Data assetization and corporate credit financing: evidence from hidden champion SMEs in China

Purpose Our study explores the association between data assetization and corporate credit financing from bank loans. By examining the hidden champion small- and medium-sized enterprises (SMEs) sample from China, we want to reveal whether firms with higher data assets improve their bank credit financing. Design/methodology/approach Our study uses the sample of China's hidden champion listed SMEs from 2011 to 2021 to examine the association between data assetization and corporate credit financing. We use the multiple regression analysis approach and the neural network model to construct accurate firm-level data assets. Findings Our study finds that firms with higher data assets improve their bank credit financing. This finding arises because data assetization helps reduce information asymmetry and obtain government subsidies. Moreover, we reveal that enterprises with more extended credit-term structures tend to prefer short-term financing as data assets increase. Regarding influencing factors, enterprises with higher risk-taking, lower industry positions and higher economic policy uncertainty will enhance the effect of data assetization on bank credit financing. Additionally, the data assetization also reduces the over-financialization among hidden champion SMEs. Originality/value Our study makes the following contributions. Firstly, we enrich the research on the data assets and economic outcomes. Secondly, we contribute to the literature on how corporate digital transformation shapes bank credit resource allocations (e.g. Liu and Wang, 2023; Zhou and Li, 2023). Finally, our study contributes to the literature on the hidden champions' financial features in emerging markets.