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首页> 外文期刊>Transportation journal >The Power and Centrality of the Transportation and Warehousing Sector within the US Economy:A Longitudinal Exploration Using Social Network Analysis
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The Power and Centrality of the Transportation and Warehousing Sector within the US Economy:A Longitudinal Exploration Using Social Network Analysis

机译：美国经济中运输和仓储部门的权力和集中度：使用社会网络分析的纵向探索

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This article uses archival panel data over two decades and social network analysis (SNA) to address the question of whether logistics and transportation have become more central to the US economy over time. Unlike traditional measures, SNA goes beyond measuring dyadic relationships and incorporates measures of power, like centrality, closeness, and between-ness that traditional measures fail to capture. Secondary data from the Benchmark Use Input-Output Tables compiled every five years by the US Bureau of Economic Analysis are examined in the SNA context. Results demonstrate the value of using SNA over conventional dyadic measures to analyze the extended network, especially with respect to the shift of power and centrality over time. The findings demonstrate that while the overall industrial landscape of the United States has become less centralized over time, transportation and warehousing have become increasingly central to the US economy. Thus, the article demonstrates that over time, logistics and warehousing have not only become more powerful, but have gone from being peripheral activities to being increasingly central and important in the larger economy. The article contrasts our findings from the SNA approach with those obtained from conventional (non-SNA-based approaches) and highlights the differences in the two approaches.
机译：本文使用了过去20年的档案面板数据和社交网络分析（SNA），以解决物流和运输是否已逐渐成为美国经济中越来越重要的问题。与传统措施不同，国民账户体系超越了测量二元关系的范畴，并纳入了权力措施，例如传统措施无法抓住的中心性，亲密性和中间性。在SNA的背景下，对由美国经济分析局每五年编制的基准使用投入产出表中的辅助数据进行了检查。结果证明了使用SNA优于传统的二分法来分析扩展网络的价值，尤其是在权力和中心性随时间推移方面。调查结果表明，随着时间的流逝，美国的整体工业格局变得越来越不集中，而运输和仓储对于美国经济却越来越重要。因此，本文证明，随着时间的流逝，物流和仓储不仅变得越来越强大，而且已经从外围活动变成在更大的经济中越来越重要和重要。本文将我们从SNA方法获得的发现与从常规（基于非SNA的方法）获得的发现进行了对比，并着重介绍了这两种方法之间的差异。

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