Empirical Study on Gray Correlation of Cross-Border E-Commerce Logistics Efficiency in Guangdong-Hong Kong-Macao Greater Bay Area

Songtao Shou (), Zongdian Ye and Luojia Zheng
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Songtao Shou: Guangzhou College of Technology and Business, Faculty of Business Administration
Zongdian Ye: Guangzhou Institute of Science and Technology, Faculty of Business Administration
Luojia Zheng: Guangzhou Institute of Science and Technology, Faculty of Business Administration

A chapter in Proceedings of the 2024 6th Management Science Informatization and Economic Innovation Development Conference (MSIEID 2024), 2025, pp 119-126 from Springer

Abstract: Abstract With the continuous advancement of globalization and the rapid development of digital technology, cross-border e-commerce has emerged as a pivotal driver of regional economic development. As a critical engine of China’s economic growth, enhancing the logistics efficiency of cross-border e-commerce in the Guangdong-Hong Kong-Macao Greater Bay Area is of paramount importance for regional economic integration and the deepening of international trade. This study employs a combination of questionnaire surveys and the grey relational analysis model to empirically investigates the relationships among the factors influencing logistics efficiency in cross-border e-commerce within the region. The findings indicate that logistics costs, logistics system specialization, regional differences, and digital logistics are highly correlated with the logistics efficiency of cross-border e-commerce enterprises in the Greater Bay Area, with logistics costs exerting the most significant influence, while the impact of digital logistics is comparatively weaker. These findings are intended to offer theoretical insights to support the continued prosperity of the regional economy in the Greater Bay Area and the sustainable development of cross-border e-commerce.

Keywords: Guangdong-Hong Kong-Macao Greater Bay Area; Cross-border e-commerce; Logistics efficiency; Grey relational analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-676-5_14

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