Drawing upon the conservation of resources theory (Hobfoll, Am Psychol 44:513-524, 1989), social exchange theory (Blau, Exchange and power in social life, 1964) and the job demand-control model (Karasek, Adm Sci Q 24:285-308, 1979), this study uncovers the theoretical mechanism that explains the relationship between workfamily conflict and organizational citizenship behavior (OCB). First, this study focuses on how employee job satisfaction mediates the relationship. Then, we investigate the moderating role of decision authority in the mediated relationship. We employ three-wave data collected from 324 employees in 102 teams to test our hypotheses. Results of hierarchical linear modeling (HLM) first shows that job satisfaction mediates the relationship between work interference with family (WIF) and OCB. In addition, employees' decision authority moderates the direct effect of WIF on OCB. Specifically, the negative relationship between WIF and OCB is stronger when employees' decision authority is high. Moreover, decision authority moderates the indirect effect of WIF on OCB via job satisfaction. Specifically, the negative relationship between WIF and job satisfaction is weaker when employees' decision authority is high. The results suggest that organizations should give employees enough decision authority over their work, as a high level of decision authority may act as a double-edged sword regarding critical organizational outcomes.
As the radar resolution improves, the extended structure of the targets in radar echoes can make a significant contribution to improving tracking performance, hence specific trackers need to be designed for these targets. However, traditional radar target tracking methods are mainly based on the accumulation of the target’s motion information, and the target’s appearance information is ignored. In this paper, a novel tracking algorithm that exploits both the appearance and motion information of a target is proposed to track a single extended target in maritime surveillance scenarios by incorporating the Bayesian motion state filter and the correlation appearance filter. The proposed algorithm consists of three modules. Firstly, a Bayesian module is utilized to accumulate the motion information of the target. Secondly, a correlation module is performed to capture the appearance features of the target. Finally, a fusion module is proposed to integrate the results of the former two modules according to the Maximum A Posteriori Criterion. In addition, a feedback structure is proposed to transfer the fusion results back to the former two modules to improve their stability. Besides, a scale adaptive strategy is presented to improve the tracker’s ability to cope with targets with varying shapes. In the end, the effectiveness of the proposed method is verified by measured radar data. The experimental results demonstrate that the proposed method achieves superior performance compared with other traditional algorithms, which simply focus on the target’s motion information. Moreover, this method is robust under complicated scenarios, such as clutter interference, target shape changing, and low signal-to-noise ratio (SNR).
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