Digital transformation and financing constraints of SMEs: evidence from China

中国 转化(遗传学) 业务 数字化转型 财务 计算机科学 政治学 万维网 化学 生物化学 基因 法学
作者
Lei Guo,Luying Xu,Junyi Wang,Jing Li
出处
期刊:Asia-Pacific journal of accounting & economics [Informa]
卷期号:31 (6): 966-986 被引量:7
标识
DOI:10.1080/16081625.2023.2257235
摘要

ABSTRACTIt is known that digital transformation can sharpen the competitive edge of SMEs, yet the relationship between digital transformation and the financing of SMEs only receives little attention. Based on the data of Chinese companies listed on SME board from 2010 to 2020, this paper empirically examines the impact of digital transformation on the financing constraints of SMEs as well as its mechanism. The study finds that digital transformation significantly eases the financing constraints of SMEs, and the conclusion remains valid after considering endogeneity issues and conducting robustness tests. The mechanism analysis manifests that SMEs improve the quality of external information disclosure through digital transformation, thereby alleviating financing constraints. In addition, the impact of digital transformation on the financing constraints of SMEs demonstrates prominent heterogeneity. The mitigation effect of digital transformation is more pronounced in areas where digital financial is well developed, in state-owned enterprises, high-tech enterprises and long-term oriented enterprises. In the context of SMEs, this paper reveals the relationship between digital transformation and corporate financing, enriching the studies on the organizational performance effects of digital transformation.KEYWORDS: digital transformationfinancing constraintSmesChina Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. The digital terms are defined in Appendix Table A1.2. Specifically, it refers to the proportion of words related to the future (such as future, plans, and expectations) in the 'Management Discussion and Analysis' section of each company's annual report. The data in this paper is extracted from management forward-looking indicators in the WinGo Textual Analysis Database.Additional informationFundingThis work was supported by the National Science Foundation of China [71972170 & 71802157]; Fundamental Research Funds for the Central Universities [SK2023040]; Social Science Foundation of Shaanxi Province [2023R214]; China Postdoctoral Science Foundation [2020T130506].
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