No abstract

The role of social capital in predicting knowledge sharing has received considerable attention in research. However, very limited research has investigated the mechanisms mediating this relationship. To address this important gap in knowledge, the purpose of this study was to examine employee vigor as a psychological mechanism mediating the effect of social capital on tacit knowledge-sharing intention. Data collected from 209 employees in multiple industries in China were empirically tested by using structural equation modeling analysis. The results show that social capital positively affects emotional energy, which then positively influences the intention to share tacit knowledge. However, neither physical strength nor cognitive liveliness mediates the path through which social capital impacts tacit knowledge-sharing intention. Our research findings unpack the impact mechanism of social capital on tacit knowledge-sharing intention, and provide practical insights into how to use social capital to facilitate employees to share tacit knowledge in organizations.

Abstract:China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring.

Human-AI teaming refers to systems in which humans and artificial intelligence (AI) agents collaborate to provide significant mission performance improvements over that which humans or AI can achieve alone. The goal is faster and more accurate decision-making by integrating the rapid data ingest, learning, and analyses capabilities of AI with the creative problem solving and abstraction capabilities of humans. The purpose of this panel is to discuss research directions in Trust Engineering for building appropriate bi-directional trust between humans and AI. Discussions focus on the challenges in systems that are increasingly complex and work within imperfect information environments. Panelists provide their perspectives on addressing these challenges through concepts such as dynamic relationship management, adaptive systems, co-discovery learning, and algorithmic transparency. Mission scenarios in command and control (C2), piloting, cybersecurity, and criminal intelligence analysis demonstrate the importance of bi-directional trust in human-AI teams.