Xin Zhao , Guoqing Zhai , Vincent Charles , Tatiana Gherman , Hyoungsuk Lee , Tuan Pan , Yuping Shang
{"title":"Enhancing enterprise investment efficiency through artificial intelligence: The role of accounting information transparency","authors":"Xin Zhao , Guoqing Zhai , Vincent Charles , Tatiana Gherman , Hyoungsuk Lee , Tuan Pan , Yuping Shang","doi":"10.1016/j.seps.2024.102092","DOIUrl":null,"url":null,"abstract":"<div><div>In the post-COVID-19 era, with global economic recovery as a critical goal, the rapid development of artificial intelligence (AI) has emerged as a key driver of economic growth and transformation. AI not only acts as a powerful catalyst for economic development but also significantly impacts enterprise investment efficiency (EIE). This paper explores the influence of AI on EIE, with a focus on the role of accounting information transparency. Using data from Shanghai and Shenzhen A-share listed enterprises between 2010 and 2021, the findings demonstrate that AI development significantly enhances EIE. These results are confirmed through robustness tests, including variable substitution, and addressing endogeneity and sample limitations. Mechanism analysis reveals that AI improves EIE by increasing the transparency of accounting information. Additionally, heterogeneity analysis shows that AI has a greater impact on the investment efficiency of high-tech and technology-intensive enterprises, non-state-owned enterprises, and those located in highly urbanised areas, such as ‘Broadband China’ pilot cities. This paper examines how AI development affects EIE through the lens of enterprise accounting information transparency, offering actionable insights for enhancing accounting disclosures and serving as a valuable resource for enterprises navigating the technological transformation of the modern era.</div></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"96 ","pages":"Article 102092"},"PeriodicalIF":6.2000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Socio-economic Planning Sciences","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038012124002921","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
In the post-COVID-19 era, with global economic recovery as a critical goal, the rapid development of artificial intelligence (AI) has emerged as a key driver of economic growth and transformation. AI not only acts as a powerful catalyst for economic development but also significantly impacts enterprise investment efficiency (EIE). This paper explores the influence of AI on EIE, with a focus on the role of accounting information transparency. Using data from Shanghai and Shenzhen A-share listed enterprises between 2010 and 2021, the findings demonstrate that AI development significantly enhances EIE. These results are confirmed through robustness tests, including variable substitution, and addressing endogeneity and sample limitations. Mechanism analysis reveals that AI improves EIE by increasing the transparency of accounting information. Additionally, heterogeneity analysis shows that AI has a greater impact on the investment efficiency of high-tech and technology-intensive enterprises, non-state-owned enterprises, and those located in highly urbanised areas, such as ‘Broadband China’ pilot cities. This paper examines how AI development affects EIE through the lens of enterprise accounting information transparency, offering actionable insights for enhancing accounting disclosures and serving as a valuable resource for enterprises navigating the technological transformation of the modern era.
期刊介绍:
Studies directed toward the more effective utilization of existing resources, e.g. mathematical programming models of health care delivery systems with relevance to more effective program design; systems analysis of fire outbreaks and its relevance to the location of fire stations; statistical analysis of the efficiency of a developing country economy or industry.
Studies relating to the interaction of various segments of society and technology, e.g. the effects of government health policies on the utilization and design of hospital facilities; the relationship between housing density and the demands on public transportation or other service facilities: patterns and implications of urban development and air or water pollution.
Studies devoted to the anticipations of and response to future needs for social, health and other human services, e.g. the relationship between industrial growth and the development of educational resources in affected areas; investigation of future demands for material and child health resources in a developing country; design of effective recycling in an urban setting.