| In the current era of big data,data,as the carrier of all business activities,evolves along with the business and management activities of firms.Summarizing and analyzing the laws of data changes,we can understand the business and management conditions of firms from multiple dimensions,so as to find ways to improve the efficiency of business management,discover the loopholes in business management,and ultimately promote the realization of firm value.Big data has an important role in promoting firm management and development,and is also an important topic of common concern for both practice and academia.However,there are risks and challenges associated with big data analytics.First,when conducting big data analytics,a large amount of hardware equipment,software systems,and human resources are required,which usually incur high costs.Secondly,if the data quality is not high or the processing and analysis capability is not sufficient,it may lead to biased decision-making.Finally,data security and data misuse issues may cause damage to a company’s property and image.Existing studies focus on the impact of big data analytics on firm performance,decision-making,and innovation;however,there is a lack of literature on the impact of big data analytics on the overall risk faced by companies,the tail risk,and the economic consequences it brings.Therefore,it is of great theoretical value and practical significance to study the impact of big data analytics on firm risk,stock price crash risk,and firm value and its impact mechanism from the micro level of big data analytics.Based on the Chinese policy and institutional background and the theoretical foundation of domestic and international literature,this study takes the non-IT industry-listed companies in Shanghai and Shenzhen Ashares from 2002 to 2020 as the research sample,and constructs the firmspecific big data analytics index.First,this study examines the impact of big data analytics on overall firm risk and the influential mechanism.Second,we validate the effect of big data analytics on tail risk and the influential mechanism,and further concerns its impact on the right-tail risk.Finally,the paper returns to the ultimate proposition of value testing,exploring the impact of big data analytics on firm value and the mechanism of the impact.It also moves the research perspective forward to focus on the factors that affect the implementation of big data analytics.Based on the above research,this paper draws the following conclusions:(1)Big data analytics significantly increases firm risk.It raises firm risk by motivating senior managers to adopt more aggressive corporate policies,including investment,R&D,and M&A policies.The positive relationship between big data analytics and firm risk increases when firms face stronger financing constraints;the positive relationship tends to moderate when firms have sufficient free cash flow.Further,we find that the effect of big data analytics on firm risk is continuous for a specific time,and this dynamic effect has a significant decaying pattern over time.(2)Big data analytics reduces the firm-specific stock price crash risk.Two important mechanisms by which big data analytics affects crash risk are bad news hoarding and bad news formation.The effect of big data analytics on crash risk is more pronounced when firms face strong external governance mechanisms;the negative relationship is weakened when firms face an asymmetric information environment.Further,we find that the effect of big data analytics on future crashes is continuous for a specific time,and this dynamic effect has a significant decaying pattern over time.Evidence from additional analysis suggests that big data analytics could predict two-sided fail risks asymmetrically.(3)Big data analytics significantly enhances firm value.Big data analytics may affect important mechanisms of firm value:R&D inputs and innovation outputs,productivity and management efficiency.The positive impact of big data analytics on firm value is more pronounced when the external and internal governance mechanisms faced by the firm are stronger.Further research shows that firms with larger firms,longer time to market,higher capital expenditures,and a smaller share of fixed assets are more likely to implement big data analytics.The contributions of this study are as follows:(1)This study measures firm-specific big data analytics with a combination of text analysis and deep learning.In contrast to the questionnaires,our measurement is more comprehensive and continuous,covering a sample of almost all A-share Chinese-listed companies,which can intuitively reflect the dynamic process of big data analytics.Moreover,unlike studies that use text analysis alone to measure big data analysis,we use deep learning to find similar words of "big data",which overcomes the subjectivity of manual selection and,using the similarity of the extended set as the weight of different keywords,avoiding the inaccuracy arising from using equal weights.(2)This study provides the first empirical evidence on the relationship between big data analytics and firm risk,revealing the potential risks faced by firms when applying big data,filling this empirical research gap,and demonstrating the transmission mechanism of corporate policies between big data analytics and firm risk.In addition,our study provides solutions to reduce firm risk,including reducing aggressive decisions,lowering financing constraints,and increasing free cash flow.This study provides a theoretical basis and empirical evidence for risk management in practice,which is innovative for both the theory and practice of firm-specific risk management.(3)This study provides the first empirical evidence that big data analytics affects the firm-specific stock price crash risk.Unlike studies on firm digitization,which has a broader scope and focuses more on the application of various digital technologies.Our study focuses on big data analytics,with more attention to data processing and analysis.The study finds that big data analytics can improve firm’s internal controls and predict bilateral tail risks.This provides theoretical guidance for firm managers to make good investment and risk management decisions,while helping to protect shareholders’ wealth and providing guidance for investors who wish to choose stable stock returns.(4)This study examines the influential mechanism of big data analytics on firm value from a new perspective of corporate governance.While studies on the impact of big data analytics on firm value exist in the existing literature,no literature has yet focused on the moderating effect of corporate governance mechanisms on their relationship.This study provides new evidence that external and internal governance mechanisms significantly enhance the positive impact of big data analytics on firm value.This finding provides new perspectives and evidence for a deeper understanding of the impact of corporate governance mechanisms on the relationship between big data analytics and firm value,and provides important insights for both researchers and practitioners seeking to better understand and leverage big data for enhancing firm value. |
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