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The purpose of this paper is to test and explain the context where motivation to learn (MTL) reduces innovative behavior in the organizational context.

The authors used questionnaire survey to collect data in a field study. In order to test the moderating effect of transfer climate, MTL on the relationship between MTL and innovative behavior, a sample of 606 employees was analyzed to examine the theoretical expectation by using multiple regression and bootstrapping.

The authors found employees motivated to learn showed less innovative behavior when perceived transfer climate is less favorable. The authors further revealed that motivation to transfer mediates the moderating effect of transfer climate for the relationship between MTL and innovative behavior.

One suggestion for further research is to investigate the relationship among the four constructs by using multi-source, multi-wave and multi-level method.

This study provides several useful guidance of how organization and manager avoid the negative effects of MTL through encouraging employees to learn new knowledge and skills, and providing employee opportunities to use their acquired knowledge and skills.

The authors contribute to the motivational literature by taking a step further to understand the effect of MTL. The authors propose and confirm that employee MTL can lead to negative outcomes when individuals perceived transfer climate is low. The results offer new insight beyond previous findings on positive or non-significant relationship between MTL and innovative behavior. The results further show that this interactive effect is induced by motivation to transfer. Particularly, low transfer climate reduces individuals’ motivation to transfer, and individuals with high MTL have low innovative behavior when they are less motivated to transfer.

The purpose of this paper is to investigate the dissipative filtering problem for a flexible manipulator (FM) with randomly occurring uncertainties and randomly occurring missing data.

The randomly occurring phenomena during the filtering procedure are described by Bernoulli sequences. Based on the idea of dissipative theory, the distributed filtering error augmented system is derived for ensuring the prescribed dissipative performance.

By constructing appropriate Lyapunov function, sufficient dissipative filtering conditions are derived such that the filtering error can be approaching zero. Then, the desired distributed filter gains are designed with the help of matrix transformation.

The merit of this paper is proposing a novel distributed filtering framework for an FM with external disturbance under the dissipative framework, which can provide a more applicable filter design.