Suchergebnisse

PurposeThe perseverative cognition framework suggests that observing ostracism has negative implications for observers due to affective rumination and that a proactive personality might make observers more vulnerable to this effect.Design/methodology/approachData from 49 team leaders and 218 team members were obtained through a three-wave survey in China. Path analysis was used to examine the theoretical model.FindingsThe results indicate that observing ostracism increased turnover intention and reduced task performance and that these relationships were mediated by affective rumination. Furthermore, these effects were stronger for observers with high proactive personality.Research limitations/implicationsWorkplace ostracism harms employees; however, its effects on observers remain underexplored. This paper extends research on the effects of ostracism by revealing that ostracism is not only harmful to the well-being of its victims but also adversely affects the work-related attitudes and behaviors of observers, especially those with proactive personality.Practical implicationsOrganizations should be aware of the harmful effects of workplace ostracism on observers, and take actions to inhibit workplace ostracism as well as reduce the negatives impacts.Originality/valueThe results reveal the cognitive mechanism of affective rumination, in which observing workplace ostracism affects observers' behaviors and attitudes, highlighting the importance of observing effect of workplace ostracism.

China is the largest developing country worldwide, with rapid economic growth and the highest population. Light pollution is an environmental factor that significantly influences the quality and health of wildlife, as well as the people of any country. The objective of this study is to model the light pollution spatial pattern, and monitor changes in trends of spatial distribution from 1992 to 2012 in China using nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System. Based on the intercalibration of nighttime light imageries of the study area from 1992 to 2012, this study obtained the change trends map. This result shows an increase in light pollution of the study area; light pollution in the spatial scale increased from 2.08% in the period from 1992–1996 to 2000–2004, to 5.64% in the period from 2000–2004 to 2008–2012. However, light pollution change trends presented varying styles in different regions and times. In the 1990s, the increasing trend in light pollution regions mostly occurred in larger urban cities, which are mainly located in eastern and coastal areas, whereas the decreasing trend areas were chiefly industrial and mining cities rich in mineral resources, in addition to the central parts of large cities. Similarly, the increasing trend regions dominated urban cities of the study area, and the expanded direction changed from larger cities to small and middle-sized cities and towns in the 2000s. The percentages of regions where light pollution transformed to severe and slight were 5.64% and 0.39%, respectively. The results can inform and help identify how local economic and environmental decisions influence our global nighttime environment, and assist government agencies in creating environmental protection measures.

Abstract
The highly selective conversion of carbon dioxide into value-added products is extremely valuable. However, even with the aid of in situ characterization techniques, it remains challenging to directly correlate extensive spectral data carrying microscopic information with macroscopic performance. Herein, we adopted advanced machine learning (ML) approaches to establish an accurate and interpretable relationship between vibrational spectral signals and catalytic performances to uncover hidden physical insights. Focusing on photocatalytic CO2 reduction, our model is shown to effectively and accurately predict the CO production activity and selectivity based solely on the infrared (IR) spectral signals, the generalizability of which is additionally demonstrated with a new Bi5O7I photocatalytic system. More importantly, further model analysis has revealed a novel strategy to steer CO selectivity, the physical sanity of which is verified by a detailed reaction mechanism analysis. This work demonstrates the tremendous potential of machine-learned spectroscopy to efficiently identify reaction control factors, which can further lay the foundation for targeted optimization and reverse design.

PurposeThe purpose of this paper is to promote a wider understanding of the importance of distinguishing between presenteeism behavior and its motivation and between the avoidance and approach dimensions of motivation, and to rectify the neglect of presenteeism's antecedents (in particular, situational ones). It develops a theoretical model that explains how situational antecedents affect presenteeism – conventionally defined as attending work while ill.Design/methodology/approachAn ordinary least-squares regression-based path analysis is employed to analyze the findings of a sample of 277 employees in service organizations in southwestern China.FindingsFindings demonstrate that the situational factor, leader–member exchange (LMX), is positively related to the approach dimension of presenteeism motivation and that of workload moderates the positive link between presenteeism motivation and behavior, such that employees who experience higher workload more frequently display presenteeism behavior.Practical implicationsFindings suggest that managers should be prudent when developing relationships with their subordinates and consider the ways in which they may most effectively encourage employees to support their organization.Originality/valueThis is the first study to consider LMX and workload as situational antecedents of presenteeism motivation and behavior.