A Large-Scale Comparative Study of Informal Social Networks in Firms
A Large-Scale Comparative Study of Informal Social Networks in Firms
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DOI：
10.1287/mnsc.2021.3997
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Abigail Z. Jacobs;D. Watts
中科院分区：
文献类型：
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作者：
Abigail Z. Jacobs;D. Watts
组织理论认同网络科学的长期观点，即组织网络应被视为多尺度的，并能够显示出紧急属性。然而，为许多（N × 1）组织收集个人级网络数据的历史困难，每个组织包括许多（N × 1）个人，阻碍了开发具体的、理论上有动机的假设的努力，这些假设将微观（即，个体层次）的网络结构与宏观组织属性。在本文中，我们试图通过对来自企业电子邮件系统的聚合匿名电子邮件数据的独特数据集进行探索性分析来刺激这种努力，该数据集包括来自65家美国上市公司的140万用户发送的18亿条消息，这些公司的规模和7个工业部门各不相同。我们发现广泛的异质性企业之间的所有测量的网络特征，我们发现强大的网络和组织的变化，作为一个结果的大小。有趣的是，我们没有发现组织网络结构和公司的年龄，行业，或性能之间有明确的关联，但是，我们确实发现，集中度与地理分散的结果是不能解释的网络大小。尽管这些结果是初步的，但它为组织理论提出了新问题，也为收集、处理和解释数字网络数据提出了新问题。本文被大卫Simchi-Levi接受，管理科学特别部分：65周年。
Theories of organizations are sympathetic to long-standing ideas from network science that organizational networks should be regarded as multiscale and capable of displaying emergent properties. However, the historical difficulty of collecting individual-level network data for many (N ≫ 1) organizations, each of which comprises many (n ≫ 1) individuals, has hobbled efforts to develop specific, theoretically motivated hypotheses connecting micro- (i.e., individual-level) network structure with macro-organizational properties. In this paper we seek to stimulate such efforts with an exploratory analysis of a unique data set of aggregated, anonymized email data from an enterprise email system that includes 1.8 billion messages sent by 1.4 million users from 65 publicly traded U.S. firms spanning a wide range of sizes and 7 industrial sectors. We uncover wide heterogeneity among firms with respect to all measured network characteristics, and we find robust network and organizational variation as a result of size. Interestingly, we find no clear associations between organizational network structure and firm age, industry, or performance; however, we do find that centralization increases with geographical dispersion—a result that is not explained by network size. Although preliminary, these results raise new questions for organizational theory as well as new issues for collecting, processing, and interpreting digital network data. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.