This study focuses on a traditional business ethics question and aims to use advanced techniques to improve the performance of corporate fraud prediction. Based on the GONE framework, we adopt the machine learning model to predict the occurrence of corporate fraud in China. We first identify a comprehensive set of fraud-related variables and organize them into each category (i.e., Greed, Opportunity, Need, and Exposure) of the GONE framework. Among the six machine learning models tested, the Random Forest (RF) model outperforms (...) the other five models in corporate fraud prediction. Based on the RF model, we show that Exposure variables play a more important role in predicting corporate fraud than other input variables. These results highlight the importance of Exposure variables in corporate fraud prediction and promote the practical use of the machine learning model in solving business ethics questions. (shrink)

The ethical controversies of firms in the sharing economy (SE) have recently drawn attention and caused debates. Ridesharing firms violate laws in many countries, but how they become legitimised remains underexplored. We apply the co-evolutionary perspective to examine how ethical controversies, legitimation and institutionalisation co-evolve in the ridesharing segment in the dynamic and changing institutional environment of China. We conducted a case study on Didi using firm-institution dual-level analysis based on stakeholder salience theory (SST) and the Orders of Worth framework. (...) Our findings uncover ethical controversies among stakeholders with different levels of salience. We also reveal a dynamic interplay between the legitimacy work of Didi and the institutional-level market regulations on ridesharing firms. We develop a process model demonstrating the co-evolution among the changes in ethical controversies, new market category emergence and legitimation of ridesharing firms. These findings shed light on the application of the co-evolutionary perspective in analysing ethical controversies among multiple stakeholders’ evolving interests in the SE context. (shrink)