Why do employees commit fraud? Theory, measurement, and validation.

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.

A novel method for financial distress prediction based on sparse neural networks with L 1 / 2 regularization.

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 L 1 / 2 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.

[The Role of c-fos in the Production of Follicle-Stimulating Hormone and the Related Signal Transduction Pathways].

As an immediate early gene, c-fos plays a critical role in stimulating the synthesis and release of pituitary FSH via GnRH. To better understanding the mechanism how c-fos works in the transcription of FSHbeta under different frequency of pulsatile GnRH stimulation, this paper reviewed the signal trans- ductions initiated by c-fos in pituitary, which include cAMP pathway, MAPK pathway, Ca2+ /calmodulin-dependent kinases pathway and nuclear factor of activated T-cells (NFAT) pathway. It will be helpful for research in molecular targeted immunotherapy and eventually effective treatment to the infertility which resulted from defection or mutation of c-fos and c-fos related signal pathway elements.

Whole-tree agarwood-inducing technique: an efficient novel technique for producing high-quality agarwood in cultivated Aquilaria sinensis trees.

Agarwood is the fragrant resin-infused wood derived from the wounded trees of Aquilaria species. It is a valuable non-timber forest product used in fragrances and as medicine. Reforestation for Aquilaria trees in combination with artificial agarwood-inducing methods serves as a way to supply agarwood and conserve of wild Aquilaria stock. However, the existing agarwood-inducing methods produce poor-quality agarwood at low yield. Our study evaluated a novel technique for producing agarwood in cultivated Aquilaria trees, called the whole-tree agarwood-inducing technique (Agar-Wit). Ten different agarwood inducers were used for comparison of Agar-Wit with three existing agarwood-inducing methods. For Aquilaria trees treated with these ten inducers, agarwood formed and spread throughout the entire tree from the transfusion point in the trunk to the roots and branches of the whole tree. Agarwood yield per tree reached 2,444.83 to 5,860.74 g, which is 4 to 28 times higher than that by the existing agarwood-inducing methods. Furthermore, this agarwood derived from Agar-Wit induction was found to have a higher quality compared with the existing methods, and similar to that of wild agarwood. This indicates Agar-Wit may have commercial potential. Induction of cultivated agarwood using this method could satisfy the significant demand for agarwood, while conserving and protecting the remaining wild Aquilaria trees.