The digital transformation, corporate uncertainty, and CEO 's risk preference ?

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Xianjun Bao, Minghui Lan, Nan Li, Yudi Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8205602/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 4 You are reading this latest preprint version Abstract Using a text-mining–based measure of digital transformation and multiple behavioral proxies of CEO risk preference for a large sample of Chinese listed firms from 2018 to 2022, we provide systematic empirical evidence that digital transformation significantly increases CEO risk aversion. Mechanism analysis shows that the reduction of firm-level uncertainty induced by digital transformation is a key channel through which CEO risk preference is suppressed. Furthermore, heterogeneity tests reveal that the behavioral effects of digital transformation vary with CEO demographic and professional attributes—such as age, tenure, compensation incentives, and technological background—as well as firm characteristics. These findings contribute to the literature on digital transformation and executive behavior by demonstrating that technological change not only restructures organizational processes but also shapes managerial risk tendencies, offering new insights into the behavioral micro-foundations of digital transformation. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Digital Transformation CEO Risk Preference Risk Aversion 1. Introduction Over the past decade, rapid advancements in digital technologies—such as artificial intelligence, cloud computing, big data analytics, blockchain, and the Internet of Things—have profoundly reshaped the global competitive landscape. Digital transformation has thus become a strategic imperative for enterprises seeking to optimize operational efficiency, enhance value creation processes, and sustain competitive advantage in an increasingly turbulent environment. According to the dynamic capabilities framework, digital technologies not only alter how firms sense, seize, and reconfigure opportunities but also fundamentally reshape organizational routines and decision-making logic. As a result, enterprises undergoing digital transformation face unprecedented environmental dynamism and complexity, such as technological obsolescence risks, information governance challenges, and disruptive organizational restructuring. These shifts heighten strategic uncertainty and impose new demands on corporate governance and executive decision-making. Within the firm, the CEO—who serves as the central architect of strategic choices—plays an irreplaceable role in determining the pace, direction, and success of digital transformation initiatives. According to upper echelons theory, the cognitive foundations, values, and personal attributes of CEOs shape how they interpret environmental signals and select strategic responses. Digital transformation is therefore not only a technological and organizational process but also a deeply behavioral and managerial one. Yet, despite the emerging body of literature examining the antecedents and consequences of digital transformation, limited attention has been paid to how digital transformation influences executives' psychological tendencies—particularly their risk preferences—and how such behavioral shifts affect corporate decision-making. This omission is notable because risk preference is a fundamental dimension of CEO behavior that affects investment intensity, innovation input, capital structure decisions, and long-term strategic orientation. From a principal–agent perspective, CEOs typically exhibit greater risk aversion than shareholders due to career concerns, reputation costs, and limited diversification of personal wealth. Digital transformation, by amplifying information complexity and intensifying environmental volatility, may further enlarge this divergence; alternatively, it may reduce uncertainty and thereby encourage greater risk-taking. Thus, whether digital transformation induces CEOs to become more conservative or more risk-seeking remains an unresolved empirical question of significant academic and practical relevance. Another important gap in the literature concerns the mechanism through which digital transformation affects CEO risk preference. While organizational scholars argue that digitalization can enhance information transparency and reduce firm-level uncertainty, behavioral economics suggests that information overload, algorithmic complexity, and digitalized reporting systems may instead obscure risks and weaken board oversight, potentially allowing CEOs to strategically manage information and avoid risky projects. These competing theoretical predictions underscore the need for systematic empirical evidence, especially in emerging markets like China, where institutional pressures, governance structures, and digital infrastructure exhibit unique characteristics that may shape executives’ behavioral responses to digital transformation. Furthermore, CEO heterogeneity—including age, tenure, compensation, and technical expertise—may moderate the effect of digital transformation on risk preference. Younger or technologically proficient CEOs may view digital transformation as an opportunity for strategic renewal, whereas older or non–R&D-background CEOs may perceive it as a source of operational uncertainty. These nuanced behavioral differences remain underexplored in the existing literature. Given these research gaps, this study addresses three specific research questions: (1) How does enterprise digital transformation influence CEOs’risk preference? (2) Through what mechanisms does digital transformation shape CEOs’behavioral tendencies? (3) How do heterogeneities at the CEO level (e.g., age, tenure, compensation, R&D background) and the firm level (e.g., ownership type, industry technology intensity) condition the direction and magnitude of this relationship? Drawing on text-mined digital transformation measures and CEO behavioral proxies for a large sample of Chinese listed firms from 2018 to 2022, this study provides novel empirical evidence that digital transformation significantly increases CEO risk aversion. The analysis further reveals that digital transformation reduces firm-level uncertainty—thereby serving as a key mechanism suppressing CEO risk preferences—and that CEO demographic and professional attributes substantially moderate these effects. This study makes several important contributions to the literature. First, it enriches the growing research stream on digital transformation by shifting the analytical focus from organizational outcomes to executive behavioral responses. While existing studies primarily examine productivity, innovation, governance, or performance consequences of digital transformation, little attention has been given to how digitalization reshapes CEOs’ cognitive structures and decision-making tendencies. By revealing a significant behavioral shift in CEO risk preference, this study provides novel evidence that digital transformation is not only a technological and organizational process but also a behavioral phenomenon. Second, the identification of firm-level uncertainty reduction as a key mediating mechanism extends current knowledge on the micro-foundations of digital transformation. By showing how digital technologies reshape information environments and reduce decision uncertainty, this study links technological change with strategic risk behavior, offering new insights into the internal logic through which digitalization affects management decisions. Third, the heterogeneity analysis provides managerial and policy implications by demonstrating that CEOs with different demographic and professional profiles respond differently to digital transformation. These findings highlight the importance of aligning executive characteristics with digital transformation strategies and contribute to ongoing discussions on leadership–technology fit in the digital era. 2. Literature Review In the process of managing a company, the CEO embodies a form of entrepreneurship, which serves as a crucial driving force for economic growth [ 7 ]. Entrepreneurship is the nuclear energy of business and social change [ 8 ]. Regarding the essence of entrepreneurship, it can generally be divided into three classic schools of thought [ 9 ] .The first is the German school, represented by Schumpeter and Baumol, which posits that the core of entrepreneurship lies in innovation, viewing entrepreneurs as innovators whose inventions and innovations drive long-term economic cycles. The second is the Chicago school, represented by Knight and Schultz. This perspective posits that the core of entrepreneurship lies in risk-bearing willingness and the entrepreneurial alertness to identify and correct market imbalances. Lastly, the Austrian school, represented by Mises and Kirzner, focuses on the entrepreneur's ability to identify market opportunities. Wennekers and Thurik argue that entrepreneurship encompasses 13 dimensions, including the perception of risk and uncertainty, the ability to organize and allocate economic resources, innovativeness, and the capacity to discover new business opportunities, among others [ 10 ]. A CEO's risk appetite manifests as a spirit of adventure, and CEOs with this adventurous spirit have a greater tolerance for risk. The entrepreneurial spirit of adventure is the most fundamental and core trait of a CEO [11、12]. It is their proactive breakthrough of conventions and active exploration of the unknown that drives companies forward, fostering economic growth and social change. Therefore, in this paper, we focus on the spirit of adventure within entrepreneurship, namely the CEO's risk appetite.The Chicago School posits that entrepreneurship is the ability to handle uncertainty and risk in the economy. Without the courage to take and shoulder risks, one cannot become an entrepreneur. Risk appetite is an innate characteristic of entrepreneurs, and a daring, exploratory spirit is their essential quality. Avoiding exploration, risk, and development entails even greater risks. It is by daring to adventure that risks can be mitigated and the opportunities contained within them seized.From the history of research on entrepreneurs and entrepreneurship, the spirit of adventure was the first entrepreneurial trait to attract economists' attention[ 13 ][ 14 ]. Even Schumpeter, a representative figure of the German School, defined entrepreneurship as: "Entrepreneurship consists of doing things that are not generally done in the ordinary course of business routine or a combination of them" [ 15 ] .This aligns with the innovative spirit emphasized by this school: to do what others have not done requires an even greater spirit of adventure, which forms the foundation of the innovative spirit. CEOs with a higher risk appetite and a more adventurous spirit are bold in experimentation, pioneers in their fields, and willing to undertake risks. They typically maintain a higher level of vigilance towards the market and are more likely to identify and capitalize on latent investment opportunities [ 16 , 17 ]. Cronqvist et al. found a significant positive correlation between a CEO's personal mortgage leverage and the company's financial leverage [ 18 ]. Cain and McKeon used the possession of a pilot's license for small aircraft as a proxy for adventurousness and discovered that firms led by pilot CEOs exhibited higher overall corporate risk [ 19 ].Risk and return are inherently linked; high-risk projects often imply high returns. Under the constraint of limited resources, investing more in high-return projects diverts resources from lower-return ones, thereby enhancing the efficiency of resource allocation. Senior executives' personal characteristics influence this efficiency. Faccio et al. found that firms led by female CEOs demonstrated significantly lower risk-taking compared to those led by male CEOs, resulting in lower resource allocation efficiency [ 20 ]. Boivie et al. showed that a CEO's organizational identification affects the firm's agency costs [ 21 ]. The antecedents of corporate digital transformation can be categorized into two aspects: transformation support and driving factors. The supporting conditions for digital transformation form a critical foundation for the process, with digital technology serving as a key element that profoundly influences the path and outcomes of transformation. According to Nambisan [ 21 , 22 , 23 ], digital technology, as a new type of information technology system, encompasses three fundamental dimensions: digital components, digital platforms, and digital infrastructure. Specifically, digital technology continuously enables the transformation process by optimizing business processes, facilitating the innovation of digital products and services, and building integrated platform ecosystems. Matarazzo et al. found that digital tools, such as applications and social media, further expand enterprises' capabilities in channel construction and user engagement. These tools not only help companies develop new distribution channels but also enhance their ability to sense and respond to market dynamics by collecting and analyzing consumer data in real time. This leads to upgrades in value creation and delivery, driving systematic innovation in business models [ 24 ]. At the organizational level, an enterprise’s adaptability in advancing digital transformation depends on its comprehensive internal attributes, such as organizational readiness, agility, cognitive models, cultural atmosphere, and digital maturity.Simsek et al. found that the capabilities, attitudes, and cognitive approaches of strategic leaders profoundly shape the pathways and internal mechanisms of digital transformation[ 25 ]. At the same time, Singh et al. found that organizational culture, cognitive readiness, organizational mindfulness, and IT infrastructure also play critical roles in the transformation process, further influencing corporate performance [ 26 ].At the network level, interactions among multiple types of actors constitute an important force driving transformation. Siachou et al. found that these relationships encompass complementarity and dependence between platform enterprises and participants, customer relationships, political connections, and knowledge acquisition within alliances [ 27 ]. Nayal et al. and Belitski et al. found that it also includes coordination and collaboration within supply chains [ 28 ], as well as diversity among knowledge partners[ 29 ].At the environmental level, enterprises operate in an external context characterized by complexity, volatility, and uncertainty, facing multiple pressures from technology, markets, and competition. Chen & Tian found that they must pay attention to challenges and opportunities arising from factors such as market and technological uncertainty[ 30 ], institutional and competitive pressure[ 31 ], as well as policy support, the construction of digital ecosystems, the process of industrial digitalization, the evolution of institutional and industrial structures, and demographic aging trends. This paper investigates the most fundamental and core aspect of entrepreneurship—the spirit of adventure (a topic of significant interest to economists)—at the most micro, individual level. It finds that a CEO's risk appetite can more effectively promote corporate digital transformation. This finding supports Kirzner's theory of entrepreneurial discovery and enriches the literature on entrepreneurship. 3. Theoretical Analysis and Research Hypothesis Corporate shareholders serve as providers of corporate equity capital.Corporate shareholders express keen interest in the appreciation of corporate equity capital, leading them to seek excess profits through high-yield venture capital. In contrast, CEOs without shareholdings exhibit divergent preferences. Corporate CEOs typically opt for secure venture capital projects due to considerations related to their reputation and a stable career development path. Conversely, the risky investment projects favored by corporate shareholders divert and consume the CEO's cash flow and resources, diminishing the CEO's individual utility and intensifying their risk aversion.The disparity in risk preferences between CEOs and corporate shareholders gives rise to an escalating principal-agent cost problem stemming from this distinction. Consequently, numerous scholars have addressed the agency cost problem resulting from disparate risk preferences through the lens of incentive and constraint mechanism design. For instance, scholars have explored the design of equity structure [ 32 ], board structure [ 33 ] to oversee the CEO's risk preference tendency, or the implementation of monetary [ 34 ], equity [ 35 ], promotion [ 36 ], and other incentive mechanisms to stimulate the CEO's risk preference.Subsequently, amid digital transformation, the enterprise management model has undergone revolutionary changes. How will the incentive and restraint mechanism of corporate shareholders on CEOs evolve?Will the CEO's risk preference align with shareholders' risk preference, or will it diverge?Subsequently, we will delve into a thorough discussion of these questions. As societal focus on digital transformation intensifies, it may be leveraged by CEOs as a deliberate tool to evade risk-taking, thereby complicating agency costs and impairing enterprises' long-term development. First, digital tools, despite their promise of transparency, can be manipulated to obfuscate risk. The inherent malleability and lack of physical traces of digital information facilitate its alteration and concealment [ 37 , 38 ]. CEOs can leverage this to manipulate data, present selectively favorable analyses, and mislead shareholders' assessment of venture projects. By obscuring true risks, CEOs can shield themselves from accountability, potentially increasing the firm's overall risk exposure. Second, digital transformation exacerbates information processing demands on shareholders. According to the theory of investors' limited attention [ 39 , 40 ], the information redundancy and overload generated by digital technologies can surpass shareholders' processing capacity. This impedes their ability to distill critical insights from vast datasets, increasing the likelihood of misjudging the firm's operational health and the CEO's true risk appetite. Key strategic shifts or emerging risks may be overlooked amidst the noise, leading to flawed oversight. Third, post-transformation business models and organizational structures become significantly more complex [ 41 ]. New digital initiatives often span multiple fields and involve networked collaborations, making it difficult for shareholders to decipher the firm's core logic, decision-making processes, and resource allocation. This complexity obscures the CEO's risk preferences and creates informational asymmetries. Inadequate disclosure further hampers shareholders' ability to effectively monitor and constrain CEO decision-making, potentially granting CEOs greater latitude for risk-averse behavior[ 41 , 42 , 43 ]. In conclusion, rather than unequivocally strengthening governance, digital transformation introduces significant challenges. It complicates the consistent acquisition and interpretation of information necessary to assess CEO risk preferences, thereby impairing effective shareholder monitoring. Concurrently, it can create avenues for CEOs to deliberately engage in risk evasion. Based on this analysis, we propose the following hypothesis: H1: Digital transformation is positively associated with CEOs' risk aversion Enterprise digital transformation can reduce CEO risk preference by lowering enterprise uncertainty. First, digital transformation can improve the operational transparency of enterprises. Through digital technology, enterprises can achieve real-time monitoring and data analysis of business processes, thereby better understanding their operational status. This transparency can help CEOs better grasp the risk points in enterprise operations, reduce the uncertainty problems faced by the enterprise, and consequently make more stable risk decisions, reducing CEO risk preference[ 44 , 45 , 46 , 47 ]. Second, digital transformation enhances enterprise risk response capability. Digital transformation enables enterprises to obtain and process risk information more quickly, thus allowing them to take measures to address potential risks in a more timely manner. This efficient risk response mechanism can reduce the risk exposure of enterprise uncertainty, thereby lowering CEO risk preference. Furthermore, digital transformation optimizes enterprise decision-making processes[ 48 ]. Digital technology can help enterprises optimize decision-making processes, improving decision-making efficiency and accuracy. This optimization can reduce the uncertainty faced by enterprises during the decision-making process, thereby reducing CEO risk preference in decision-making[ 49 ]. Finally, digital transformation enhances enterprise competitiveness. Through digital transformation, enterprises can gain stronger competitive advantages, thereby improving profitability. This enhancement in competitiveness can make enterprises more stable and sustainable, reducing the uncertainty problems they face, and consequently lowering CEO risk preference. In summary, digital transformation reduces enterprise uncertainty and thereby lowers CEO risk preference by improving enterprise operational transparency, enhancing risk response capability, optimizing decision-making processes, and boosting enterprise competitiveness[ 50 , 51 ]. H2: As the degree of digital transformation increases, corporate uncertainty decreases, reinforcing CEO risk aversion. 4. Research Design 4.1Variables Selection 4.1.1.Corporate Digital Transformation Following the research methods of Qi and Cai [ 52 ], this study quantifies the index of enterprise digitization degree using the text mining method. The study compiles and organizes the keywords related to digital transformation, then calculate the frequency of keyword occurrence to measure the digital transformation of the enterprise 4.1.2.CEO Risk Preference Investment decisions reflect the CEO's risk preference. In investment decision-making projects, transactional financial assets, available-for-sale financial assets, and investment real estate are based on the fair value model, and the uncertainty of risks and returns is greater than other investment projects. Therefore, based on the risk preference measuremen[ 53 , 54 ], this study calculates the proportion of the total annual amount of three venture capitals to the total assets of the current year. A higher value indicates a higher CEO's risk preference. 4.1.3.Enterprise Uncertainty Following the methodology of Hassaballa (2019), this study employs text mining techniques to construct an enterprise uncertainty index. Specifically, we extract textual content from the Management Discussion & Analysis (MD&A) sections of listed companies' annual reports and utilize Python's jieba library for word segmentation. The index is calculated by quantifying the frequency of uncertainty-related terms within the MD&A texts. The lexicon of uncertainty-related terms includes: uncertainty; unclear; indistinct; unknown; unpredictable; hard to estimate; hard to forecast; hard to predict; hard to anticipate; risk; danger; crisis; threat. Table 1 Variable definition Variable Type Variable Symbol Name Variable Definition Explanatory variable Risk_prefer CEO risk preferences The total expenditure of the enterprise 's investment in trading financial assets, available for sale financial assets and investment real estate / total assets at the end of the year Explained variable DT Enterprise digital transformation The digital word frequency obtained by text mining method is logarithmically processed. Control Variable Age Company Age Observation year - establishment year Cap Capital Expenditure The ratio of capital expenditure to total assets TOP1 Ownership Concentration Proportion of the largest shareholder ROA Profitability Net profit / total assets excluding non-operating income and expenditure Ind Proportion of independent directors Independent directors / the total number of board members Cash Free Cash Flow Net cash flow / total assets PPE Fixed Assets Ratio Net fixed assets / total assets Growth Enterprise Growth ( Net profit for the year - Net profit for the same period of the previous year ) / Net profit for the same period of the previous year Tobinq Enterprise Value Price per share × number of tradable shares + price per share × number of non-tradable shares + book value of liabilities ) / total assets Lev Assets-Liability Ratio Total liabilities / total assets Size Size of Enterprise The total assets are logarithmically processed Duality job consolidation The general manager serving as chairman takes 1, otherwise 0. Year Year Annual dummy variable Industry Industry Industry dummy variable Province Province Province dummy variable 4.1.4.Control Variables In accordance with prior research, this paper considers various control variables, encompassing company age (Age), capital expenditure (Cap), ownership concentration (TOP1), corporate profitability (ROA), proportion of independent directors (Ind), free cash flow (Cash), fixed asset ratio (PPE), enterprise growth (Growth), enterprise value (Tobinq), asset-liability ratio (Lev), enterprise size (Size), duality (Duality), year (Year), industry (Industry), and province (Province). The specific details can be found in Table 1 . 4.2. Model design To examine the influence of digital transformation on CEO risk preference, we construct the following model: where Risk_prefer i,t represents the CEO risk preference of corporate i in time t . DT it represents the digital transformation of corporate i in time t . Industry t and Year t represent the industry and year effects of the firm, respectively. 4.3 Data sources The study utilizes data from A-share listed companies spanning from 2015 to 2022. Samples labeled with ST, * ST, SST, and PT are excluded. The data is sourced from the CNRDS database. Ultimately, we obtain 15,153 sample observations, and all indicators undergo Winsorize processing at the 1% and 99% quantiles. Table 2 presents the descriptive statistical. The maximum and minimum values of the core variable CEO risk preference (Risk_prefer) are 0.865 and 0.000, signifying significant variations in CEO risk preference among different enterprises. There exists a discernible gap between the average and median of digital transformation (DT), and the range is considerable, indicating a notable diversity and uneven distribution in the degree of digital transformation among sample enterprises. The remaining variables, also detailed in Table 2 , fall within the normal range, with no abnormal values. Table 2 Descriptive Statistic variable N mean sd p50 min max Risk_prefer 15153 0.060 0.104 0.015 0.000 0.865 DT 15153 1.324 0.600 1.163 0.693 4.427 Age 15153 3.007 0.297 3.045 2.079 3.555 Cap 15153 0.002 0.006 0.000 0.000 0.040 TOP1 15153 33.05 14.83 30.50 8.350 74.89 ROA 15153 0.035 0.074 0.0394 -0.291 0.208 Ind 15153 0.382 0.058 0.367 0.287 0.571 Cash 15153 0.169 0.128 0.134 0.0117 0.624 PPE 15153 0.179 0.142 0.146 0.002 0.651 Growth 15153 0.0112 0.495 0.000 -0.815 0.923 Tobinq 15153 1.885 1.238 1.514 0.000 8.457 Lev 15153 0.404 0.203 0.399 0.000 0.884 Size 15153 22.320 1.328 22.10 19.95 26.38 Duality 15153 0.342 0.474 0.000 0.000 1.000 5. Empirical Research 5.1 Digital transformation and CEO risk preference We test H1 using empirical model (1). Table 3 presents the regression results. To address potential autocorrelation or heteroscedasticity issues, this study conducts Cluster clustering adjustment at the company level, gradually controlling for the fixed effects of year, industry, and province.Among these adjustments, (1) without controlling for industries and provinces, the regression coefficient of digital transformation (DT) on CEO risk preference (Risk_prefer) is 0.003, and the relationship is not statistically significant; (2) Controlling for industry and year, the regression coefficient of digital transformation (DT) on CEO risk preference (Risk_prefer) is − 0.057 at the 5% level.. The findings indicate that the regression coefficient of digital transformation (DT) on CEO risk preference (Risk_prefer) is − 0.057 at the 5% level. Empirical results indicate a significant negative correlation between digital transformation (DT) and Chief Executive Officer's risk preference (Risk_prefer) in accordance with hypothesis H1. Table 3 Digital Transformation and CEO Risk Preference (1) (2) (3) Risk_prefer Risk_prefer Risk_prefer DT 0.003 -0.057 ** -0.057 ** (0.160) (-2.419) (-2.425) Age 0.007 -0.066 -0.068 (0.142) (-1.381) (-1.422) Cap -0.120 *** -0.128 *** -0.120 *** (-7.857) (-7.500) (-7.091) TOP1 0.004 *** 0.003 *** 0.003 *** (3.871) (3.155) (2.942) ROA 0.307 * 0.390 ** 0.345 ** (1.823) (2.335) (2.109) Ind 0.117 0.157 0.121 (0.601) (0.818) (0.640) Cash -2.060 *** -2.246 *** -2.236 *** (-16.828) (-18.046) (-17.906) PPE -1.985 *** -1.883 *** -1.850 *** (-18.680) (-17.281) (-17.076) Growth -0.022 -0.006 -0.004 (-1.298) (-0.336) (-0.214) Tobinq 0.031 *** 0.031 *** 0.030 *** (2.790) (2.748) (2.704) Lev -1.784 *** -1.830 *** -1.836 *** (-19.106) (-19.321) (-19.405) Size 0.027 ** 0.017 0.023 * (2.300) (1.399) (1.875) Duality 0.049 * 0.048 * 0.039 (1.772) (1.782) (1.473) Constant 0.976 *** 1.571 *** 1.593 *** (3.771) (5.112) (4.993) Year Yes Yes Yes Industry No Yes Yes Province No No Yes N 15153 15153 15153 R 2 0.169 0.209 0.217 Columns (1)-(3) of Table 4 present the paths of enterprise uncertainty. Column (1) controls for year fixed effects, column (2) additionally controls for industry fixed effects, and column (3) further controls for province fixed effects. The results show that digital transformation has significant negative effects on enterprise uncertainty (β1 = -0.108, -0.113, and − 0.112), indicating the potential mechanism of reducing enterprise uncertainty through which digital transformation can reduce CEO risk preference. Table 4 Mechanism Tests on Firm Uncertainty (1) (2) (3) FW FW FW DT -0.108 *** -0.113 *** -0.112 *** (-4.515) (-4.443) (-4.354) Age -0.073 -0.067 -0.059 (-1.347) (-1.212) (-1.062) Cap -0.078 *** -0.059 ** -0.050 ** (-3.665) (-2.461) (-2.063) TOP1 0.000 0.001 0.001 (0.189) (0.979) (0.842) ROA -0.372 * -0.434 ** -0.356 * (-1.740) (-2.016) (-1.672) Ind -0.454 * -0.438 * -0.381 (-1.929) (-1.877) (-1.624) Cash 0.573 *** 0.670 *** 0.674 *** (4.194) (4.889) (4.966) PPE 0.186 * 0.249 * 0.222 (1.668) (1.789) (1.584) Growth 0.056 ** 0.037 0.023 (2.242) (1.508) (0.966) Tobinq -0.055 *** -0.053 *** -0.050 *** (-5.432) (-5.078) (-4.796) Lev -0.261 *** -0.308 *** -0.282 *** (-2.639) (-3.026) (-2.779) Size -0.029 ** -0.032 ** -0.038 ** (-2.036) (-2.085) (-2.477) Duality -0.036 -0.031 -0.024 (-1.174) (-1.008) (-0.802) _cons 2.196 *** 2.325 *** 2.302 *** (6.600) (5.190) (5.126) Year Yes Yes Yes Industry No Yes Yes Province No No Yes N 11206 11206 11206 R 2 0.075 0.097 0.106 5.2 Robustness tests:High-order fixed effect model To further mitigate endogeneity issues and draw insights from the research methods of other scholars [ 55 ], this paper employs a more rigorous fixed effect model for regression analysis. In terms of controlling potential variables, this study not only includes the fixed effects of time and industry but also extends its consideration to the influence of the fixed effect dimension of time and industry.Specifically, this paper utilizes the time-industry, time-industry double cross fixed effect model for analysis. This model better captures the differences between various times and industries, along with their evolving impact over time. Through this model, the study can diminish the influence of endogeneity problems on research results. Column (3) of Table 3 reports the test results after employing the cross-product fixed effect model. The results consistent with the benchmark regression results. This indicates that, even after considering a more stringent fixed effect model, the conclusions of this paper remain robust.In summary, the use of a more rigorous fixed effect model in the analysis further alleviates endogeneity problems and provides more accurate and robust research results. These findings hold significant implications for understanding the impact of enterprise digital transformation. 5.3 Endogeneity test 5.3.1PSM-DID model In order to more robustly reveal the causal relationship between digital transformation ( DT ) and CEO risk preference ( Risk prefer ), this paper uses the PSM-DID model to solve the possible endogenous problems. The test results are shown in Table 5 ( 1 ) - ( 3 ). First of all, this paper takes the median of the sample of the digital transformation of the explanatory variable enterprise as the standard. When the score of the digital transformation of the enterprise is greater than the median, Treat takes 1, otherwise Treat takes 0, and then the sample with a higher degree of digital transformation of the enterprise is set as the processing group, and the sample group with a lower degree of digital transformation of the enterprise is set as the control group ;secondly, the company 's age ( Age ), capital expenditure ( Cap ), ownership concentration ( TOP1 ), corporate profitability ( ROA ), proportion of independent directors ( Ind ), free cash flow ( Cash ), fixed asset ratio ( PPE ), enterprise growth ( Growth ), enterprise value ( Tobinq ), asset-liability ratio ( Lev ), enterprise scale ( Size ), duality ( Duality ), year ( Year ),Control variables such as industry and province are used as skew variables to estimate propensity scores. Finally, the nearest neighbor method is selected according to the estimated propensity score, and the matching ratio is determined to be 1 : 1.Finally, the regression is carried out according to the matched samples. The results are reported in Table 5 ( 2 ) - ( 3 ). After controlling the industry year ( Year ), industry ( Industry ) and year ( Year ), industry ( Industry ) province ( Province ), the regression coefficient of enterprise digital transformation to CEO risk preference ( Risk _ prefer ) is − 0.047 at the level of 10%, which is consistent with the above regression results. 5.3.2Instrumental variable tests This study employs the one-period lag of enterprise digital transformation as the instrumental variable for both enterprise digital transformation and CEO risk preference. When estimating the impact of enterprise digital transformation on CEO risk preference, data from the previous period of enterprise digital transformation are utilized. This instrumental variable approach is grounded in the concept of instrumental variables, which entails identifying a tool variable highly correlated with the endogenous explanatory variable but unrelated to the error term to replace the endogenous explanatory variable. The advantage of using the one-period lag of enterprise digital transformation as an instrumental variable lies in its ability to mitigate endogeneity issues. By utilizing one-period lag data, it avoids the simultaneous impact of the previous enterprise digital transformation on CEO risk preference, thereby enhancing the accuracy of estimation results. Following the use of the lag phase of enterprise digital transformation as an instrumental variable, the estimation results in this study still exhibit a significant negative correlation. This signifies the robustness of the negative and significant findings regarding the impact of enterprise digital transformation on CEO risk preference. Consequently, the study addresses endogeneity concerns by employing the one-period lag of the digital transformation, yielding more precise and robust estimation results. Table 5 Endogeneity test (1) (2) (3) Variable PSM IV-2SLS Risk_prefer DT -0.047 * -0.058 ** -0.057 ** (-1.859) (-2.555) (-2.424) Age -0.090 -0.030 -0.062 (-1.610) (-0.806) (-1.294) Cap -0.121 *** -0.121 *** -0.127 *** (-6.726) (-9.556) (-7.434) TOP1 0.003 *** 0.005 *** 0.003 *** (3.163) (7.338) (3.201) ROA 0.283 0.689 *** 0.411 ** (1.585) (4.264) (2.383) Ind 0.177 0.148 0.170 (0.823) (0.970) (0.875) Cash -2.133 *** -2.339 *** -2.273 *** (-14.923) (-21.686) (-17.974) PPE -1.787 *** -1.798 *** -1.899 *** (-14.655) (-21.073) (-17.198) Growth 0.000 -0.021 -0.012 (0.004) (-1.046) (-0.707) Tobinq 0.028 ** 0.010 0.030 ** (2.220) (1.082) (2.565) Lev -1.822 *** -2.058 *** -1.832 *** (-16.866) (-27.234) (-19.119) Size 0.033 ** 0.005 0.017 (2.370) (0.540) (1.343) Duality 0.029 0.035 * 0.047 * (0.990) (1.699) (1.739) Constant 1.218 *** 2.094 *** 1.534 *** (3.554) (8.298) (4.372) Year Yes Yes Yes Industry Yes Yes Yes Province Yes Yes Ind_Year Yes N 10419 10281 15153 R 2 0.214 0.243 0.207 6. Individual heterogeneity test of CEOs Due to the heterogeneity of CEO individual level, the impact of enterprise digital transformation on CEO risk preference will be affected. Therefore, based on the age, salary, tenure, and R & D background of the CEO, this paper further subdivides the whole sample into the older CEO group and the younger CEO group, high salary group and low salary group, longer-term group and shorter-term group, and no R & D background group and R & D background group. Among them,the CEO age is set to 1 above the mean value and 0 below the mean value; CEO salary higher than the average is set to 1, lower than the salary is set to 0; CEO tenure above the mean is set to 1, and the CEO tenure below the mean is set to 0; CEO with R & D background is set to 1, and no R & D background is set to 0. 6.1 Individual heterogeneity test of CEO age The impact of digital transformation on CEO risk preference might be influenced by the age of the CEO.Older CEOs experience a notably negative impact on their risk preference due to digital transformation; conversely, the effect is not significant for younger CEOs.The majority of senior executives exhibit a propensity to uphold the existing state of affairs and exhibit reluctance towards risk-taking.Consequently, the digital tools introduced by digital transformation prompt senior executives to utilize them for concealing risk information.Furthermore, the challenge for shareholders lies in managing an extensive volume of redundant information, potentially exacerbating the risk aversion inclination among senior individuals,thereby fostering greater caution in decision-making. Young CEOs typically possess a more profound comprehension of and receptiveness to novel developments within the enterprise, displaying a willingness to embrace emerging technologies and innovative business models.These CEOs may discern the opportunities and potential within the business, demonstrating a readiness to undertake risks in pursuit of experimentation.Consequently, young CEOs exhibit a heightened willingness to embrace risks and explore innovative transformation pathways to realize the company's long-term developmental objectives.Thus, in contrast to their older counterparts, the influence of digital transformation on the risk appetite of young CEOs is not significant. 6.2Individual-Level heterogeneity in CEO compensation When the annual salary of the CEO is low, digital transformation significantly decreases the CEO's risk preference, with a high coefficient.Conversely, with a high CEO salary, digital transformation still significantly reduces CEO risk preference, albeit with a lower coefficient. CEOs earning a lower annual salary may focus more on short-term economic gains and risk Mitigation.Consequently, CEOs with lower salaries, due to digital transformation, employ digital tools to conceal risk information. Dealing with a surplus of redundant information becomes challenging for shareholders, potentially reinforcing the risk-averse behavior of lower-salaried CEOs. Lower-salaried CEOs may prioritize personal income and occupational safety, resulting in a higher inclination towards risk aversion.Conversely, CEOs earning higher salaries are likely to prioritize the company's long-term development and strategic objectives.Higher-salaried CEOs often possess greater resources and decision-making autonomy,emphasizing innovation and competitive advantage.Despite digital transformation negatively affecting their risk appetite, the coefficient is lower, likely due to their heightened focus on the crucial role of digital transformation in long-term enterprise development.In conclusion, the impact of digital transformation on CEO risk preference is contingent on salary level. CEOs with lower annual salaries may prioritize short-term earnings and risk control,intensifying the negative impact of digital transformation on their risk appetite. Conversely, CEOs with higher salaries may concentrate on long-term development and strategic goals, mitigating the adverse effect of digital transformation on their risk appetite. Table 6 Individual-Level Heterogeneity among CEOs (1) (2) (3) (4) Senior CEO Junior CEO Low compensation High compensation Variable Risk_prefer Risk_prefer Risk_prefer Risk_prefer DT -0.074*** -0.041 -0.066* -0.050* (-2.611) (-1.101) (-1.861) (-1.747) Age -0.011 -0.141** -0.065 -0.063 (-0.176) (-1.984) (-0.899) (-1.061) Cap -0.106*** -0.148*** -0.110*** -0.131*** (-5.087) (-5.437) (-4.187) (-6.537) TOP1 0.002** 0.003** 0.004*** 0.002** (2.121) (2.346) (3.294) (2.119) ROA 0.353* 0.312 0.519** 0.110 (1.716) (1.260) (2.496) (0.486) Ind 0.090 0.151 0.014 0.174 (0.384) (0.499) (0.055) (0.718) Cash -2.058*** -2.507*** -1.965*** -2.447*** (-13.874) (-12.784) (-11.261) (-15.222) PPE -1.669*** -2.201*** -1.460*** -2.161*** (-13.094) (-12.407) (-10.251) (-15.220) Growth -0.009 -0.003 -0.017 0.004 (-0.442) (-0.120) (-0.676) (0.186) Tobinq 0.014 0.062*** 0.027 0.029** (1.046) (3.137) (1.407) (2.171) Lev -1.713*** -1.999*** -1.625*** -1.981*** (-14.764) (-13.875) (-11.875) (-16.556) Size 0.011 0.054*** 0.032 0.010 (0.741) (2.729) (1.510) (0.650) Duality 0.065* 0.009 -0.024 0.080** (1.873) (0.215) (-0.599) (2.390) Constant 1.476*** 1.538** 1.372** 1.831*** (4.153) (2.566) (2.503) (4.928) Year Yes Yes Yes Yes Industry Yes Yes Yes Yes Province Yes Yes Yes Yes N 9152 6001 5462 9691 R 2 0.215 0.235 0.204 0.234 Note: *p < 0.1, **p < 0.05, ***p < 0.01; t-values for clustering to the firm level are in parentheses. 6.3 Individual-Level heterogeneity in CEO tenure In instances of long CEO tenure, digital transformation significantly diminishes CEO risk preference, yielding a low coefficient. Conversely, with short CEO tenure, digital transformation similarly exerts a substantial negative impact on the CEO's risk preference, resulting in a higher Coefficient.For CEOs with extended tenures, their accumulated experience and authority empower them to effectively control and mitigate risks.Extended CEO tenure typically provides more resources and time to address challenges in enterprise development, facilitating improved adjustment and optimization of the company's operation and business model.Long-term CEOs may focus on the opportunities and competitive advantages arising from digital transformation, recognizing its importance for future enterprise development. Consequently, they exhibit a willingness to assume higher risks, fostering sustained enterprise development. Consequently, although the negative impact of digital transformation on their risk preference is substantial, the associated coefficient remains low.CEOs with short tenure typically have limited time and resources to comprehend the company's business, operations, and risks.Limited time and resources may hinder their ability to attain an in-depth understanding of all aspects of the company, impeding the formulation of comprehensive risk decisions.Additionally, due to the heightened uncertainty and risk faced by short-term CEOs, they may exhibit a greater inclination toward pursuing stability and security.They may believe that maintaining the status quo and avoiding significant risks is a more secure option, leading them to allocate more time and resources to risk management rather than innovation and change.Additionally, CEOs with short tenure may prioritize personal reputation and career development.They may consider excessively risky decisions as potentially detrimental to their reputation and career development.Consequently, to mitigate potential risks, short-term CEOs may lean towards employing digital tools to conceal risk information, embracing conservative strategies, and refraining from making overly radical decisions.Furthermore, the information redundancy effect induced by digitization may increase the challenge for shareholders in overseeing the company.Consequently, in comparison to CEOs with lengthy tenures, those with shorter tenures exhibit more pronounced risk aversion effects. 6.4 Individual-Level heterogeneity in CEO R&D background The influence of digital transformation on the risk preference of a CEO may depend on whether the CEO possesses an R&D background.In instances where the CEO lacks an R&D background, digital transformation markedly diminishes CEO risk preference. Conversely, when the CEO possesses an R&D background, the impact of digital transformation on CEO risk preference is negligible.CEOs lacking an R&D background may possess limited knowledge about technology, R&D, and market risks associated with digital transformation.Owing to the absence of pertinent experience and knowledge, these CEOs may exhibit heightened caution and conservatism, expressing concerns about the risk and uncertainty facing the enterprise.They may lean towards utilizing existing digital tools or seeking technical personnel to conceal risk information, adopting conservative strategies, and refraining from taking excessive risks to uphold the stability and business performance of the company. Consequently, digital transformation may amplify their risk aversion, exerting a marked negative impact on risk appetite. Conversely, CEOs possessing an R&D background typically possess a more profound understanding of technology, R&D, and market risks associated with digital transformation. They can adeptly assess and manage these risks, formulating appropriate strategies to address the challenges. Owing to their extensive knowledge and experience in digital transformation, these CEOs may prioritize innovation and competitive advantages, demonstrating a willingness to undertake associated risks to attain the company's long-term development goals. Additionally, the CEO's R&D background may influence their decision-making style and innovation consciousness. CEOs with an R&D background may prioritize technological innovation and R&D capabilities,demonstrating heightened innovation awareness and risk-taking ability.They may exhibit a greater willingness to promote digital transformation and experiment with new business models and innovative strategies to achieve the sustainable development of enterprises.CEOs lacking an R&D background may focus more on stability and business performance,demonstrating heightened sensitivity to the risks associated with digital transformation.In summary, the influence of digital transformation on CEO risk appetite is contingent upon whether the CEO possesses an R&D background. Table 7 Individual-Level Heterogeneity among CEOs (1) (2) (3) (4) Short Tenure Long Tenure Lack R&D Background Has R&D Background Variable Risk_prefer Risk_prefer Risk_prefer Risk_prefer DT -0.049* -0.059* -0.075** -0.056 (-1.701) (-1.821) (-2.521) (-1.249) Age 0.008 -0.153** -0.031 -0.253*** (0.126) (-2.507) (-0.516) (-2.674) Cap -0.110*** -0.130*** -0.139*** -0.101*** (-5.444) (-5.300) (-5.624) (-3.390) TOP1 0.003*** 0.002* 0.003** 0.001 (2.696) (1.671) (2.302) (0.453) ROA 0.551** 0.268 0.495** 0.108 (2.476) (1.211) (2.318) (0.326) Ind -0.023 0.367 -0.007 0.684* (-0.102) (1.364) (-0.027) (1.869) Cash -2.158*** -2.357*** -2.372*** -2.377*** (-12.969) (-14.568) (-14.237) (-10.780) PPE -1.739*** -1.981*** -1.948*** -1.905*** (-12.341) (-13.618) (-13.815) (-9.184) Growth -0.026 0.019 -0.036* 0.067** (-1.134) (0.808) (-1.703) (2.018) Tobinq 0.009 0.062*** 0.043*** 0.022 (0.765) (3.245) (2.931) (1.020) Lev -1.843*** -1.827*** -1.860*** -2.206*** (-15.187) (-14.516) (-15.404) (-12.684) Size 0.023 0.028* 0.041** 0.003 (1.508) (1.713) (2.356) (0.117) Duality 0.063* 0.037 0.030 0.041 (1.911) (0.958) (0.876) (0.791) Constant 1.470*** 1.615*** 1.391*** 2.516*** (4.247) (3.548) (3.253) (4.068) Year Yes Yes Yes Yes Industry Yes Yes Yes Yes Province Yes Yes Yes Yes N 8103 7050 9164 3837 R 2 0.214 0.226 0.225 0.245 7. Heterogeneity test at the enterprise level and industy level Because the heterogeneity characteristics at the enterprise and industry levels directly influence the CEO's risk perception, judgment, and understanding, resulting in variations in CEO risk preferences.Accordingly, considering the enterprise industry type and property rights nature, this study further categorizes the entire sample into non-high-tech and high-tech industries.The study additionally explores the influence mechanism of digital transformation and CEO risk preference within the state-owned and private enterprise groups.Specifically, enterprise property rights are coded as 1 for state-owned enterprises and 0 for private enterprises. Following CSRC's industry classification guidelines for listed companies, high-tech industries are identified by categories C26, C27, C28, C29, C34, C35, C36, C38, C39, C40, C41, I64, I65, and M73, denoted as the high-tech enterprise group (coded as 1), while the remaining categories constitute the non-high-tech enterprise group (coded as 0). 7.1 Corporate-Level heterogeneity in Ownership nature In private enterprises, digital transformation significantly negatively affects CEO risk preference. Following digital transformation, private enterprises exhibit a stronger CEO risk aversion effect compared to state-owned enterprises.State-owned enterprises typically have the government as a shareholder, resulting in a more stable and transparent governance structure.In contrast, private enterprises feature a more diverse ownership structure and a relatively flexible governance framework.These disparities in ownership and governance may introduce heightened uncertainty for CEOs of private enterprises post-digital transformation, necessitating CEOs to bear increased risks.Consequently, to safeguard professional stability, the CEO's risk aversion effect is more pronounced.Additionally, state-owned enterprises typically enjoy more government support and resources, encompassing capital, technology, and talent.Private enterprises may encounter greater challenges in acquiring resources and financial support, posing numerous hurdles to their subsequent development post-digital transformation.The increased complexity of digital tools and issues related to information redundancy may contribute to a more pronounced risk aversion effect among enterprise CEOs.Ultimately, while private enterprises typically exhibit greater flexibility in market competition, they concurrently face heightened risks. Following digital transformation, CEOs of private enterprises must prioritize market research and strategic planning to mitigate potential risks and losses.State-owned enterprises demonstrate a relatively strong risk tolerance and a focus on long-term development and stability. Consequently, the risk aversion of CEOs in private enterprises surpasses that in state-owned enterprises. 7.2 Industry-Level heterogeneity in High-Tech nature In non-high-tech industries, digital transformation significantly negatively influences CEO risk preference. In contrast to high-tech industries, non-high-tech industries exhibit a more complex organizational structure, and their information capacity becomes more redundant following digital transformation. This phenomenon may further enhance the risk-averse tendency of corporate CEOs.CEOs often exhibit a propensity to maintain the status quo and a reluctance to undertake risks.High-tech companies typically possess advanced technical capabilities and innovation prowess, leading CEOs to exhibit a greater willingness to experiment with new technologies and business models.The trajectory of digital transformation is a pivotal trend in the evolution of high-tech industries. CEOs in these sectors typically prioritize innovation and embrace change, demonstrating a greater willingness to undertake substantial risks in driving enterprise development. Table 8 Individual-Level Heterogeneity among CEOs (1) (2) (3) (4) Private Enterprise State-Owned Enterprise Non-High-Tech Industry High-Tech Industry Variable Risk_prefer Risk_prefer Risk_prefer Risk_prefer DT -0.055* -0.048 -0.092** -0.043 (-1.854) (-1.230) (-2.376) (-1.432) Age -0.138** 0.056 0.113 -0.214*** (-2.225) (0.679) (1.312) (-3.594) Cap -0.151*** -0.046** -0.084 -0.122*** (-6.897) (-2.257) (-1.310) (-6.801) TOP1 0.004*** -0.001 0.006*** 0.003** (3.090) (-0.451) (3.481) (2.517) ROA 0.323 -0.056 1.089*** 0.080 (1.607) (-0.170) (3.722) (0.379) Ind 0.096 0.430 0.108 0.040 (0.382) (1.502) (0.317) (0.181) Cash -2.564*** -1.433*** -2.407*** -2.492*** (-16.466) (-7.062) (-9.750) (-16.711) PPE -2.275*** -1.210*** -1.629*** -2.043*** (-14.774) (-8.090) (-9.071) (-14.865) Growth -0.000 -0.018 -0.060** 0.042** (-0.016) (-0.731) (-2.010) (2.068) Tobinq 0.031** 0.023 -0.027 0.005 (2.371) (0.971) (-1.262) (0.386) Lev -2.067*** -1.321*** -1.767*** -2.256*** (-16.713) (-8.931) (-9.607) (-18.855) Size 0.047** -0.015 0.018 0.011 (2.225) (-0.924) (0.744) (0.787) Duality 0.015 0.125 0.028 0.042 (0.492) (1.556) (0.496) (1.418) Constant 1.523*** 1.705*** 1.412*** 1.986*** (2.588) (3.677) (2.733) (5.372) Year Yes Yes Yes Yes Industry Yes Yes Yes Yes Province Yes Yes Yes Yes N 9690 4270 5478 9340 R 2 0.219 0.281 0.229 0.255 8. Economic consequences : enterprise innovation Upon analyzing the data in Table 8 , an unexpected conclusion emerges.While digital transformation exacerbates the CEO's risk aversion tendency, it does not hinder high-risk R&D investments; instead, it markedly enhances overall R&D investment and output for enterprises. This observation indicates a substantial mitigation of the adverse impact of the CEO's risk aversion tendency on enterprise innovation activities.Enterprises gain increased benefits and competitive advantages through digital transformation, achieved by enhancing production efficiency, reducing costs, and optimizing resource allocation.Leveraging these benefits and competitive advantages, enterprises can augment their R&D investments, thereby advancing digital transformation, innovation, and overall development.Consequently, digital transformation has the potential to attract additional funds and resources for enterprises, fostering further innovation and development.Additionally, digital transformation significantly enhances the R&D output of enterprises through the optimization of R&D processes, improved R&D efficiency, and innovation capabilities.Digital transformation empowers enterprises to rapidly acquire and integrate diverse resources, efficiently manage and coordinate various departments, thereby enhancing the efficiency and quality of R&D outcomes.Furthermore, digital transformation facilitates communication and collaboration between enterprises and external partners, collaboratively advancing R&D progress and the transformation of outcomes. In summary, within the realm of digital transformation, the impact of the CEO's risk aversion tendency is significantly diminished. The digital transformation of enterprises not only enhances R&D investment and output but also delivers increased benefits and competitive advantages, thereby fostering innovation and overall development. Table 9 R&D input and output of enterprises (1) Patenting (2) R&D Investment (3) R&D Investment /Operating Income Variable Patents1 RDSpendSum RDSpendSumRatio DT 0.146*** 0.083*** 0.025* (2.967) (3.115) (1.907) Age -0.184** -0.208*** -0.173*** (-2.098) (-4.267) (-7.070) Cap 0.045 0.227*** 0.203*** (1.212) (14.371) (13.096) TOP1 0.005** 0.001 -0.002*** (2.110) (0.702) (-3.657) ROA -0.276 0.435* -1.516*** (-0.642) (1.886) (-11.243) Ind -0.085 -0.173 -0.065 (-0.197) (-0.837) (-0.675) Cash 0.283 0.256** 0.277*** (1.327) (2.559) (4.454) PPE -0.389 0.051 -0.143*** (-1.507) (0.355) (-2.707) Growth -0.073* 0.049** -0.003 (-1.663) (2.013) (-0.322) Tobinq 0.033 0.071*** 0.069*** (1.645) (7.312) (9.630) Lev 0.093 -0.145 -0.744*** (0.501) (-1.569) (-15.776) Size 0.703*** 0.928*** 0.021*** (22.155) (64.765) (3.233) Duality 0.129** 0.027 0.047*** (2.381) (1.136) (3.482) Constant -14.085*** -2.958*** 0.557*** (-16.584) (-6.864) (3.428) Year Yes Yes Yes Industry Yes Yes Yes Province Yes Yes Yes N 2597 14176 14044 R 2 0.490 0.668 0.502 9. Research conclusions and implications 9.1 Conclusion. The present study investigates the influence of digital transformation on CEO risk preference.Findings reveal that the digital transformation of enterprises significantly negatively affects CEO risk preference and this relationship is moderated by CEO personal traits. Moreover, it is found that digital transformation reduces CEO risk preference by mitigating firm-level uncertainty. The study discovers that digital transformation enhances both the innovation input and output. By enabling rapid responses to market changes and enhancing production efficiency, digital transformation prompts increased investment in innovation by enterprises. Simultaneously, digital transformation aids enterprises in enhancing product quality and service levels, thereby improving overall innovation output.These favorable economic outcomes further substantiate the critical role of digital transformation in fostering enterprise development.In conclusion, this study unveils the impact of digital transformation on CEO risk preference, elucidates its underlying mechanisms, and discusses the economic ramifications of digital transformation.These findings carry significant theoretical and practical implications for comprehending CEO risk management during the enterprise digital transformation process. 9.2 Implications. 9.2.1 Implement a robust follow-up management system for digital transformation. Enterprises should develop a comprehensive follow-up management system for digital transformation, encompassing the assessment, revision, and enhancement of the existing system, along with research and responses to emerging challenges.Enterprises should adopt an open-minded approach, actively assimilate external advanced experiences and practices, and consistently enhance the efficiency and effectiveness of digital transformation follow-up management.Clarify the responsibilities and authorities of different departments in the follow-up management of digital transformation, ensuring seamless collaboration across all departments.For instance, assign the IT department with the responsibility for maintaining and upgrading digital systems, delegate the business department for the utilization and promotion of digital applications, and task the human resources department with recruitment and training of digital talents.During the digital transformation process, various issues and challenges may arise.Establish an effective feedback management mechanism to promptly gather employees' opinions and suggestions. In response to identified issues, enterprises should implement appropriate measures for improvement, such as optimizing the digital system, enhancing staff training, and adjusting the digital strategy.Simultaneously, establish a problem tracking mechanism to ensure timely resolution of issues and prevent their recurrence. 9.2.2Enhance risk management post-digital transformation. Following digital transformation, establish a robust risk management mechanism to delineate the risk management process and assign responsibilities.This mechanism should encompass risk identification, assessment, control, and monitoring to enable enterprises to promptly detect and respond to potential risks.Simultaneously, enterprises should establish risk management documentation, recording risk events and processes to serve as a reference for subsequent risk management.Enterprises should reinforce risk identification, assessment, and control measures to safeguard the stability and safety of operations.The foundation of risk management lies in risk identification. Enterprises should gather information from diverse channels, including market research, internal reports, and expert advice, to promptly detect potential risks.Simultaneously, enterprises should establish a risk identification mechanism to systematically conduct post-digital transformation business risk assessments, ensuring the timely detection and response to potential risks.Risk assessment is a crucial step in determining the level and impact of risks. Enterprises should employ scientific methods and tools to assess identified risks, determining their likelihood and impact. The evaluation results should serve as a foundation for enterprises to formulate risk management strategies.Effectively controlling risks is crucial for preventing or minimizing losses to the enterprise. Enterprises should devise appropriate risk management strategies and control measures based on the results of risk assessments.Such measures may involve the formulation of emergency plans, enhancing system security protection, and fostering employee safety awareness.Simultaneously, enterprises should regularly evaluate and adjust control measures to ensure their efficacy and adaptability.Monitoring risks is pivotal to ensuring continuous and effective risk management. Enterprises should establish a risk monitoring mechanism to systematically monitor business risks post-digital transformation, promptly detecting and responding to potential risks.Simultaneously, enterprises should establish a risk reporting system to routinely report risk management to senior leaders, ensuring comprehensive understanding and mastery of risk management by leadership. 9.2.3Institute a comprehensive incentive mechanism to motivate CEOs to undertake risks. The CEO's risk tolerance and coping ability can be enhanced through the implementation of both a reward mechanism and a fault tolerance mechanism.Initially, to incentivize CEOs to undertake risks, digital enterprises can establish corresponding incentive mechanisms.This reward mechanism can be tied to the company's performance, innovation, and proficiency in risk control.For instance, CEOs who attain notable outcomes in the digital transformation process can receive bonuses, promotion opportunities, or other forms of recognition.Such a reward mechanism can fuel the enthusiasm and innovative spirit of CEOs, fostering a greater willingness to undertake risks.Additionally, CEOs may encounter various uncertainties and challenges in the subsequent phases of digital transformation.To encourage experimentation and innovation, digital enterprises should institute a fault-tolerant mechanism.This mechanism should encompass tolerance and comprehension of failure, along with offering protection and support in case of failure.Upon facing challenges or failures in digital transformation, companies should provide essential support and encouragement to assist CEOs in learning from failures and progressing forward.Lastly, an open corporate culture is a crucial environment for fostering a culture of risk-taking among CEOs. Digital enterprises ought to foster a corporate culture that promotes innovation, embraces failure tolerance, and values individuality.In such a cultural atmosphere, CEOs can freely express their ideas and opinions, fostering the courage to experiment with new methods and strategies. Simultaneously, an open corporate culture can facilitate information sharing and collaboration within the enterprise, enhancing overall risk response capabilities. 9.2.4 Institute a supervisory mechanism to mitigate CEO risk aversion. Digital enterprises should institute a more refined shareholder supervision mechanism to incentivize CEOs to undertake risks.Shareholders constitute the ownership of enterprises, and their rights and interests necessitate complete protection.Firstly, the establishment of a robust shareholder supervision mechanism can ensure that the CEO thoroughly considers the interests of shareholders in the decision-making process, preventing any harm to shareholders' rights and interests arising from personal self-interest or short-sighted behavior.Simultaneously, the shareholder supervision mechanism can prevent CEOs from committing significant mistakes or engaging in misconduct, thereby ensuring the stable development. Secondly, by intensifying shareholders' oversight of CEOs, the enterprise can foster information transparency and promote scientific decision-making.Simultaneously, the shareholder supervision mechanism can encourage enterprises to establish a more comprehensive internal control system, ensuring compliance and robustness.Overall, in the digital age, the competitiveness of enterprises hinges on their innovation capability, market responsiveness, and risk management proficiency. Implementing a sound shareholder supervision mechanism can motivate CEOs to proactively undertake risks, fostering innovation and the development of enterprises.This will assist enterprises in maintaining a leading position in the intense market competition and enhancing their overall competitiveness. Declarations Author Contribution N.L. conceived and designed the study, led the empirical strategy, and drafted the initial version of the manuscript. 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Leading digital transformation in incumbent firms: A strategic entrepreneurship framing[J]. Strateg. Entrepreneurship J. 18 (1), 91–102 (2024). Singh, S. K. et al. Top management knowledge value, knowledge sharing practices, open innovation and organizational performance[J]. J. Bus. Res. 128 , 788–798 (2021). Siachou, E., Vrontis, D. & Trichina, E. Can traditional organizations be digitally transformed by themselves? The moderating role of absorptive capacity and strategic interdependence[J]. J. Bus. Res. 124 , 408–421 (2021). Nayal, K. et al. The impact of sustainable development strategy on sustainable supply chain firm performance in the digital transformation era[J]. Bus. Strategy Environ. 31 (3), 845–859 (2022). Belitski, M., Delgado-Márquez, B. L. & Pedauga, L. E. Your innovation or mine? The effects of partner diversity on product and process innovation[J]. J. Prod. Innov. Manage . 41 (1), 112–137 (2024). Chen, H. & Tian, Z. Environmental uncertainty, resource orchestration and digital transformation: A fuzzy-set QCA approach[J]. J. Bus. Res. 139 , 184–193 (2022). Singh, S., Sharma, M. & Dhir, S. Modeling the effects of digital transformation in Indian manufacturing industry[J]. Technol. Soc. 67 , 101763 (2021). Faccio, M., Marchica, M. T. & Mura, R. Large shareholder diversification and corporate risk-taking[J]. Rev. Financial Stud. 24 (11), 3601–3641 (2011). Su, K., Liu, H. & Zhang, H. Board size, social trust, and corporate risk taking: Evidence from China[J]. Manag. Decis. Econ. 40 (6), 596–609 (2019). Dittmann, I., Yu, K. C. & Zhang, D. How important are risk-taking incentives in executive compensation?[J]. Rev. Financ. 21 (5), 1805–1846 (2017). Chen, Y., Truong, C. & Veeraraghavan, M. CEO risk-taking incentives and the cost of equity capital[J]. J. Bus. Finance Acc. 42 (7–8), 915–946 (2015). Hu, Y., Li, W. & Zhang, A. Political promotion incentives and firm risk: Evidence from state-owned enterprises in China[J]. Emerg. Markets Finance Trade . 59 (1), 156–169 (2023). Yang, D. Evaluation of enterprise financial risk level under digital transformation with artificial neural network[J] (Security and Communication Networks, 2022). Pizzi, S. et al. Assessing the impacts of digital transformation on internal auditing: A bibliometric analysis[J]. Technol. Soc. 67 , 101738 (2021). Bamberger, K. A. Technologies of compliance: Risk and regulation in a digital age[J]. Tex. L Rev. 88 , 669 (2009). Sawhney, M. Prandelli,E.Communities of Creation:Managing Distributed Innovation in Turbulent Markets[J]. Calif. Manage. Rev. 2000 , 42 (4):24– . Kornberger, M. The Visible Hand and the Crowd: Analyzing Organization Design in Distributed Innovation Systems[J]. Strategic Organization,2017,15(2):174 ~ 193. Stohl, C., Stohl, M. & Leonardi, P. M. Digital age managing opacity: Information visibility and the paradox of transparency in the digital age[J]. Int. J. Communication . 10 (15), 123–137 (2016). Warren, J. D., Moffitt, K. C. & Byrnes, P. How big data will change accounting[J].Accounting Horizons,2015, 29 (2):397–407 . Verhoef, P. C. et al. Digital transformation: A multidisciplinary reflection and research agenda[J]. J. Bus. Res. 2021 , 122 (1): 889–901 . Min, H. A. Intelligence in Supply Chain Management:Theory and Applications[J]. Int. J. Logistics:Research Appl. 2010 , 13 (1):13–39 . Li, F. A., ༲eport & ༲eadability Current Earnings, and Earnings Persistence[J].Journal of Accounting and Economics, 45, (2-3): 221།247. (2008). Merkley, K. J. N. Disclosure and Earnings Performance: Evidence from ༲and Disclosures[J].The Accounting ༲eview,2014,89,(2): 725-757. Lateef, A. & Omotayo F.O.Information Audit as an Important Tool in Organizational Management: A Review of Literature[J].BusinessInformationReview,2019, 36 (1):15–22 . Kahneman, D. Attention and Effort[M] (Englewood Cliffs, N.J:Prentice Hall, 1973). Hirshleifer, D., Lim, S. S. & Teoh, S. H. Driven to Distraction: Extraneous Events and Underreaction to Earnings News [J]. J. Finance 2009 , 64 (5): 2289–2325 . Vial, G. Understanding Digital Transformation: A Review and a Research Agenda[J]. J. Strategic InformationSystems 2019 , 28 (2):118–144 . Qi, Yudong & Cai Chengwei.. The Multiple Effects and Mechanism of Digitalization on the Performance of Manufacturing Enterprises [J]. Study Explor. , (7): 108–119. (2020). Li Shihui, Q. et al. Audit Fees, CEO Risk Preference, and Corporate Misconduct [J]. Audit Res. , (03): 84–95. (2021). Hassanta,Hollanders Vanlentl,etal.Firm-level political risk:measurement and effects[J]. Q. J. Economic 2019 , 134 (4):2135–2202 . Moser, P. Compulsory Licensing: Evidence from the Trading with the Enemy Act [J]. Am. Econ. Rev. 102 (1), 396–427 (2012). Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eOver the past decade, rapid advancements in digital technologies\u0026mdash;such as artificial intelligence, cloud computing, big data analytics, blockchain, and the Internet of Things\u0026mdash;have profoundly reshaped the global competitive landscape. Digital transformation has thus become a strategic imperative for enterprises seeking to optimize operational efficiency, enhance value creation processes, and sustain competitive advantage in an increasingly turbulent environment. According to the dynamic capabilities framework, digital technologies not only alter how firms sense, seize, and reconfigure opportunities but also fundamentally reshape organizational routines and decision-making logic. As a result, enterprises undergoing digital transformation face unprecedented environmental dynamism and complexity, such as technological obsolescence risks, information governance challenges, and disruptive organizational restructuring. These shifts heighten strategic uncertainty and impose new demands on corporate governance and executive decision-making.\u003c/p\u003e\u003cp\u003eWithin the firm, the CEO\u0026mdash;who serves as the central architect of strategic choices\u0026mdash;plays an irreplaceable role in determining the pace, direction, and success of digital transformation initiatives. According to upper echelons theory, the cognitive foundations, values, and personal attributes of CEOs shape how they interpret environmental signals and select strategic responses. Digital transformation is therefore not only a technological and organizational process but also a deeply behavioral and managerial one. Yet, despite the emerging body of literature examining the antecedents and consequences of digital transformation, limited attention has been paid to how digital transformation influences executives' psychological tendencies\u0026mdash;particularly their risk preferences\u0026mdash;and how such behavioral shifts affect corporate decision-making.\u003c/p\u003e\u003cp\u003eThis omission is notable because risk preference is a fundamental dimension of CEO behavior that affects investment intensity, innovation input, capital structure decisions, and long-term strategic orientation. From a principal\u0026ndash;agent perspective, CEOs typically exhibit greater risk aversion than shareholders due to career concerns, reputation costs, and limited diversification of personal wealth. Digital transformation, by amplifying information complexity and intensifying environmental volatility, may further enlarge this divergence; alternatively, it may reduce uncertainty and thereby encourage greater risk-taking. Thus, whether digital transformation induces CEOs to become more conservative or more risk-seeking remains an unresolved empirical question of significant academic and practical relevance. Another important gap in the literature concerns the mechanism through which digital transformation affects CEO risk preference. While organizational scholars argue that digitalization can enhance information transparency and reduce firm-level uncertainty, behavioral economics suggests that information overload, algorithmic complexity, and digitalized reporting systems may instead obscure risks and weaken board oversight, potentially allowing CEOs to strategically manage information and avoid risky projects. These competing theoretical predictions underscore the need for systematic empirical evidence, especially in emerging markets like China, where institutional pressures, governance structures, and digital infrastructure exhibit unique characteristics that may shape executives\u0026rsquo; behavioral responses to digital transformation. Furthermore, CEO heterogeneity\u0026mdash;including age, tenure, compensation, and technical expertise\u0026mdash;may moderate the effect of digital transformation on risk preference. Younger or technologically proficient CEOs may view digital transformation as an opportunity for strategic renewal, whereas older or non\u0026ndash;R\u0026amp;D-background CEOs may perceive it as a source of operational uncertainty. These nuanced behavioral differences remain underexplored in the existing literature.\u003c/p\u003e\u003cp\u003eGiven these research gaps, this study addresses three specific research questions: (1) How does enterprise digital transformation influence CEOs\u0026rsquo;risk preference? (2) Through what mechanisms does digital transformation shape CEOs\u0026rsquo;behavioral tendencies? (3) How do heterogeneities at the CEO level (e.g., age, tenure, compensation, R\u0026amp;D background) and the firm level (e.g., ownership type, industry technology intensity) condition the direction and magnitude of this relationship? Drawing on text-mined digital transformation measures and CEO behavioral proxies for a large sample of Chinese listed firms from 2018 to 2022, this study provides novel empirical evidence that digital transformation significantly increases CEO risk aversion. The analysis further reveals that digital transformation reduces firm-level uncertainty\u0026mdash;thereby serving as a key mechanism suppressing CEO risk preferences\u0026mdash;and that CEO demographic and professional attributes substantially moderate these effects.\u003c/p\u003e\u003cp\u003eThis study makes several important contributions to the literature. First, it enriches the growing research stream on digital transformation by shifting the analytical focus from organizational outcomes to executive behavioral responses. While existing studies primarily examine productivity, innovation, governance, or performance consequences of digital transformation, little attention has been given to how digitalization reshapes CEOs\u0026rsquo; cognitive structures and decision-making tendencies. By revealing a significant behavioral shift in CEO risk preference, this study provides novel evidence that digital transformation is not only a technological and organizational process but also a behavioral phenomenon. Second, the identification of firm-level uncertainty reduction as a key mediating mechanism extends current knowledge on the micro-foundations of digital transformation. By showing how digital technologies reshape information environments and reduce decision uncertainty, this study links technological change with strategic risk behavior, offering new insights into the internal logic through which digitalization affects management decisions. Third, the heterogeneity analysis provides managerial and policy implications by demonstrating that CEOs with different demographic and professional profiles respond differently to digital transformation. These findings highlight the importance of aligning executive characteristics with digital transformation strategies and contribute to ongoing discussions on leadership\u0026ndash;technology fit in the digital era.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eIn the process of managing a company, the CEO embodies a form of entrepreneurship, which serves as a crucial driving force for economic growth [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Entrepreneurship is the nuclear energy of business and social change [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Regarding the essence of entrepreneurship, it can generally be divided into three classic schools of thought [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] .The first is the German school, represented by Schumpeter and Baumol, which posits that the core of entrepreneurship lies in innovation, viewing entrepreneurs as innovators whose inventions and innovations drive long-term economic cycles. The second is the Chicago school, represented by Knight and Schultz. This perspective posits that the core of entrepreneurship lies in risk-bearing willingness and the entrepreneurial alertness to identify and correct market imbalances. Lastly, the Austrian school, represented by Mises and Kirzner, focuses on the entrepreneur's ability to identify market opportunities. Wennekers and Thurik argue that entrepreneurship encompasses 13 dimensions, including the perception of risk and uncertainty, the ability to organize and allocate economic resources, innovativeness, and the capacity to discover new business opportunities, among others [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA CEO's risk appetite manifests as a spirit of adventure, and CEOs with this adventurous spirit have a greater tolerance for risk. The entrepreneurial spirit of adventure is the most fundamental and core trait of a CEO [11、12]. It is their proactive breakthrough of conventions and active exploration of the unknown that drives companies forward, fostering economic growth and social change. Therefore, in this paper, we focus on the spirit of adventure within entrepreneurship, namely the CEO's risk appetite.The Chicago School posits that entrepreneurship is the ability to handle uncertainty and risk in the economy. Without the courage to take and shoulder risks, one cannot become an entrepreneur. Risk appetite is an innate characteristic of entrepreneurs, and a daring, exploratory spirit is their essential quality. Avoiding exploration, risk, and development entails even greater risks. It is by daring to adventure that risks can be mitigated and the opportunities contained within them seized.From the history of research on entrepreneurs and entrepreneurship, the spirit of adventure was the first entrepreneurial trait to attract economists' attention[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Even Schumpeter, a representative figure of the German School, defined entrepreneurship as: \"Entrepreneurship consists of doing things that are not generally done in the ordinary course of business routine or a combination of them\" [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] .This aligns with the innovative spirit emphasized by this school: to do what others have not done requires an even greater spirit of adventure, which forms the foundation of the innovative spirit.\u003c/p\u003e\u003cp\u003eCEOs with a higher risk appetite and a more adventurous spirit are bold in experimentation, pioneers in their fields, and willing to undertake risks. They typically maintain a higher level of vigilance towards the market and are more likely to identify and capitalize on latent investment opportunities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Cronqvist et al. found a significant positive correlation between a CEO's personal mortgage leverage and the company's financial leverage [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Cain and McKeon used the possession of a pilot's license for small aircraft as a proxy for adventurousness and discovered that firms led by pilot CEOs exhibited higher overall corporate risk [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].Risk and return are inherently linked; high-risk projects often imply high returns. Under the constraint of limited resources, investing more in high-return projects diverts resources from lower-return ones, thereby enhancing the efficiency of resource allocation. Senior executives' personal characteristics influence this efficiency. Faccio et al. found that firms led by female CEOs demonstrated significantly lower risk-taking compared to those led by male CEOs, resulting in lower resource allocation efficiency [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Boivie et al. showed that a CEO's organizational identification affects the firm's agency costs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe antecedents of corporate digital transformation can be categorized into two aspects: transformation support and driving factors. The supporting conditions for digital transformation form a critical foundation for the process, with digital technology serving as a key element that profoundly influences the path and outcomes of transformation. According to Nambisan [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], digital technology, as a new type of information technology system, encompasses three fundamental dimensions: digital components, digital platforms, and digital infrastructure. Specifically, digital technology continuously enables the transformation process by optimizing business processes, facilitating the innovation of digital products and services, and building integrated platform ecosystems. Matarazzo et al. found that digital tools, such as applications and social media, further expand enterprises' capabilities in channel construction and user engagement. These tools not only help companies develop new distribution channels but also enhance their ability to sense and respond to market dynamics by collecting and analyzing consumer data in real time. This leads to upgrades in value creation and delivery, driving systematic innovation in business models [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. At the organizational level, an enterprise\u0026rsquo;s adaptability in advancing digital transformation depends on its comprehensive internal attributes, such as organizational readiness, agility, cognitive models, cultural atmosphere, and digital maturity.Simsek et al. found that the capabilities, attitudes, and cognitive approaches of strategic leaders profoundly shape the pathways and internal mechanisms of digital transformation[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. At the same time, Singh et al. found that organizational culture, cognitive readiness, organizational mindfulness, and IT infrastructure also play critical roles in the transformation process, further influencing corporate performance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].At the network level, interactions among multiple types of actors constitute an important force driving transformation. Siachou et al. found that these relationships encompass complementarity and dependence between platform enterprises and participants, customer relationships, political connections, and knowledge acquisition within alliances [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nayal et al. and Belitski et al. found that it also includes coordination and collaboration within supply chains [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], as well as diversity among knowledge partners[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].At the environmental level, enterprises operate in an external context characterized by complexity, volatility, and uncertainty, facing multiple pressures from technology, markets, and competition. Chen \u0026amp; Tian found that they must pay attention to challenges and opportunities arising from factors such as market and technological uncertainty[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], institutional and competitive pressure[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], as well as policy support, the construction of digital ecosystems, the process of industrial digitalization, the evolution of institutional and industrial structures, and demographic aging trends.\u003c/p\u003e\u003cp\u003eThis paper investigates the most fundamental and core aspect of entrepreneurship\u0026mdash;the spirit of adventure (a topic of significant interest to economists)\u0026mdash;at the most micro, individual level. It finds that a CEO's risk appetite can more effectively promote corporate digital transformation. This finding supports Kirzner's theory of entrepreneurial discovery and enriches the literature on entrepreneurship.\u003c/p\u003e"},{"header":"3. Theoretical Analysis and Research Hypothesis","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCorporate shareholders serve as providers of corporate equity capital.Corporate shareholders express keen interest in the appreciation of corporate equity capital, leading them to seek excess profits through high-yield venture capital. In contrast, CEOs without shareholdings exhibit divergent preferences. Corporate CEOs typically opt for secure venture capital projects due to considerations related to their reputation and a stable career development path. Conversely, the risky investment projects favored by corporate shareholders divert and consume the CEO's cash flow and resources, diminishing the CEO's individual utility and intensifying their risk aversion.The disparity in risk preferences between CEOs and corporate shareholders gives rise to an escalating principal-agent cost problem stemming from this distinction. Consequently, numerous scholars have addressed the agency cost problem resulting from disparate risk preferences through the lens of incentive and constraint mechanism design. For instance, scholars have explored the design of equity structure [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], board structure [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] to oversee the CEO's risk preference tendency, or the implementation of monetary [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], equity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], promotion [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and other incentive mechanisms to stimulate the CEO's risk preference.Subsequently, amid digital transformation, the enterprise management model has undergone revolutionary changes. How will the incentive and restraint mechanism of corporate shareholders on CEOs evolve?Will the CEO's risk preference align with shareholders' risk preference, or will it diverge?Subsequently, we will delve into a thorough discussion of these questions.\u003c/p\u003e\u003cp\u003eAs societal focus on digital transformation intensifies, it may be leveraged by CEOs as a deliberate tool to evade risk-taking, thereby complicating agency costs and impairing enterprises' long-term development. First, digital tools, despite their promise of transparency, can be manipulated to obfuscate risk. The inherent malleability and lack of physical traces of digital information facilitate its alteration and concealment [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. CEOs can leverage this to manipulate data, present selectively favorable analyses, and mislead shareholders' assessment of venture projects. By obscuring true risks, CEOs can shield themselves from accountability, potentially increasing the firm's overall risk exposure. Second, digital transformation exacerbates information processing demands on shareholders. According to the theory of investors' limited attention [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], the information redundancy and overload generated by digital technologies can surpass shareholders' processing capacity. This impedes their ability to distill critical insights from vast datasets, increasing the likelihood of misjudging the firm's operational health and the CEO's true risk appetite. Key strategic shifts or emerging risks may be overlooked amidst the noise, leading to flawed oversight. Third, post-transformation business models and organizational structures become significantly more complex [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. New digital initiatives often span multiple fields and involve networked collaborations, making it difficult for shareholders to decipher the firm's core logic, decision-making processes, and resource allocation. This complexity obscures the CEO's risk preferences and creates informational asymmetries. Inadequate disclosure further hampers shareholders' ability to effectively monitor and constrain CEO decision-making, potentially granting CEOs greater latitude for risk-averse behavior[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn conclusion, rather than unequivocally strengthening governance, digital transformation introduces significant challenges. It complicates the consistent acquisition and interpretation of information necessary to assess CEO risk preferences, thereby impairing effective shareholder monitoring. Concurrently, it can create avenues for CEOs to deliberately engage in risk evasion. Based on this analysis, we propose the following hypothesis:\u003c/p\u003e\u003cp\u003eH1: Digital transformation is positively associated with CEOs' risk aversion\u003c/p\u003e\u003cp\u003eEnterprise digital transformation can reduce CEO risk preference by lowering enterprise uncertainty. First, digital transformation can improve the operational transparency of enterprises. Through digital technology, enterprises can achieve real-time monitoring and data analysis of business processes, thereby better understanding their operational status. This transparency can help CEOs better grasp the risk points in enterprise operations, reduce the uncertainty problems faced by the enterprise, and consequently make more stable risk decisions, reducing CEO risk preference[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Second, digital transformation enhances enterprise risk response capability. Digital transformation enables enterprises to obtain and process risk information more quickly, thus allowing them to take measures to address potential risks in a more timely manner. This efficient risk response mechanism can reduce the risk exposure of enterprise uncertainty, thereby lowering CEO risk preference. Furthermore, digital transformation optimizes enterprise decision-making processes[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Digital technology can help enterprises optimize decision-making processes, improving decision-making efficiency and accuracy. This optimization can reduce the uncertainty faced by enterprises during the decision-making process, thereby reducing CEO risk preference in decision-making[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Finally, digital transformation enhances enterprise competitiveness. Through digital transformation, enterprises can gain stronger competitive advantages, thereby improving profitability. This enhancement in competitiveness can make enterprises more stable and sustainable, reducing the uncertainty problems they face, and consequently lowering CEO risk preference. In summary, digital transformation reduces enterprise uncertainty and thereby lowers CEO risk preference by improving enterprise operational transparency, enhancing risk response capability, optimizing decision-making processes, and boosting enterprise competitiveness[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eH2: As the degree of digital transformation increases, corporate uncertainty decreases, reinforcing CEO risk aversion.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Research Design","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e4.1Variables Selection\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1.Corporate Digital Transformation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFollowing the research methods of Qi and Cai [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], this study quantifies the index of enterprise digitization degree using the text mining method. The study compiles and organizes the keywords related to digital transformation, then calculate the frequency of keyword occurrence to measure the digital transformation of the enterprise\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2.CEO Risk Preference\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eInvestment decisions reflect the CEO's risk preference. In investment decision-making projects, transactional financial assets, available-for-sale financial assets, and investment real estate are based on the fair value model, and the uncertainty of risks and returns is greater than other investment projects. Therefore, based on the risk preference measuremen[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], this study calculates the proportion of the total annual amount of three venture capitals to the total assets of the current year. A higher value indicates a higher CEO's risk preference.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e4.1.3.Enterprise Uncertainty\u003c/h2\u003e\u003cp\u003eFollowing the methodology of Hassaballa (2019), this study employs text mining techniques to construct an enterprise uncertainty index. Specifically, we extract textual content from the Management Discussion \u0026amp; Analysis (MD\u0026amp;A) sections of listed companies' annual reports and utilize Python's jieba library for word segmentation. The index is calculated by quantifying the frequency of uncertainty-related terms within the MD\u0026amp;A texts. The lexicon of uncertainty-related terms includes: uncertainty; unclear; indistinct; unknown; unpredictable; hard to estimate; hard to forecast; hard to predict; hard to anticipate; risk; danger; crisis; threat.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable definition\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable Symbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariable Definition\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCEO risk preferences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe total expenditure of the enterprise 's investment in trading financial assets, available for sale financial assets and investment real estate / total assets at the end of the year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplained variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnterprise digital transformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe digital word frequency obtained by text mining method is logarithmically processed.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e\u003cp\u003eControl Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCompany Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eObservation year - establishment year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCapital Expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe ratio of capital expenditure to total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOwnership Concentration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProportion of the largest shareholder\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProfitability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet profit / total assets excluding non-operating income and expenditure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eInd\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProportion of independent directors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndependent directors / the total number of board members\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFree Cash Flow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet cash flow / total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePPE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFixed Assets Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet fixed assets / total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnterprise Growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e( Net profit for the year - Net profit for the same period of the previous year ) / Net profit for the same period of the previous year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTobinq\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnterprise Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrice per share \u0026times; number of tradable shares\u0026thinsp;+\u0026thinsp;price per share \u0026times; number of non-tradable shares\u0026thinsp;+\u0026thinsp;book value of liabilities ) / total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAssets-Liability Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal liabilities / total assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSize of Enterprise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe total assets are logarithmically processed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDuality\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ejob consolidation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe general manager serving as chairman takes 1, otherwise 0.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnnual dummy variable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndustry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndustry dummy variable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProvince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProvince dummy variable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e4.1.4.Control Variables\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn accordance with prior research, this paper considers various control variables, encompassing company age (Age), capital expenditure (Cap), ownership concentration (TOP1), corporate profitability (ROA), proportion of independent directors (Ind), free cash flow (Cash), fixed asset ratio (PPE), enterprise growth (Growth), enterprise value (Tobinq), asset-liability ratio (Lev), enterprise size (Size), duality (Duality), year (Year), industry (Industry), and province (Province). The specific details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Model design\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo examine the influence of digital transformation on CEO risk preference, we construct the following model:\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1764246899.png\" style=\"width: 687px;\"\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eRisk_prefer\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e represents the CEO risk preference of corporate \u003cem\u003ei\u003c/em\u003e in time \u003cem\u003et\u003c/em\u003e. \u003cem\u003eDT\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents the digital transformation of corporate \u003cem\u003ei\u003c/em\u003e in time \u003cem\u003et\u003c/em\u003e. \u003cem\u003eIndustry\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eYear\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e represent the industry and year effects of the firm, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Data sources\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study utilizes data from A-share listed companies spanning from 2015 to 2022. Samples labeled with ST, * ST, SST, and PT are excluded. The data is sourced from the CNRDS database. Ultimately, we obtain 15,153 sample observations, and all indicators undergo Winsorize processing at the 1% and 99% quantiles. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistical. The maximum and minimum values of the core variable CEO risk preference (Risk_prefer) are 0.865 and 0.000, signifying significant variations in CEO risk preference among different enterprises. There exists a discernible gap between the average and median of digital transformation (DT), and the range is considerable, indicating a notable diversity and uneven distribution in the degree of digital transformation among sample enterprises. The remaining variables, also detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, fall within the normal range, with no abnormal values.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistic\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003esd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep50\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003emin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003emax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.427\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.555\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e74.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInd\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePPE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTobinq\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDuality\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Empirical Research","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Digital transformation and CEO risk preference\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWe test H1 using empirical model (1). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the regression results. To address potential autocorrelation or heteroscedasticity issues, this study conducts Cluster clustering adjustment at the company level, gradually controlling for the fixed effects of year, industry, and province.Among these adjustments, (1) without controlling for industries and provinces, the regression coefficient of digital transformation (DT) on CEO risk preference (Risk_prefer) is 0.003, and the relationship is not statistically significant; (2) Controlling for industry and year, the regression coefficient of digital transformation (DT) on CEO risk preference (Risk_prefer) is \u0026minus;\u0026thinsp;0.057 at the 5% level.. The findings indicate that the regression coefficient of digital transformation (DT) on CEO risk preference (Risk_prefer) is \u0026minus;\u0026thinsp;0.057 at the 5% level. Empirical results indicate a significant negative correlation between digital transformation (DT) and Chief Executive Officer's risk preference (Risk_prefer) in accordance with hypothesis H1.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDigital Transformation and CEO Risk Preference\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProvince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eColumns (1)-(3) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e present the paths of enterprise uncertainty. Column (1) controls for year fixed effects, column (2) additionally controls for industry fixed effects, and column (3) further controls for province fixed effects. The results show that digital transformation has significant negative effects on enterprise uncertainty (β1 = -0.108, -0.113, and \u0026minus;\u0026thinsp;0.112), indicating the potential mechanism of reducing enterprise uncertainty through which digital transformation can reduce CEO risk preference.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMechanism Tests on Firm Uncertainty\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFW\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.108\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.113\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.112\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-4.515)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-4.443)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-4.354)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.347)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.212)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.062)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.078\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.059\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.050\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-3.665)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.461)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.063)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.189)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.979)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.842)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.372\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.434\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.356\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.740)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.672)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInd\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.454\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.438\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.381\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.929)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.877)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.624)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.573\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.670\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.674\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.194)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.889)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.966)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePPE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.186\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.249\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.668)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.789)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.584)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.056\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.242)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.508)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.966)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTobinq\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.055\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.050\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-5.432)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-5.078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-4.796)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.261\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.308\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.282\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.639)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-3.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.779)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.029\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.032\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.038\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.477)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDuality\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.802)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e_cons\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.196\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.325\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.302\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6.600)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.126)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProvince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Robustness tests:High-order fixed effect model\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo further mitigate endogeneity issues and draw insights from the research methods of other scholars [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], this paper employs a more rigorous fixed effect model for regression analysis. In terms of controlling potential variables, this study not only includes the fixed effects of time and industry but also extends its consideration to the influence of the fixed effect dimension of time and industry.Specifically, this paper utilizes the time-industry, time-industry double cross fixed effect model for analysis. This model better captures the differences between various times and industries, along with their evolving impact over time. Through this model, the study can diminish the influence of endogeneity problems on research results. Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the test results after employing the cross-product fixed effect model. The results consistent with the benchmark regression results. This indicates that, even after considering a more stringent fixed effect model, the conclusions of this paper remain robust.In summary, the use of a more rigorous fixed effect model in the analysis further alleviates endogeneity problems and provides more accurate and robust research results. These findings hold significant implications for understanding the impact of enterprise digital transformation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Endogeneity test\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e5.3.1PSM-DID model\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn order to more robustly reveal the causal relationship between digital transformation ( DT ) and CEO risk preference ( Risk prefer ), this paper uses the PSM-DID model to solve the possible endogenous problems. The test results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e ( 1 ) - ( 3 ). First of all, this paper takes the median of the sample of the digital transformation of the explanatory variable enterprise as the standard. When the score of the digital transformation of the enterprise is greater than the median, Treat takes 1, otherwise Treat takes 0, and then the sample with a higher degree of digital transformation of the enterprise is set as the processing group, and the sample group with a lower degree of digital transformation of the enterprise is set as the control group ;secondly, the company 's age ( Age ), capital expenditure ( Cap ), ownership concentration ( TOP1 ), corporate profitability ( ROA ), proportion of independent directors ( Ind ), free cash flow ( Cash ), fixed asset ratio ( PPE ), enterprise growth ( Growth ), enterprise value ( Tobinq ), asset-liability ratio ( Lev ), enterprise scale ( Size ), duality ( Duality ), year ( Year ),Control variables such as industry and province are used as skew variables to estimate propensity scores. Finally, the nearest neighbor method is selected according to the estimated propensity score, and the matching ratio is determined to be 1 : 1.Finally, the regression is carried out according to the matched samples. The results are reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e ( 2 ) - ( 3 ). After controlling the industry year ( Year ), industry ( Industry ) and year ( Year ), industry ( Industry ) province ( Province ), the regression coefficient of enterprise digital transformation to CEO risk preference ( \u003cem\u003eRisk _ prefer\u003c/em\u003e ) is \u0026minus;\u0026thinsp;0.047 at the level of 10%, which is consistent with the above regression results.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2Instrumental variable tests\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study employs the one-period lag of enterprise digital transformation as the instrumental variable for both enterprise digital transformation and CEO risk preference. When estimating the impact of enterprise digital transformation on CEO risk preference, data from the previous period of enterprise digital transformation are utilized. This instrumental variable approach is grounded in the concept of instrumental variables, which entails identifying a tool variable highly correlated with the endogenous explanatory variable but unrelated to the error term to replace the endogenous explanatory variable. The advantage of using the one-period lag of enterprise digital transformation as an instrumental variable lies in its ability to mitigate endogeneity issues. By utilizing one-period lag data, it avoids the simultaneous impact of the previous enterprise digital transformation on CEO risk preference, thereby enhancing the accuracy of estimation results. Following the use of the lag phase of enterprise digital transformation as an instrumental variable, the estimation results in this study still exhibit a significant negative correlation. This signifies the robustness of the negative and significant findings regarding the impact of enterprise digital transformation on CEO risk preference. Consequently, the study addresses endogeneity concerns by employing the one-period lag of the digital transformation, yielding more precise and robust estimation results.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEndogeneity test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePSM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eIV-2SLS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.047\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.058\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.057\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.859)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.555)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.424)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.610)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.806)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.294)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.121\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.121\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.127\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-6.726)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-9.556)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-7.434)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.163)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.338)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.201)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.689\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.411\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.585)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.383)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInd\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.970)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.875)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.133\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.339\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.273\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-14.923)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-21.686)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-17.974)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePPE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.787\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.798\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.899\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-14.655)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-21.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-17.198)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.707)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTobinq\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.028\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.565)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.822\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.832\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-16.866)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-27.234)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-19.119)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.370)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.540)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.343)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDuality\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.047\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.990)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.699)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.739)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.218\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.094\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.534\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.554)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(8.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.372)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProvince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInd_Year\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"6. Individual heterogeneity test of CEOs","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDue to the heterogeneity of CEO individual level, the impact of enterprise digital transformation on CEO risk preference will be affected. Therefore, based on the age, salary, tenure, and R \u0026amp; D background of the CEO, this paper further subdivides the whole sample into the older CEO group and the younger CEO group, high salary group and low salary group, longer-term group and shorter-term group, and no R \u0026amp; D background group and R \u0026amp; D background group. Among them,the CEO age is set to 1 above the mean value and 0 below the mean value; CEO salary higher than the average is set to 1, lower than the salary is set to 0; CEO tenure above the mean is set to 1, and the CEO tenure below the mean is set to 0; CEO with R \u0026amp; D background is set to 1, and no R \u0026amp; D background is set to 0.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Individual heterogeneity test of CEO age\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe impact of digital transformation on CEO risk preference might be influenced by the age of the CEO.Older CEOs experience a notably negative impact on their risk preference due to digital transformation; conversely, the effect is not significant for younger CEOs.The majority of senior executives exhibit a propensity to uphold the existing state of affairs and exhibit reluctance towards risk-taking.Consequently, the digital tools introduced by digital transformation prompt senior executives to utilize them for concealing risk information.Furthermore, the challenge for shareholders lies in managing an extensive volume of redundant information, potentially exacerbating the risk aversion inclination among senior individuals,thereby fostering greater caution in decision-making. Young CEOs typically possess a more profound comprehension of and receptiveness to novel developments within the enterprise, displaying a willingness to embrace emerging technologies and innovative business models.These CEOs may discern the opportunities and potential within the business, demonstrating a readiness to undertake risks in pursuit of experimentation.Consequently, young CEOs exhibit a heightened willingness to embrace risks and explore innovative transformation pathways to realize the company's long-term developmental objectives.Thus, in contrast to their older counterparts, the influence of digital transformation on the risk appetite of young CEOs is not significant.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e6.2Individual-Level heterogeneity in CEO compensation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhen the annual salary of the CEO is low, digital transformation significantly decreases the CEO's risk preference, with a high coefficient.Conversely, with a high CEO salary, digital transformation still significantly reduces CEO risk preference, albeit with a lower coefficient. CEOs earning a lower annual salary may focus more on short-term economic gains and risk Mitigation.Consequently, CEOs with lower salaries, due to digital transformation, employ digital tools to conceal risk information. Dealing with a surplus of redundant information becomes challenging for shareholders, potentially reinforcing the risk-averse behavior of lower-salaried CEOs. Lower-salaried CEOs may prioritize personal income and occupational safety, resulting in a higher inclination towards risk aversion.Conversely, CEOs earning higher salaries are likely to prioritize the company's long-term development and strategic objectives.Higher-salaried CEOs often possess greater resources and decision-making autonomy,emphasizing innovation and competitive advantage.Despite digital transformation negatively affecting their risk appetite, the coefficient is lower, likely due to their heightened focus on the crucial role of digital transformation in long-term enterprise development.In conclusion, the impact of digital transformation on CEO risk preference is contingent on salary level. CEOs with lower annual salaries may prioritize short-term earnings and risk control,intensifying the negative impact of digital transformation on their risk appetite. Conversely, CEOs with higher salaries may concentrate on long-term development and strategic goals, mitigating the adverse effect of digital transformation on their risk appetite.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndividual-Level Heterogeneity among CEOs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSenior CEO\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eJunior CEO\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eLow compensation\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eHigh compensation\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.442)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.676)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.186)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTobinq\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.062***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.407)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.171)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.713***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.999***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.625***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.981***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-14.764)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-13.875)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-11.875)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-16.556)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.054***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.741)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.729)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.650)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDuality\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.065*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.080**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.873)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.215)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.599)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.390)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.476***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.538**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.372**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.831***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.566)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.928)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProvince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; t-values for clustering to the firm level are in parentheses.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Individual-Level heterogeneity in CEO tenure\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn instances of long CEO tenure, digital transformation significantly diminishes CEO risk preference, yielding a low coefficient. Conversely, with short CEO tenure, digital transformation similarly exerts a substantial negative impact on the CEO's risk preference, resulting in a higher Coefficient.For CEOs with extended tenures, their accumulated experience and authority empower them to effectively control and mitigate risks.Extended CEO tenure typically provides more resources and time to address challenges in enterprise development, facilitating improved adjustment and optimization of the company's operation and business model.Long-term CEOs may focus on the opportunities and competitive advantages arising from digital transformation, recognizing its importance for future enterprise development. Consequently, they exhibit a willingness to assume higher risks, fostering sustained enterprise development. Consequently, although the negative impact of digital transformation on their risk preference is substantial, the associated coefficient remains low.CEOs with short tenure typically have limited time and resources to comprehend the company's business, operations, and risks.Limited time and resources may hinder their ability to attain an in-depth understanding of all aspects of the company, impeding the formulation of comprehensive risk decisions.Additionally, due to the heightened uncertainty and risk faced by short-term CEOs, they may exhibit a greater inclination toward pursuing stability and security.They may believe that maintaining the status quo and avoiding significant risks is a more secure option, leading them to allocate more time and resources to risk management rather than innovation and change.Additionally, CEOs with short tenure may prioritize personal reputation and career development.They may consider excessively risky decisions as potentially detrimental to their reputation and career development.Consequently, to mitigate potential risks, short-term CEOs may lean towards employing digital tools to conceal risk information, embracing conservative strategies, and refraining from making overly radical decisions.Furthermore, the information redundancy effect induced by digitization may increase the challenge for shareholders in overseeing the company.Consequently, in comparison to CEOs with lengthy tenures, those with shorter tenures exhibit more pronounced risk aversion effects.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e6.4 Individual-Level heterogeneity in CEO R\u0026amp;D background\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe influence of digital transformation on the risk preference of a CEO may depend on whether the CEO possesses an R\u0026amp;D background.In instances where the CEO lacks an R\u0026amp;D background, digital transformation markedly diminishes CEO risk preference. Conversely, when the CEO possesses an R\u0026amp;D background, the impact of digital transformation on CEO risk preference is negligible.CEOs lacking an R\u0026amp;D background may possess limited knowledge about technology, R\u0026amp;D, and market risks associated with digital transformation.Owing to the absence of pertinent experience and knowledge, these CEOs may exhibit heightened caution and conservatism, expressing concerns about the risk and uncertainty facing the enterprise.They may lean towards utilizing existing digital tools or seeking technical personnel to conceal risk information, adopting conservative strategies, and refraining from taking excessive risks to uphold the stability and business performance of the company. Consequently, digital transformation may amplify their risk aversion, exerting a marked negative impact on risk appetite. Conversely, CEOs possessing an R\u0026amp;D background typically possess a more profound understanding of technology, R\u0026amp;D, and market risks associated with digital transformation. They can adeptly assess and manage these risks, formulating appropriate strategies to address the challenges. Owing to their extensive knowledge and experience in digital transformation, these CEOs may prioritize innovation and competitive advantages, demonstrating a willingness to undertake associated risks to attain the company's long-term development goals. Additionally, the CEO's R\u0026amp;D background may influence their decision-making style and innovation consciousness. CEOs with an R\u0026amp;D background may prioritize technological innovation and R\u0026amp;D capabilities,demonstrating heightened innovation awareness and risk-taking ability.They may exhibit a greater willingness to promote digital transformation and experiment with new business models and innovative strategies to achieve the sustainable development of enterprises.CEOs lacking an R\u0026amp;D background may focus more on stability and business performance,demonstrating heightened sensitivity to the risks associated with digital transformation.In summary, the influence of digital transformation on CEO risk appetite is contingent upon whether the CEO possesses an R\u0026amp;D background.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndividual-Level Heterogeneity among CEOs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eShort Tenure\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLong Tenure\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eLack R\u0026amp;D Background\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eHas R\u0026amp;D Background\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.049*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.059*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.075**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.701)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.821)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.521)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-1.249)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.153**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.253***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.507)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.516)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-2.674)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.110***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.130***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.139***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.101***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-5.444)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-5.300)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-5.624)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-3.390)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.696)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.671)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.302)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.453)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.551**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.495**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.476)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.318)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.326)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInd\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.684*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.364)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.869)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.158***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.357***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.372***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.377***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-12.969)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-14.568)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-14.237)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-10.780)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePPE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.739***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.981***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.948***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.905***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-12.341)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-13.618)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-13.815)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-9.184)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.036*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.808)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.703)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTobinq\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.062***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.043***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.765)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.245)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.843***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.827***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.860***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.206***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-15.187)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-14.516)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-15.404)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-12.684)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.028*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.041**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.508)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.713)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.356)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.117)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDuality\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.063*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.911)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.958)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.876)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.791)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.470***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.615***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.391***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.516***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.548)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.253)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.068)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProvince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Heterogeneity test at the enterprise level and industy level","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBecause the heterogeneity characteristics at the enterprise and industry levels directly influence the CEO's risk perception, judgment, and understanding, resulting in variations in CEO risk preferences.Accordingly, considering the enterprise industry type and property rights nature, this study further categorizes the entire sample into non-high-tech and high-tech industries.The study additionally explores the influence mechanism of digital transformation and CEO risk preference within the state-owned and private enterprise groups.Specifically, enterprise property rights are coded as 1 for state-owned enterprises and 0 for private enterprises. Following CSRC's industry classification guidelines for listed companies, high-tech industries are identified by categories C26, C27, C28, C29, C34, C35, C36, C38, C39, C40, C41, I64, I65, and M73, denoted as the high-tech enterprise group (coded as 1), while the remaining categories constitute the non-high-tech enterprise group (coded as 0).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e7.1 Corporate-Level heterogeneity in Ownership nature\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn private enterprises, digital transformation significantly negatively affects CEO risk preference. Following digital transformation, private enterprises exhibit a stronger CEO risk aversion effect compared to state-owned enterprises.State-owned enterprises typically have the government as a shareholder, resulting in a more stable and transparent governance structure.In contrast, private enterprises feature a more diverse ownership structure and a relatively flexible governance framework.These disparities in ownership and governance may introduce heightened uncertainty for CEOs of private enterprises post-digital transformation, necessitating CEOs to bear increased risks.Consequently, to safeguard professional stability, the CEO's risk aversion effect is more pronounced.Additionally, state-owned enterprises typically enjoy more government support and resources, encompassing capital, technology, and talent.Private enterprises may encounter greater challenges in acquiring resources and financial support, posing numerous hurdles to their subsequent development post-digital transformation.The increased complexity of digital tools and issues related to information redundancy may contribute to a more pronounced risk aversion effect among enterprise CEOs.Ultimately, while private enterprises typically exhibit greater flexibility in market competition, they concurrently face heightened risks. Following digital transformation, CEOs of private enterprises must prioritize market research and strategic planning to mitigate potential risks and losses.State-owned enterprises demonstrate a relatively strong risk tolerance and a focus on long-term development and stability. Consequently, the risk aversion of CEOs in private enterprises surpasses that in state-owned enterprises.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e7.2 Industry-Level heterogeneity in High-Tech nature\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn non-high-tech industries, digital transformation significantly negatively influences CEO risk preference. In contrast to high-tech industries, non-high-tech industries exhibit a more complex organizational structure, and their information capacity becomes more redundant following digital transformation. This phenomenon may further enhance the risk-averse tendency of corporate CEOs.CEOs often exhibit a propensity to maintain the status quo and a reluctance to undertake risks.High-tech companies typically possess advanced technical capabilities and innovation prowess, leading CEOs to exhibit a greater willingness to experiment with new technologies and business models.The trajectory of digital transformation is a pivotal trend in the evolution of high-tech industries. CEOs in these sectors typically prioritize innovation and embrace change, demonstrating a greater willingness to undertake substantial risks in driving enterprise development.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndividual-Level Heterogeneity among CEOs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePrivate Enterprise\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eState-Owned Enterprise\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eNon-High-Tech Industry\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eHigh-Tech Industry\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRisk_prefer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.055*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.092**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.854)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.376)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-1.432)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.138**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.214***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.679)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.312)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-3.594)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.151***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.046**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.122***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-6.897)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-6.801)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.451)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.481)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.517)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.089***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.607)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.722)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.379)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInd\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.382)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.502)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.317)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.181)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.564***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.433***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.407***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.492***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-16.466)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-7.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-9.750)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-16.711)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePPE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.275***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.210***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.629***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.043***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-14.774)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-8.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-9.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-14.865)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.060**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.042**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.731)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.068)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTobinq\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.031**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.371)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.971)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-1.262)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.386)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.067***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.321***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.767***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.256***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-16.713)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-8.931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-9.607)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-18.855)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.924)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.744)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.787)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDuality\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.492)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.556)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.496)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.418)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.523***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.705***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.412***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.986***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.588)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.677)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.733)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(5.372)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProvince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9340\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"8. Economic consequences : enterprise innovation","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eUpon analyzing the data in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, an unexpected conclusion emerges.While digital transformation exacerbates the CEO's risk aversion tendency, it does not hinder high-risk R\u0026amp;D investments; instead, it markedly enhances overall R\u0026amp;D investment and output for enterprises.\u003c/p\u003e\u003cp\u003eThis observation indicates a substantial mitigation of the adverse impact of the CEO's risk aversion tendency on enterprise innovation activities.Enterprises gain increased benefits and competitive advantages through digital transformation, achieved by enhancing production efficiency, reducing costs, and optimizing resource allocation.Leveraging these benefits and competitive advantages, enterprises can augment their R\u0026amp;D investments, thereby advancing digital transformation, innovation, and overall development.Consequently, digital transformation has the potential to attract additional funds and resources for enterprises, fostering further innovation and development.Additionally, digital transformation significantly enhances the R\u0026amp;D output of enterprises through the optimization of R\u0026amp;D processes, improved R\u0026amp;D efficiency, and innovation capabilities.Digital transformation empowers enterprises to rapidly acquire and integrate diverse resources, efficiently manage and coordinate various departments, thereby enhancing the efficiency and quality of R\u0026amp;D outcomes.Furthermore, digital transformation facilitates communication and collaboration between enterprises and external partners, collaboratively advancing R\u0026amp;D progress and the transformation of outcomes.\u003c/p\u003e\u003cp\u003eIn summary, within the realm of digital transformation, the impact of the CEO's risk aversion tendency is significantly diminished. The digital transformation of enterprises not only enhances R\u0026amp;D investment and output but also delivers increased benefits and competitive advantages, thereby fostering innovation and overall development.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eR\u0026amp;D input and output of enterprises\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e(1)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePatenting\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e(2)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eR\u0026amp;D Investment\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e(3)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eR\u0026amp;D Investment /Operating Income\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePatents1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRDSpendSum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRDSpendSumRatio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.146***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.967)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.907)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.184**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.208***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.173***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-4.267)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-7.070)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.227***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.203***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.212)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(14.371)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(13.096)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTOP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.110)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.702)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-3.657)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.435*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.516***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.642)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.886)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-11.243)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInd\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.197)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.837)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.675)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.256**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.277***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.327)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.559)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.454)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePPE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.143***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.507)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.355)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.744***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.501)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.569)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-15.776)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.703***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.928***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(22.155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(64.765)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.233)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDuality\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.129**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.047***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.482)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-14.085***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.958***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.557***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-16.584)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-6.864)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.428)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProvince\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"9. Research conclusions and implications","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e9.1 Conclusion.\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe present study investigates the influence of digital transformation on CEO risk preference.Findings reveal that the digital transformation of enterprises significantly negatively affects CEO risk preference and this relationship is moderated by CEO personal traits. Moreover, it is found that digital transformation reduces CEO risk preference by mitigating firm-level uncertainty. The study discovers that digital transformation enhances both the innovation input and output. By enabling rapid responses to market changes and enhancing production efficiency, digital transformation prompts increased investment in innovation by enterprises. Simultaneously, digital transformation aids enterprises in enhancing product quality and service levels, thereby improving overall innovation output.These favorable economic outcomes further substantiate the critical role of digital transformation in fostering enterprise development.In conclusion, this study unveils the impact of digital transformation on CEO risk preference, elucidates its underlying mechanisms, and discusses the economic ramifications of digital transformation.These findings carry significant theoretical and practical implications for comprehending CEO risk management during the enterprise digital transformation process.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e9.2 Implications.\u003c/h2\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003ch2\u003e9.2.1 Implement a robust follow-up management system for digital transformation.\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEnterprises should develop a comprehensive follow-up management system for digital transformation, encompassing the assessment, revision, and enhancement of the existing system, along with research and responses to emerging challenges.Enterprises should adopt an open-minded approach, actively assimilate external advanced experiences and practices, and consistently enhance the efficiency and effectiveness of digital transformation follow-up management.Clarify the responsibilities and authorities of different departments in the follow-up management of digital transformation, ensuring seamless collaboration across all departments.For instance, assign the IT department with the responsibility for maintaining and upgrading digital systems, delegate the business department for the utilization and promotion of digital applications, and task the human resources department with recruitment and training of digital talents.During the digital transformation process, various issues and challenges may arise.Establish an effective feedback management mechanism to promptly gather employees' opinions and suggestions. In response to identified issues, enterprises should implement appropriate measures for improvement, such as optimizing the digital system, enhancing staff training, and adjusting the digital strategy.Simultaneously, establish a problem tracking mechanism to ensure timely resolution of issues and prevent their recurrence.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section3\"\u003e\u003ch2\u003e9.2.2Enhance risk management post-digital transformation.\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFollowing digital transformation, establish a robust risk management mechanism to delineate the risk management process and assign responsibilities.This mechanism should encompass risk identification, assessment, control, and monitoring to enable enterprises to promptly detect and respond to potential risks.Simultaneously, enterprises should establish risk management documentation, recording risk events and processes to serve as a reference for subsequent risk management.Enterprises should reinforce risk identification, assessment, and control measures to safeguard the stability and safety of operations.The foundation of risk management lies in risk identification. Enterprises should gather information from diverse channels, including market research, internal reports, and expert advice, to promptly detect potential risks.Simultaneously, enterprises should establish a risk identification mechanism to systematically conduct post-digital transformation business risk assessments, ensuring the timely detection and response to potential risks.Risk assessment is a crucial step in determining the level and impact of risks. Enterprises should employ scientific methods and tools to assess identified risks, determining their likelihood and impact. The evaluation results should serve as a foundation for enterprises to formulate risk management strategies.Effectively controlling risks is crucial for preventing or minimizing losses to the enterprise. Enterprises should devise appropriate risk management strategies and control measures based on the results of risk assessments.Such measures may involve the formulation of emergency plans, enhancing system security protection, and fostering employee safety awareness.Simultaneously, enterprises should regularly evaluate and adjust control measures to ensure their efficacy and adaptability.Monitoring risks is pivotal to ensuring continuous and effective risk management. Enterprises should establish a risk monitoring mechanism to systematically monitor business risks post-digital transformation, promptly detecting and responding to potential risks.Simultaneously, enterprises should establish a risk reporting system to routinely report risk management to senior leaders, ensuring comprehensive understanding and mastery of risk management by leadership.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\u003ch2\u003e9.2.3Institute a comprehensive incentive mechanism to motivate CEOs to undertake risks.\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe CEO's risk tolerance and coping ability can be enhanced through the implementation of both a reward mechanism and a fault tolerance mechanism.Initially, to incentivize CEOs to undertake risks, digital enterprises can establish corresponding incentive mechanisms.This reward mechanism can be tied to the company's performance, innovation, and proficiency in risk control.For instance, CEOs who attain notable outcomes in the digital transformation process can receive bonuses, promotion opportunities, or other forms of recognition.Such a reward mechanism can fuel the enthusiasm and innovative spirit of CEOs, fostering a greater willingness to undertake risks.Additionally, CEOs may encounter various uncertainties and challenges in the subsequent phases of digital transformation.To encourage experimentation and innovation, digital enterprises should institute a fault-tolerant mechanism.This mechanism should encompass tolerance and comprehension of failure, along with offering protection and support in case of failure.Upon facing challenges or failures in digital transformation, companies should provide essential support and encouragement to assist CEOs in learning from failures and progressing forward.Lastly, an open corporate culture is a crucial environment for fostering a culture of risk-taking among CEOs.\u003c/p\u003e\u003cp\u003eDigital enterprises ought to foster a corporate culture that promotes innovation, embraces failure tolerance, and values individuality.In such a cultural atmosphere, CEOs can freely express their ideas and opinions, fostering the courage to experiment with new methods and strategies.\u003c/p\u003e\u003cp\u003eSimultaneously, an open corporate culture can facilitate information sharing and collaboration within the enterprise, enhancing overall risk response capabilities.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003e9.2.4 Institute a supervisory mechanism to mitigate CEO risk aversion.\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDigital enterprises should institute a more refined shareholder supervision mechanism to incentivize CEOs to undertake risks.Shareholders constitute the ownership of enterprises, and their rights and interests necessitate complete protection.Firstly, the establishment of a robust shareholder supervision mechanism can ensure that the CEO thoroughly considers the interests of shareholders in the decision-making process, preventing any harm to shareholders' rights and interests arising from personal self-interest or short-sighted behavior.Simultaneously, the shareholder supervision mechanism can prevent CEOs from committing significant mistakes or engaging in misconduct, thereby ensuring the stable development. Secondly, by intensifying shareholders' oversight of CEOs, the enterprise can foster information transparency and promote scientific decision-making.Simultaneously, the shareholder supervision mechanism can encourage enterprises to establish a more comprehensive internal control system, ensuring compliance and robustness.Overall, in the digital age, the competitiveness of enterprises hinges on their innovation capability, market responsiveness, and risk management proficiency.\u003c/p\u003e\u003cp\u003eImplementing a sound shareholder supervision mechanism can motivate CEOs to proactively undertake risks, fostering innovation and the development of enterprises.This will assist enterprises in maintaining a leading position in the intense market competition and enhancing their overall competitiveness.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.L. conceived and designed the study, led the empirical strategy, and drafted the initial version of the manuscript. X.B. contributed to the research design, implemented the econometric analyses, and conducted robustness checks. M.L. was responsible for data collection, variable construction, and visualization, and contributed to the interpretation of the empirical results. Y.Z. contributed to the theoretical framework, literature review, and discussion of policy and managerial implications. All authors discussed the results, revised the manuscript critically for important intellectual content, and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEggers, J. P. \u0026amp; Park, K. F. Incumbent adaptation to technological change: The past, present, and future of research on heterogeneous incumbent response[J]. \u003cem\u003eAcad. Manag. Ann.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 357\u0026ndash;389 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhanagha, S. et al. 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Rev.\u003c/em\u003e \u003cb\u003e102\u003c/b\u003e (1), 396\u0026ndash;427 (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital Transformation, CEO, Risk Preference, Risk Aversion","lastPublishedDoi":"10.21203/rs.3.rs-8205602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8205602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing a text-mining\u0026ndash;based measure of digital transformation and multiple behavioral proxies of CEO risk preference for a large sample of Chinese listed firms from 2018 to 2022, we provide systematic empirical evidence that digital transformation significantly increases CEO risk aversion. Mechanism analysis shows that the reduction of firm-level uncertainty induced by digital transformation is a key channel through which CEO risk preference is suppressed. Furthermore, heterogeneity tests reveal that the behavioral effects of digital transformation vary with CEO demographic and professional attributes\u0026mdash;such as age, tenure, compensation incentives, and technological background\u0026mdash;as well as firm characteristics. These findings contribute to the literature on digital transformation and executive behavior by demonstrating that technological change not only restructures organizational processes but also shapes managerial risk tendencies, offering new insights into the behavioral micro-foundations of digital transformation.\u003c/p\u003e","manuscriptTitle":"The digital transformation, corporate uncertainty, and CEO 's risk preference ?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 12:39:26","doi":"10.21203/rs.3.rs-8205602/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-27T08:20:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-27T07:38:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T07:35:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-25T17:06:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e82d5861-a3d3-4df0-8a83-5d879b58cb34","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58628639,"name":"Business and commerce/Business and management"},{"id":58628640,"name":"Social science/Business and management"},{"id":58628641,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-02-23T16:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-8205602","link":"https://doi.org/10.1038/s41598-026-40064-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-16 15:58:32","publishedOnDateReadable":"February 16th, 2026"},"versionCreatedAt":"2025-11-27 12:39:26","video":"","vorDoi":"10.1038/s41598-026-40064-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40064-x","workflowStages":[]},"version":"v1","identity":"rs-8205602","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8205602","identity":"rs-8205602","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}