate governance variables: (1) separation of the chairman and CEO positions; (2) proportion of independent directors; (3) shareholding ratios of directors and senior executives; (4) board of directors and board of supervisors size; (5) the total compensation of the top three senior executives. Based on eigenvalue significance, we select the first three principal components to build a proxy for corporate governance quality.

To test the robustness of our findings, we follow Chen et al. [61] to select seven corporate governance variables and develop an alternative measure of corporate governance quality ($CGa$). These variables incorporate: shareholding ratios of the second to tenth largest shareholders, board size, supervisor board size, shareholding ratio of senior executives, board meeting frequency, total compensation of the top three highest-paid executives, and CEO-chairman duality. Analogously, we derive a proxy for corporate governance quality via principal component analysis for this alternative measure.

4.2.2. Main Explanatory Variable

Our key explanatory variable, defined as the average corporate governance quality of technologically linked firms $\left({CG}^{Linked}\right)$, is constructed based on the strength of technological peer linkages. Specifically, for firm $i$ in year $t$, the average governance quality of its technologically linked firms is calculated as the weighted average of the governance quality of the linked firms.

$${CG}_{i,t}^{Linked}={\sum }_{j=1,j\ne i}^N{w}_{i,j,t}{CG}_{j,t}$$

(1)

Here, the weight $w_{i,j,t}$ is determined based on the relative strength of the technological linkage between firm $j$ and firm $i$ in year $t$.

$$w_{i,j,t}={\displaystyle \frac{L_{i,j,t}}{{\sum }_{j=1,j\ne i}^N{L}_{i,j,t}}}$$

(2)

The calculation of the strength of the technological peer linkages between firm $i$ and firm $j$ at time t ($L_{i,j,t}$) refers to the approach of Zeng and Kuang [2]. First, we transform the patent classification frequencies of invention patents held by firm $i$ and firm $j$ in year $t$ into a vector format. Second, to capture the likelihood of knowledge spillover across technological domains, we consider the patent distribution matrix across technological fields. Finally, the Mahalanobis metric is employed to compute the technological adjacency matrix, where the element in the i-th row and the j-th column of the matrix denotes the strength of the technological linkage between firm $i$ and firm $j$ in year $t$.

4.2.3. Cross-Firm Price Efficiency

Following the work of Zeng and Kuang [2], we employ stock price delay among technologically linked firms to measure cross-firm price efficiency. Specifically, this delay measure quantifies the speed at which information from technologically linked firms is impounded into the stock prices of the focal firms.

To begin with, the traditional market pricing model is augmented by incorporating industry returns and returns of technologically linked peers:

$$r_{i,t}={\alpha }_i+{\beta }_{i,m}^0{r}_{m,t}+{\beta }_{i,ind}^0{r}_{ind,t}+{\beta }_{i,s}^0{Signal}_{i,t}+{\sum }_{n=1}^K{\beta }_{i,s}^n{Signal}_{i,t-n}+{\epsilon }_{i,t}$$

(3)

Here, ${Signal}_{i,t-n}$ represents the weighted average of the stock returns of firms technologically linked to firm $i$ on day $t-n$.

$${Signal}_{i,t-n}={\sum }_{j=1}^N{w}_{i,j,t}{r}_{j,t-n}$$

(4)

Next, we estimate the coefficient ${\beta }_{i,s}^n$ using a rolling quarterly window. A larger magnitude of the coefficient suggests a longer lag for information from linked firms to be integrated into the focal firm’s stock price, reflecting slower cross-firm information dissemination and lower cross-firm price efficiency. Thus, the cross-firm price efficiency of firm $i$ in quarter $q$ is calculated as follows:

$${IDelay}_{i,q}=1-{\displaystyle \frac{{\sum }_{n=1}^{K}n\times \frac{\left|{\beta }_{i,s}^n\right|}{se\left({\beta }_{i,s}^n\right)}}{\frac{{|\beta }_{i,s}^0|}{se\left({\beta }_{i,s}^0\right)}+{\sum }_{n=1}^K\frac{\left|{\beta }_{i,s}^n\right|}{se\left({\beta }_{i,s}^n\right)}}}$$

(5)

In the last step, annual cross-firm price efficiency is calculated as the average of the quarterly cross-firm price efficiency in the specified year.

4.2.4. Intensity of Market Competition

To measure the intensity of market competition, we construct two Herfindahl-Hirschman Index (HHI) metrics: one based on operating income ($SalesHHI$) and the other on principal operating income ($PsalesHHI$). To facilitate results interpretation, we invert both indices by subtracting them from 1, obtaining $IPsalesHHI$ and $ISalesHHI$. Consequently, higher values of $IPsalesHHI$ and $ISalesHHI$ indicate stronger market competition.

4.2.5. Idiosyncratic Stock Return of Technologically Linked Firms

Following Machokoto et al. [24], the Carhart [62] four-factor model is extended by incorporating an industry factor to compute the idiosyncratic stock returns.

$$R_{i,t}={\alpha }_{i,t}+{\beta }_{i,t}^M\left(R{M}_t-R{F}_t\right)+{\beta }_{i,t}^{SMB}SM{B}_t+{\beta }_{i,t}^{HML}HM{L}_t+{\beta }_{i,t}^{MOM}MO{M}_t+{\beta }_{i,t}^{IND}{IND}_t+{\eta }_{i,t}$$

(6)

where $R_{i,t}$, $R{M}_t$, and $R{F}_t$ represent the stock return of firm $i$, the market return, and the risk-free rate in month t. $SM{B}_t$,$HM{L}_t$, and $MO{M}_t$ denote the size, value, and momentum factors. ${IND}_t$ is the industry average stock return. Firms are required to have at least 24 months of non-missing historical return data. The regression coefficients are estimated using a 60-month rolling-window. The monthly idiosyncratic return of each stock is then obtained by subtracting the expected return predicted by the model from the actual stock return.

$${Resi}_{i,t}=R_{i,t}-{\widehat{R}}_{i,t}$$

(7)

Subsequently, the monthly idiosyncratic stock returns of the company are compounded to derive the annual idiosyncratic stock return. We then compute the weighted average idiosyncratic return of technologically linked firms, using the relative strength of technological linkages as weights.

4.2.6. Control Variables

Following Santhosh et al. [40,41,63], we control the following factors that may influence corporate governance decisions: firm size ($Size$, return on total assets ($ROTA$), leverage ratio ($Lev$), equity balance degree ($Ebd$), independent director ratio ($Indep$), ownership type ($SOE$), real earnings management ($TREM$), analyst attention ($Analyst$), firm age ($Age$), as well as industry and year fixed effects ($Ind$ and $Year$).

Table 1. Description of the main variable setting and calculation.

Variables	Symbol	Variable Name	Meaning of Variables
Dependent Variable	${CG}_{i,t}$	Corporate governance quality	Weighted aggregation of the first three principal components extracted from seven corporate governance indicators
Core Explanatory Variable	${CG}_{i,t-1}^{linked}$	Average corporate governance quality of technologically linked firms	Average corporate governance quality of technologically linked firms, constructed based on the strength of technological peer relationships.
Moderating/Grouping Variables	$IDelay$	Cross-firm price efficiency	1−Delay measure of cross-firm price efficiency proposed by Zeng and Kuang [2]
	$IPsalesHHI$	Inverse HHI (principal revenue)	1−HHI derived from principal business revenue
	$ISalesHHI$	Inverse HHI (operating revenue)	1−HHI derived from operating revenue
	$Rd$	R&D intensity	R&D investment/Operating revenue
	$Re$	R&D efficiency	The number of patent citations/ln(1 + Research and development expenditure)
	$Edu$	Average educational attainment of the executive team	Total educational qualifications of executives/Number of executives (high school or below = 1, junior college = 2, undergraduate = 3, master’s degree = 4, and doctoral degree = 5)
	$Overseas$	Average overseas background of the executive team	Average overseas background scores of executives (overseas employment or study = 1, no overseas background = 0)
Control Variables	$Size$	Firm size	ln (the number of individual shares outstanding×Annual closing price + 1)
	$ROTA$	Return on total assets (adjusted)	(Total profit + Financial expenses)/Total assets
	$Lev$	Leverage ratio	Total liabilities/Total assets
	$Indep$	Independent director ratio	Number of independent directors/Total board size
	$TREM$	Real earnings management	Measured following the models of Roychowdhury [64] and Dechow [65]
	$Age$	Firm age	Observation year−IPO year
	$SOE$	Ownership type	Assigned 1 if the firm is state-owned, 0 otherwise.
	$Yretwd$	Annual stock return	Annual total stock return considering price changes and cash dividend reinvestment
	$Ebd$	Equity balance degree	The shareholding ratio of the 2nd–5th largest shareholders/Shareholding ratio of the largest shareholder
	$Analyst$	Analyst attention	Annual number of analyst teams covering the firm (counted by team)
	TA	Total assets	The firm’s total assets in the current year
	$Growth$	Business revenue growth	(Current year operating income−Last year operating income)/Last year’s operating income
	$CFO$	Cash flow from operating activities	Net cash flow from operating activities/Total assets at the beginning of the year
	$DisAcc$	Discretionary Accruals	Discretionary portion of total accruals estimated via the modified Jones model
Company Performance-Related Variables	$EVAR$	EVA Rate	EVA/Quarterly average total investment
	$EVANAR$	Net asset EVA rate	EVA/Average net assets
	$ROE$	Return on equity	Net Profit/Average Shareholders’ Equity
	$ROA$	Return on total assets	Net income/Average total assets
	$ROTA$	Return on total assets (adjusted)	(Total profit + Financial expenses)/Average total assets
	$ROIC$	Return on invested capital	NOPAT/Quarterly average total investment
	$ESG$	Corporate ESG performance	Measured using Bloomberg ESG rating indicators

Table 1. Description of the main variable setting and calculation.

Variables	Symbol	Variable Name	Meaning of Variables
Dependent Variable	${CG}_{i,t}$	Corporate governance quality	Weighted aggregation of the first three principal components extracted from seven corporate governance indicators
Core Explanatory Variable	${CG}_{i,t-1}^{linked}$	Average corporate governance quality of technologically linked firms	Average corporate governance quality of technologically linked firms, constructed based on the strength of technological peer relationships.
Moderating/Grouping Variables	$IDelay$	Cross-firm price efficiency	1−Delay measure of cross-firm price efficiency proposed by Zeng and Kuang [2]
	$IPsalesHHI$	Inverse HHI (principal revenue)	1−HHI derived from principal business revenue
	$ISalesHHI$	Inverse HHI (operating revenue)	1−HHI derived from operating revenue
	$Rd$	R&D intensity	R&D investment/Operating revenue
	$Re$	R&D efficiency	The number of patent citations/ln(1 + Research and development expenditure)
	$Edu$	Average educational attainment of the executive team	Total educational qualifications of executives/Number of executives (high school or below = 1, junior college = 2, undergraduate = 3, master’s degree = 4, and doctoral degree = 5)
	$Overseas$	Average overseas background of the executive team	Average overseas background scores of executives (overseas employment or study = 1, no overseas background = 0)
Control Variables	$Size$	Firm size	ln (the number of individual shares outstanding×Annual closing price + 1)
	$ROTA$	Return on total assets (adjusted)	(Total profit + Financial expenses)/Total assets
	$Lev$	Leverage ratio	Total liabilities/Total assets
	$Indep$	Independent director ratio	Number of independent directors/Total board size
	$TREM$	Real earnings management	Measured following the models of Roychowdhury [64] and Dechow [65]
	$Age$	Firm age	Observation year−IPO year
	$SOE$	Ownership type	Assigned 1 if the firm is state-owned, 0 otherwise.
	$Yretwd$	Annual stock return	Annual total stock return considering price changes and cash dividend reinvestment
	$Ebd$	Equity balance degree	The shareholding ratio of the 2nd–5th largest shareholders/Shareholding ratio of the largest shareholder
	$Analyst$	Analyst attention	Annual number of analyst teams covering the firm (counted by team)
	TA	Total assets	The firm’s total assets in the current year
	$Growth$	Business revenue growth	(Current year operating income−Last year operating income)/Last year’s operating income
	$CFO$	Cash flow from operating activities	Net cash flow from operating activities/Total assets at the beginning of the year
	$DisAcc$	Discretionary Accruals	Discretionary portion of total accruals estimated via the modified Jones model
Company Performance-Related Variables	$EVAR$	EVA Rate	EVA/Quarterly average total investment
	$EVANAR$	Net asset EVA rate	EVA/Average net assets
	$ROE$	Return on equity	Net Profit/Average Shareholders’ Equity
	$ROA$	Return on total assets	Net income/Average total assets
	$ROTA$	Return on total assets (adjusted)	(Total profit + Financial expenses)/Average total assets
	$ROIC$	Return on invested capital	NOPAT/Quarterly average total investment
	$ESG$	Corporate ESG performance	Measured using Bloomberg ESG rating indicators

4.3. Model Specification

To test Hypothesis 1, we follow the framework proposed by Ahern et al. [66] and Glaeser et al. [48], and specify the following fixed-effect panel model:

$${CG}_{i,t}={\beta }_0+{\beta }_1{CG}_{i,t-1}^{Linked}+\sum {{\gamma }_{k}Control}_{i,t}^k+Ind+Year+{\epsilon }_{i,t}$$

(8)

A statistically significant and positive coefficient ${\beta }_1$ would suggest the presence of a peer effect on corporate governance quality.

To examine Hypotheses 2a and 2b, the sample firms are split evenly into two subgroups based on the focal firm’s own governance quality and the average governance quality of its technologically linked peers, respectively. Should the information-based mechanism hold, peer effects on corporate governance would be stronger for firms with inferior governance quality and those whose peers exhibit higher average governance standards.

To validate the competition-based mechanism (Hypothesis 2c), we introduce an interaction term between peer governance quality and market competition intensity ($IHHI$) into model (8). If the Hypothesis 2c holds, the coefficient ${\beta }_2$ in model (9) should be positive and statistically significant.

$${CG}_{i,t}={\beta }_0+{\beta }_1{CG}_{i,t-1}^{Linked}+{\beta }_2{CG}_{i,t-1}^{Linked}\times {IHHI}_{i,t-1}+\sum {{\gamma }_{k}Control}_{i,t}^k+Ind+Year+{\epsilon }_{i,t}$$

(9)

Here, the variable ${IHHI}_{i,t-1}$ denotes the inverse HHI measures based on principal business revenue ($IPsalesHHI$) and operating revenue ($ISalesHHI$).

To test Hypothesis 2d, we include an interaction term into model (8) to capture the interplay between peer governance quality and a dummy variable based on cross-firm price efficiency:

$${CG}_{i,t}={\beta }_0+{\beta }_1{CG}_{i,t-1}^{Linked}+{\beta }_2{CG}_{i,t-1}^{Linked}\times I_{IDelay>Median}+\sum {{\gamma }_{k}Control}_{i,t}^k+Ind+Year+{\epsilon }_{i,t}$$

(10)

Here, the variable $I_{IDelay>Median}$ is a dummy variable equal to 1 if the cross-firm price efficiency of firm $i$ is above the sample median, and 0 otherwise.

To examine the heterogeneity of peer effects conditional on peer corporate performance (H3a), the sample firms are divided into two equal subgroups based on the average idiosyncratic stock returns and financial performance of their technological peers, respectively. We then estimate the regression model (8) for each subgroup. If the Hypothesis 3a holds, the coefficient ${\beta }_1$ in model (8) should be more pronounced for firms whose technological peers demonstrate superior idiosyncratic stock returns or financial performance.

To test hypotheses 3b and 3c, we conduct the same subgroup approach used to validate Hypothesis 3a, where the sample is split evenly by R&D intensity and R&D efficiency, respectively. Hypotheses 3b and 3c are supported if the peer effects on governance are stronger for firms with higher R&D intensity and efficiency.

Following Machokoto et al. [24], we develop model (11) to examine the economic implications arising from peer effects on governance (Hypothesis 4):

$$Y_{i,t}={\beta }_0+{\beta }_1{CG}_{i,t-1}^{linked}+{\beta }_k\sum {Control}_{i,t}+Ind+Year+{\epsilon }_{i,t}$$

(11)

In the model, the dependent variable ${ Y}_{i,t}$ represents the financial and ESG performance of firm $i$ in year t. Consistent with prior research on financial performance [67,68,69,70], we use a comprehensive set of measures as proxies, including the economic value added ratio ($EVAR$), economic value added ratio by net assets ($EVANAR$), return on equity ($ROE$), return on assets ($ROA$), return on investment capital ($ROIC$), return on total assets ($ROTA$). ESG performance is measured using Bloomberg ESG ratings. Control variables include total assets ($TA$), leverage ratio ($Lev$), growth rate of business revenue ($Growth$), ownership type ($SOE$), cash flow from operating activities ($CFO$), annual stock return with cash dividends reinvested ($Yretwd$), discretionary accruals ($DisAcc$), as well as industry fixed effects ($Ind$) and year fixed effects ($Year$) to account for unobserved heterogeneity across industries and time. The definitions of the aforementioned variables are provided in Table 1.

5. Empirical Results Analysis

5.1. Descriptive Statistics

Table 2. Descriptive statistics of main variables.

	Min	Max	Mean	Std. Dev.
CG	−0.586	1.955	−0.002	0.605
CGLinked	−0.234	0.121	0.016	0.083
Size	11.900	18.561	14.808	1.353
ROTA	−0.357	0.251	0.047	0.080
Lev	0.054	1.130	0.451	0.223
Ebd	0.019	2.786	0.734	0.616
SOE	0	1	0.404	0.491
Indep	0	57.140	35.524	8.830
Analyst	1	80	9.447	10.086
TREM	−0.784	0.629	−0.000	0.218
lnAge	1.099	3.332	2.266	0.630

reports the descriptive statistics for the main variables employed in the empirical analysis. Specifically, the average value of corporate governance quality ($CG$) stands at −0.002, ranging from −0.586 (minimum) to 1.955 (maximum) with a standard deviation of 0.605. These statistics suggest substantial cross-firm variations in governance quality. Furthermore, the average corporate governance quality of technological peers is positive at 0.016, with a standard deviation of 0.083, considerably lower than that of the firm-level $CG$ measure.

5.2. Empirical Results

5.2.1. Evidence of Peer Effects on Corporate Governance

Table 3. Test for the existence of governance peer effects among innovation-linked firms.

	CG	CG	CG	CGa
	(1)	(2)	(3)	(4)
CGLinked	0.639 **		0.447 **
	(2.21)		(2.25)
CGLinked				0.287 **
				(2.10)
Size		−0.036 ***	−0.038 ***	−0.048 ***
		(−7.59)	(−7.90)	(−6.15)
ROTA		0.079	0.094	0.163 ***
		(1.21)	(1.45)	(3.51)
Lev		−0.038	−0.032	−0.146 ***
		(−1.12)	(−0.99)	(−4.26)
Ebd		0.060 ***	0.060 ***	0.296 ***
		(4.76)	(4.76)	(17.39)
SOE		−0.288 ***	−0.287 ***	−0.125 ***
		(−21.96)	(−21.86)	(−11.50)
Indep		0.012 ***	0.012 ***	0.004 ***
		(17.43)	(17.35)	(5.34)
Analyst		0.007 ***	0.007 ***	0.004 ***
		(13.70)	(13.64)	(4.56)
TREM		−0.084 **	−0.080 **	−0.123 ***
		(−2.54)	(−2.46)	(−5.77)
lnAge		−0.216 ***	−0.217 ***	−0.227 ***
		(−22.87)	(−23.15)	(−23.74)
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	19,846	19,846	19,846	20,081
R-squared	0.107	0.306	0.357	0.370

Note: ***, ** indicate statistical significance at the 1% and 5% levels, respectively. The t-statistics are reported in parentheses. Standard errors are clustered at the industry level. Industry classification follows the China Securities Regulatory Commission (CSRC) standards, with two-digit codes used for the manufacturing sector and one-digit codes for other industries. The same conventions apply to all subsequent analyses unless otherwise specified.

reports the regression results of model (8), providing strong evidence for peer effects on corporate governance. In column (1), the regression coefficient for the corporate governance quality of linked peers is 0.639 and statistically significant at the 5% level. As shown in column (3), including control variables reduces the coefficient slightly to 0.447, while its statistical significance remains intact. A comparison between column (2) and column (3) clearly shows that adding the peer-based governance variable substantially improves the explanatory power of the model.

In addition, column (4) reports the results using an alternative corporate governance quality measure constructed in Section 4.2.1. The magnitude of the coefficient declines to 0.287 but remains statistically significant at 5% level. These findings provide consistent evidence of cross-firm imitation in corporate governance practices, supporting Hypothesis 1.

5.2.2. Information-Based Mechanism

Table 4. Tests of the information-based imitation of corporate governance strategies.

	Dependent Variable: CG
	(1)	(2)	(3)	(4)
	Firm’s Own Governance Quality		Peer Firms’ Governance Quality
	Low	High	Low	High
CGLinked	0.123 ***	0.318	0.188	0.491 **
	(4.83)	(1.22)	(1.21)	(2.63)
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	9768	10,074	9789	9791
R-squared	0.499	0.302	0.359	0.346

Note: ***, ** indicate statistical significance at the 1% and 5% levels, respectively.

reports the test results for information-based imitation in corporate governance strategies. Columns (1) and (2) present results for subsamples split by the focal firm’s own governance quality. For firms with lower governance quality, the coefficient on peer firms’ average governance quality is 0.123 and statistically significant at the 1% level. By contrast, the corresponding coefficient for firms with higher governance quality is statistically insignificant.

Columns (3) and (4) present the results for subsamples partitioned by the governance quality of technological peers. For firms whose peers demonstrate higher governance quality, the coefficient on the peer average governance quality is 0.491 and statistically significant at the 5% level. Notably, this coefficient is not only substantially larger in magnitude but also statistically more significant than the estimate for the subsample with lower-quality peer governance.

Typically, firms with higher governance quality are considered more sophisticated in governance decision-making. Our findings, therefore, strongly support the information-based mechanism underlying corporate governance peer effects. Hypotheses 2a and 2b are thus verified.

5.2.3. Competition-Based and Market Feedback Mechanisms

Columns (1) to (2) of

Table 5. Tests of competition-based and market feedback mechanisms.

	Dependent Variable: CG
	(1)	(2)	(3)
CGLinked	0.290	0.016	0.506
	(1.69)	(0.05)	(1.72)
ISalesHHI	0.043
	(0.46)
ISalesHHI × CGLinked	0.461 *
	(1.76)
IPsalesHHI		0.030
		(0.32)
IPsalesHHI × CGLinked		0.750 **
		(2.43)
Idelay			0.124
			(0.53)
Idelay × CGLinked			5.008 ***
			(3.13)
Controls	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes
N	19,581	19,581	11,396
R-squared	0.348	0.348	0.301

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

report tests of the competition-based mechanism underlying peer effects on corporate governance. In particular, column (1) shows that market competition intensity is positively associated with corporate governance quality, although the coefficient is not statistically significant. The interaction term between the market competition dummy and peer average governance quality is 0.461 and significant at the 10% level. These findings are further supported by the results in column (2), where market competition is measured using principal business revenue. Taken together, these findings strongly support the Hypothesis H2c that competitive pressure induces imitative governance behavior.

Column (3) of Table 5 reports test results of the market feedback mechanism. The results show that cross-firm price efficiency is positively related to the firm’s governance quality, although this relationship is not statistically significant. Meanwhile, the coefficient of the interaction term between the price efficiency dummy and peer average governance quality is 5.008 and statistically significant at the 1% level. This finding implies that cross-firm price efficiency facilitates the imitation of governance practices from technologically connected peers. H2d is thus supported.

5.3. Heterogeneity Tests and Further Analyses

5.3.1. Corporate Performance and Peer Effect on Governance

Table 6. Impact of peer firms’ performance on the peer effects.

	Dependent Variable: CG
	(1)	(2)	(3)	(4)
	Peer Firms’ Idiosyncratic Stock Return		Peer Firms’ Financial Performance
	Low	High	Low	High
CGLinked	0.341 **	0.421 **	0.245	0.509 **
	(2.19)	(2.84)	(1.51)	(2.41)
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	9844	9757	9641	10,223
R-squared	0.360	0.353	0.376	0.345

Note: ** indicate statistical significance at the 5% levels.

reports the regression results from the heterogeneity analysis of the corporate governance peer effects, conditional on the performance of peer firms. We first split the sample evenly into two subgroups based on the annual idiosyncratic returns of technological peers. Columns (1) and (2) of Table 6 show that the coefficient on the peer average governance quality is 0.421 for the subgroup with higher peer idiosyncratic returns, while it decreases to 0.341 for the subgroup with lower peer idiosyncratic returns.

Subsequently, we partition the sample equally according to peer firms’ financial performance. Financial performance is measured using the first principal component extracted from a set of performance metrics: economic value-added ratio ($EVAR$), economic value-added ratio on net assets ($EVANAR$), return on equity ($ROE$), return on assets ($ROA$), return on invested capital ($ROIC$), and return on total assets ($ROTA$). Detailed variable definitions are provided in Section 4.2. This component explains approximately 88% of the total sample variation, confirming its validity as a composite measure of firm financial performance. As expected, a substantial difference in the coefficient of average governance quality appears between the two subgroups. Specifically, the coefficient is 0.509 and statistically significant for firms with financially superior peers, but falls sharply to 0.245 and becomes insignificant for those with poorer-performing peers.

These findings are consistent with managerial incentive theory. Corporate performance is closely linked to managers’ bonus compensation and stock option values, and thus represents a key benchmark for managerial objectives. Against this background, managers have stronger incentives to imitate the governance strategies of high-performing peers, since such practices are perceived as effective ways to improve their own firm’s performance and thereby enhance personal rewards. Hypothesis 3a is therefore validated.

5.3.2. Corporate Innovation and Peer Effect on Governance

Given that the peer relationships in this study are defined by technological linkages, it is reasonable to expect that the peer effect on corporate governance is more pronounced for firms with greater R&D engagement. To test this hypothesis, the sample firms are categorized into two subgroups based on two innovation characteristics: R&D intensity and R&D efficiency. The results in

Table 7. Impact of R&D attributes on the peer effects.

	Dependent Variable: CG
	(1)	(2)	(3)	(4)
	R&D Intensity		R&D Efficiency
	Low	High	Low	High
CGLinked	−0.018	1.110 **	0.276 *	1.222 **
	(−0.08)	(2.17)	(1.84)	(2.37)
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	7930	7784	5778	8040
R-squared	0.330	0.253	0.312	0.336

Note: **, * indicate statistical significance at the 5% and 10% levels, respectively.

provide strong support for this hypothesis.

Columns (1) and (2) of Table 7 present regression results for subsamples grouped by R&D intensity. Notably, the coefficient on peer average governance quality is 1.110 and statistically significant for firms with higher R&D intensity, whereas the coefficient is −0.018 and insignificant for firms with lower R&D intensity. These results indicate that higher R&D intensity strengthens the imitation of governance practices among technologically connected firms. Hypothesis 3b is thus verified.

Consistent evidence is shown in columns (3) and (4) of Table 7, where the sample is divided by R&D efficiency. The coefficient on peer average governance quality for firms with higher R&D efficiency is approximately four times larger than that for firms with lower R&D efficiency. This finding demonstrates that R&D efficiency positively moderates the governance peer effects among technologically linked firms. Hypothesis 3c is therefore supported.

5.3.3. Further Analysis

Based on factor input structures, firms can be broadly classified into labor-intensive, capital-intensive, and technology-intensive types. Technology-intensive firms rely on technological innovation as their core driver to maintain market position and sustainable competitive advantage. Accordingly, technological peer relationships are likely to exert a stronger influence on the governance practices of technology-intensive firms. We therefore conduct a heterogeneity analysis across firms with different factor intensities.

To identify whether a firm is technology-intensive, we measure technology intensity as the ratio of R&D investment to principal business income. Firms with technology intensity above the sample median are classified as technology-intensive firms. Analogously, capital intensity is measured as the ratio of fixed assets to total assets at the end of the respective year, with firms above the median defined as capital-intensive. Firms that satisfy neither criterion are classified as labor-intensive.

Table 8. Intensity of production factors and peer effects.

	Technology-Intensive	Capital-Intensive	Labor-Intensive
	(1)	(2)	(3)
CGLinked	1.110 **	0.138	0.049
	(2.17)	(1.39)	(0.12)
Controls	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes
N	7784	10,176	3497
R-squared	0.253	0.373	0.323

Note: ** indicate statistical significance at the 5% levels.

displays the regression results of model (8) for subsamples grouped by factor intensity. For the technology-intensive group, the coefficient on peer corporate governance quality is 1.110 and statistically significant at the 5% level. In contrast, the corresponding coefficients for the capital-intensive and labor-intensive groups are much smaller (0.138 and 0.049, respectively) and statistically insignificant. These findings indicate that peer effects on governance are far more pronounced among technology-intensive firms than among labor-intensive and capital-intensive firms.

5.4. Economic Implications of Peer Effects

Table 9. Economic implications of cross-firm imitation.

	EVAR	EVANAR	ROE	ROA	ROTA	ROIC	ESG
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
CGLinked	0.089 ***	0.138 ***	0.164 ***	0.051 ***	0.034 ***	0.092 ***	6.825 **
	(4.64)	(6.47)	(7.02)	(4.02)	(3.34)	(4.82)	(2.79)
TA	0.000 **	0.000 **	0.000 **	0.000 **	0.000 **	0.000 *	0.000 ***
	(2.45)	(2.31)	(2.24)	(2.18)	(2.17)	(1.94)	(3.94)
Lev	−0.023 **	−0.104 ***	−0.139 ***	−0.068 ***	−0.036 ***	−0.040 ***	4.504 ***
	(−2.44)	(−7.15)	(−7.73)	(−11.23)	(−5.93)	(−4.47)	(4.16)
Growth	0.023 ***	0.038 ***	0.044 ***	0.015 ***	0.015 ***	0.024 ***	−0.168
	(7.87)	(10.06)	(8.52)	(9.13)	(8.69)	(8.30)	(−1.07)
SOE	0.011 ***	0.014 ***	0.019 ***	0.006 ***	0.004 ***	0.010 ***	1.020 ***
	(3.84)	(3.78)	(4.25)	(4.72)	(3.96)	(3.63)	(6.25)
CFO	0.899 ***	1.177 ***	1.238 ***	0.611 ***	0.664 ***	0.896 ***	9.938 ***
	(23.84)	(24.72)	(20.61)	(23.68)	(24.91)	(23.90)	(6.74)
Yretwd	0.007 ***	0.011 ***	0.015 ***	0.006 ***	0.006 ***	0.008 ***	−0.318 ***
	(4.33)	(6.26)	(5.29)	(5.04)	(5.00)	(5.14)	(−3.07)
DisAcc	0.770 ***	1.030 ***	1.152 ***	0.530 ***	0.559 ***	0.767 ***	4.265 ***
	(13.70)	(14.37)	(13.47)	(13.92)	(14.27)	(13.76)	(5.88)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	31,776	31,624	31,596	31,843	31,843	31,776	8486
R-squared	0.556	0.502	0.440	0.658	0.659	0.570	0.621

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

presents the empirical results investigating the economic implications of cross-firm imitation in governance strategies. In the test, we adopt a comprehensive set of financial performance and ESG indicators as dependent variables, whose detailed definitions are provided in Section 4.3.

As shown in Table 9, the coefficients on peer average governance quality are all positive and statistically significant at the 1% level across financial performance measures, and remain significant at the 5% level when ESG performance is considered as the dependent variable. These findings indicate that mimicking the governance practices of technologically linked peers reflects rational learning rather than blind herding. In other words, cross-firm imitation of corporate governance generates positive economic spillovers among technologically connected firms.

Combined with our earlier findings, innovation-oriented firms with relatively weak governance quality tend to imitate peers with higher governance quality and superior performance, and such imitation ultimately delivers favorable economic outcomes. Hypothesis 4 is thus supported.

5.5. Robustness Test

5.5.1. Controlling for the Industry-Based Peer Effects

To ensure the robustness of our main findings and rule out the possibility that the documented governance peer effect arises from intra-industry learning rather than imitation among technologically linked firms, we explicitly control for governance imitation among industry peers.

To construct industry-based peer variables, we follow the industry classification standards issued by the China Securities Regulatory Commission (CSRC). The average governance quality is then calculated for broad industry sectors (Peer1-CG) and industry subcategories (Peer2-CG), respectively.

Table 10. Correlation between technology-based and industry-based variables.

	Peer1-CG	Peer2-CG	CGLinked
Peer1-CG	1.000
Peer2-CG	0.995	1.000
CGLinked	0.568	0.568	1.000

reports the correlation between the technology-based and industry-based peer governance quality variables. Obviously, the two industry-based variables are highly correlated, while the technology-based peer variable shows only a moderate correlation with the industry-based variables (0.568). Regressing the technology-based peer variable on each industry-based measure yields R-squared values of 0.323 and 0.322, respectively. These results provide preliminary evidence that technological and industry peer relationships capture distinct information content.

Table 11. Robustness test: controlling for industry-based peer effects.

	CG	CG	CG	CG
	(1)	(2)	(3)	(4)
CGLinked		0.339 **		0.340 **
		(2.85)		(2.86)
Peer1-CG	0.058 **	0.052 **
	(2.73)	(2.37)
Peer2-CG			0.049 **	0.043 *
			(2.31)	(1.96)
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	18,947	18,947	18,947	18,947
R-squared	0.356	0.356	0.356	0.356

Note: **, * indicate statistical significance at the 5% and 10% levels, respectively.

reports the robustness test results after incorporating the industry-based peer average governance quality into model (8). Columns (1) and (3) present the results when the technology-based peer variable is replaced by peer governance measures constructed at the industry sector and subcategory levels, respectively. The coefficients for industry peer average governance quality are both positive and statistically significant at the 5% level. After further incorporating technology-based peer variables, as shown in columns (2) and (4), both the coefficients for technology-based and industry-based variables remain statistically significant. Particularly, the coefficient for technology-based peer average governance quality drops from 0.447 in Table 3 to approximately 0.340, yet remains statistically significant at the 5% level.

This finding confirms that the technology-based peer effect contains information content beyond the conventional peer effect observed within industries.

5.5.2. Addressing the Endogeneity Problem

Manski [71] underscored that the key challenge in studying peer effects lies in model identification. Due to the “reflection problem”, commonly used linear models often fail to achieve identification. To mitigate this issue, we employ the lagged explanatory variable in model (8).

A further concern arises from omitted variable bias. For instance, common firm characteristics such as investment policies and financial constraints may simultaneously influence the governance strategies of both the focal firm and its peers. To formally address endogeneity stemming from omitted variables, we follow the approach of prior studies [24,51] and estimate the model using instrumental variable two-stage least squares (IV-2SLS) and generalized method of moments (IV-GMM).

Following the approach of Leary and Roberts [51] and Machokoto et al. [24], we employ peer firms’ idiosyncratic stock returns and idiosyncratic risks as the instrumental variables. The selection of these instruments is justified by three considerations: First, empirical evidence consistently demonstrates strong associations between both idiosyncratic risks and stock returns, and corporate governance. For instance, Drobetz et al. [72] document a positive relationship between governance practices and abnormal returns, while poorly governed firms tend to exhibit higher risk levels [73]. Second, as firm-specific measures, idiosyncratic stock returns and risks are unlikely to be correlated with governance decisions of other firms. Third, unlike the accounting-based metrics, stock returns are inherently less susceptible to managerial manipulation, ensuring the reliability of the instruments.

To validate the instrumental variables, we conduct a series of identification tests: the Kleibergen–Paap rk LM test for underidentification, the Cragg–Donald Wald F test for weak identification, and the Hansen–J test for overidentification. As presented in

Table 12. Test of peer effects with instrumental variables.

	IV-2SLS 1st Stage	IV-2SLS 2nd Stage	IV-GMM 2nd Stage
	(1)	(2)	(3)
	CGLinked	CG	CG
CGLinked		0.800 ***	0.872 ***
		(4.09)	(4.28)
RiskLinked	−2.156 ***
	(−14.24)
ResiLinked	0.292 ***
	(12.89)
Controls	Yes	Yes	Yes
Kleibergen–Paap rk LM statistic	8.260 **	8.260 **	8.260 **
Cragg-Donald Wald F statistic	1436.670 ***	1436.670 ***	1436.670 ***
Hansen J statistic	1.442	1.442	1.442
Year FE	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes
N	18,638	18,638	18,638
R-squared	0.821	0.258	0.258

Note: ***, ** indicate statistical significance at the 1% and 5% levels, respectively.

, the results reveal a strong correlation between the instruments and the peer average of governance quality, allowing us to reject the null hypotheses of underidentification and weak identification. Furthermore, the Hansen–J statistics are small and statistically insignificant, indicating that we can reject the null hypothesis of overidentification, thus confirming the exogeneity of the instruments.

Table 12 presents the regression results using idiosyncratic stock returns and idiosyncratic risks of peer firms as instrumental variables. Column (1) reports the first-stage IV-2SLS results, which show a positive association between peer governance quality and idiosyncratic returns, as well as a negative association between peer governance quality and idiosyncratic risks. These results are consistent with findings from existing literature.

Columns (2) and (3) present the second-stage estimates for IV-2SLS and IV-GMM, respectively. Notably, the coefficients for peer average governance quality are 0.800 (IV-2SLS) and 0.872 (IV-GMM), both substantially larger in magnitude than the coefficient reported in Table 3 and statistically significant at the 1% level. The consistency across both instrumental variable frameworks reinforces the reliability of our core findings, confirming a strong and robust peer effect on corporate governance practices (in the second-stage regressions of both IV-2SLS and IV-GMM, the specific values of the Kleibergen–Paap rk LM statistic, Cragg-Donald Wald F statistic, and Hansen J statistic differ slightly across the two specifications but are negligible. When rounded to three decimal places, the values are identical).

To further address endogeneity concerns, we employ the same instrumental variable approach designed for tests of moderating mechanisms. Detailed two-stage instrumental variable results for the information bridging, competitive isomorphism, and market feedback mechanisms are reported in Appendix A.

5.5.3. Characteristics of the Executives

According to the Upper Echelons theory proposed by Hambrick and Mason [74], executives with advanced educational attainment develop more sophisticated cognitive frameworks [75], which strengthens their ability to process and integrate complex information from technologically connected firms. On this basis, if the information-based mechanism holds, firms managed by executives with stronger educational backgrounds should demonstrate more pronounced imitation in corporate governance decisions.

Additionally, prior research [76,77] underscores that executives with international experience develop stronger cross-border and cross-cultural communication skills, as well as higher social adaptability. These traits increase their willingness to learn from and more effectively adopt governance practices of technologically linked peers. Hence, we examine the heterogeneity of governance peer effects across executives’ educational attainment and overseas experience as robustness tests of the information-based mechanism.

Table 13. Characteristics of executives and peer effects.

	Educational Credentials		Overseas Experience
	(1)	(2)	(3)	(4)
	Low	High	Low	High
CGLinked	0.122	0.731 ***	0.226	0.612 **
	(0.59)	(3.48)	(1.22)	(2.72)
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	9202	10,210	9120	10,118
R-squared	0.363	0.354	0.397	0.296

Note: ***, ** indicate statistical significance at the 1% and 5% levels, respectively.

reports the regression results of model (8) for the subsamples grouped by executives’ average educational attainment and average overseas experience, respectively. Columns (1) and (3) show that for firms led by executives with lower educational levels and limited international experience, the coefficients on peer average governance quality stand at 0.122 and 0.226, respectively, neither of which is statistically significant. Conversely, columns (2) and (4) demonstrate that these coefficients rise to 0.731 and 0.612 for firms with highly educated executives and executives with extensive overseas experience, which are significant at the 1% and 5% levels, respectively. This subsample analysis reveals a strong positive association between executives’ educational and international backgrounds and governance peer effects, providing further empirical support for the information-based mechanism documented in Section 5.2.2.

6. Discussion and Conclusions

6.1. Discussion of Results

This study empirically shows that corporate governance decisions are significantly influenced by technologically linked firms, confirming the existence of governance peer effects. Such effects operate mainly through three channels: information diffusion, competitive pressure, and market feedback, and display notable heterogeneity across firm characteristics. We also systematically examine the economic consequences of these governance peer effects. Our findings extend the traditional perspective of governance imitation rooted in social networks, providing a new framework for understanding corporate governance peer effects.

This paper contributes to the existing literature in multiple dimensions. First, prior studies usually define peers as geographically adjacent firms [78], industry competitors [51], or firms connected by social ties such as interlocking directors [79] and common shareholders [80]. In contrast, this study identifies peer boundaries based on technological linkages, enriching the conceptual scope of peer effects. For technological network construction, existing research often measures technological proximity via patent text similarity or classification overlap [9,10], which only captures knowledge spillovers within the same field. By using the Mahalanobis method to build technological networks, this study more comprehensively captures cross-field knowledge diffusion and transmission, improving the methodology of network measurement.

Second, in the literature on corporate governance peer effects, previous research has focused on the roles of institutional isomorphism [81] and common shareholders [33,34] in driving governance convergence, while neglecting governance imitation stemming from cross-firm technological networks. This paper identifies the imitation of governance practices among technologically connected firms, offering new empirical evidence for this research stream.

Third, information-based imitation [82] and competition-based imitation [54] have been widely verified as mechanisms of peer effects. On this basis, this study further constructs cross-firm information efficiency indicators and introduces market feedback as an additional channel, providing a more integrated explanation for how governance peer effects function.

Finally, existing studies offer limited discussion on the economic consequences of corporate governance peer effects. To fill this gap, we demonstrate that governance peer effects significantly enhance firm performance and ESG ratings. This research expands the boundaries of the literature by examining the economic outcomes of governance peer effects from the perspectives of value creation and sustainable development.

6.2. Conclusions

This study empirically investigates the existence, formation mechanisms, and economic consequences of governance decision imitation among firms in cross-firm technological networks. The key conclusions are summarized as follows:

First, robust empirical evidence confirms the existence of governance peer effects among technologically linked firms. Specifically, firms in these networks tend to align their governance decisions with peers, and this effect is particularly pronounced for firms with strong innovation incentives. This validates that technological networks serve as an important channel for transmitting governance peer effects.

Second, three mechanisms jointly drive governance convergence in innovation networks. Information-based imitation relies on effective information flow and integration, as shown by stronger peer effects for firms with low own governance quality or high peer governance quality. Competition-driven imitation stems from market competitive pressure, with firms in more competitive markets more likely to mimic peers’ governance practices. Market feedback imitation further complements this logic by extending information transmission theory to governance contexts, collectively clarifying the multi-dimensional drivers of governance peer effects in technological networks.

Third, governance peer effects exhibit significant heterogeneity. Governance convergence is more pronounced when peers have strong overall performance; firms with intensive R&D engagement are more likely to imitate peers’ governance practices. Notably, technology-intensive firms show a stronger tendency to imitate peers’ governance compared to labor- and capital-intensive firms.

Fourth, regarding economic consequences, governance peer effects driven by technological network interactions exert a positive impact on focal firms’ performance. This confirms that imitating technological peers’ governance strategies is rational and enhances firm operating performance, validating the practical value of governance peer effects.

6.3. Avenues for Future Research

This study adopts the Mahalanobis method to quantify the classification similarity of invention patents held by different listed firms, and thereby constructs a generalized cross-firm technological network. Future research may refine and extend the methodologies for constructing technological networks—for instance, by incorporating text analysis and other alternative approaches—to further improve the comprehensiveness and accuracy of network measurement.

In addition, the empirical focus of this paper is restricted to Chinese A-share listed companies. Subsequent studies could expand the research scope to include firms from other countries and regions. Such an extension would help validate the cross-country generalizability of the present findings and offer more universal theoretical and practical implications for research on corporate governance peer effects.