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The current research examined a moderated mediation model for the relationships among indicators of objective career success (salary and job level), subjective career success (career satisfaction) and turnover intention, as well as the boundary conditions of this process. Based on a survey study among a sample of Chinese managers (N = 324), we found that both salary and job level were negatively related to turnover intention, with these relations fully mediated by career satisfaction.The results further showed that the relation between job level and career satisfaction was weaker among managers who perceived a higher level of organizational career management, but stronger among managers with a higher managerial career anchor. In support of our hypothesized model, the indirect effect of job level on turnover intention through career satisfaction existed only among managers who perceived a lower level of organizational career management or managers with a higher managerial career anchor. These findings carry implications for research on career success and turnover intention.

The current research aimed to develop a multidimensional measure of career locus of control (LOC) and examine its predictive validity on objective and subjective career success among Chinese employees. Items of career LOC were generated based on literature review of the significant predictors of career success, as well as the open-ended responses among Chinese employees (N ¼ 30). Principal component analysis (Study 1, N ¼ 204) revealed that career LOC consists of three factors: internal factor, external factor, and chance factor, which was consistent with the framework proposed by Levenson (1974). Results of confirmatory factor analysis among another sample of Chinese employees (Study 2, N ¼ 646) supported this three-factor structure. Predictive validity analysis showed that after controlling for the effects of demographic, organizational and industrial variables, internal career LOC was positively related to both objective and subjective career success; chance factor was negatively related to subjective career success, but not related to objective career success. Theoretical and practical implications of this research were discussed.

Based on a resource perspective, the authors investigated how leader-follower power distance value incongruence influences employees' withdrawal behavior. Data were collected twice in China, and the sample included 66 leaders and 350 followers. Leader-follower power distance value incongruence was found to be associated with the psychological workplace strain experienced by followers, indicating that incongruence was a stressor for this group and further influenced their withdrawal behavior. Moreover, incongruence had asymmetrical effects; that is, followers experienced higher psychological workplace strain when their power distance was lower than that of their leaders, compared with when their power distance was higher. The authors also found that the leader's role can make a difference, as the leaders' political skill mitigated the effect of value incongruence on their followers' psychological workplace strain. The study provides a novel extension of value congruence theory and also contributes to the field of value conflict management.

The corrosion behavior of Q345 weathering bridge steel (Q345WS) with oxide scale in simulated different environments was investigated by corrosion kinetics, SEM, XRD and polarization curves. The results demonstrate that the corrosion behavior of Q345WS in three wet-dry cyclic environments are influenced by oxide scale. The corrosion tendency of Q345WS with oxide scale is V-shaped curve due to the formation of loose rust layer in simulated deicing salt and mixed environments, while that of Q345WS in stimulated industrial environment is divided into two different linear stages. The main role of oxide scale is similar in different environments, which has a protective effect in the early stage of all corrosion. Nevertheless, potential difference is easy to be caused in the middle of corrosion, and it has little effect on the later period.

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Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN–LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN–LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN–LSTM neural network combined with IR spectroscopy helps to identify different pasta products.