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. 2023 May 18;18(5):e0286034.
doi: 10.1371/journal.pone.0286034. eCollection 2023.

A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis

Qiwei Wang  1 , Xiaoya Zhu  2 , Manman Wang  1 , Fuli Zhou  1 , Shuang Cheng  1
Affiliations

A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis

Qiwei Wang et al. PLoS One. .

Abstract

The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com. Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler's five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Research techniques used in this study.
Fig 2
Fig 2. Results of K-means clustering for Jingdong.
Fig 3
Fig 3. Results of K-means clustering for Taobao.
Fig 4
Fig 4. Combined results of K-means clustering for both Jingdong and Taobao.
Fig 5
Fig 5. SSE of K-means.
Fig 6
Fig 6. Silhouette coefficient of K-means.
Fig 7
Fig 7. The theoretical framework of OCPB influencing factors.
See this image and copyright information in PMC

References

    1. Mejía-Trejo J. COVID-19 ads on purchase intention of online consumer behavior as business innovation activity: A contribution to the uses and gratification theory. Electron Commer R A. 2021; 49:101086.
    1. Vanhala M, Lu C, Peltonen J, Sundqvist S, Nummenmaa J, JÃĪrvelin K. The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research. J Bus Res. 2020; 106:46–59.
    1. Richard M-O, Habibi MR. Advanced modeling of online consumer behavior: The moderating roles of hedonism and culture. J Bus Res. 2016; 69(3):1103–19.
    1. Venkatesh V, Speier-Pero C, Schuetz S. Why do people shop online? A comprehensive framework of consumers’ online shopping intentions and behaviors. Inform Technol Peopl. 2022; 35(5):1590–620.
    1. Camacho LJ, Ramírez-Correa PE, Salazar-Concha C. Consumer Ethnocentrism and Country of Origin: Effects on Online Consumer Purchase Behavior in Times of a Pandemic. Sustain. 2022; 14(1):348.

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