Suchergebnisse

In light of social exchange theory and the gratitude norm of Confucianism, we explored the relationships between family-supportive supervisor behaviors (FSSBs), employee gratitude, and work engagement. We also investigated power distance orientation as a potential contingent factor
affecting these relationships. We tested our hypotheses using a two-wave survey conducted with 325 employees of 15 companies in China. When we controlled for supervisor–employee work support, we found that employee gratitude partially mediated the positive association between FSSBs and
work engagement. Further, power distance orientation negatively moderated the direct effect of FSSBs on employee gratitude and the mediating effect of gratitude of FSSBs on work engagement. Theoretical and practical management implications are discussed.

Stroke is the leading cause of death and disability among people in China, and it leads to heavy burdens for patients, their families and society. An accurate prediction of the risk of stroke has important implications for early intervention and treatment. In light of recent advances in machine learning, the application of this technique in stroke prediction has achieved plentiful promising results. To detect the relationship between potential factors and the risk of stroke and examine which machine learning method significantly can enhance the prediction accuracy of stroke. We employed six machine learning methods including logistic regression, naive Bayes, decision tree, random forest, K-nearest neighbor and support vector machine, to model and predict the risk of stroke. Participants were 233 patients from Sichuan and Chongqing. Four indicators (accuracy, precision, recall and F1 metric) were examined to evaluate the predictive performance of the different models. The empirical results indicate that random forest yields the best accuracy, recall and F1 in predicting the risk of stroke, with an accuracy of .7548, precision of .7805, recall of .7619 and F1 of .7711. Additionally, the findings show that age, cerebral infarction, PM 8 (an anti-atrial fibrillation drug), and drinking are independent risk factors for stroke. Further studies should adopt a broader assortment of machine learning methods to analyze the risk of stroke, by which better accuracy can be expected. In particular, RF can successfully enhance the forecasting accuracy for stroke.

PurposeBased on the work–home resources model regarding the work domain and the home domain as a whole resource exchange system with directional resource flows, this study proposed that perceived overqualification could lead to personal resources drain, especially for employees with high work–family centrality (i.e. valuing work more than family). Furthermore, the drained personal resources of the focal employees brought in more spouse undermining and less spouse support at home.Design/methodology/approachA quantitative approach in which Study 1 involving 259 pairs and Study 2 involving 260 pairs of employees and their spouses from China provided support to the first-stage moderated mediation model.FindingsResults revealed that when employees' work–family centrality is high, perceived overqualification could elicit personal resources drain and induce more spouse undermining and less spouse support. On the contrary, when employees' work–family centrality is low, perceived overqualification could reduce personal resources drain and render less spouse undermining and more spouse support. The two studies consistently provided support for most of the hypotheses.Practical implicationsThe research results suggest that organizations could take some feasible measures to help overqualified employees articulate the value of work–family centrality to manage overqualified employees' work–family resources further, bringing appropriate sequential behaviors at home.Originality/valueResearch on perceived overqualification has primarily focused on its consequences in the work domain, paying scant attention to whether it can influence the home domain outside work. This research contributes to this line of literature by investigating how and when perceived overqualification leads to family outcomes.