Cracking the silicon ceiling: how female directors shape corporate robot adoption

Cracking the silicon ceiling: how female directors shape corporate robot adoption | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cracking the silicon ceiling: how female directors shape corporate robot adoption Jianjun Dong, Yuqing Xu, Xuekun Suo, Han Lin, Mingchuan Yu, Ming Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8572539/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Drawing on expectation states theory, this study investigates the impact of board gender diversity on industrial robot adoption within the context of the era of industrial intelligence. Based on a sample of Chinese A-share listed manufacturing firms from 2011 to 2022, the research utilizes panel data regression analysis to empirically test this relationship, employing instrumental variables and robustness checks to address endogeneity concerns. The findings reveal that female board representation significantly promotes the adoption of industrial robots, suggesting that gender-diverse boards are more receptive to technological innovation. Furthermore, the analysis indicates that this positive effect is heterogeneous: it is enhanced by higher product pricing power but weakened by stronger internal controls. As one of the first empirical studies to connect demographic diversity with industrial robot adoption, this research contributes to the literature on corporate governance and technology management. The results suggest that manufacturing firms, particularly those with strong market positions, should consider gender diversity as a strategic asset for enhancing technological competitiveness, while providing policymakers and investors with new insights into the strategic value of diverse leadership. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Female board representation industrial robot adoption internal control pricing power expectation states theory Figures Figure 1 Figure 2 Figure 3 I. Introduction Combining business with artificial intelligence aims to build new pathways for enterprises to cultivate core competitiveness (Duan et al., 2023 ; Koch et al., 2021 ; Kong et al., 2023 ). Industrial robots, as AI-enabled generalized technology, are increasingly adopted due to their dual externality: improving productivity and generating positive environmental spillovers (Acemoglu & Restrepo, 2018 ; Chen et al., 2024 ). Amid fading demographic dividends and urgent industrial upgrading needs, they represent a strategic option for enterprises (Duan et al., 2023 ). Promoting environmental protection while creating economic value is an effective strategy for securing new market opportunities and competitive advantages (Li et al., 2025 ; Zhao et al., 2024 ). Therefore, exploring factors affecting industrial robot adoption holds significant theoretical and practical importance. Given the critical importance of industrial robots, researchers have endeavored to identify the relevant antecedents of industrial robot adoption through different perspectives, which are categorized into two main areas: institutional theories and market competition. Institutional theories highlight the role of environmental institutions (Acemoglu & Restrepo, 2020 ; Zhu et al., 2023 ), with AI advancements (Goldsmith-Pinkham et al., 2020 ). Enabling robots to reduce industrial pollution (Rodrigue et al., 2024 ; Yu et al., 2023 ) and ensure environmental compliance (Huang et al., 2022 ). Market competition drives adoption through robots' productivity effects (Autor, 2015 ). While insightful, these views primarily frame adoption as externally driven. Crucially, adoption is a strategic decision contingent on organizational decision-makers' willingness and ability (Islam et al., 2025 ), influenced by factors like gender (Duan et al., 2023 ; Kong et al., 2023 ) and top management team traits (Adams & Ferreira, 2009 ; Zou et al., 2018 ). Scholars have further revealed that women tend to pursue social and economic goals through technology adoption more than men (Liu et al., 2014 ), suggesting board gender diversity—by increasing social diversity—may play a key role in industrial robot adoption. Female board representation refers to the percentage of female directors on a company's board, also known as board gender diversity (Guldiken et al., 2019 ). Rising education and recognition of women's leadership increase female senior roles (Mao et al., 2023 ). This representation impacts firm competitiveness through gender-neutral hiring, meritocratic promotion (Dezsö & Ross, 2012 ), and diverse decision-making benefits like innovation and long-term value creation (Luo et al., 2018 ). Female managers’ traits may facilitate technology adoption (Kong et al., 2023 ), accelerate technology introduction and improve decision-making efficiency (Zou et al., 2018 ; Adams & Ferreira, 2009 ). However, China presents a distinct context. Despite government gender equality advocacy, deeply ingrained traditional patriarchal norms create a persistent gap between policy and corporate reality. For instance, in companies with strong patriarchal norms, female directors' unique insights may be limited in technology adoption decisions. Despite growing research on female board representation, its role as a motivator for industrial robot adoption remains neglected. This study addresses this gap by establishing a correlation between female board representation and industrial robotics adoption. Drawing on the expectation states theory, a gender-specific framework is developed to examine this role. The theory provides an interactive perspective on board diversity expectations, identifying gender diversity enhances the willingness and efficiency of adopting industrial robots through the differentiated ability expectation mechanism in the expected state theory and the dual-dimensional drive (vertical skill advantages and horizontal responsibility consensus) in the practical logic. In addition, internal control weakens this correlation, while pricing power strengthens it. Empirical analysis of Chinese manufacturing companies (2011–2022) supports the framework and hypotheses. This study contributes theoretically and practically. Theoretically, it innovatively links gender diversity with industrial robot adoption in emerging tech, filling literature gaps (Adams et al., 2023 ; Adams & Ferreira, 2009 ; Chen et al., 2018 ; Fall et al., 2021 ). It empirically demonstrates female directors' unique role in promoting adoption, opening new avenues for diversity research and offering fresh perspectives on their complex technological impact from a gender equality stance. It further clarifies gender diversity's diverse manifestations across organizational contexts, laying groundwork for defining adoption boundary conditions and deepening understanding of internal mechanisms. Practically, it provides new perspectives and evidence for enterprise robot adoption, helping firms—especially high-tech enterprises—capitalize on opportunities to enhance productivity and efficiency. II Theory, literature review and hypotheses development Industrial robot adoption Industrial robotics, a “high-impact” innovation boosting productivity and reducing environmental costs (Lin & Xu, 2024 ), faces conflicting drivers in research. While environmental regulations and competitive markets are seen by some as triggers for adoption through enhanced competitiveness (Dong et al., 2024b ; Goldsmith-Pinkham et al., 2020 ), others argue strict regulations increase costs, inhibiting adoption. Studies have shown that firms adopt robots to optimize processes, reduce costs in response to environmental pressures, resource constraints, and market demands (Acemoglu et al., 2014 ; Autor, 2015 ). In response to external drivers, organizations tend to acquire and develop resources for managing processes (Dong et al., 2024a ) in response to industrial robot adoption, where corporate governance matters: poor governance reduces outputs (Huang et al., 2023 ). Another study found that internal control, a set of activities implemented by companies to monitor business processes and control risks, negatively impacts robot adoption (Islam et al., 2025 ), while pricing power as the ability of firms to adjust pricing strategies, facilitates adoption (Maine et al., 2015 ). In summary, despite this focus on external push and governance, the role of female board representation in industrial robot adoption remains underexplored. Socially assigned roles of gender: expectation states theory Gender diversity in corporate boardrooms' impact on firms’ strategic choices and performance has been explored through various theoretical perspectives (Ahern & Dittmar, 2012 ), analyzing its effects on corporate strategy and operations (Saeed et al., 2024 ; Triana et al., 2019 ). This study expands existing research by incorporating expectation states theory to explain female directors' preferences for adopting industrial robots. The theory focuses on gender diversity in team collaboration and its contribution to group goal consensus (Correll & Ridgeway, 2006 ). Expectation states theory posits that inherent biases about others' capabilities influence group dynamics, evoking stereotypes in task-oriented settings (Correll, 2004 ; Yuan & Sun, 2024 ). It's pertinent in environments with low female representation, acknowledging gender disparities' impact on decision-making (Fuentes-Fuentes et al., 2023 ). Gender, a social construction sustaining unequal relations, shapes behavior, practice, and individual identities within organizations (Correll, 2004 ). The theory elucidates how gender-based status and differing competence perceptions affect social interactions in organizational contexts (Ridgeway & Correll, 2004 ). Expectation states theory states that biases about group characteristics influence interactions (Correll & Ridgeway, 2006 ), and characteristic differences can trigger stereotypes in task-specific situations (Correll, 2004 ). It is applicable where women are underrepresented, as it explicitly addresses gender differences' impact on decision-making and outcomes (Fuentes-Fuentes et al., 2023 ). At the interaction level, it shapes organizational behavioral norms and practices; at the individual level, it shapes identities and roles (Correll, 2004 ). The theory describes how gender-determined status and competence beliefs affect organizational interactions (Ridgeway & Correll, 2004 ). The theory also states that expectations of task completion and evaluations are influenced by vertical characteristics (specialized skills, e.g., men's competence in practical activities) and horizontal characteristics (group distinctiveness, e.g., females' team-orientation) (Fuentes-Fuentes et al., 2023 ; Eagly, 2009 ). Dominant femininity perceptions affect behavior by shaping perceived expectations (Fuentes-Fuentes et al., 2023 ). Thus, the framework formed by these two characteristics is pertinent in industrial robot adoption. Female board representation and industrial robot adoption In a context where gender diversity is increasingly valuing skills over representation, female directors are expected to provide unique insights on technology adoption, impacting firms (Saeed et al., 2024 ; Triana et al., 2019 ). Management implements industrial robotics to demonstrate its value. Based on expectation states theory, gender characteristics and task-related skills are crucial for evaluating contributions (Correll & Ridgeway, 2006 ; Fuentes-Fuentes et al., 2023 ). In industrial robot adoption, gender diversity and specialized skills interact, shaping perceptions of individual contributions. In the vertical dimension, managers specializing in industrial robot adoption are scrutinized for critical knowledge and skills, such as those maintaining relationships with developers/suppliers or securing favorable loans. Culturally gendered competence stereotypes (e.g., Confucian expectations) often assign women group-oriented strengths: communication (Correll, 2004 ; Dezsö & Ross, 2012 ), collaboration, financing, and team cohesion (Karavitis et al., 2021 ; Luo et al., 2018 ). This skillset aligns with industrial robot adoption’s demands for multi-party coordination, technology integration, and capital deployment (Duan et al., 2023 ; Kong et al., 2023 ), an area where women show higher involvement (Sang et al., 2020 ). Consequently, gendered competence expectations increase the likelihood that women’s contributions to robotics adoption are valued (Correll & Ridgeway, 2006 ). Furthermore, the adoption of industrial robots may boost their credibility, standing, and acknowledgment in the company, given perceptions of their superior competency in this domain (Correll, 2001 ), thereby increasing their effectiveness in achieving adoption goals. Horizontally, individual differences create heterogeneity in industrial robot adoption outcomes among female directors. Existing research shows women exhibit higher social responsibility than men (Zou et al., 2018 ), that is, they are more inclined to participate in decision making on strategic activities that are friendly to the environment and stakeholders (Javed et al., 2023 ). Female managers' stronger social responsibility may create a subgroup effect that fosters internal consensus and concerted action when adopting eco-friendly robotics that enable business transformation. This increases management's willingness to adopt industrial robots. For the corporate strategic level, industrial robot adoption is undoubtedly a green-related technological innovation initiative (Li et al., 2025 ), that is, requiring integration of robotics compatible with green manufacturing across organizational processes and technological resources. To a certain extent, the corresponding investment of technological resources and conceptualization are required (Yuan & Sun, 2024 ). In this sense, females play a key role both in facilitating strategic decisions and accelerating adoption, advancing robots' strategic integration. Thus, even if not all possess relevant competencies, greater female representation increases the likelihood of adoption-related decisions and actions. Therefore, Hypothesis 1 is formulated as follows. H1: Female board representation is positively associated with the adoption of industrial robots. The moderating role of internal control While industrial robot adoption offers benefits, it also inevitably faces high costs and necessitates process changes (Islam et al., 2025 ). As a core management function, internal control—ensuring operations and risk reduction—significantly impacts management teams' adoption efforts (Li et al., 2024 ). Expectation states theory indicates managers' gender influences decisions (Correll, 2004 ). Under systemic pressures, internal control rigor critically shapes female managers' strategies for promoting adoption (Nasir et al., 2021 ). Moderate control enhances autonomy, fostering cross-functional cooperation and innovation, whereas high control impedes adoption and reduces flexibility. For one thing, under relaxed internal control, female managers view industrial robot adoption challenges as inherent to innovation. This flexibility fosters collaboration and problem solving (Nakata & Im, 2010 ). In such an organizational climate, female managers strategically balance control with adoption to mitigate risks and enhance competitiveness through innovation, creating greater economic value (Rodríguez et al., 2008 ). In addition, low internal control Low control also provides motivation and autonomy, eases resource competition, and reduces internal constraints' impact on adoption, thus boosting female managers' inclination toward robotics implementation. Conversely, overly strict internal control constrains female managers' cognitive flexibility (Li et al., 2024 ), reinforcing adherence to traditional practices. Industrial robot adoption entails high investment, learning costs, and risks of uneven resource allocation (Chang et al., 2023 ), which strict control exacerbates by prioritizing risk minimization over innovation. This limits firms to rigid processes and makes it difficult to quickly adjust strategies and business models when necessary. Expectation states theory suggests challenging environments amplify female managers' caution: while relaxed control limits diversity's impact, strict control heightens its role in adoption decisions. Therefore, Hypothesis 2 is hypothesized as follows: H2: Internal control negatively moderates the adoption of industrial robots by female board representation. The moderating role of pricing power Pricing power denotes a firm’s ability to align product or service pricing with market acceptance and industry standards through market analysis, strategy adjustments, and resource optimization (Dutta et al., 2003 ; Maine et al., 2015 ). Previous studies have pointed out that pricing power critically sustains profitability and market share, thereby influencing management's decisions on corporate strategies (Maine et al., 2015 ). Further, the process of how female managers evaluate firms based on the level of “fit” mentioned above and thus influence decision-making is revealed in this paper. Notably, female directors associate with multi-stakeholder strategies (Meng et al., 2024 ). Expectation states theory posits that senior directors maintain implicit leadership through long-term strategies that align competencies with roles to enhance organizational impact (Meng et al., 2024 ). Thus, female board representation's impact on industrial robot adoption hinges on pricing power. On the one hand, When firms possess high pricing power, female managers become more receptive to industrial robot adoption's uncertainties, as the firm better withstands market fluctuations (Liozu & Hinterhuber, 2013 ). In this case, they leverage this advantage to predict demand, adjust pricing, optimize costs, reduce risks, and actively participate in adoption decisions (Raja et al., 2020 ). On the other hand, high pricing power enhances perceived adoption benefits: female managers recognize such firms can precisely position offerings and respond swiftly to market, technological innovations to maximize the return on investment (Liozu, 2015 ). This increases their likelihood of lowering adoption thresholds, enhancing decision efficiency, and actively promoting robotics implementation. Conversely, low pricing power heightens industrial robot adoption challenges. If female managers perceive pricing strategies cannot cover robot costs (Liozu & Hinterhuber, 2013 ), skepticism and resistance may weaken willingness to embrace industrial robotics. Low pricing power inefficiently converts technology investments into returns and may trigger avoidable financial risks (Liozu, 2015 ), motivating female managers to maintain current production models rather than explore robots' productivity gains. Based on this, the following prediction is proposed that female managers in low pricing power firms likely constrain robot adoption compared with firms with high pricing power. Therefore, Hypothesis 3 is hypothesized as follows: H3 Pricing power positively moderates the adoption of industrial robots by female board representation. Figure 1 illustrates the conceptual framework with explanatory, moderating, and explained variables. III. Methodology Sample and Data To evaluate the above hypothesis, a sample of Chinese manufacturing companies listed between 2011 and 2022 was analyzed. First, companies categorized as special treatment (ST) or special transfers (PT) are excluded. This step is intended to exclude data points that could unduly influence the statistical analysis and to ensure a more representative sample. The second step excludes samples that lack variable data. Finally, a sample of this observation was obtained. The industrial robot data used in this study is derived from the International Federation of Robotics (IFR), an authoritative global non-profit providing country-industry-year robotics statistics directly from manufacturers. Since industrial robots primarily serve manufacturing, this paper focuses on manufacturing sector data. Personnel data are obtained from the industrial enterprise database, which is widely used for research on China's economy, and financial data are derived from the CSMAR, which is known for supporting empirical research and model testing. Variable Measuremen t Female board representation The percentage of women serving on the board is designated as the independent variable. According to previous studies (Adams et al., 2023 ), the female directors’ proportion is utilized as our primary scale of female board representation (Female). For robustness, we also consider two alternatives: a binary variable assigned a value of one if there is at least one female manager on the board, and zero if there are none (Dezsö & Ross, 2012 ). This metric aligns with managers' pivotal roles in industrial robot adoption (Kong et al., 2023 ). Industrial robot adoption The dependent variable, industrial robot adoption (Robot), is measured using an enterprise-level penetration index. Following prior research (Acemoglu & Restrepo, 2020 ; Goldsmith-Pinkham et al., 2020 ), this paper integrates IFR industry-level robotics data with Chinese manufacturing listed firms' microdata, adopting the "Batik instrument" approach. The utilization of industrial robots in China has shown a pronounced upward trend only since 2010, and accounting for potential lagged effects of female board representation, dependent and moderating variables use one-period lagged data (2012–2022), while independent variables cover 2011–2021. The specific measurement method is as follows: Step 1: Calculate the industrial robot penetration rate indicator at the industry level: $$\:{PR}_{it}^{CH}=\frac{{MR}_{it}^{CH}}{{L}_{i,t=2011}^{CH}}$$ \(\:{MR}_{it}^{CH}\) represents the stock of industrial robots in Chinese industry i in year t , \(\:{L}_{i,t=2011}^{CH}\) denotes the employment level in Chinese industry i in the base year 2011, \(\:{PR}_{it}^{CH}\) measures the penetration rate of industrial robots in Chinese industry i in year t . Step 2: Construct an indicator for the penetration rate of industrial robots at the enterprise level: $$\:{CHFexposure\:to\:robuts}_{jit}=\frac{{PWP}_{jit=2012}}{{ManuPWP}_{t=2012}}\ast\:{PR}_{it}^{CH}$$ This indicator measures the penetration rate of industrial robots in industry j in year t . \(\:\frac{{PWP}_{jit=2012}}{{ManuPWP}_{t=2012}}\) represents the ratio of the proportion of employees in the production sector of industry i and enterprise j in the manufacturing sector in 2012 (base period) to the median proportion of employees in the production sector of all enterprises in the manufacturing sector in 2012. For enterprises j , changes in the penetration rate of industrial robots mainly reflect changes in the technological characteristics of the domestic industry, and are unrelated to the characteristic factors of the enterprises themselves. Step 3: Using US industry-level industrial robot data to construct an instrument variable for robot penetration at the Chinese enterprise level: $$\:{\stackrel{-}{CHFexposure\:to\:robuts}}_{jit}=\frac{{PWP}_{jit=2012}}{{ManuPWP}_{t=2012}}\ast\:\frac{{MR}_{it}^{US}}{{L}_{i,t=1990}^{CH}}$$ \(\:{MR}_{it}^{US}\:\) indicates the stock of industrial robots in the US industry i in year t . \(\:{L}_{i,t=1990}^{CH}\) represents the number of people employed in the industry i in the US in 1990 (base period). \(\:{PR}_{it}^{US}=\frac{{MR}_{it}^{US}}{{L}_{i,t=1990}^{CH}}\) indicates the penetration rate of industrial robots in the US industry i in year t . This paper employs U.S. industrial robot data as an instrumental variable for two reasons: First (technological exogeneity): As a global leader in industrial robotics, U.S. industry robot adoption trends effectively reflect the direction of technological progress in the sector, rather than being influenced by country-specific factors. Second (exclusivity constraint): The near-perfect U.S. labor market means its robot application affects China primarily through technology spillovers, not endogenous factors like labor market distortions. Therefore, the level of industrial robot application in the United States meets the dual conditions for a tool variable: Correlation: U.S. trends align with technological characteristics in China's comparable industries; Exogeneity: Unrelated to China's domestic adoption drivers, e.g., policy subsidies, labor cost fluctuations. Internal control Based on previous experience, internal control index (IC) is chosen to measure the degree of internal control in firms in this study (Yuan & Sun, 2024 ), sourced from the DIB database and risk management database (Li et al., 2024 ). Data were logarithmically transformed to ensure comparability and reduce bias and extreme values. The index evaluates listed firms' internal control systems through five indicators: i.e., legitimacy of operation and management, security of company property, validity and completeness of financial reports and associated information, operational efficiency and effectiveness, and realization of the development strategy. Pricing power According to previous studies, pricing power (PP), measuring a firm's market position and monopoly degree (Nguyen & Nguyen, 2022 ), is quantified using the Lerner index (Spierdijka & Zaourasa, 2018 ). Lerner's index, also referred to Lerner's index of monopoly power, which gauges monopoly strength by measuring price deviation from marginal cost. When the Lerner index approaches 0, it signifies that the firm possesses negligible market power, with the price being nearly equivalent to its marginal cost. Conversely, when the Lerner index nears 1, it indicates that the firm holds considerable market power, enabling it to set prices significantly above its marginal cost. The formula for calculating the Lerner Index of an enterprise is \(\:\:L=(P-MC)/P\) . (Where L represents the Lerner Index, P represents the price, and MC represents marginal cost, which refers to the variation in total cost that results from producing one additional unit or reducing production by one unit at a specific level of output.) Control variables Various factors impact industrial robot adoption. Based on the existing literature, a set of corporate characteristics that may affect industrial robot adoption are selected (Duan et al., 2023 ; Kong et al., 2023 ) in order to minimize their potential impact (Goldsmith-Pinkham et al., 2020 ; Zhu et al., 2023 ): firm size ( Size , i.e., log of annual total assets), firm leverage ( Lev , i.e., total liabilities/total assets), firm growth ( Growth , i.e., revenue for the current year/revenue for the previous year-1), return on assets ( ROA , i.e., net profit/total assets), cash flow ratio ( Cashflow , i.e., operating cash flow/current liabilities), proportion of fixed assets ( FIXED , i.e., fixed assets/total assets), firm age ( Firm Age , i.e., log of the number of years since the firm’s founding) and ownership concentration ( Top1 , i.e., the number of shares held by the largest shareholder/the total number of shares). The definitions and measurements of the variables involved in the study are described in detail in Table I. IV. Results Empirical Results The descriptive analysis of the sample is displayed in TableⅡ, including the mean, standard deviation, and correlation coefficients among the variables. All correlations between variables were less than 0.7, which is within acceptable limits. Table III tests Hypothesis 1 on the positive link between female board representation and industrial robot adoption. Model 1, the baseline regression, shows a significant positive correlation ( β = 0.717, p < 0.01). Model 2, incorporating eight control variables, confirms this positive relationship ( β = 0.847, p < 0.05), strongly validating Hypothesis 1. The coefficient of 0.847 implies that 1 percentage point increase in female representation is associated with an average increase of 0.847 units in this penetration index of industrial robots in enterprises. The rise in coefficient from Model 1 to Model 2 suggests that firm characteristics may moderate the effect. For instance, return on assets negatively correlates with adoption ( β = -0.041), while the fixed asset ratio shows a strong positive correlation ( β = 0.661, p < 0.01), indicating a substantial impact compared to other control variables. The moderating effects of internal control and pricing power were tested in Models 3 and 4, respectively. Consistent with earlier findings, Model 3 showed that female board representation maintained a significant positive correlation with industrial robot adoption ( β = 0.839, p < 0.01). However, the interaction between female board representation and internal control negatively correlated with industrial robot adoption ( β = -0.006, p < 0.05), indicating that internal control moderates this relationship. As shown in Fig. 2 , the relationship between female board representation and industrial robot adoption is positive and flat under lax internal control but significantly negative under strict internal control, highlighting its non-negligible moderating effect. When considering pricing power, the interaction coefficient between female board representation and pricing power ( β = 4.509, p < 0.05) shifts from significant at the 1% level to significant at the 5% level, compared to the coefficient for female representation alone ( β = 0.817, p < 0.01). Pricing power is a crucial moderating factor in the relationship between female board representation and industrial robot adoption. High pricing power strengthens this positive correlation, as shown in Fig. 3 . Robustness Analysis Robust test Robust tests are conducted to ensure dependability of the research results. Table IV shows benchmark model test results using variable substitution, adding control variables, and eliminating extreme values. First, change the independent variable measurement with a 0–1 binary variable (1 for female directors in the company, 0 otherwise). Table IV shows a positive regression coefficient ( β = 0.430, p < 0.01) in the first column, keeping core results unchanged. Next, modify the independent variable with the blindex index for female board representation and re - run regression analysis. The second column in Table IV shows a significant positive correlation ( β = 0.641, p < 0.01) at the 1% level, strongly reinforcing the core findings. Some literature indicates corporate governance factors impact the causality (Spierdijka & Zaourasa, 2018 ), so the following three control variables at the level of corporate governance are added to the sample : ratio of independent directors ( Indep , the number of independent directors/the number of directors), board size ( Board , log of the number of board members), and dual positions ( Dual , the chairman and the general manager are the same person 1, otherwise 0). Regression on the augmented sample (third column) reveals a significant positive correlation ( β = 0.868, p < 0.01) among main variables. Finally, to address extreme values, continuous variables are Winsorized at the 1% level. The fourth column results still show a significant positive correlation ( β = 0.793, p < 0.01). Overall, these robust tests bolster our findings. Endogeneity Test Female board representation positively affects industrial robot adoption, though firms may adjust executive gender ratios due to technical or managerial skill needs. In addition, economic, cultural, and policy differences between provinces also affect female board representation and industrial robot adoption. To address endogeneity, which includes reverse causality and omitted variables, this study employs an instrumental variable approach using provincial average female board representation. As an indicator of the overall level of female board representation of firms in the province, the average female board representation in the province is highly correlated with the female board representation of individual firms. In addition, Provincial averages don’t directly affect individual firms’ robot adoption decisions. This design controls provincial confounders while meeting instrumental variable requirements. Table V presents the results: the Kleibergen-Paap rk LM statistic is 548.602 with p = 0, rejecting the non-identifiability hypothesis of the instrumental variable. The Kleibergen-Paap rk Wald F statistic is 629.940, rejecting the weak instrumental variable hypothesis, validating the choice of provincial average female board representation as a reasonable instrumental variable. Moreover, the regression result ( β = 3.150, p < 0.05) shows a significant positive effect of female board representation on industrial robot adoption. Overall, these tests align with the benchmark regression, confirming result robustness considering endogeneity. Heterogeneity analysis To explore the disparities among various groups, this paper further examines the heterogeneity of female board representation's impact on industrial robot adoption across three dimensions: enterprise qualification, the property nature, and whether enterprises are high-polluting. According to China's Measures for the Administration of the Identification of High-tech Enterprises, enterprises meeting three criteria are deemed high-tech: being in a state-supported high-tech field, having registered in China for a year, and continuously developing core independent intellectual property rights. Samples are thus split into high-tech and non-high-tech. As per Table Ⅵ, model 1 represents high-tech, model 2 non-high-tech. High-tech enterprises have a significantly positive influence coefficient on female board representation ( β = 1.033, p < 0.01), while for non-high-tech, the coefficient ( β = 0.167) is positive but insignificant. This shows high-tech enterprises have a greater demand for industrial robot adoption than non-high-tech ones. Based on asset ownership, enterprises are grouped into state-owned and non-state-owned. Regression analysis on their samples forms Model 3 for state-owned and Model 4 for non-state-owned. In state-owned samples, the female board representation coefficient ( β = 0.302) is positive but insignificant. In non-state-owned samples, it shows a highly significant positive correlation ( β = 0.994, p < 0.01). This aligns with the baseline regression results. The findings imply non-state-owned enterprises, compared to state-owned ones, may focus more on profitability. Under “profit maximization”, robot use cuts labor costs and boosts profits, so they rely more on industrial robots (Duan et al., 2023 ). Furthermore, state-owned enterprises are greatly affected by the national gender equality policy, so state-owned enterprises may have more "symbolic" female directors, while the gender diversity of non-state-owned enterprises is more likely to reflect the actual capacity structure, so the impact of female directors of non-state-owned enterprises on industrial robots is more significant. A growing number of highly polluting enterprises have integrated industrial robots, but the environmental effects are uncertain. Enterprises are categorized as highly polluting or not according to state regulations. Using relevant guidance, industries like manufacturing, mining, and energy production/supply are identified as highly polluting. Table Ⅵ's columns 5 and 6 show analysis results for samples of such enterprises and non-highly polluting ones. For highly polluting enterprises, the coefficient of female board representation on robot adoption is positively correlated ( β = 0.641) but insignificant; for non-highly polluting ones, it's notably positive ( β = 0.877, p < 0.01). This indicates a smaller impact in highly polluting enterprises. Government and stakeholders should encourage them to adopt smart and clean equipment via tax incentives and subsidies to boost production and environmental responsibility (Zhu et al., 2023 ). V. Discussion and Conclusion Industrial robot adoption boosts efficiency but remains understudied (Duan et al., 2023 ). Accelerating its drivers is critical (Islam et al., 2025 ), with top executives' vision, technical expertise, decision-making style, and gender being central to implementation (Islam et al., 2025 ). While management diversity and technology adoption have been examined (Zou et al., 2018 ), few studies specifically address gender diversity's impact on industrial robotics (Kong et al., 2023 ). Based on the expectation states theory, this study reveals female board representation increases industrial robot adoption, particularly under lax internal controls or high pricing power. This aligns with findings that female-led firms exhibit stronger social responsibility and robotics adoption tendencies (Kong et al., 2023 ; Zou et al., 2018 ). At this point, enhancing board gender diversity is thus crucial for industrial intelligence advancement. The study actively invited manufacturing enterprises for authentic feedback. Many practitioners and managers emphasized that female directors enhance decision-making comprehensiveness and inclusiveness, ensuring productivity gains from technological innovation align with employee well-being and social responsibility. They balance efficiency with humanistic care in robot adoption. The sector is actively recruiting female leaders to contribute development insights. Theoretical contributions First, this study bridges gender diversity with industrial robotics adoption—an emerging technology gap in extant literature. While prior research examines gender diversity's impact on performance, innovation, and decision-making in traditional industries (Saeed et al., 2024 ), it overlooks technology adoption in industrial intelligence contexts. Our empirical analysis reveals female directors' unique role in advancing robot adoption, pioneering new theoretical directions for diversity research. Paradoxically, despite growing gender equality awareness, women's status remains restricted in certain sectors (Hong, 2024 ), offering fresh perspectives on diversity's complex role in technology adoption. Second, by introducing expectation states theory, this study provides a solid theoretical foundation for explaining the impact of gender diversity on industrial robotics adoption. While the theory emphasizes sociocultural gender bias's behavioral influence, the paper extends it by revealing female directors' critical role in technology adoption. They drive adoption through unique perspectives, decision-making styles and exert a board-level "multiplier effect," significantly enhancing team decision quality. This enriches expectation states theory and provides a new framework for analyzing executive gender structures in industrial intelligence contexts. Third, this study not only explores the direct impact of female directors on the adoption of industrial robots but further reveals the moderating role of internal control and pricing power in this relationship. It finds that tight internal control diminishes the driving role of female directors, while strong pricing power amplifies it. This finding provides a new theoretical perspective for understanding the differential role of gender diversity in different organizational contexts, as well as an important theoretical basis for examining the boundary conditions of technology adoption. These moderators deepen mechanistic understanding and open new research pathways. Fourth, although existing literature highlights female directors' importance, feminist perspectives remain scarce. This study explores their unique role in industrial robotics adoption, showing how gender diversity drives technology uptake by disrupting traditional power structures. This perspective offers new insights for gender and management theory and supports firms in leveraging gender diversity during industrial intelligence. Fifth, examining female directors' role in industrial robotics adoption reveals how optimizing executive team structure helps organizations adapt to technological change. Gender diversity enhances firms' technology adoption capabilities, adaptability, and competitiveness in dynamic environments. This finding provides new insights into organizational adaptation theory and provides a theoretical basis for studying decision-making in industrial intelligence. Practical Implications This paper offers practical guidelines for executives using empirical data. First, recognizing the value of gender diversity, firms should increase female board representation to drive industrial robot adoption, through policies promoting board diversity and stakeholder awareness. Second, relaxing rigid internal controls facilitates industrial robot diffusion (Li et al., 2024 ). Management should embrace flexibility and innovation to enhance efficiency (Sang et al., 2020 ). Furthermore, firms must strengthen pricing power via enhanced market analysis, cost optimization, and strategic pricing adjustments. Limitations This study also has limitations. First, due to data constraints, we used annual industrial robot adoption metrics which may not fully capture adoption dynamics. Second, some unconsidered factors like board composition, educational backgrounds, experiences, organizational longevity of both managers and board members, as well as the specific characteristics of the industrial robotics market may influence firms' robot adoption decisions. Future research should validate findings through diverse data sources and primary methods. Ultimately, the descriptive results reflect China's current manufacturing landscape. As a developing economy, China’s institutional/technological infrastructure differs from other nations. Since robot adoption and female board representation are context-specific, findings require testing elsewhere. Exploring additional institutional or regional cultural factors would be interesting in the future. Declarations Ethical Approval This article does not contain any studies with human participants performed by any of the authors. Informed Consent This article does not contain any studies with human participants performed by any of the authors. Author Contribution Jianjun Dong, Yuqing Xu, and Han Lin wrote the manuscript. Xuekun Suo and Mingchuan Yu performed the data analysis and designed the tables. Yuan Ming created the figures and revised the overall manuscript. All authors reviewed the manuscript. Acknowledgement We gratefully acknowledge the support of this study from the National Natural Science Foundation of China (Grants No. 72271126, 72201162, 72372079). 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Emerg Markets Finance Trade 54:2965–2981. https://doi.org/10.1080/1540496X.2018.1453355 Tables TableⅠDefinition of variables Variables Symbol Measurements Reference Independent Variable Female board representation Female The number of female directors / Total number of directors Dependent Variable Industrial robot adoption Robot industrial robot penetration index Moderating Variables Internal control IC DIB internal control index Pricing power PP Lerner Index L = (P - MC) / P Control Variables Firm Size Size Natural logarithm of (annual total assets) Firm Leverage Lev Total liabilities at year-end / Total assets at year-end Firm Age Firm Age Natural logarithm of (the number of years since a firm’s establishment) Ownership concentration Top1 Number of shares held by the largest shareholder / Total number of shares Firm Growth Growth Revenue for the current year/ Revenue for the previous year-1 Return on Assets ROA Net profit / Total assets Cashflow Ratio Cashflow Operating cash flow / Current liabilities Proportion of fixed assets FIXED Fixed assets / Total assets Note: Standard errors in parentheses, * p <0.1, ** p <0.05, *** p <0.01 Source: Authors own work. TableⅡ Correlation coefficients between variables Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 1.Robot 6.959 4.072 1 2.Female 0.187 0.110 0.057*** 1 3.Size 22.05 1.179 0.031*** -0.081*** 1 4.Firm Age 2.877 0.332 0.094*** 0.028*** 0.173*** 1 5.Lev 0.394 0.191 0.009 -0.163*** 0.469*** 0.130*** 1 6.ROA 0.045 0.068 -0.005 0.039*** 0.044*** -0.038*** -0.340*** 1 7.Cashflow 0.050 0.070 0.021*** 0.030*** 0.104*** 0.067*** -0.159*** 0.446*** 1 8.Growth 0.334 8.446 0.013*** 0.016** 0.009 0.002 0.011 -0.008 -0.030*** 1 9.FIXED 0.227 0.134 -0.006 -0.127*** 0.111*** 0.040*** 0.176*** -0.124*** 0.153*** -0.010 1 10.TOP1 33.46 14.21 -0.030*** -0.011 0.113*** -0.101*** -0.001 0.130*** 0.092*** 0.008 0.040*** 1 11.IC 643.5 116.9 -0.030*** -0.019*** 0.128*** -0.107*** -0.076*** 0.321*** 0.126*** -0.010 -0.073*** 0.122*** 1 12.PP 0.118 0.133 0.033*** 0.095*** 0.040*** -0.008 0.603*** 0.603*** 0.367*** -0.003 -0.136*** 0.045*** 0.196*** 1 Note: Standard errors in parentheses, * p <0.1, ** p <0.05, *** p <0.01 Source: Authors own work Table Ⅲ Baseline and moderating effects regression results Model (1) Model (2) Model (3) Model (4) VARIABLES Robot Robot Robot Robot Female 0.717*** 0.847*** 0.839*** 0.817*** (0.276) (0.282) (0.282) (0.282) Female × IC -0.006** (0.002) IC -0.000 (0.000) Female × PP 4.509** (1.971) PP 0.067 (0.281) Size 0.007 0.005 0.006 (0.031) (0.031) (0.031) Firm Age 0.036 0.033 0.036 (0.099) (0.099) (0.099) Lev 0.224 0.239 0.227 (0.193) (0.194) (0.194) ROA -0.041 0.062 -0.174 (0.542) (0.563) (0.619) Cashflow 0.021 0.003 -0.001 (0.481) (0.481) (0.483) Growth 0.005 0.005 0.005 (0.003) (0.003) (0.003) FIXED 0.661*** 0.681*** 0.669*** (0.256) (0.256) (0.256) TOP1 -0.002 -0.002 -0.002 (0.002) (0.002) (0.002) Constant 5.461*** 5.008*** 5.095*** 5.021*** (0.232) (0.720) (0.726) (0.720) Observations 18,585 18,585 18,585 18,585 R-squared 0.044 0.045 0.045 0.045 Ind FE YES YES YES YES Year FE YES YES YES YES r2_a 0.042 0.043 0.043 0.040 Note: Standard errors in parentheses, * p <0.1, ** p <0.05, *** p <0.01 Source: Authors own work. Table Ⅳ Robustness test results (1) (2) (3) (3) VARIABLES Robot Robot Robot Robot Female 0.430*** 0.641*** 0.868*** 0.793*** (0.137) (0.242) (0.285) (0.287) Size -0.005 0.004 0.007 0.003 (0.031) (0.031) (0.032) (0.032) Firm Age 0.029 0.036 0.034 0.022 (0.099) (0.099) (0.099) (0.102) Lev 0.214 0.224 0.224 0.279 (0.193) (0.193) (0.193) (0.202) ROA -0.051 -0.043 -0.052 0.331 (0.542) (0.542) (0.542) (0.674) Cashflow 0.056 0.027 0.022 -0.173 (0.481) (0.481) (0.481) (0.537) Growth 0.005 0.005 0.005 -0.103 (0.003) (0.003) (0.003) (0.096) FIXED 0.624** 0.654** 0.663*** 0.691*** (0.255) (0.256) (0.257) (0.261) TOP1 -0.002 -0.002 -0.002 -0.002 (0.002) (0.002) (0.002) (0.002) Indep -0.005 (0.006) Dual -0.006 (0.066) Board -0.008 (0.197) Constant 5.012*** 5.043*** 5.233*** 5.121*** (0.719) (0.721) (0.845) (0.739) Observations 18,585 18,585 18,585 18,585 R-squared 0.045 0.045 0.045 0.045 Ind FE YES YES YES YES Year FE YES YES YES YES r2_a 0.043 0.042 0.042 0.042 Note: Standard errors in parentheses, * p <0.1, ** p <0.05, *** p <0.01 Source: Authors own work. Table Ⅴ Instrumental variable regression results (1) (2) VARIABLES Female Robot Female 3.150** (1.571) IVPRO 0.811*** (0.032) Control FE YES YES Constant 3.793*** (1.091) Observations 18,585 R-squared 0.042 Kleibergen-Paap rk LM statistic 548.602 [0.000] Kleibergen-Paap rk Wald F statistic 629.940 {16.38} Ind FE YES YES Year FE YES YES r2_a 0.159 0.039 Note: * p <0.1, ** p <0.05, *** p <0.01, () values are robust standard errors, [] values are P -values, and {} values are Stock-Yogo weak identification critical value at the 10% level of the test. Source: Authors own work. Additional Declarations No competing interests reported. Supplementary Files process.do data.dta Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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09:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8572539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8572539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505810,"identity":"07e33a29-a35d-471e-b664-72e31a61e5e1","added_by":"auto","created_at":"2026-02-26 13:33:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133697,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical framework\u003c/p\u003e\n\u003cp\u003eSource: Authors own work.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8572539/v1/0b8b1b90d5d7f93600271e2d.jpeg"},{"id":103322917,"identity":"cdd0f355-a5de-44f7-b065-6a8d67d249cf","added_by":"auto","created_at":"2026-02-24 12:17:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35273,"visible":true,"origin":"","legend":"\u003cp\u003eThe moderating role of internal control\u003c/p\u003e\n\u003cp\u003eSource: Authors own work.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8572539/v1/b32e9a4b0b192f8c4519a3b5.png"},{"id":103322918,"identity":"9a8ae1fb-e049-4c2e-8b62-a5083fa91dfe","added_by":"auto","created_at":"2026-02-24 12:17:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33814,"visible":true,"origin":"","legend":"\u003cp\u003eThe moderating role of pricing power\u003c/p\u003e\n\u003cp\u003eSource: Authors own work.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8572539/v1/36aa230a3166e7b76f5ab2d5.png"},{"id":105562703,"identity":"f11f3cfd-4e94-464e-8e50-6a782ecae7ff","added_by":"auto","created_at":"2026-03-27 12:44:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1400551,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8572539/v1/45320a88-988c-400c-aa1d-784e2651e3fb.pdf"},{"id":103506546,"identity":"949c562b-ace3-45f7-8a68-8732c8a78b88","added_by":"auto","created_at":"2026-02-26 13:37:33","extension":"do","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":9999,"visible":true,"origin":"","legend":"","description":"","filename":"process.do","url":"https://assets-eu.researchsquare.com/files/rs-8572539/v1/28d2faae1bcfd8ba4532f451.do"},{"id":103322919,"identity":"9204f123-d724-45e1-8e59-589d3e115dfe","added_by":"auto","created_at":"2026-02-24 12:17:38","extension":"dta","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14456197,"visible":true,"origin":"","legend":"","description":"","filename":"data.dta","url":"https://assets-eu.researchsquare.com/files/rs-8572539/v1/17ad528ffce31a9e6873182e.dta"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cracking the silicon ceiling: how female directors shape corporate robot adoption","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003eCombining business with artificial intelligence aims to build new pathways for enterprises to cultivate core competitiveness (Duan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Koch et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Industrial robots, as AI-enabled generalized technology, are increasingly adopted due to their dual externality: improving productivity and generating positive environmental spillovers (Acemoglu \u0026amp; Restrepo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Amid fading demographic dividends and urgent industrial upgrading needs, they represent a strategic option for enterprises (Duan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Promoting environmental protection while creating economic value is an effective strategy for securing new market opportunities and competitive advantages (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, exploring factors affecting industrial robot adoption holds significant theoretical and practical importance.\u003c/p\u003e \u003cp\u003eGiven the critical importance of industrial robots, researchers have endeavored to identify the relevant antecedents of industrial robot adoption through different perspectives, which are categorized into two main areas: institutional theories and market competition. Institutional theories highlight the role of environmental institutions (Acemoglu \u0026amp; Restrepo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with AI advancements (Goldsmith-Pinkham et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Enabling robots to reduce industrial pollution (Rodrigue et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and ensure environmental compliance (Huang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Market competition drives adoption through robots' productivity effects (Autor, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While insightful, these views primarily frame adoption as externally driven. Crucially, adoption is a strategic decision contingent on organizational decision-makers' willingness and ability (Islam et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), influenced by factors like gender (Duan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and top management team traits (Adams \u0026amp; Ferreira, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zou et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Scholars have further revealed that women tend to pursue social and economic goals through technology adoption more than men (Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), suggesting board gender diversity\u0026mdash;by increasing social diversity\u0026mdash;may play a key role in industrial robot adoption.\u003c/p\u003e \u003cp\u003eFemale board representation refers to the percentage of female directors on a company's board, also known as board gender diversity (Guldiken et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rising education and recognition of women's leadership increase female senior roles (Mao et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This representation impacts firm competitiveness through gender-neutral hiring, meritocratic promotion (Dezs\u0026ouml; \u0026amp; Ross, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and diverse decision-making benefits like innovation and long-term value creation (Luo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Female managers\u0026rsquo; traits may facilitate technology adoption (Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), accelerate technology introduction and improve decision-making efficiency (Zou et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Adams \u0026amp; Ferreira, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, China presents a distinct context. Despite government gender equality advocacy, deeply ingrained traditional patriarchal norms create a persistent gap between policy and corporate reality. For instance, in companies with strong patriarchal norms, female directors' unique insights may be limited in technology adoption decisions.\u003c/p\u003e \u003cp\u003eDespite growing research on female board representation, its role as a motivator for industrial robot adoption remains neglected. This study addresses this gap by establishing a correlation between female board representation and industrial robotics adoption. Drawing on the expectation states theory, a gender-specific framework is developed to examine this role. The theory provides an interactive perspective on board diversity expectations, identifying gender diversity enhances the willingness and efficiency of adopting industrial robots through the differentiated ability expectation mechanism in the expected state theory and the dual-dimensional drive (vertical skill advantages and horizontal responsibility consensus) in the practical logic. In addition, internal control weakens this correlation, while pricing power strengthens it. Empirical analysis of Chinese manufacturing companies (2011\u0026ndash;2022) supports the framework and hypotheses.\u003c/p\u003e \u003cp\u003eThis study contributes theoretically and practically. Theoretically, it innovatively links gender diversity with industrial robot adoption in emerging tech, filling literature gaps (Adams et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Adams \u0026amp; Ferreira, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fall et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It empirically demonstrates female directors' unique role in promoting adoption, opening new avenues for diversity research and offering fresh perspectives on their complex technological impact from a gender equality stance. It further clarifies gender diversity's diverse manifestations across organizational contexts, laying groundwork for defining adoption boundary conditions and deepening understanding of internal mechanisms. Practically, it provides new perspectives and evidence for enterprise robot adoption, helping firms\u0026mdash;especially high-tech enterprises\u0026mdash;capitalize on opportunities to enhance productivity and efficiency.\u003c/p\u003e \u003cp\u003e \u003cb\u003eII Theory, literature review and hypotheses development\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIndustrial robot adoption\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIndustrial robotics, a \u0026ldquo;high-impact\u0026rdquo; innovation boosting productivity and reducing environmental costs (Lin \u0026amp; Xu, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), faces conflicting drivers in research. While environmental regulations and competitive markets are seen by some as triggers for adoption through enhanced competitiveness (Dong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Goldsmith-Pinkham et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), others argue strict regulations increase costs, inhibiting adoption. Studies have shown that firms adopt robots to optimize processes, reduce costs in response to environmental pressures, resource constraints, and market demands (Acemoglu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Autor, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn response to external drivers, organizations tend to acquire and develop resources for managing processes (Dong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) in response to industrial robot adoption, where corporate governance matters: poor governance reduces outputs (Huang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another study found that internal control, a set of activities implemented by companies to monitor business processes and control risks, negatively impacts robot adoption (Islam et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while pricing power as the ability of firms to adjust pricing strategies, facilitates adoption (Maine et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, despite this focus on external push and governance, the role of female board representation in industrial robot adoption remains underexplored.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSocially assigned roles of gender: expectation states theory\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGender diversity in corporate boardrooms' impact on firms\u0026rsquo; strategic choices and performance has been explored through various theoretical perspectives (Ahern \u0026amp; Dittmar, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), analyzing its effects on corporate strategy and operations (Saeed et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Triana et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This study expands existing research by incorporating expectation states theory to explain female directors' preferences for adopting industrial robots. The theory focuses on gender diversity in team collaboration and its contribution to group goal consensus (Correll \u0026amp; Ridgeway, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExpectation states theory posits that inherent biases about others' capabilities influence group dynamics, evoking stereotypes in task-oriented settings (Correll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Yuan \u0026amp; Sun, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It's pertinent in environments with low female representation, acknowledging gender disparities' impact on decision-making (Fuentes-Fuentes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Gender, a social construction sustaining unequal relations, shapes behavior, practice, and individual identities within organizations (Correll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The theory elucidates how gender-based status and differing competence perceptions affect social interactions in organizational contexts (Ridgeway \u0026amp; Correll, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExpectation states theory states that biases about group characteristics influence interactions (Correll \u0026amp; Ridgeway, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and characteristic differences can trigger stereotypes in task-specific situations (Correll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). It is applicable where women are underrepresented, as it explicitly addresses gender differences' impact on decision-making and outcomes (Fuentes-Fuentes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the interaction level, it shapes organizational behavioral norms and practices; at the individual level, it shapes identities and roles (Correll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The theory describes how gender-determined status and competence beliefs affect organizational interactions (Ridgeway \u0026amp; Correll, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe theory also states that expectations of task completion and evaluations are influenced by vertical characteristics (specialized skills, e.g., men's competence in practical activities) and horizontal characteristics (group distinctiveness, e.g., females' team-orientation) (Fuentes-Fuentes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Eagly, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Dominant femininity perceptions affect behavior by shaping perceived expectations (Fuentes-Fuentes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, the framework formed by these two characteristics is pertinent in industrial robot adoption.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFemale board representation and industrial robot adoption\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn a context where gender diversity is increasingly valuing skills over representation, female directors are expected to provide unique insights on technology adoption, impacting firms (Saeed et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Triana et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Management implements industrial robotics to demonstrate its value. Based on expectation states theory, gender characteristics and task-related skills are crucial for evaluating contributions (Correll \u0026amp; Ridgeway, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fuentes-Fuentes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In industrial robot adoption, gender diversity and specialized skills interact, shaping perceptions of individual contributions.\u003c/p\u003e \u003cp\u003eIn the vertical dimension, managers specializing in industrial robot adoption are scrutinized for critical knowledge and skills, such as those maintaining relationships with developers/suppliers or securing favorable loans. Culturally gendered competence stereotypes (e.g., Confucian expectations) often assign women group-oriented strengths: communication (Correll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Dezs\u0026ouml; \u0026amp; Ross, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), collaboration, financing, and team cohesion (Karavitis et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This skillset aligns with industrial robot adoption\u0026rsquo;s demands for multi-party coordination, technology integration, and capital deployment (Duan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), an area where women show higher involvement (Sang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, gendered competence expectations increase the likelihood that women\u0026rsquo;s contributions to robotics adoption are valued (Correll \u0026amp; Ridgeway, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, the adoption of industrial robots may boost their credibility, standing, and acknowledgment in the company, given perceptions of their superior competency in this domain (Correll, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), thereby increasing their effectiveness in achieving adoption goals.\u003c/p\u003e \u003cp\u003eHorizontally, individual differences create heterogeneity in industrial robot adoption outcomes among female directors. Existing research shows women exhibit higher social responsibility than men (Zou et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), that is, they are more inclined to participate in decision making on strategic activities that are friendly to the environment and stakeholders (Javed et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Female managers' stronger social responsibility may create a subgroup effect that fosters internal consensus and concerted action when adopting eco-friendly robotics that enable business transformation. This increases management's willingness to adopt industrial robots.\u003c/p\u003e \u003cp\u003eFor the corporate strategic level, industrial robot adoption is undoubtedly a green-related technological innovation initiative (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), that is, requiring integration of robotics compatible with green manufacturing across organizational processes and technological resources. To a certain extent, the corresponding investment of technological resources and conceptualization are required (Yuan \u0026amp; Sun, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this sense, females play a key role both in facilitating strategic decisions and accelerating adoption, advancing robots' strategic integration. Thus, even if not all possess relevant competencies, greater female representation increases the likelihood of adoption-related decisions and actions. Therefore, Hypothesis 1 is formulated as follows.\u003c/p\u003e \u003cp\u003eH1: Female board representation is positively associated with the adoption of industrial robots.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe moderating role of internal control\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhile industrial robot adoption offers benefits, it also inevitably faces high costs and necessitates process changes (Islam et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a core management function, internal control\u0026mdash;ensuring operations and risk reduction\u0026mdash;significantly impacts management teams' adoption efforts (Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Expectation states theory indicates managers' gender influences decisions (Correll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Under systemic pressures, internal control rigor critically shapes female managers' strategies for promoting adoption (Nasir et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moderate control enhances autonomy, fostering cross-functional cooperation and innovation, whereas high control impedes adoption and reduces flexibility.\u003c/p\u003e \u003cp\u003eFor one thing, under relaxed internal control, female managers view industrial robot adoption challenges as inherent to innovation. This flexibility fosters collaboration and problem solving (Nakata \u0026amp; Im, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In such an organizational climate, female managers strategically balance control with adoption to mitigate risks and enhance competitiveness through innovation, creating greater economic value (Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In addition, low internal control Low control also provides motivation and autonomy, eases resource competition, and reduces internal constraints' impact on adoption, thus boosting female managers' inclination toward robotics implementation.\u003c/p\u003e \u003cp\u003eConversely, overly strict internal control constrains female managers' cognitive flexibility (Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), reinforcing adherence to traditional practices. Industrial robot adoption entails high investment, learning costs, and risks of uneven resource allocation (Chang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which strict control exacerbates by prioritizing risk minimization over innovation. This limits firms to rigid processes and makes it difficult to quickly adjust strategies and business models when necessary. Expectation states theory suggests challenging environments amplify female managers' caution: while relaxed control limits diversity's impact, strict control heightens its role in adoption decisions. Therefore, Hypothesis 2 is hypothesized as follows:\u003c/p\u003e \u003cp\u003eH2: Internal control negatively moderates the adoption of industrial robots by female board representation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe moderating role of pricing power\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePricing power denotes a firm\u0026rsquo;s ability to align product or service pricing with market acceptance and industry standards through market analysis, strategy adjustments, and resource optimization (Dutta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Maine et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Previous studies have pointed out that pricing power critically sustains profitability and market share, thereby influencing management's decisions on corporate strategies (Maine et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Further, the process of how female managers evaluate firms based on the level of \u0026ldquo;fit\u0026rdquo; mentioned above and thus influence decision-making is revealed in this paper. Notably, female directors associate with multi-stakeholder strategies (Meng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Expectation states theory posits that senior directors maintain implicit leadership through long-term strategies that align competencies with roles to enhance organizational impact (Meng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, female board representation's impact on industrial robot adoption hinges on pricing power.\u003c/p\u003e \u003cp\u003eOn the one hand, When firms possess high pricing power, female managers become more receptive to industrial robot adoption's uncertainties, as the firm better withstands market fluctuations (Liozu \u0026amp; Hinterhuber, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this case, they leverage this advantage to predict demand, adjust pricing, optimize costs, reduce risks, and actively participate in adoption decisions (Raja et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, high pricing power enhances perceived adoption benefits: female managers recognize such firms can precisely position offerings and respond swiftly to market, technological innovations to maximize the return on investment (Liozu, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This increases their likelihood of lowering adoption thresholds, enhancing decision efficiency, and actively promoting robotics implementation.\u003c/p\u003e \u003cp\u003eConversely, low pricing power heightens industrial robot adoption challenges. If female managers perceive pricing strategies cannot cover robot costs (Liozu \u0026amp; Hinterhuber, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), skepticism and resistance may weaken willingness to embrace industrial robotics. Low pricing power inefficiently converts technology investments into returns and may trigger avoidable financial risks (Liozu, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), motivating female managers to maintain current production models rather than explore robots' productivity gains. Based on this, the following prediction is proposed that female managers in low pricing power firms likely constrain robot adoption compared with firms with high pricing power. Therefore, Hypothesis 3 is hypothesized as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003ePricing power positively moderates the adoption of industrial robots by female board representation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the conceptual framework with explanatory, moderating, and explained variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u0026lt; Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here \u0026gt;\u003c/p\u003e"},{"header":"III. Methodology","content":"\u003cp\u003e \u003cb\u003eSample and Data\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the above hypothesis, a sample of Chinese manufacturing companies listed between 2011 and 2022 was analyzed. First, companies categorized as special treatment (ST) or special transfers (PT) are excluded. This step is intended to exclude data points that could unduly influence the statistical analysis and to ensure a more representative sample. The second step excludes samples that lack variable data. Finally, a sample of this observation was obtained.\u003c/p\u003e \u003cp\u003eThe industrial robot data used in this study is derived from the International Federation of Robotics (IFR), an authoritative global non-profit providing country-industry-year robotics statistics directly from manufacturers. Since industrial robots primarily serve manufacturing, this paper focuses on manufacturing sector data. Personnel data are obtained from the industrial enterprise database, which is widely used for research on China's economy, and financial data are derived from the CSMAR, which is known for supporting empirical research and model testing.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVariable Measuremen\u003c/b\u003e \u003cb\u003et\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFemale board representation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe percentage of women serving on the board is designated as the independent variable. According to previous studies (Adams et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the female directors\u0026rsquo; proportion is utilized as our primary scale of female board representation (Female). For robustness, we also consider two alternatives: a binary variable assigned a value of one if there is at least one female manager on the board, and zero if there are none (Dezs\u0026ouml; \u0026amp; Ross, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This metric aligns with managers' pivotal roles in industrial robot adoption (Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndustrial robot adoption\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe dependent variable, industrial robot adoption (Robot), is measured using an enterprise-level penetration index. Following prior research (Acemoglu \u0026amp; Restrepo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Goldsmith-Pinkham et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this paper integrates IFR industry-level robotics data with Chinese manufacturing listed firms' microdata, adopting the \"Batik instrument\" approach. The utilization of industrial robots in China has shown a pronounced upward trend only since 2010, and accounting for potential lagged effects of female board representation, dependent and moderating variables use one-period lagged data (2012\u0026ndash;2022), while independent variables cover 2011\u0026ndash;2021.\u003c/p\u003e \u003cp\u003eThe specific measurement method is as follows:\u003c/p\u003e \u003cp\u003eStep 1: Calculate the industrial robot penetration rate indicator at the industry level:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{PR}_{it}^{CH}=\\frac{{MR}_{it}^{CH}}{{L}_{i,t=2011}^{CH}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{MR}_{it}^{CH}\\)\u003c/span\u003e \u003c/span\u003e represents the stock of industrial robots in Chinese industry \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{L}_{i,t=2011}^{CH}\\)\u003c/span\u003e\u003c/span\u003e denotes the employment level in Chinese industry \u003cem\u003ei\u003c/em\u003e in the base year 2011, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PR}_{it}^{CH}\\)\u003c/span\u003e\u003c/span\u003e measures the penetration rate of industrial robots in Chinese industry \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eStep 2: Construct an indicator for the penetration rate of industrial robots at the enterprise level:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{CHFexposure\\:to\\:robuts}_{jit}=\\frac{{PWP}_{jit=2012}}{{ManuPWP}_{t=2012}}\\ast\\:{PR}_{it}^{CH}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis indicator measures the penetration rate of industrial robots in industry \u003cem\u003ej\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{PWP}_{jit=2012}}{{ManuPWP}_{t=2012}}\\)\u003c/span\u003e\u003c/span\u003e represents the ratio of the proportion of employees in the production sector of industry \u003cem\u003ei\u003c/em\u003e and enterprise \u003cem\u003ej\u003c/em\u003e in the manufacturing sector in 2012 (base period) to the median proportion of employees in the production sector of all enterprises in the manufacturing sector in 2012. For enterprises \u003cem\u003ej\u003c/em\u003e, changes in the penetration rate of industrial robots mainly reflect changes in the technological characteristics of the domestic industry, and are unrelated to the characteristic factors of the enterprises themselves.\u003c/p\u003e \u003cp\u003eStep 3: Using US industry-level industrial robot data to construct an instrument variable for robot penetration at the Chinese enterprise level:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{\\stackrel{-}{CHFexposure\\:to\\:robuts}}_{jit}=\\frac{{PWP}_{jit=2012}}{{ManuPWP}_{t=2012}}\\ast\\:\\frac{{MR}_{it}^{US}}{{L}_{i,t=1990}^{CH}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{MR}_{it}^{US}\\:\\)\u003c/span\u003e \u003c/span\u003eindicates the stock of industrial robots in the US industry \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{L}_{i,t=1990}^{CH}\\)\u003c/span\u003e\u003c/span\u003e represents the number of people employed in the industry \u003cem\u003ei\u003c/em\u003e in the US in 1990 (base period). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PR}_{it}^{US}=\\frac{{MR}_{it}^{US}}{{L}_{i,t=1990}^{CH}}\\)\u003c/span\u003e\u003c/span\u003e indicates the penetration rate of industrial robots in the US industry \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThis paper employs U.S. industrial robot data as an instrumental variable for two reasons: First (technological exogeneity): As a global leader in industrial robotics, U.S. industry robot adoption trends effectively reflect the direction of technological progress in the sector, rather than being influenced by country-specific factors. Second (exclusivity constraint): The near-perfect U.S. labor market means its robot application affects China primarily through technology spillovers, not endogenous factors like labor market distortions. Therefore, the level of industrial robot application in the United States meets the dual conditions for a tool variable: Correlation: U.S. trends align with technological characteristics in China's comparable industries; Exogeneity: Unrelated to China's domestic adoption drivers, e.g., policy subsidies, labor cost fluctuations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInternal control\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on previous experience, internal control index (IC) is chosen to measure the degree of internal control in firms in this study (Yuan \u0026amp; Sun, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), sourced from the DIB database and risk management database (Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Data were logarithmically transformed to ensure comparability and reduce bias and extreme values. The index evaluates listed firms' internal control systems through five indicators: i.e., legitimacy of operation and management, security of company property, validity and completeness of financial reports and associated information, operational efficiency and effectiveness, and realization of the development strategy.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePricing power\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccording to previous studies, pricing power (PP), measuring a firm's market position and monopoly degree (Nguyen \u0026amp; Nguyen, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), is quantified using the Lerner index (Spierdijka \u0026amp; Zaourasa, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Lerner's index, also referred to Lerner's index of monopoly power, which gauges monopoly strength by measuring price deviation from marginal cost. When the Lerner index approaches 0, it signifies that the firm possesses negligible market power, with the price being nearly equivalent to its marginal cost. Conversely, when the Lerner index nears 1, it indicates that the firm holds considerable market power, enabling it to set prices significantly above its marginal cost.\u003c/p\u003e \u003cp\u003eThe formula for calculating the Lerner Index of an enterprise is\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:L=(P-MC)/P\\)\u003c/span\u003e\u003c/span\u003e. (Where \u003cem\u003eL\u003c/em\u003e represents the Lerner Index, \u003cem\u003eP\u003c/em\u003e represents the price, and \u003cem\u003eMC\u003c/em\u003e represents marginal cost, which refers to the variation in total cost that results from producing one additional unit or reducing production by one unit at a specific level of output.)\u003c/p\u003e \u003cp\u003e \u003cb\u003eControl variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eVarious factors impact industrial robot adoption. Based on the existing literature, a set of corporate characteristics that may affect industrial robot adoption are selected (Duan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in order to minimize their potential impact (Goldsmith-Pinkham et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e): \u003cem\u003efirm size\u003c/em\u003e (\u003cem\u003eSize\u003c/em\u003e, i.e., log of annual total assets), \u003cem\u003efirm leverage\u003c/em\u003e (\u003cem\u003eLev\u003c/em\u003e, i.e., total liabilities/total assets), \u003cem\u003efirm growth\u003c/em\u003e (\u003cem\u003eGrowth\u003c/em\u003e, i.e., revenue for the current year/revenue for the previous year-1), \u003cem\u003ereturn on assets\u003c/em\u003e (\u003cem\u003eROA\u003c/em\u003e, i.e., net profit/total assets), \u003cem\u003ecash flow ratio\u003c/em\u003e (\u003cem\u003eCashflow\u003c/em\u003e, i.e., operating cash flow/current liabilities), \u003cem\u003eproportion of fixed assets\u003c/em\u003e (\u003cem\u003eFIXED\u003c/em\u003e, i.e., fixed assets/total assets), \u003cem\u003efirm age\u003c/em\u003e (\u003cem\u003eFirm Age\u003c/em\u003e, i.e., log of the number of years since the firm\u0026rsquo;s founding) and \u003cem\u003eownership concentration\u003c/em\u003e (\u003cem\u003eTop1\u003c/em\u003e, i.e., the number of shares held by the largest shareholder/the total number of shares).\u003c/p\u003e \u003cp\u003eThe definitions and measurements of the variables involved in the study are described in detail in Table I.\u003c/p\u003e \u003cp\u003e\u0026lt; Insert Table I here \u0026gt;\u003c/p\u003e"},{"header":"IV. Results","content":"\u003cp\u003e \u003cb\u003eEmpirical Results\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe descriptive analysis of the sample is displayed in TableⅡ, including the mean, standard deviation, and correlation coefficients among the variables. All correlations between variables were less than 0.7, which is within acceptable limits.\u003c/p\u003e \u003cp\u003e\u0026lt; Insert Table Ⅱ here\u0026gt;\u003c/p\u003e \u003cp\u003eTable III tests Hypothesis 1 on the positive link between female board representation and industrial robot adoption. Model 1, the baseline regression, shows a significant positive correlation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.717, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Model 2, incorporating eight control variables, confirms this positive relationship (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.847, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), strongly validating Hypothesis 1. The coefficient of 0.847 implies that 1 percentage point increase in female representation is associated with an average increase of 0.847 units in this penetration index of industrial robots in enterprises. The rise in coefficient from Model 1 to Model 2 suggests that firm characteristics may moderate the effect. For instance, return on assets negatively correlates with adoption (\u003cem\u003eβ\u003c/em\u003e = -0.041), while the fixed asset ratio shows a strong positive correlation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.661, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating a substantial impact compared to other control variables.\u003c/p\u003e \u003cp\u003e\u0026lt; Insert Table III here \u0026gt;\u003c/p\u003e \u003cp\u003eThe moderating effects of internal control and pricing power were tested in Models 3 and 4, respectively. Consistent with earlier findings, Model 3 showed that female board representation maintained a significant positive correlation with industrial robot adoption (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.839, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, the interaction between female board representation and internal control negatively correlated with industrial robot adoption (\u003cem\u003eβ\u003c/em\u003e = -0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that internal control moderates this relationship. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the relationship between female board representation and industrial robot adoption is positive and flat under lax internal control but significantly negative under strict internal control, highlighting its non-negligible moderating effect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u0026lt; Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here \u0026gt;\u003c/p\u003e \u003cp\u003eWhen considering pricing power, the interaction coefficient between female board representation and pricing power (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.509, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) shifts from significant at the 1% level to significant at the 5% level, compared to the coefficient for female representation alone (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.817, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Pricing power is a crucial moderating factor in the relationship between female board representation and industrial robot adoption. High pricing power strengthens this positive correlation, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u0026lt; Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here \u0026gt;\u003c/p\u003e \u003cp\u003e \u003cb\u003eRobustness Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRobust test\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRobust tests are conducted to ensure dependability of the research results. Table IV shows benchmark model test results using variable substitution, adding control variables, and eliminating extreme values. First, change the independent variable measurement with a 0\u0026ndash;1 binary variable (1 for female directors in the company, 0 otherwise). Table IV shows a positive regression coefficient (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.430, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the first column, keeping core results unchanged. Next, modify the independent variable with the blindex index for female board representation and re - run regression analysis. The second column in Table IV shows a significant positive correlation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.641, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) at the 1% level, strongly reinforcing the core findings.\u003c/p\u003e \u003cp\u003eSome literature indicates corporate governance factors impact the causality (Spierdijka \u0026amp; Zaourasa, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), so the following three control variables at the level of corporate governance are added to the sample : \u003cem\u003eratio of independent directors\u003c/em\u003e (\u003cem\u003eIndep\u003c/em\u003e, the number of independent directors/the number of directors), \u003cem\u003eboard size\u003c/em\u003e (\u003cem\u003eBoard\u003c/em\u003e, log of the number of board members), and \u003cem\u003edual positions\u003c/em\u003e (\u003cem\u003eDual\u003c/em\u003e, the chairman and the general manager are the same person 1, otherwise 0). Regression on the augmented sample (third column) reveals a significant positive correlation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.868, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) among main variables. Finally, to address extreme values, continuous variables are Winsorized at the 1% level. The fourth column results still show a significant positive correlation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.793, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Overall, these robust tests bolster our findings.\u003c/p\u003e \u003cp\u003e\u0026lt; Insert Table IV here \u0026gt;\u003c/p\u003e \u003cp\u003e \u003cb\u003eEndogeneity Test\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFemale board representation positively affects industrial robot adoption, though firms may adjust executive gender ratios due to technical or managerial skill needs. In addition, economic, cultural, and policy differences between provinces also affect female board representation and industrial robot adoption. To address endogeneity, which includes reverse causality and omitted variables, this study employs an instrumental variable approach using provincial average female board representation. As an indicator of the overall level of female board representation of firms in the province, the average female board representation in the province is highly correlated with the female board representation of individual firms. In addition, Provincial averages don\u0026rsquo;t directly affect individual firms\u0026rsquo; robot adoption decisions. This design controls provincial confounders while meeting instrumental variable requirements.\u003c/p\u003e \u003cp\u003eTable V presents the results: the Kleibergen-Paap rk LM statistic is 548.602 with p\u0026thinsp;=\u0026thinsp;0, rejecting the non-identifiability hypothesis of the instrumental variable. The Kleibergen-Paap rk Wald F statistic is 629.940, rejecting the weak instrumental variable hypothesis, validating the choice of provincial average female board representation as a reasonable instrumental variable. Moreover, the regression result (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.150, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) shows a significant positive effect of female board representation on industrial robot adoption. Overall, these tests align with the benchmark regression, confirming result robustness considering endogeneity.\u003c/p\u003e \u003cp\u003e\u0026lt; Insert Table V here \u0026gt;\u003c/p\u003e \u003cp\u003e \u003cb\u003eHeterogeneity analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo explore the disparities among various groups, this paper further examines the heterogeneity of female board representation's impact on industrial robot adoption across three dimensions: enterprise qualification, the property nature, and whether enterprises are high-polluting.\u003c/p\u003e \u003cp\u003eAccording to China's Measures for the Administration of the Identification of High-tech Enterprises, enterprises meeting three criteria are deemed high-tech: being in a state-supported high-tech field, having registered in China for a year, and continuously developing core independent intellectual property rights. Samples are thus split into high-tech and non-high-tech. As per Table Ⅵ, model 1 represents high-tech, model 2 non-high-tech. High-tech enterprises have a significantly positive influence coefficient on female board representation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.033, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while for non-high-tech, the coefficient (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.167) is positive but insignificant. This shows high-tech enterprises have a greater demand for industrial robot adoption than non-high-tech ones.\u003c/p\u003e \u003cp\u003eBased on asset ownership, enterprises are grouped into state-owned and non-state-owned. Regression analysis on their samples forms Model 3 for state-owned and Model 4 for non-state-owned. In state-owned samples, the female board representation coefficient (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.302) is positive but insignificant. In non-state-owned samples, it shows a highly significant positive correlation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.994, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This aligns with the baseline regression results. The findings imply non-state-owned enterprises, compared to state-owned ones, may focus more on profitability. Under \u0026ldquo;profit maximization\u0026rdquo;, robot use cuts labor costs and boosts profits, so they rely more on industrial robots (Duan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, state-owned enterprises are greatly affected by the national gender equality policy, so state-owned enterprises may have more \"symbolic\" female directors, while the gender diversity of non-state-owned enterprises is more likely to reflect the actual capacity structure, so the impact of female directors of non-state-owned enterprises on industrial robots is more significant.\u003c/p\u003e \u003cp\u003eA growing number of highly polluting enterprises have integrated industrial robots, but the environmental effects are uncertain. Enterprises are categorized as highly polluting or not according to state regulations. Using relevant guidance, industries like manufacturing, mining, and energy production/supply are identified as highly polluting. Table Ⅵ's columns 5 and 6 show analysis results for samples of such enterprises and non-highly polluting ones. For highly polluting enterprises, the coefficient of female board representation on robot adoption is positively correlated (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.641) but insignificant; for non-highly polluting ones, it's notably positive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.877, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates a smaller impact in highly polluting enterprises. Government and stakeholders should encourage them to adopt smart and clean equipment via tax incentives and subsidies to boost production and environmental responsibility (Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"V. Discussion and Conclusion","content":"\u003cp\u003eIndustrial robot adoption boosts efficiency but remains understudied (Duan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accelerating its drivers is critical (Islam et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), with top executives' vision, technical expertise, decision-making style, and gender being central to implementation (Islam et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While management diversity and technology adoption have been examined (Zou et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), few studies specifically address gender diversity's impact on industrial robotics (Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the expectation states theory, this study reveals female board representation increases industrial robot adoption, particularly under lax internal controls or high pricing power. This aligns with findings that female-led firms exhibit stronger social responsibility and robotics adoption tendencies (Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zou et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At this point, enhancing board gender diversity is thus crucial for industrial intelligence advancement.\u003c/p\u003e \u003cp\u003eThe study actively invited manufacturing enterprises for authentic feedback. Many practitioners and managers emphasized that female directors enhance decision-making comprehensiveness and inclusiveness, ensuring productivity gains from technological innovation align with employee well-being and social responsibility. They balance efficiency with humanistic care in robot adoption. The sector is actively recruiting female leaders to contribute development insights.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheoretical contributions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, this study bridges gender diversity with industrial robotics adoption\u0026mdash;an emerging technology gap in extant literature. While prior research examines gender diversity's impact on performance, innovation, and decision-making in traditional industries (Saeed et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), it overlooks technology adoption in industrial intelligence contexts. Our empirical analysis reveals female directors' unique role in advancing robot adoption, pioneering new theoretical directions for diversity research. Paradoxically, despite growing gender equality awareness, women's status remains restricted in certain sectors (Hong, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), offering fresh perspectives on diversity's complex role in technology adoption.\u003c/p\u003e \u003cp\u003eSecond, by introducing expectation states theory, this study provides a solid theoretical foundation for explaining the impact of gender diversity on industrial robotics adoption. While the theory emphasizes sociocultural gender bias's behavioral influence, the paper extends it by revealing female directors' critical role in technology adoption. They drive adoption through unique perspectives, decision-making styles and exert a board-level \"multiplier effect,\" significantly enhancing team decision quality. This enriches expectation states theory and provides a new framework for analyzing executive gender structures in industrial intelligence contexts.\u003c/p\u003e \u003cp\u003eThird, this study not only explores the direct impact of female directors on the adoption of industrial robots but further reveals the moderating role of internal control and pricing power in this relationship. It finds that tight internal control diminishes the driving role of female directors, while strong pricing power amplifies it. This finding provides a new theoretical perspective for understanding the differential role of gender diversity in different organizational contexts, as well as an important theoretical basis for examining the boundary conditions of technology adoption. These moderators deepen mechanistic understanding and open new research pathways.\u003c/p\u003e \u003cp\u003eFourth, although existing literature highlights female directors' importance, feminist perspectives remain scarce. This study explores their unique role in industrial robotics adoption, showing how gender diversity drives technology uptake by disrupting traditional power structures. This perspective offers new insights for gender and management theory and supports firms in leveraging gender diversity during industrial intelligence.\u003c/p\u003e \u003cp\u003eFifth, examining female directors' role in industrial robotics adoption reveals how optimizing executive team structure helps organizations adapt to technological change. Gender diversity enhances firms' technology adoption capabilities, adaptability, and competitiveness in dynamic environments. This finding provides new insights into organizational adaptation theory and provides a theoretical basis for studying decision-making in industrial intelligence.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePractical Implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis paper offers practical guidelines for executives using empirical data. First, recognizing the value of gender diversity, firms should increase female board representation to drive industrial robot adoption, through policies promoting board diversity and stakeholder awareness. Second, relaxing rigid internal controls facilitates industrial robot diffusion (Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Management should embrace flexibility and innovation to enhance efficiency (Sang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, firms must strengthen pricing power via enhanced market analysis, cost optimization, and strategic pricing adjustments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study also has limitations. First, due to data constraints, we used annual industrial robot adoption metrics which may not fully capture adoption dynamics. Second, some unconsidered factors like board composition, educational backgrounds, experiences, organizational longevity of both managers and board members, as well as the specific characteristics of the industrial robotics market may influence firms' robot adoption decisions. Future research should validate findings through diverse data sources and primary methods. Ultimately, the descriptive results reflect China's current manufacturing landscape. As a developing economy, China\u0026rsquo;s institutional/technological infrastructure differs from other nations. Since robot adoption and female board representation are context-specific, findings require testing elsewhere. Exploring additional institutional or regional cultural factors would be interesting in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJianjun Dong, Yuqing Xu, and Han Lin wrote the manuscript. Xuekun Suo and Mingchuan Yu performed the data analysis and designed the tables. Yuan Ming created the figures and revised the overall manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe gratefully acknowledge the support of this study from the National Natural Science Foundation of China (Grants No. 72271126, 72201162, 72372079).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu D, Autor D, Dorn D, Hanson GH, Price B (2014) Return of the solow paradox? IT, productivity, and employment in US manufacturing. 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style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eFemale board representation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eThe number of female directors / Total number of directors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eIndustrial robot adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eindustrial robot penetration index\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerating Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eInternal control\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eDIB internal control index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003ePricing power\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003ePP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eLerner Index\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL = (P - MC) / P\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eFirm Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eNatural logarithm of (annual total assets)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eFirm Leverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eTotal liabilities at year-end / Total assets at year-end\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eFirm Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eFirm Age\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eNatural logarithm of (the number of years since a firm\u0026rsquo;s establishment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eOwnership concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eTop1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eNumber of shares held by the largest shareholder / Total number of shares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eFirm Growth\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eRevenue for the current year/ Revenue for the previous year-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eReturn on Assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eNet profit / Total assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eCashflow Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eCashflow\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eOperating cash flow / Current liabilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003eProportion of fixed assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eFIXED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eFixed assets / Total assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Standard errors in parentheses, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.1, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e\n\u003cp\u003eSource: Authors own work.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTableⅡ Correlation coefficients between variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.Robot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e6.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e4.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.Female\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.057***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e3.Size\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e22.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.031***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.081***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e4.Firm Age\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e2.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.094***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.028***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.173***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e5.Lev\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.163***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.469***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.130***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e6.ROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.039***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.044***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.038***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.340***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e7.Cashflow\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.021***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.030***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.104***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.067***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.159***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.446***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e8.Growth\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e8.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.013***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.016**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.030***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e9.FIXED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.127***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.111***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.040***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.176***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.124***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.153***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e10.TOP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e33.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.030***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.113***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.101***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.130***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.092***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.040***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e11.IC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e643.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e116.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.030***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.128***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.107***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.076***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.321***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.126***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.073***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.122***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003e12.PP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.033***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.095***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.040***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.603***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.603***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.367***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.136***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.045***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.196***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Standard errors in parentheses, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.1, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e\n\u003cp\u003eSource: Authors own work\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable Ⅲ Baseline and moderating effects regression results\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eModel (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eModel (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eModel (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eModel (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eVARIABLES\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.717***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.847***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.839***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.817***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eIC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003ePP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4.509**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(1.971)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003ePP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.281)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eFirm Age\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.542)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.619)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eCashflow\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.483)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eFIXED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.661***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.681***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.669***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5.461***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5.008***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5.095***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5.021***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e(0.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-squared\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eInd FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cem\u003er2_a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Standard errors in parentheses, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.1, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e\n\u003cp\u003eSource: Authors own work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable Ⅳ Robustness test results\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eVARIABLES\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.430***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.641***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.868***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.793***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.242)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.287)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eFirm Age\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.542)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.542)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.542)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eCashflow\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.537)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.096)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eFIXED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.624**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.654**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.663***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.691***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.257)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.261)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eIndep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eBoard\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.012***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.043***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.233***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.121***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e(0.739)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-squared\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eInd FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003er2_a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Standard errors in parentheses, *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.1, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e\n\u003cp\u003eSource: Authors own work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable Ⅴ Instrumental variable regression results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eVARIABLES\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cem\u003eRobot\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e3.150**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(1.571)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eIVPRO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e0.811***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eControl FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e3.793***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(1.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e18,585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-squared\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eKleibergen-Paap rk LM statistic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e548.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eKleibergen-Paap rk Wald F statistic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e629.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e{16.38}\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eInd FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003er2_a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.1, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, () values are robust standard errors, [] values are \u003cem\u003eP\u003c/em\u003e-values, and {} values are Stock-Yogo weak identification critical value at the 10% level of the test.\u003c/p\u003e\n\u003cp\u003eSource: Authors own work.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Female board representation, industrial robot adoption, internal control, pricing power, expectation states theory","lastPublishedDoi":"10.21203/rs.3.rs-8572539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8572539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrawing on expectation states theory, this study investigates the impact of board gender diversity on industrial robot adoption within the context of the era of industrial intelligence. Based on a sample of Chinese A-share listed manufacturing firms from 2011 to 2022, the research utilizes panel data regression analysis to empirically test this relationship, employing instrumental variables and robustness checks to address endogeneity concerns. The findings reveal that female board representation significantly promotes the adoption of industrial robots, suggesting that gender-diverse boards are more receptive to technological innovation. Furthermore, the analysis indicates that this positive effect is heterogeneous: it is enhanced by higher product pricing power but weakened by stronger internal controls. As one of the first empirical studies to connect demographic diversity with industrial robot adoption, this research contributes to the literature on corporate governance and technology management. The results suggest that manufacturing firms, particularly those with strong market positions, should consider gender diversity as a strategic asset for enhancing technological competitiveness, while providing policymakers and investors with new insights into the strategic value of diverse leadership.\u003c/p\u003e","manuscriptTitle":"Cracking the silicon ceiling: how female directors shape corporate robot adoption","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 12:17:31","doi":"10.21203/rs.3.rs-8572539/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8efec55f-56c1-476e-aac9-6931fd16c266","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63385547,"name":"Business and commerce/Business and management"},{"id":63385548,"name":"Social science/Business and management"},{"id":63385549,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-03-20T14:41:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 12:17:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8572539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8572539","identity":"rs-8572539","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}