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Grounded in social exchange theory (SET), the purpose of this paper is to examine the relationship between empowering leadership and unethical pro-organizational behavior (UPB), as mediated by duty orientation (including duty to members, duty to mission and duty to codes). Further, this study proposes that perceived leader expediency moderates indirectly between empowering leadership and UPB.

This paper tests this social exchange model across a survey study using time-lagged data collections from 215 employees of a service company in China.

The results show that duty orientation mediates the relationship between empowering leadership and UPB. In addition, perceived leader expediency moderates the indirect relationship between empowering leadership and UPB through duty orientation (i.e. duty to members and duty to missions).

This research aids in understanding the impact of empowering leadership on follower outcomes by investigating the dark side of empowering leadership and examining the relationships between empowering leadership, duty orientation and UPB. The present study also challenges the notion that the phrase “the greater the empowerment, the better the outcomes” suggests that organizations should offer a conditional approach to the empowerment of followers by their leaders.

At present, smartphones are embedded with accelerometers, gyroscopes, magnetometers and WiFi sensors. Most researchers have delved into the use of these sensors for localization. However, there are still many problems in reducing fingerprint mismatching and fusing these positioning data. The purpose of this paper is to improve positioning accuracy by reducing fingerprint mismatching and designing a weighted fusion algorithm.

For the problem of magnetic mismatching caused by singularity fingerprint, derivative Euclidean distance uses adjacent fingerprints to eliminate the influence of singularity fingerprint. To improve the positioning accuracy and robustness of the indoor navigation system, a weighted extended Kalman filter uses a weighted factor to fuse multisensor data.

The scenes of the teaching building, study room and office building are selected to collect data to test the algorithm’s performance. Experiments show that the average positioning accuracies of the teaching building, study room and office building are 1.41 m, 1.17 m, and 1.77 m, respectively.

The algorithm proposed in this paper effectively reduces fingerprint mismatching and improve positioning accuracy by adding a weighted factor. It provides a feasible solution for indoor positioning.