高参与人力资源实践对员工创新行为的影响：一个中介与调节模型

基于员工工作成长综合模型,探讨员工感知的高参与人力资源实践如何通过促进员工工作繁荣正向影响员工创新行为,以及员工所在部门谦卑型领导风格对这一过程的跨层次调节作用。对402份部门主管与员工匹配两阶段问卷调查结果表明,工作繁荣在高参与人力资源实践与员工创新行为间具有完全中介作用;谦卑型领导在高参与人力资源实践促进员工工作繁荣过程中发挥跨层次调节作用,即当部门领导表现出高谦卑性风格时,高参与人力资源实践对工作繁荣的正向影响更强;此外,部门谦卑型领导跨层调节工作繁荣对高参与人力资源实践与员工创新行为间的中介作用,当采取高谦卑型领导风格时,该中介作用更强。研究结论丰富和发展了员工工作成长综合模型视角下高参与人力资源实践对员工创新行为影响的研究,可为管理者有效激发员工创新行为提供决策依据。     

创客政策感知对其创新行为的影响机理——知识获取的中介与创新自我效能感的调节

基于心理动力场理论视角,分析创客政策感知对其创新行为的影响机理,重点考察知识获取的中介作用以及创新自我效能感的调节效应。通过对380名创客样本数据进行结构方程模型分析,研究发现：①创客政策感知对创新行为的正向影响显著;②知识获取在创客政策感知对创新行为的影响中起完全中介作用;③创新自我效能感在政策感知与创新行为的正向关系中调节效应不显著,但在知识获取与创新行为的正向关系中调节效应显著,且正向调节知识获取在政策感知与创新行为关系中的中介作用。研究结论进一步深化了对创客政策感知与其创新行为关系的认识,对推动创客创新实践具有一定借鉴作用。     

Goals,Conflict, Politics,and Performance of Cross-Functional Sourcing Teams—Results from a Social Team Experiment

Strategic sourcing is carried out in cross-functional teams to account for the complexity and multidimensionality of modern procurement decisions. However, such teams not only enable the integration of distinct interdependent skill sets and viewpoints, they are also characterized by functional goal misalignment. We focus on the resulting behavioral challenges, namely conflict and politics, and their effects on team satisfaction and rationality, which ultimately leads to observed outcomes. We test our hypotheses in a structural equation model based on data gathered from 468 participants in a social team experiment. We find a mediated effect of goal misalignment on political behavior via two types of team conflict. Political behavior, in turn, obstructs rational team sourcing decisions and reduces team members’ satisfaction with the process. Our study indicates that behavioral challenges in the framework of Organizational Buying Behavior not only co-occur but affect each other via mediation. Hence, managers need to closely monitor the escalation chains’ origin, task conflict, which constitutes a necessary condition for further emotional dissent and political biasing. We contribute to the understanding of the challenges in cross-functional sourcing teams, thereby providing advice to executives in their pursuit to rationalize and improve sourcing team decisions and their outcomes.     

关于推进我国现代农业发展中集体行为的思考——基于以色列农业集体模式的经验

自实施乡村振兴战略以来,我国农业发展迎来向现代农业转型的重要契机,现代农业的突出特点是个体基础上的集体活动,因此对农业集体行为的研究,是积极发挥好政府和财政作用的题中应有之义.本文在讨论了农业领域中集体行为的理论逻辑和我国发展的现实逻辑基础之上,结合以色列农业集体经济模式的经验,认为推动我国农业现代化必须符合中国实际,凸显社会主义的集体力量,一是要以集体行为推动产业基础形成,面向市场;二是要以集体行为促进保障基础的建设,应对市场;三是利用集体力量推动内源性农业基地建设.     

企业内部知识流动行为及其影响因素研究

知识流动的过程同时也是企业技术核心能力提升的过程。结合知识流动网络结构模型,分析了企业内部知识网络构成,阐述了企业内部网络知识流动的特征。从结点和子网两层次分析了知识流动行为的微观机理:结点间的知识流动使知识得以在网络中共享和扩散,结点与子网间知识的循环流动形成子网知识增量,子网间的知识流动最终提升企业的技术核心能力。企业内部知识网络知识流动行为主要受知识主体、知识受体、知识距离三方面因素的影响。     

P2P借贷市场与股票市场间的溢出机制:中国股市2015年异常波动期间的证据

本文认为,P2P借贷市场与股票市场之间存在两类溢出联动机制,即替代效应机制和互补效应机制。替代效应指的是P2P投资者资金配置导致两个市场行情呈现负相关;互补效应指的是P2P借款者杠杆融资导致两个市场行情正相关。在此基础上,本文利用事件分析法,量化研究了股市异常波动时期市场间溢出的具体表现。从P2P市场对股市溢出来看,P2P市场是拉动股市大幅上涨的重要资金渠道,但没有证据表明P2P市场是造成股市大幅下跌的直接原因。从股市对P2P市场溢出来看,股市大幅上涨对P2P市场的影响较弱,但股市大幅下跌会对P2P市场行情产生显著影响。上述现象均可由替代效应机制或互补效应机制进行解释,且这两类机制在不同股市周期和股票板块之间具有较大的异质性。     

How to “improve” prediction using behavior modification

Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making. Improving predictive accuracy is therefore extremely valuable. Data science researchers design algorithms, models, and approaches to improve prediction. Prediction is also improved with larger and richer data. Beyond improving algorithms and data, platforms can stealthily achieve better prediction accuracy by pushing users’ behaviors towards their predicted values, using behavior modification techniques, thereby demonstrating more certain predictions. Such apparent “improved” prediction can result from employing reinforcement learning algorithms that combine prediction and behavior modification. This strategy is absent from the machine learning and statistics literature. Investigating its properties requires integrating causal with predictive notation. To this end, we incorporate Pearl’s causal $do(.)$ operator into the predictive vocabulary. We then decompose the expected prediction error given behavior modification and identify the components impacting predictive power. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated. Behavior modification can make users’ behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when business customers use predictions in practice. Outcomes pushed towards their predictions can be at odds with customers’ intentions, and harmful to manipulated users.