This study investigates the relationship between board composition and corporate social responsibility (CSR) performance. Specifically, we examine the impact of board composition (aspects like political experience, academic experience, overseas background, and gender diversity) on CSR performance. We test our hypotheses using data collected from 839 Chinese public firms during the period from 2008 to 2016. Applying generalized least squares regression, the study shows that the political experience, academic experience, and overseas background of the board members are positively related to the firm's CSR performance. Moreover, we discuss the distinctive relationship between gender diversity and CSR performance in the context of Chinese culture. We extend the CSR literature by examining unique aspects of board composition in the Chinese context and offer fruitful implications for both scholars and practitioners.

In this paper, we examine the role of a firm's entrepreneurial orientation (EO) in the advancement of corporate social responsibility (CSR) performance. We argue that a firm's innovativeness, proactiveness, and risk‐taking would lead it to employ more socially responsible practices and generate benefits to society. Moreover, we theorize that this influence would differ, depending on the firm ownership. Specifically, our argument is based on the Chinese context where state‐controlled firms dominate the economy. We test our hypotheses by utilizing secondary data on 738 Chinese public firms over an 8‐year period (2008–2015). Our empirical results demonstrate a positive and significant relationship between EO and CSR performance among state‐controlled firms. However, this relationship is not significant among privately controlled firms.

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Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and temporal frequency of these surface water maps by using locally trained classifiers and gap-filling missing values via imputation in all available satellite images. We developed the Surface Water IMputation (SWIM) classification framework using R and the Google Earth Engine computing platform to improve water classification compared to the JRC study. The novel contributions of the SWIM classification framework include (1) a cluster-based algorithm to improve classification sensitivity to a variety of surface water conditions and produce approximately unbiased estimation of surface water area, (2) a method to gap-fill every available Landsat image for a region of interest to generate submonthly classifications at the highest possible temporal frequency, (3) an outlier detection method for identifying images that contain classification errors due to failures in cloud masking. Validation and several case studies demonstrate the SWIM classification framework outperforms the JRC dataset in spatiotemporal analyses of small waterbody dynamics with previously unattainable sensitivity and temporal frequency. Most importantly, this study shows that reliable surface water classifications can be obtained for all pixels in every available Landsat image, even those containing cloud cover, after performing gap-fill imputation. By using this technique, the SWIM framework supports monitoring water extent on a submonthly basis, which is especially applicable to assessing the impact of short-term flood and drought events. Additionally, our results contribute to addressing the challenges of training machine learning classifiers with biased ground truth data and identifying images that contain regions of anomalous classification errors.

Owing to rapid property degradation after ambient exposure and incompatibility with conventional device fabrication process, electrical transport measurements on air‐sensitive 2D materials have always been a big issue. Here, for the first time, a facile one‐step polymer‐encapsulated electrode transfer (PEET) method applicable for fragile 2D materials is developed, which showed great advantages of damage‐free electrodes patterning and in situ polymer encapsulation preventing from H2O/O2 exposure during the whole electrical measurements process. The ultrathin SmTe2 metals grown by chemical vapor deposition (CVD) are chosen as the prototypical air‐sensitive 2D crystals for their poor air‐stability, which will become highly insulating when fabricated by conventional lithographic techniques. Nevertheless, the intrinsic electrical properties of CVD‐grown SmTe2 nanosheets can be readily investigated by the PEET method instead, showing ultralow contact resistance and high signal/noise ratio. The PEET method can be applicable to other fragile ultrathin magnetic materials, such as (Mn,Cr)Te, to investigate their intrinsic electrical/magnetic properties.