Can absent leadership be positive in team conflicts? An examination of leaders' avoidance behavior in China

Purpose – Although conflict avoidance is one of the most commonly used conflict resolution styles in China, there has surprisingly been no explicit investigation of the effects of leaders’ avoidance. This paper therefore examines how leaders’ avoidance influences followers’ attitudes and well-being in China. Design/methodology/approach – Data was collected from 245 subordinates in three large companies in the People’s Republic of China through an online survey. Multiple regression analysis was adopted to test three sets of competing hypotheses. Findings – Leaders’ avoidance behaviour is positively related to followers’ perception of justice, supervisory trust and emotional well-being in Chinese organizations. Originality/value - Our paper joins growing attempts to consider conflict management in the context of leadership. To the best of our knowledge, this is the first study to examine empirically the relationships between a team leader’s avoidance behaviour and his or her subordinates’ perceptions of justice, supervisory trust, and emotional well-being in a single study. The findings are provoking by illustrating positive effect of leader's conflict avoidance behaviour in China. Our paper supports that conflict avoidance could be a sustainable rather than one-off strategy by a leader, and that identifying conditions (e.g. culture) that affect the outcomes of conflict avoidance is important

Optimal Incentive Contract with Endogenous Monitoring Technology

Recent technology advances have enabled firms to flexibly process and analyze sophisticated employee performance data at a reduced and yet significant cost. We develop a theory of optimal incentive contracting where the monitoring technology that governs the above procedure is part of the designer's strategic planning. In otherwise standard principal-agent models with moral hazard, we allow the principal to partition agents' performance data into any finite categories and to pay for the amount of information the output signal carries. Through analysis of the trade-off between giving incentives to agents and saving the monitoring cost, we obtain characterizations of optimal monitoring technologies such as information aggregation, strict MLRP, likelihood ratio-convex performance classification, group evaluation in response to rising monitoring costs, and assessing multiple task performances according to agents' endogenous tendencies to shirk. We examine the implications of these results for workforce management and firms' internal organizations

Investigation on energetic optimization problems of stochastic thermodynamics with iterative dynamic programming

The energetic optimization problem, e.g., searching for the optimal switch- ing protocol of certain system parameters to minimize the input work, has been extensively studied by stochastic thermodynamics. In current work, we study this problem numerically with iterative dynamic programming. The model systems under investigation are toy actuators consisting of spring-linked beads with loading force imposed on both ending beads. For the simplest case, i.e., a one-spring actuator driven by tuning the stiffness of the spring, we compare the optimal control protocol of the stiffness for both the overdamped and the underdamped situations, and discuss how inertial effects alter the irreversibility of the driven process and thus modify the optimal protocol. Then, we study the systems with multiple degrees of freedom by constructing oligomer actuators, in which the harmonic interaction between the two ending beads is tuned externally. With the same rated output work, actuators of different constructions demand different minimal input work, reflecting the influence of the internal degrees of freedom on the performance of the actuators.Comment: 14 pages, 7 figures, communications in computational physics, in pres

Generative Face Completion

In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for patches to synthesize, our algorithm directly generates contents for missing regions based on a neural network. The model is trained with a combination of a reconstruction loss, two adversarial losses and a semantic parsing loss, which ensures pixel faithfulness and local-global contents consistency. With extensive experimental results, we demonstrate qualitatively and quantitatively that our model is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.Comment: Accepted by CVPR 201