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Employees’ voluntary green behavior (EVGB) is indispensable in realizing organizations’ environmental sustainability objectives. Leaders can act as catalysts to shape the behavior of their employees. On EVGB, noticeably the missing link is investigating the influence of servant leadership and the mechanism through which it operates. Building upon self-determination and psychological empowerment theories, this research examined the impact of servant leadership on EVGB through the simple and sequential mediation of psychological empowerment and autonomous motivation for the environment (AME). Through systematic sampling, dyadic data were collected from 315 pairs of subordinates and supervisors working in the power sector organizations of Pakistan. Results were obtained by employing the partial least squares structural modeling (PLS-SEM) technique with Smart-PLS 3.2.8 software. Findings revealed that psychological empowerment and AME simply and sequentially mediate the influence of servant leadership on EVGB. Implications for theory and organizational practitioners are offered, accompanied by suggestions for future research.

Employees' pro-environmental behavior serves as a cornerstone in realizing organizations' sustainability initiatives. Leadership plays a vital role in shaping and nurturing the desired employee behaviors. Building upon social learning and self-determination theories, we developed and tested a research model that investigated the mediating role of employees' green intrinsic motivation and the moderating influence of their green self-efficacy on the relationship between green servant leadership and proenvironmental behavior. Data were collected in two waves from 323 pairs of employee-manager working in Pakistan's energy sector and analyzed with partial least squares path modeling (PLS-PM). The findings endorsed that green servant leadership influences pro-environmental behavior through the mediating mechanism of green intrinsic motivation. Further, green self-efficacy played an interactive role with green servant leadership in shaping pro-environmental behavior. This research responds to multiple calls for research and advances the knowledge on servant leadership and pro-environmental behavior.

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Aims This study examines the role of servant leadership through the mechanism of psychological safety in curbing nurses' burnout during the COVID‐19 pandemic. Background During the COVID‐19 pandemic, studies have shown an increased level of stress and burnout among health care workers, especially nurses. This study responds to the call for research to explore the mechanisms of servant leadership in predicting nurses' burnout by employing the perspective of conservation of resources theory. Methods Through a cross‐sectional quantitative research design, data were collected in three waves from 443 nurses working in Pakistan's five public sector hospitals. Data were analysed by employing the partial least squares path modelling (PLS‐PM) technique. Results Servant leadership (β = −0.318; 95% CI = 0.225, 0.416) and psychological safety (β = −0.342; CI = 0.143, 0.350) have an inverse relationship with nurses' burnout and explain 63.1% variance. Conclusions Servant leadership significantly reduces nurses' burnout, and psychological safety mediates this relationship. Implications for Nursing Management Human resource management policies in health care must emphasize training nursing leaders in servant leadership behaviour.

With the development of bioinformatics, tumor classification from gene expression data becomes an important useful technology for cancer diagnosis. Since a gene expression data often contains thousands of genes and a small number of samples, gene selection from gene expression data becomes a key step for tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection field, as it has the characters of data driving and requiring no additional information. However, traditional rough set method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set model, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. Moreover, this paper addresses an entropy measure under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is designed based on neighborhood granules and the entropy measure. Some experiments on two gene expression data show that the proposed gene selection is an effective method for improving the accuracy of tumor classification.

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