Purpose Despite the intensive research on corporate social responsibility (CSR) and firm financial performance, little is known about how the linkage between CSR and firm financial performance is heterogeneous across industries and how the performance implications are differentiated among specific categories of CSR activities. The purpose of this paper is to explore how the association between a firm’s engagement in CSR and firm financial performance is heterogeneous across industries and CSR categories. Design/methodology/approach Using a sample of 17,083 firm-year observations representing 1,877 firms from the largest 3,000 US companies during years 1991 and 2011, the authors compare the association between CSR and firm financial performance across ten industry sectors defined by Global Industry Classification Standard and across the four CSR categories classified by Mandl and Dorr (2007). Findings The authors find that the association between the overall CSR activities and firm performance is heterogeneous across industries. CSR has significant positive implications for firms from most, but not all, industries. Comparing the performance implication of CSR practices targeting different stakeholder groups, the empirical results indicate that different types of CSR have different influences on financial performance of firms from different industry sectors. Research limitations/implications This study provides new angles for managers in maximizing firm performance through CSR activities and suggests an important and interesting direction for researchers who engage in CSR research. Due to its heterogeneous nature, the CSR-performance relationship needs to be examined more specifically – across industries and different CSR categories. Findings from studies incorporating both company industrial sector and CSR categories would provide more meaningful and practical implications for managers. Practical implications This study provides important managerial implications. First, to maximize firm performance through CSR activities, managers must interpret the linkage between CSR and firm financial performance from the perspective of a specific industrial sector and acknowledge the importance of CSR practices across different CSR categories. Second, the findings suggest that CSR practices aiming at different stakeholder groups generate different financial returns in different industries. Firms engage in CSR to satisfy different stakeholder groups. When budgets are tight, managers may give higher priority to the CSR practices that have stronger effects on firm financial performance. Originality/value This study advances our understanding of the CSR-financial performance relationship by exploring its heterogeneous nature across industry sectors and across specific categories. To obtain the biggest gain from CSR spending, managers must have a good understanding how a specific CSR category can contribute to the financial performance of their particular company in their particular industry.

Abstract:Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30ˆ30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.

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To extract the sensitive bands for estimating the winter wheat growth status and yields, field experiments were conducted. The crop variables including aboveground biomass (AGB), soil and plant analyzer development (SPAD) value, yield, and canopy spectra were determined. Statistical methods of correlation analysis, partial least squares (PLS), and stepwise multiple linear regression (SMLR) were used to extract sensitive bands and estimate the crop variables with calibration set. The predictive model based on the selected bands was tested with validation set. The results showed that the crop variables were significantly correlated with spectral reflectance. The major spectral regions were selected with the B-coefficient and variable importance on projection (VIP) parameter derived from the PLS analysis. The calibrated SMLR model based on the selected wavelengths demonstrated an excellent performance as the R2, TC, and RMSE were 0.634, 0.055, and 843.392 for yield; 0.671, 0.017, and 1.798 for SPAD; and 0.760, 0.081, and 1.164 for AGB. These models also performed accurately and robustly by using the field validation data set. It indicated that these wavelengths retained in models were important. The determined wavelengths for yield, SPAD, and AGB were 350, 410, 730, 1015, 1185 and 1245 nm; 355, 400, 515, 705, 935, 1090, and 1365 nm; and 470, 570, 895, 1170, 1285, and 1355 nm, respectively. This study illustrated that it was feasible to predict the crop variables by using the multivariate method. The step-by-step procedure to select the significant bands and optimize the prediction model of crop variables may serve as a valuable approach. The findings of this study may provide a theoretical and practical reference for rapidly and accurately monitoring the crop growth status and predicting the yield of winter wheat.

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