Tone manipulation and its implications for trade credit misallocation in supply chains
基于信号理论，研究买方年报中的乐观语调操纵如何扭曲供应商认知，导致商业信用错配，并发现供应链透明度可缓解此问题。
Purpose Drawing on signaling theory, this paper investigates how buyers' rhetorical tone manipulation embedded in annual reports distorts suppliers' perceptions and contributes to trade credit misallocation within supply chains. Moreover, it explores which kinds of firms are easier to exploit through such textual manipulation and provides a way for suppliers to mitigate it: greater supply chain transparency (SCT). Design/methodology/approach This study applies secondary data analysis. We obtain the textual analysis metrics from the Chinese Research Data Services (CNRDS) platform. Other data are collected from the China Stock Market and Accounting Research (CSMAR) database, the Bloomberg database, and the State Intellectual Property Office (SIPO). A two-way fixed effects model is conducted. Instrumental variable estimation and the propensity score matching approach are employed to address potential endogeneity. Findings Using textual analysis of 4,915 Chinese firm-year observations (2007–2022), this study finds that buyers strategically embed the annual reports' optimistic tone, which constitutes a distorted signal. Exploiting suppliers’ information disadvantage, buyers drive trade credit misallocation, reflected in inflated days payable outstanding (DPO). This inefficiency is stronger for buyers with reputable legal or technical standing, while the impact of reputation is attenuated as SCT rises. Originality/value This study bridges the gap between operations and finance domains by illustrating how qualitative disclosures distort financial dynamics within supply chains. It further advances tone management research by revealing that manipulative tone in the annual reports of well-reputed buyer firms is more likely to mislead suppliers. Practically, it calls for suppliers to integrate tone analysis into credit evaluations and identifies SCT as an effective instrument for curbing information asymmetry in trade credit decisions.