Charting the path to consumer satisfaction: An innovative investigation into fresh e-commerce through text mining and spatiotemporal analysis
| dc.contributor.author | Yang, Yixing | |
| dc.contributor.author | Wei, Lili | |
| dc.contributor.author | Fan, Liangxian | |
| dc.contributor.author | Zhao, Qiuhan | |
| dc.date.accessioned | 2025-03-05T11:11:43Z | |
| dc.date.available | 2025-03-05T11:11:43Z | |
| dc.date.issued | 2025-03-05 | |
| dc.description.abstract | The rapid expansion of fresh food e-commerce introduces significant challenges in logistics service quality due to the perishability of products. However, the effects of these challenges on customer satisfaction, particularly across different regions and seasons, have been insufficiently explored. This study addresses this gap by analyzing Jingdong (JD)’s e-commerce reviews using latent dirichlet allocation (LDA) topic models to extract dimensions of logistics service quality, combined with sentiment analysis to evaluate both service quality and customer satisfaction. The research investigates how logistics service quality influences satisfaction, accounting for spatial and temporal variations. Key findings reveal that logistics attributes such as quality assurance, reliability, and convenience play a crucial role in shaping customer satisfaction, with their relative importance differing by region and season. For example, convenience is more critical in remote areas, whereas affluent regions place greater emphasis on empathy. Additionally, higher temperatures amplify the impact of logistics attributes. Repeat customers tend to demand higher service quality compared to first-time buyers. These insights provide actionable recommendations for e-commerce firms seeking to optimize logistics services and enhance their competitiveness. | |
| dc.identifier.uri | https://dspace.tul.cz/handle/15240/176746 | |
| dc.language.iso | en | |
| dc.relation.isrefereed | true | |
| dc.title | Charting the path to consumer satisfaction: An innovative investigation into fresh e-commerce through text mining and spatiotemporal analysis | |
| dc.type | Article |