Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

PurposeThe purpose of this paper is to investigate how perceived supervisor support (PSS) affects employees' innovation implementation behavior (IIB), the psychological mechanisms of this relationship, and the role of perceived coworker support (PCS).Design/methodology/approachUsing a three-phase survey, data were collected from 307 employees of a state-owned coal company located in the central region of China. The study tests the hypotheses by using hierarchical regression analyses. The mediating effects and the moderated mediating effects are further examined by using bias-corrected bootstrapping methods.FindingsAffective commitment (AC) fully mediates the positive relationship between PSS and IIB, and this mediating effect can be moderated by PCS.Practical implicationsCompanies should foster supportive supervisors and colleagues by investing in appropriate training programs. In addition, managers should emphasize the psychological changes of employees and provide more supportive feelings for them.Originality/valueThe study explicitly tests the entire causal chain implied by organizational support theory in predicting IIB. It specifies the different role of two similar support constructs (i.e. supervisor support and coworker support) in affecting employees' attitudes and behaviors.

No abstract

No abstract

No abstract

No abstract

In this paper, we study the asymptotic equipartition property (AEP) for a nonhomogeneous Markov information source. We first give a limit theorem for the averages of the functions of two variables of this information source by using the convergence theorem for the martingale difference sequence. As corollaries, we get several limit theorems and a limit theorem of the relative entropy density, which hold for any nonhomogeneous Markov information source. Then, we get a class of strong laws of large numbers for nonhomogeneous Markov information sources. Finally, we prove the AEP for a class of nonhomogeneous Markov information sources.

In this paper, we study the strong law of large numbers and Shannon-McMillan (S-M) theorem for Markov chains indexed by an infinite tree with uniformly bounded degree. The results generalize the analogous results on a homogeneous tree. Keywords: Markov chains, Shannon-McMillan theorem, strong law of large numbers, uniformly bounded tree MSC(2000): 60F15, 60J10