Mining Face-to-Face Interaction Networks using Sociometric Badges: Predicting Productivity in an IT Configuration Task
Mining Face-to-Face Interaction Networks using Sociometric Badges: Predicting Productivity in an IT Configuration Task
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DOI：
10.2139/ssrn.1130251
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
Lynn Wu;Benjamin N. Waber;Sinan Aral;Erik Brynjolfsson;A. Pentland
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文献类型：
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作者：
Lynn Wu;Benjamin N. Waber;Sinan Aral;Erik Brynjolfsson;A. Pentland
社会网络理论（如Granonetter 1973，Burt 1992）和信息丰富度理论（Daft & Lengel 1987）都被独立地用来理解信息密集型工作环境中的知识转移。社会网络理论解释了网络结构如何与信息的扩散和分布协变，但在很大程度上忽略了信息和知识传递的沟通渠道（或媒体）的特征。另一方面，信息丰富度理论明确地关注不同类型的知识转移的通信渠道要求，但忽略了网络中信息转移的人口层次拓扑结构。本文旨在弥合这两套理论，以了解什么类型的社会结构是最有利于转移知识和提高工作绩效的面对面的通信网络。使用一套新的数据收集工具，技术和方法，我们能够记录一组IT配置专家在一个月内进行工作时的面对面交互网络，音调对话变化和物理接近度的精确数据。将这些数据与详细的性能和生产力指标联系起来，我们发现了四个主要结果。首先，与之前研究中发现的电子邮件网络相比，生产工人的面对面通信网络显示出非常不同的拓扑结构。在面对面的网络中，网络凝聚力与更高的工作效率呈正相关，而在电子邮件通信中则相反。第二，在执行复杂任务时，面对面网络中的网络凝聚力与更高的工作绩效相关。这一结果表明，网络凝聚力可以补充信息丰富的通信媒介，以传递完成复杂任务所需的复杂或隐性知识。第三，潜在社交网络（表征可用通信伙伴网络的网络）的最有效网络结构不同于任务内社交网络（表征在执行特定任务期间实现的通信伙伴网络的网络）。最后，在面对面的网络中，凝聚力的影响要比在物理接近的网络中强得多，这表明实际对话中的信息流（而不仅仅是物理接近）正在推动我们的结果。我们的工作桥梁两个有影响力的研究机构，以比较面对面的网络结构和网络结构在电子通信。我们还提供了一套新颖的工具和技术，用于在真实的工作环境中发现和记录精确的面对面交互数据。
Social network theories (e.g. Granovetter 1973, Burt 1992) and information richness theory (Daft & Lengel 1987) have both been used independently to understand knowledge transfer in information intensive work settings. Social network theories explain how network structures covary with the diffusion and distribution of information, but largely ignore characteristics of the communication channels (or media) through which information and knowledge are transferred. Information richness theory on the other hand focuses explicitly on the communication channel requirements for different types of knowledge transfer but ignores the population level topology through which information is transferred in a network. This paper aims to bridge these two sets of theories to understand what types of social structures are most conducive to transferring knowledge and improving work performance in face-to-face communication networks. Using a novel set of data collection tools, techniques and methodologies, we were able to record precise data on the face-to-face interaction networks, tonal conversational variation and physical proximity of a group of IT configuration specialists over a one month period while they conducted their work. Linking these data to detailed performance and productivity metrics, we find four main results. First, the face-to-face communication networks of productive workers display very different topological structures compared to those discovered for email networks in previous research. In face-to-face networks, network cohesion is positively correlated with higher worker productivity, while the opposite is true in email communication. Second, network cohesion in face-to-face networks is associated with even higher work performance when executing complex tasks. This result suggests that network cohesion may complement information-rich communication media for transferring the complex or tacit knowledge needed to complete complex tasks. Third, the most effective network structures for latent social networks (those that characterize the network of available communication partners) differ from in-task social networks (those that characterize the network of communication partners that are actualized during the execution of a particular task). Finally, the effect of cohesion is much stronger in face-to-face networks than in physical proximity networks, demonstrating that information flows in actual conversations (rather than mere physical proximity) are driving our results. Our work bridges two influential bodies of research in order to contrast face-to-face network structure with network structure in electronic communication. We also contribute a novel set of tools and techniques for discovering and recording precise face-to-face interaction data in real world work settings.