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Gaining insights for service improvement through unstructured text from online reviews

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  • Zhang, Chenxi
  • Xu, Zeshui

Abstract

This paper aims to address the challenges of dealing with information redundancy in the context of service quality improvement by proposing an innovative online review-based service evaluation method. The proposed method identifies key service attributes that significantly influence consumer satisfaction and assist service providers in prioritizing service improvement. Specifically, this paper presents four identification dimensions and corresponding indicators of service attributes, followed by the application of Proportional Marginal Variance Decomposition (PMVD) method to determine the key service attributes related to consumer satisfaction. Taking 96,322 online reviews of 1398 hotels across four types in Beijing as the empirical validation, this paper highlights the noteworthy discrepancies in key service attributes across different hotel types and reveals the significant dimensions associated with these key service attributes. Furthermore, the calculated PMVD scores serve as valuable references for service managers in determining the ultimate improvement priorities. Overall, our findings emphasize the importance of identifying key service attribute through online review opinion mining, which help avoid wasting resources on service attributes that are repeatedly mentioned in online reviews but are not actually related to consumer satisfaction. The proposed method offers a comprehensive and effective measurement of service quality from various perspectives, providing practical insights for service providers seeking to enhance their performance.

Suggested Citation

  • Zhang, Chenxi & Xu, Zeshui, 2024. "Gaining insights for service improvement through unstructured text from online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:joreco:v:80:y:2024:i:c:s0969698924001942
    DOI: 10.1016/j.jretconser.2024.103898
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    References listed on IDEAS

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    1. Jin, Wangyan & Chen, Yuangao & Yang, Shuiqing & Zhou, Shasha & Jiang, Hui & Wei, June, 2023. "Personalized managerial response and negative inconsistent review helpfulness: The mediating effect of perceived response helpfulness," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    2. Zhai, Mengfan & Wang, Xinyue & Zhao, Xijie, 2024. "The importance of online customer reviews characteristics on remanufactured product sales: Evidence from the mobile phone market on Amazon.com," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    3. Yang Liu & Jian-Wu Bi & Zhi-Ping Fan, 2017. "A Method for Ranking Products Through Online Reviews Based on Sentiment Classification and Interval-Valued Intuitionistic Fuzzy TOPSIS," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1497-1522, November.
    4. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    5. Song, Yongming & Li, Guangxu & Li, Tie & Li, Yanhong, 2021. "A purchase decision support model considering consumer personalization about aspirations and risk attitudes," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    6. Wang, Zheng & Wang, Lun & Ji, Ying & Zuo, Lulu & Qu, Shaojian, 2022. "A novel data-driven weighted sentiment analysis based on information entropy for perceived satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    7. Jian-Wu Bi & Yang Liu & Zhi-Ping Fan & Erik Cambria, 2019. "Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 7068-7088, November.
    8. Jin Zhang & Cong Wang & Guoqing Chen, 2021. "A Review Selection Method for Finding an Informative Subset from Online Reviews," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 280-299, January.
    9. Navid Aghakhani & Onook Oh & Dawn G. Gregg & Jahangir Karimi, 2021. "Online Review Consistency Matters: An Elaboration Likelihood Model Perspective," Information Systems Frontiers, Springer, vol. 23(5), pages 1287-1301, September.
    10. Martí Bigorra, Anna & Isaksson, Ove & Karlberg, Magnus, 2019. "Aspect-based Kano categorization," International Journal of Information Management, Elsevier, vol. 46(C), pages 163-172.
    11. Ismagilova, Elvira & Dwivedi, Yogesh K. & Slade, Emma, 2020. "Perceived helpfulness of eWOM: Emotions, fairness and rationality," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    12. Kong, Juan & Lou, Chen, 2023. "Do cultural orientations moderate the effect of online review features on review helpfulness? A case study of online movie reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    13. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    14. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    15. Ahani, Ali & Nilashi, Mehrbakhsh & Yadegaridehkordi, Elaheh & Sanzogni, Louis & Tarik, A. Rashid & Knox, Kathy & Samad, Sarminah & Ibrahim, Othman, 2019. "Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 331-343.
    16. Tian, Wei & Liu, Yunliang & Heo, Yeonsook & Yan, Da & Li, Zhanyong & An, Jingjing & Yang, Song, 2016. "Relative importance of factors influencing building energy in urban environment," Energy, Elsevier, vol. 111(C), pages 237-250.
    17. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
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