Is short-term firm performance an indicator of a sustainable financial performance? Empirical evidence

Purpose This study aims to thoroughly examine and understand the relationship between working capital management (WCM) and the sustainable financial performance (FP) in the context of the New Zealand companies listed on stock exchange. Design/methodology/approach This study has applied various regression techniques to examine WCM and the sustainable FP relationship. The data set period is from 2009 to 2019. The results are robust upon various layers of robustness parameters. The system-generalized method of moments is applied for managing endogeneity issue. Findings The research reveals compelling evidence of a meaningful connection between WCM and sustainable FP indicators. The study specifically highlights the significant negative associations between the cash conversion cycle, average collection period and average age of inventory with the firm’s sustainable FP. Through robust analyses and various parameter adjustments, the study ensures the credibility and reliability of its conclusions, further reinforcing the impact of WCM on the financial health of New Zealand-listed firms. Practical implications This study provides future directions for researchers to explore the dynamic relationship between WCM and a firm sustainable FP because it is still a demanding and challenging area. Future research may care to explore the optimal way to reduce the cash conversion cycle, average collection period and average age of inventory for New Zealand firms. The current study does provide insights to NZ financial managers, which is useful for improving sustainable FP by efficiently managing WCM. Originality/value WCM is problematic and constitutes a notable challenge; it requires further research, especially in small economies such as New Zealand. Hence, it is an updated and fresh attempt based on a larger data set to measure the empirical relationship between WCM and the sustainable performance of New Zealand-listed firms. Furthermore, the current study uses dynamic panel data estimation techniques in addition to multiple regression techniques.