团队网络特征对团队内非伦理行为扩散的影响： 社会网络视角研究

非伦理行为因其对团队的严重破坏而成为学术研究热点。个体嵌入在团队内部的社会网络中，因此除了个体特征、团队特征、情境特征等因素之外，网络特征对非伦理行为的扩散也有重要影响。由于社会网络具有动态演化特征，其研究也应采用动态范式。运用计算机仿真方法可以模拟现实中的社会网络结构及关系演变，并据此探究网络特征对非伦理行为扩散的影响机制。仿真结果表明，团队社会网络特征（关系密度和关系强度）对团队内非伦理行为扩散有正向作用，而团队特征（团队规模和团队内外控构成比例）调节这一正向关系。

The Influence of Group Social Network Characters on the Diffusion of Unethical Behavior: A Social Network Perspective

The diffusion of unethical behavior in groups often leads to ethical crisis， which causes huge damages to the group. Previous studies have made some achievements on the antecedent of its diffusion， but still have limitations. First， although individuals are embedded in the group internal social network， and the diffusion of unethical behavior is a kind of phenomenon in social networks， social network perspective has not got enough attention， and the research from this perspective is obviously insufficient. Second， most of the existing researches use static methods， which do not conform to the dynamic characteristics of unethical behavior diffusion. Third， in methodology， field study can’t fully reflect the dynamic evolution of social network， which also limits the efficiency of the study progress. Given these limitations， this paper aims to look into social network perspectives using computer simulation methods to explore how group social network’s characters influence the diffusion of unethical behaviors， aiming to expand the understanding of unethical behavior diffusion process. Our study will make up for the above three shortcomings， deepen the theoretical understanding on how social network characters influence the diffusion of unethical behaviors and also break the limitation of previous research methods. We use computer simulation method as the main tool. Computer simulation method has significant advantages in the study of complex， dynamic systems. Simulation software （such as NetLogo as we used） can simulate real dynamic world， allowing researchers to observe moving bodies interacting in the space， recording the changes of the attribute and macro system. Our results prove that group social network’s characters （the density and the strength of relationship） have a significant positive influence on unethical behavior diffusion. Concrete manifestations are: the density of relationship in group social network positively influences unethical behavior diffusion， the higher the density is， the more possible unethical behavior would diffuse in group; the strength of relationship in group social network positively influences unethical behavior diffusion， the higher the strength was， the more possible unethical behavior would diffuse in group. Furthermore， we also discuss how group characters （group size and group locus-of-control composition） would moderate such relations. We find that group size moderates the relationship between group social network and group unethical behavior diffusion， with a greater group size， the influence of density and strength of relationship on unethical behavior diffusion would be more significant. Meanwhile， group locus-of-control composition moderates the relationship between group social network and group unethical behavior diffusion， when there are more internal-control individuals in the group， the influence of density and strength of relationship on unethical behavior diffusion will be more significant. These findings claim the importance of social network in unethical behavior diffusion study. More importantly， the consideration of group size and group locus-of-control composition will promote the subsequent combination of social network features with individual， group characteristics and situational perspectives， and finally build a more considerate unethical behavior diffusion model. Besides， this study also confirms that the computer simulation method has its feasibility and advantage in organizational behavior field， which is a beneficial attempt in research methodology. Finally， the limitation of our study is discussed. Due to the lack of individual level attitude interaction and introducing realistic situation into our simulation， we still cannot simulate real world in the largest extent. We hope such shortcomings will be made up in the future researches．