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How to boost e-commerce for poverty alleviation? A perspective on competitiveness analysis using online reviews

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Abstract

As a creative mode, e-commerce for poverty alleviation (e-CPA) plays a significant role in fighting against poverty globally. To boost e-commerce for poverty alleviation, this study proposes an innovative framework with mixed text mining methods and visualized tools to conduct competitiveness analysis based on consumer online reviews. Specifically, sentiment analysis was first utilized to calculate attribute performance of the target product, and attribute importance was obtained using multiple linear regression. Drawing on importance-performance competitor analysis and dynamic importance-performance competitor analysis, we conducted competitiveness analysis of e-CPA products from both static and dynamic perspectives. Finally, consumer dissatisfaction factors were extracted and visualized via a heat map to generate product improvement strategies. Online reviews from Jingdong were crawled to demonstrate the proposed framework's effectiveness. This work has strong theoretical contributions to research on e-CPA and competitiveness analysis, and also provides e-CPA merchants with concrete and efficient practical suggestions to improve products.

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Acknowledgements

The authors acknowledge the Center for Big Data & Intelligent Decision-Making of Dalian University of Technology for providing computing resources.

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Authors and Affiliations

  1. Institute of Systems Engineering, Dalian University of Technology, No. 2 Linggong Rd., Dalian, 116024, People’s Republic of China

    Jie Wu, Junming Zhang & Narisa Zhao

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  1. Jie Wu
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Correspondence to Narisa Zhao.

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There is no conflict of interest in the manuscript which is approved by all authors for publication. We would like to declare that all or part of the work described in the manuscript is original research, which has not been published elsewhere and is not prepared for publication elsewhere.

Appendices

Appendix A E-commerce merchant information

See Table 3.

Appendix B Survey of product attributes valued by consumers

See Fig. 15 and Table 4.

Appendix C Domain attribute lexicon

See Table

Table 5 Domain vocabularies (partial results)
Full size table

5.

Appendix D Attribute performance and importance from a dynamic perspective

See Tables

Table 6 Detailed data of 2019
Full size table

6,

Table 7 Detailed data of 2020
Full size table

7, and

Table 8 Detailed data of 2021
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8.

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Wu, J., Zhang, J. & Zhao, N. How to boost e-commerce for poverty alleviation? A perspective on competitiveness analysis using online reviews. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09708-2

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