班轮运输连接数据分析和研究框架

IF 2 Q3 BUSINESS
W. Yap
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引用次数: 0

摘要

本研究提出了一个框架,将航运连通性的潜在数据领域概念化,用于数据分析的应用,可用于就此类联系的状态和实际应用的潜在领域产生更深入的见解。设计/方法/方法研究方法包括全面介绍不同的角度来评估航运连通性和集装箱航运服务中包含的数据水平,并提出潜在的应用来分析盈利能力、绩效、竞争力、风险和环境影响。研究发现处理大量数据能力的进步为综合方法提供了空间,该方法利用来自不同利益相关者的所有可用数据来分析班轮运输连通性。研究表明,集装箱运输服务中包含的不同类型的数据是如何相互关联的,并且可以组织用于数据分析的应用。研究的局限性/启示研究的启示提供给航运公司,港口管理者和运营商和政策制定者。实际意义本研究提出了一个概念性框架,该框架捕获了集装箱运输服务中涉及的数据范围,以及如何以集成的方式实际应用数据分析。原创性/价值本文是文献中第一个详细讨论构成班轮运输连接应用数据分析的航运服务中存在的不同级别的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Framework for liner shipping connectivity data analytics and research
PurposeThis research proposes a framework to conceptualise the potential realm of data regarding shipping connectivity for application of data analytics which can be used to generate deeper insights with respect to the state of such linkages and potential areas for practical application.Design/methodology/approachThe study method involved comprehensive presentation of different perspectives of assessing shipping connectivity and levels of data contained within container shipping services and proposed potential application to analyse profitability, performance, competitiveness, risk and environmental impact.FindingsAdvances in capabilities to handle large volumes of data offer scope for an integrated approach which utilises all available data from various stakeholders in analyses of liner shipping connectivity. Research shows how different types of data contained in container shipping services are related and can be organised for application of data analytics.Research limitations/implicationsResearch implications are offered to shipping lines, port managers and operators and policymakers.Practical implicationsThis research presented a conceptual framework that captures the range of data involved in container shipping services and how data analytics can be practically applied in an integrated manner.Originality/valueThis paper is the first in literature to discuss in detail the different levels of data that reside within shipping services that constitute liner shipping connectivity for application of data analytics.
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来源期刊
CiteScore
4.80
自引率
0.00%
发文量
19
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