组织公民行为理论及其应用研究

组织公民行为是近年来组织行为学领域中深受关注的内容之一。该文对近几年国内外关于组织公民行为的研究进行了综述,首先介绍了组织公民行为产生的背景,其次简要论述了组织公民行为的理论框架,包括组织公民行为的作用、特征维度、影响因素等,接着分析了组织公民行为的结构和测量,最后对组织公民行为的应用研究及其未来发展趋势作了简单的讨论。     

组织公民行为理论及其应用研究

组织公民行为是近年来组织行为学领域中深受关注的内容之一。该对近几年国内外关十组织公民行为的研究进行了综述,首先介绍了组织公民行为产生的背景,其次简要论述了组织公民行为的理论框架,包括组织公民行为的作用、特征维度、影响因素等,接着分析了组织公民行为的结构和测量,最后对组织公民行为的应用研究及其未来发展趋势作了简单的讨论。     

上司不当督导与下属绩效行为的作用机制及其干预策略

不当督导与绩效行为的关系是组织行为学领域一个新的研究热点问题。本研究拟以情绪事件理论、替代侵犯理论、归因理论为指导, 采用问卷调查、投射测验、情景模拟等多种研究方法, 分析愤怒、羞愧、职场中的他人行为及自我归因在不当督导和绩效行为之间的作用, 揭示其非线性的作用机制, 丰富组织行为学的研究内容。本研究借鉴情绪调节理论及方法, 寻求有效的不当督导干预策略, 以期指导员工走出不当督导-消极情绪-不良绩效行为恶性循环的泥沼, 同时也检验本研究关于不当督导和绩效行为理论模型的科学性。     

组织行为学研究的现状：意义与建议

本文对2008~2011年这4个年度发表在英文和中文组织行为学方面重要学术期刊上的研究进行总结, 旨在让国内学者了解现今组织行为学的现状和发展。我们在分析研究内容、研究方法的基础上, 就当前中国组织行为学研究提出了一些总结性的看法, 包括：研究问题很大程度上追随国际潮流, 但缺乏对本土重要问题的关注; 研究方法过多地集中于定量的问卷调查法, 但缺乏对多种不同方法的应用; 研究的分析层次过多地集中在个体层次上, 而对多层次的组织和团队管理现象关注不足。为更好地促进组织行为学科的发展, 本文建议：应该注重立足中国的组织和管理现象来挖掘研究问题; 采用多种研究方法, 特别应鼓励开展定性研究; 注重跨层次研究和研究的情境化等。本文通过介绍近年来组织行为领域的全面情况并详细分析其中的若干个例, 为如何克服以上不足提供具体的范例和方法。     

资源保存理论在组织行为学中的应用：演变与挑战

资源保存理论(conservation of resources theory, COR)是组织行为学研究近30年中被引最高的理论之一。COR的提出源自Hobfoll对临床应激障碍的观察与思考, 历经了多次修订后该理论逐步由一个压力-反应模型发展成了一个以个体资源存量及其动态变化解释行为动因的动机理论, 形成了一个具有较广泛外延的理论体系。组织行为学是资源保存理论标志性文献的施引文献的核心来源。大量组织行为学研究对COR中的资源损失漩涡、获得螺旋等核心过程进行了实证检验, 涉及广泛的研究议题, 使COR成为了理解员工心理与行为动因的最具影响力的理论之一。COR在组织行为学中的应用面临一定的挑战, 其中有来自认知评价理论、适应理论的观点挑战, 也有其作为一个“跨界理论”带来的挑战。组织行为学研究在应用COR时应充分尊重其理论要义, 避免削足适履。     

行为科学研究与应用介绍——加拿大V.V.Baqa博士讲话摘要

导言我应中国行为科学学会及北京行为科学学会的邀请来这里讲行为科学研究与应用的问,感到很高兴,通过提问题,提意见,可向你们学到很多东西。第一部分:职工与组织的联系或结合。组织行为学的一个内容是讨论职工与组织的相互关系的问题,组织行为学的研究有两个趋向:(1)偏重心理学,进行微观和应用方面的研究。从这一方面研究决定性因素:个人与群体的行为;(2)社会学的宏观方面的研究,着重经验。这两个领域会聚起来,宏观与     

国外积极偏离行为的研究述评

偏离行为的研究在社会学和组织行为学领域都有较丰富的成果,然而其关注点主要集中于对社会或组织产生负面作用的消极偏离行为。近年来相关领域的学者越来越关注积极偏离行为（Positive Deviance, PD）的研究。该文综述了关于积极偏离行为的定义的各方观点,同时分析了积极偏离行为与相似构念的差异,并综述了积极偏离行为的测量方式,以及积极偏离行为的前因变量和结果变量。最后对积极偏离行为未来的研究方向进行了展望。     

自我管理研究回顾与展望

自我管理是个体主动应用认知及行为策略对自身的思维、情绪、行为以及所处环境等进行目标管理的过程。自我管理对于人们的学习、生活、毕生发展等有着非常重要的意义。该文通过对自我管理的概念、理论基础、评估方法及已有的测量工具等内容的系统回顾,总结了自我管理强调个体的主动性、目标导向性以及行为和认知策略的运用等特点,在对已有研究存在的主要不足进行分析的基础上,指出自我管理研究将更加关注普通正常个体的整体自我管理水平的测量和培训     

忠诚还是叛逆?中国组织情境下的员工越轨创新行为

越轨创新行为是破除创新瓶颈, 摆脱“创新者的窘境”的有效手段, 是新时代创新的重要途径。目的的合理性和行为的偏离性, 使越轨创新行为同时具有了“忠诚”和“叛逆”的色彩。以越轨创新究竟是“忠诚”还是“叛逆”这一问题作为切入点, 分别从越轨创新行为的测量、形成机制和影响效能角度构建了一个围绕越轨创新行为的系统性研究。子研究1：基于创新过程视角, 探讨中国组织情境下越轨创新行为的结构维度, 开发本土化量表。子研究2：分别从个体、领导和组织三个层次、动态探讨越轨创新行为的形成机制。子研究3：基于事件系统理论, 从越轨创新成功/失败两方面, 构建和验证越轨创新行为对组织效能的影响机制。三个子研究相互联系并逐步递进。理论贡献在于深化学术界对越轨创新行为的理解, 推动越轨创新理论的构建, 并且拓展越轨理论、创新理论、积极组织行为学理论及事件系统理论的应用。     

多维范式下神经组织行为学的哲学基础、理论框架和研究方法

神经组织行为学是指通过探究组织现象背后的生物学运作机理, 从神经生理视角发展并重构组织行为学框架的新兴多领域交叉学科。多维范式下的神经组织行为学包括从还原论到涌现论的哲学基础, 基于社会情境认知理论、跨层次研究和逆向推理的理论框架, 以及神经成像法和ANS测量法并行的研究方法。未来研究应注意神经组织行为学可能给组织理论带来的变革, 以及研究方法的未来走向。     

If We Produce Discrepancies,Then How? Testing a Computational Process Model of Positive Goal Revision

Within the self‐regulation literature on goals, both discrepancy reduction and discrepancy production are considered important theoretical and practical processes. Yet, discrepancy production has only been examined in a limited number of goal‐striving contexts, and the analytical strategies employed (e.g., difference scores) are difficult to interpret. This study extends discrepancy production research to multiple goal contexts where the goals are in conflict. Computational modeling and an organizational simulation were used to test a control theory explanation of discrepancy production. The occurrence of discrepancy production in the computational model and participants was assessed using hierarchical linear modeling. Comparing the data from the computational model with participants' data indicated a good fit. Implications of the findings and methods are discussed.     

Computational modeling of organisational learning by self-modeling networks

Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.     

Disentangling narrow and coarse semantic networks in the brain: The role of computational models of word meaning

There has been a recent boom in research relating semantic space computational models to fMRI data, in an effort to better understand how the brain represents semantic information. In the first study reported here, we expanded on a previous study to examine how different semantic space models and modeling parameters affect the abilities of these computational models to predict brain activation in a data-driven set of 500 selected voxels. The findings suggest that these computational models may contain distinct types of semantic information that relate to different brain areas in different ways. On the basis of these findings, in a second study we conducted an additional exploratory analysis of theoretically motivated brain regions in the language network. We demonstrated that data-driven computational models can be successfully integrated into theoretical frameworks to inform and test theories of semantic representation and processing. The findings from our work are discussed in light of future directions for neuroimaging and computational research.     

Thurstonian-Based Analyses: Past, Present, and Future Utilities

Current psychometric models of choice behavior are strongly influenced by Thurstone’s (1927, 1931) experimental and statistical work on measuring and scaling preferences. Aided by advances in computational techniques, choice models can now accommodate a wide range of different data types and sources of preference variability among respondents induced by such diverse factors as person-specific choice sets or different functional forms for the underlying utility representations. At the same time, these models are increasingly challenged by behavioral work demonstrating the prevalence of choice behavior that is not consistent with the underlying assumptions of these models. I discuss new modeling avenues that can account for such seemingly inconsistent choice behavior and conclude by emphasizing the interdisciplinary frontiers in the study of choice behavior and the resulting challenges for psychometricians. The author would like to thank R. Darrell Bock whose work inspired many of the ideas presented here. The paper benefitted from helpful comments by Albert Maydeu-Olivares and Rung-Ching Tsai. The reported research was supported in parts by the Social Sciences and Humanities Research Council of Canada.     

The past,present, and future of computational models of cognitive development

Does modeling matter? We address this question by providing a broad survey of the computational models of cognitive development that have been proposed and studied over the last three decades. We begin by noting the advantages and limitations of computational models. We then describe four key dimensions across which models of development can be organized and classified. With this taxonomy in hand, we focus on how the modeling enterprise has evolved over time. In particular, we separate the timeline into three overlapping historical waves and highlight how each wave of models has not only been shaped by developmental theory and behavioral research, but in return also provided valuable insights and innovations to the study of cognitive development.     

An Introduction to the Art. Review of Nonlinear Dynamics: Techniques and Applications in Psychology, by R. H. Heath

Richard Heath is a professor in the Discipline of Psychology at the University of Sunderland, England. He obtained his B.Sc. (Hons.) at the University of Newcastle, Australia in 1970 and a Ph.D. in psychology as a Commonwealth Scholar with Professor Stephen Link at McMaster University, Canada, in 1976. Heath's research has emphasized the role of new mathematical and computational techniques to the study and modeling of complex and nonlinear aspects of cognition. His experimental work has examined signal detection, categorization, memory, fatigue, handwriting, and the detection of behavior change. He has also developed nonlinear system identification models of attention and interference, nonstationary versions of the random walk model of choice response time, and adaptive, novelty sensitive models of human memory.Andrew Heathcote is an associate professor in the School of Behavioural Sciences, University of Newcastle, Australia. He obtained a B.Sc. (Hons.) at the University of Tasmania in 1984 and a Ph.D. in psychology as a Commonwealth Scholar with Professor D. Mewhort at Queen's University, Canada, in 1991. His research interests include nonlinear analysis and modeling of response time, skill acquisition, and recognition memory.     

Dialogues on prediction errors

The recognition that computational ideas from reinforcement learning are relevant to the study of neural circuits has taken the cognitive neuroscience community by storm. A central tenet of these models is that discrepancies between actual and expected outcomes can be used for learning. Neural correlates of such prediction-error signals have been observed now in midbrain dopaminergic neurons, striatum, amygdala and even prefrontal cortex, and models incorporating prediction errors have been invoked to explain complex phenomena such as the transition from goal-directed to habitual behavior. Yet, like any revolution, the fast-paced progress has left an uneven understanding in its wake. Here, we provide answers to ten simple questions about prediction errors, with the aim of exposing both the strengths and the limitations of this active area of neuroscience research.     

领导职业支持对组织公民行为的影响:心理资本的作用

本研究探讨领导职业支持对工作团队成员组织公民行为的影响机制,研究采用问卷调查法,以10个地市的66个工作团队303名成员为调查对象,在控制被试的教育程度、工作经验及人口统计学变量之后,运用多层线性模型分析领导职业支持与团队成员心理资本及其组织公民行为的关系。结果发现：（1）领导职业支持对团队成员心理资本和组织公民行为有积极促进作用;（2）无论在团队层次还是员工层次,领导职业支持对成员组织公民行为有积极促进作用,成员心理资本在二者之间起完全中介作用。     

Unsilencing Critical Conversations in Social-Studies Teacher Education using Agent-based Modeling

In this article, we argue that when complex sociopolitical issues such as ethnocentrism and racial segregation are represented as complex, emergent systems using agent-based computational models (in short agent-based models or ABMs), discourse about these representations can disrupt social studies teacher candidates' dispositions of teaching social studies without engaging in critical conversations about race and power. Our study extends the literature on agent-based computing to the domain of social studies education, and demonstrates how preservice teachers' participation in agent-based modeling activities can help them adopt a more critical stance toward designing learning activities for their future classrooms.