Modeling consensus emergence in groups using longitudinal multilevel methods

Lang Jonas W. B.; Bliese Paul D.; Voogt Alex

摘要

Abstract >Organizational researchers have long been interested in studying bottom‐up multilevel processes where lower level units (e.g., employees) in organizations interact to jointly create characteristics of higher level units (e.g., work groups). This article contributes to the literature on bottom‐up processes by detailing a statistical approach—the consensus emergence model (CEM)—that allows researchers to study emergence of shared perceptions and feelings or climates in groups over time. The described methodological approach extends standard multilevel methodology by examining residual variances within a growth model to account for dynamic change in group consensus. The CEM provides a formal test for consensus emergence. The approach also allows researchers to test explanatory models of consensus emergence by including person‐level, group‐level, and observation‐level predictors. We illustrate the CEM by applying the method to data from two longitudinal studies of work units. The first study investigated job satisfaction in military companies. Our second study examined professional archeologists working in groups on a field excavation mission and focused on fatigue at the end of the work day. Our analyses demonstrate the CEM's ability to detect and study emergence, and suggest that the CEM may be a valuable tool to help extend the study of emergence in organizational research. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><abstract xmlns=”http://www.wiley.com/namespaces/wiley“type=“main”xml:lang=“en”><title type=“main”>摘要>长期以来，组织研究人员一直对研究自下而上的多层次过程感兴趣，在这种过程中，组织中较低级别的单位（如员工）相互作用，共同创造较高级别单位（如工作组）的特征本文通过详细介绍一种统计方法，即共识涌现模型（CEM），为自下而上过程的文献做出了贡献。该模型允许研究人员研究随着时间的推移，群体中共同感知、感受或气候的出现。所述方法学方法通过检查增长模型中的剩余方差来解释群体共识的动态变化，从而扩展了标准的多水平方法学。CEM为达成共识提供了正式测试。该方法还允许研究人员通过包括个人层面、群体层面和观察层面的预测因子来测试共识出现的解释模型。我们通过将该方法应用于两个工作单元纵向研究的数据来说明CEM。第一项研究调查了军工企业的工作满意度。我们的第二项研究考察了在野外挖掘任务中分组工作的专业考古学家，重点研究了工作日结束时的疲劳。我们的分析证明了CEM检测和研究涌现的能力，并表明CEM可能是有助于在组织研究中扩展涌现研究的一个有价值的工具 </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-29061/'>《Personnel psychology》</a> <b style="margin: 0 2px;">|</b><span>2018年第2期</span><b style="margin: 0 2px;">|</b><span>共27页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Lang Jonas W. B.&option=202" target="_blank" rel="nofollow">Lang Jonas W. B.;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Bliese Paul D.&option=202" target="_blank" rel="nofollow">Bliese Paul D.;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Voogt Alex&option=202" target="_blank" rel="nofollow">Voogt Alex;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Ghent University;</p> <p>University of South Carolina;</p> <p>Drew University;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/385.html" title="个性心理学（人格心理学）">个性心理学（人格心理学）;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> </p> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/academic-journal-cn_numerical-mathematics-theory-methods-applications_thesis/0201271866651.html">An Improved Model Reduction Method on AIMs for N-S Equations Using Multilevel Finite Element Method and Hierarchical Basis</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=M.Nauman Aslam&option=202" 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Spatial Panel Econometrics and Multilevel Modeling</a> <b>[R] </b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Mills, J B&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">Mills, J B,</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext= Fricker, J D&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> Fricker, J D </a> <span>2014</span> </span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：Bypasses的区域经济影响：结合空间面板计量经济学和多层次建模的纵向研究</span> </p> </li> </ul> <ul style="display: none;"> <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/academic-conference-cn_meeting-18647_thesis/020222062734.html">Simulation of Viscous Non-Newtonian Flow under the Influence of Longitudinal Vibration by the Lattice Boltzmann Method</a> <b>[C]</b> <span> <a 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HAPPE CLEMENT DEUS</a> <span> . 2011</span> </span> </div> </li> </ul> <ul style="display: none;"> <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/patent-detail/06120114188195.html">一种基于Consensus-ADMM的带估计约束的非线性集值滤波方法</a> <b>[P]</b> . <span> 中国专利： CN114282152A </span> <span> . 2022-04-05</span> </div> </li> <li> <div> <b>2. </b><a class="enjiyixqcontent" href="/patent-detail/06120113177378.html">基于Consensus-ADMM优化的可重构纯相位稀疏阵列综合方法、装置及介质</a> <b>[P]</b> . <span> 中国专利： CN113221377A </span> <span> . 2021-08-06</span> </div> </li> <li> <div> <b>3. </b><a class="enjiyixqcontent" href="/patent-detail/06130499943627.html">Systems, methods, and devices that enable intelligent consensus, smart consensus, and weighted consensus models for distributed ledger technology in a cloud-based computing environment.</a> <b>[P]</b> . <span> 外国专利：  JP2021512416A </span> <span> . 2021-05-13</span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：在基于云的计算环境中为分布式分区技术提供智能共识，智能共识，智能共识和加权共识模型的系统，方法和设备。 </span> </p> </li> <li> <div> <b>4. </b><a class="enjiyixqcontent" href="/patent-detail/06130404183726.html">SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING INTELLIGENT CONSENSUS, SMART CONSENSUS, AND WEIGHTED CONSENSUS MODELS FOR DISTRIBUTED LEDGER TECHNOLOGIES IN A CLOUD BASED COMPUTING ENVIRONMENT</a> <b>[P]</b> . <span> 外国专利：  US2019238316A1 </span> <span> . 2019-08-01</span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：在基于云的计算环境中实施智能共识，智能共识和加权共识模型的系统，方法和装置 </span> </p> </li> <li> <div> <b>5. </b><a class="enjiyixqcontent" href="/patent-detail/06130431708154.html">Driving resistance determining method for motor vehicle, involves determining model error from reference model by comparing calculated actual-vehicle longitudinal acceleration signal with reference vehicle longitudinal acceleration signal</a> <b>[P]</b> . <span> 外国专利：  DE102006029366A1 </span> <span> . 2008-01-03</span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：机动车辆的行驶阻力确定方法，包括通过将计算出的实际车辆纵向加速度信号与参考车辆纵向加速度信号进行比较，从参考模型中确定模型误差。 </span> </p> </li> </ul> </div> </div> </div> <div class="theme cardcommon" style="overflow: auto;display:none"> <h3 class="all_title" id="enpatent55">相关主题</h3> <ul id="subject"> </ul> </div> </div> </div> </div> <div class="right rightcon"> <div class="details_img cardcommon clearfix" style="margin-bottom: 10px;display:none;" > </div> </div> </div> <div id="thesis_get_original1" class="downloadBth" style="bottom: 19px;z-index: 999;" onclick="ywcd('0704029003665','4',7,2,1,'',this,24)" class="delivery" prompt="010401" title="通过人工服务将文献原文发送至邮箱" >获取原文</div> <div 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