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)

We examine the impact of family firms’ historical ownership on corporate fraud. Our results show that restructured family firms from state-owned enterprises are more likely to violate and commit more fraud than entrepreneurial family firms. This finding is robust to the difference-in-difference-in-differences estimation, an instrument variables regression, fixed effects research design, and propensity score matching (PSM) approach analysis. Mechanism analysis shows that restructured family firms result in lower financial performance, high labor redundancy, inefficient investments, and cash volatility. Therefore, restructured family (...) firms have a stronger incentive to conceal these problems through corporate fraud. Furthermore, the effects of family firms’ historical ownership on corporate fraud are weakened for a more extended period after SOE ownership reform and the restructuring approach adopted by equity takeover. (shrink)

This paper examines the effect of financial shared service centers (FSSCs) on corporate misconduct. Using a sample of Chinese public companies with hand-collected FSSC data, we find that the adoption of FSSCs is negatively associated with the likelihood and frequency of corporate misconduct. The results hold to a battery of robustness tests. Moreover, we show that the negative association between FSSCs and corporate misconduct is more pronounced in firms that have no management equity ownership, disclose internal control weaknesses, and have (...) more subsidiaries. Additional analyses indicate that FSSCs can help mitigate both disclosure-related and nondisclosure-related misconduct. (shrink)

The academic literature has viewed drivers of corruption in isolation and, consequently, failed to examine their synergistic effect. Such an isolated view provides incomplete information, leads to a misleading conclusion, and causes great difficulty in curbing corruption. This paper conducts a systematic literature review to identify the drivers of corruption in the construction industry. Subsequently, it develops a system dynamics (SD) model by conceptualizing corruption as a complex system of interacting drivers. Building on stakeholder and open systems theories, the proposed (...) SD model shows how the complex reinforcing relationship between authoritative, organizational, cultural, and financial drivers of corruption further increases corrupt practices. The new model also provides lessons that can be helpful in the development of policy frameworks to control corruption in the construction industry. To achieve success in the fight against corruption, the findings of this research suggest that (1) corruption must be understood at both the organizational and state levels, (2) anticorruption practices must be informed by ethically grounded stakeholder management strategies, and (3) anticorruption reforms must go hand-in-hand with strategies to tackle the economic downturn. (shrink)