Previous research on corporate governance has extensively explored the motives of corporate fraud. However, this research has paid little attention to employees, the real executors of fraud, resulting in the psychological and behavioral decision-making process of employees who commit fraud in enterprises becoming a “black box” that has not yet been opened. Based on the theory of planned behavior, our study integrates the existing research findings on driving factors of employee fraud and anti-fraud practical experience, extracts the key factors of employee fraud motive, and develops a multidimensional scale of employee fraud motive. The exploratory factor analysis (EFA) generates three subscales, comprising 14 items, measuring attitude, subjective norm and perceived behavioral control of employee fraud motive. The confirmatory factor analysis (CFA) supports the reliability, discriminant validity and convergent validity of the new scale. The multiple regression results show that the score of employee fraud motive is positively correlated with the amount of employee fraud occurrence, indicating that the predictive validity of the scale holds. Overall, the scale developed in our study displays good reliability and validity, and is worth spreading.

Corporate financial distress is related to the interests of the enterprise and stakeholders. Therefore, its accurate prediction is of great significance to avoid huge losses from them. Despite significant effort and progress in this field, the existing prediction methods are either limited by the number of input variables or restricted to those financial predictors. To alleviate those issues, both financial variables and non-financial variables are screened out from the existing accounting and finance theory to use as financial distress predictors. In addition, a novel method for financial distress prediction (FDP) based on sparse neural networks is proposed, namely FDP-SNN, in which the weight of the hidden layer is constrained with regularization to achieve the sparsity, so as to select relevant and important predictors, improving the predicted accuracy. It also provides support for the interpretability of the model. The results show that non-financial variables, such as investor protection and governance structure, play a key role in financial distress prediction than those financial ones, especially when the forecast period grows longer. By comparing those classic models proposed by predominant researchers in accounting and finance, the proposed model outperforms in terms of accuracy, precision, and AUC performance.

Like the chief executive officer (CEO), the chief financial officer (CFO) is an important corporate player. However, compared to the role of CEOs, research on the factors influencing corporate innovation has paid very little attention to the role of CFOs. Based on the perspective of role theory, we measure CFO role performance by organizational identification to explore the role of CFOs in corporate innovation. Employing the availability of CFO organizational identification data from a survey of listed firms in China, we find that: (1) CFO organizational identification is negatively associated with innovation output in state-owned enterprises (SOEs) and positively associated with innovation output in non-state-owned enterprises (non-SOEs); (2) corporate misconduct experience positively moderates the relationship between CFO organizational identification and innovation in SOEs; (3) CFO financial industry experience positively moderates the relationship between CFO organizational identification and innovation in non-SOEs. Our results show that CFOs play the supervisor role in innovation in SOEs and the supporter role in innovation in non-SOEs. Our research provides theoretical and practical references for companies to sustainably drive innovation.