Unraveling how poor logistics service quality of cross-border E-commerce influences customer complaints based on text mining and association analysis

Yu Zhang and Huimin Huang

Journal of Retailing and Consumer Services, 2025, vol. 84, issue C

Abstract: Logistics issues in cross-border online shopping have become an important hotspot for customer complaints. However, limited research has explored how poor logistics service quality (LSQ) can trigger customer complaints. This study systematically discusses the complex causal relationship between poor LSQ and customer complaints based on the expectation-disconfirmation theory, through analyzing 200 typical cases collected from China's professional online consumer dispute mediation platforms. Six categories of poor LSQ contributing to customer complaints were identified through text mining: insecurity, uneconomic, unreliable, untimely, low information quality, and low contact quality using the grounded theory approach. In the second stage, five valid strong association rules were generated using association rule mining (ARM), demonstrating that the factors leading to customer complaints were interrelated rather than independent. Specifically, the "delayed delivery" indicator of untimely is associated with the "outdated information" indicator of low information quality; the "long transport times" and “delayed delivery†indicators of untimely are associated with the "poor service attitude" indicator of low contact quality; the "damaged goods" indicator of insecurity is associated with the "unguaranteed goods claims" indicator of unreliable, and the "outdated information" indicator of low information quality is associated with the "poor service attitude" indicator of low contact quality. These findings enable cross-border e-commerce practitioners and logistics service providers to implement targeted strategies to promote LSQ, minimizing customers' negative expectation disconfirmation and reducing customer complaints.

Keywords: Cross-border e-commerce; Logistics service quality; Customer complaints; Text mining; Association rule mining (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:84:y:2025:i:c:s0969698925000165

DOI: 10.1016/j.jretconser.2025.104237

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