Jie Shen

The literature on antecedents of corporate social responsibility strategies of firms has been predominately content driven. Informed by the managerial sense-making process perspective, we develop a contingency theoretical framework explaining how political ideology of managers affects the choice of CSR strategy for their firms through their CSR mindset. We also explain to what extent the outcome of this process is shaped by the firm’s internal institutional arrangements and external factors impacting on the firm. We develop and test several hypotheses using data collected from 129 Chinese managers. The results show that managers with a stronger socialist ideology are likely to develop a mindset favouring CSR, which induces the adoption of a proactive CSR strategy. The CSR mindset mediates the link between socialist ideology and CSR strategy. The strength of the relationship between the CSR mindset and the choice of CSR strategy is moderated by customer response to CSR, industry competition, the role of government, and CSR-related managerial incentives.

Building on the human resource management behavioral and organizational climate literature, this study explores the linkage between socially responsible HRM and employee support for perceived external corporate social responsibility and the underlying social and psychological process. Multilevel analysis of data gathered over two separate periods confirmed that the relationship between SRHRM and employee support for external CSR initiatives of the employing organization is mediated by the organizational CSR climate. Moreover, the indirect effect is contingent on perceived internal CSR. This study extends CSR research into the HRM domain and develops a better understanding of the micro-foundations of CSR by integrating the micro- and macro-perspectives of CSR. Based on the study findings, this paper also discusses theoretical contributions and future research directions.

Underwater object detection plays an important role in research and practice, as it provides condensed and informative content that represents underwater objects. However, detecting objects from underwater images is challenging because underwater environments significantly degenerate image quality and distort the contrast between the object and background. To address this problem, this paper proposes an optical prior-based underwater object detection approach that takes advantage of optical principles to identify optical collimation over underwater images, providing valuable guidance for extracting object features. Unlike data-driven knowledge, the prior in our method is independent of training samples. The fundamental novelty of our approach lies in the integration of an image prior and the object detection task. This novelty is fundamental to the satisfying performance of our approach in underwater environments, which is demonstrated through comparisons with state-of-the-art object detection methods.