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Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors

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  • Xu, Xun
  • Wang, Xuequn
  • Li, Yibai
  • Haghighi, Mohammad

Abstract

With the rapid development of information technology, customers not only shop online—they also post reviews on social media. This user-generated content (UGC) can be useful to understand customers’ shopping experiences and influence future customers’ purchase intentions. Therefore, business intelligence and analytics are increasingly being advocated as a way to analyze customers’ UGC in social media and support firms’ marketing activities. However, because of its open structure, UGC such as customer reviews can be difficult to analyze, and firms find it challenging to harness UGC. To fill this gap, this study aims to examine customer satisfaction and dissatisfaction toward attributes of hotel products and services based on online customer textual reviews. Using a text mining approach, latent semantic analysis (LSA), we identify the key attributes driving customer satisfaction and dissatisfaction toward hotel products and service attributes. Additionally, using a regression approach, we examine the effects of travel purposes, hotel types, star level, and editor recommendations on customers’ perceptions of attributes of hotel products and services. This study bridges customer online textual reviews with customers’ perceptions to help business managers better understand customers’ needs through UGC.

Suggested Citation

  • Xu, Xun & Wang, Xuequn & Li, Yibai & Haghighi, Mohammad, 2017. "Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors," International Journal of Information Management, Elsevier, vol. 37(6), pages 673-683.
    • Handle: RePEc:eee:ininma:v:37:y:2017:i:6:p:673-683
    DOI: 10.1016/j.ijinfomgt.2017.06.004
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    Cited by:

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    2. Ferreira Ana & Correia Marisol B. & Renda Ana Isabel, 2024. "Systematic Literature Review on the Profile of Tourists in Four- and Five-star Hotels Based on Online Reviews," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 14(1), pages 20-34.
    3. Nilashi, Mehrbakhsh & Abumalloh, Rabab Ali & Samad, Sarminah & Alrizq, Mesfer & Alyami, Sultan & Alghamdi, Abdullah, 2023. "Analysis of customers' satisfaction with baby products: The moderating role of brand image," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    4. Martí Bigorra, Anna & Isaksson, Ove & Karlberg, Magnus, 2019. "Aspect-based Kano categorization," International Journal of Information Management, Elsevier, vol. 46(C), pages 163-172.
    5. Saito, Taiga & Takahashi, Akihiko & Koide, Noriaki & Ichifuji, Yu, 2019. "Application of online booking data to hotel revenue management," International Journal of Information Management, Elsevier, vol. 46(C), pages 37-53.
    6. Yang, Bai & Liu, Ying & Liang, Yan & Tang, Min, 2019. "Exploiting user experience from online customer reviews for product design," International Journal of Information Management, Elsevier, vol. 46(C), pages 173-186.
    7. Moro, Sérgio & Ramos, Pedro & Esmerado, Joaquim & Jalali, Seyed Mohammad Jafar, 2019. "Can we trace back hotel online reviews’ characteristics using gamification features?," International Journal of Information Management, Elsevier, vol. 44(C), pages 88-95.
    8. Jaklič, Jurij & Grublješič, Tanja & Popovič, Aleš, 2018. "The role of compatibility in predicting business intelligence and analytics use intentions," International Journal of Information Management, Elsevier, vol. 43(C), pages 305-318.
    9. Jimenez-Marquez, Jose Luis & Gonzalez-Carrasco, Israel & Lopez-Cuadrado, Jose Luis & Ruiz-Mezcua, Belen, 2019. "Towards a big data framework for analyzing social media content," International Journal of Information Management, Elsevier, vol. 44(C), pages 1-12.
    10. Zhao, Lu & Zhang, Mingli & Tu, Jianbo & Li, Jialing & Zhang, Yan, 2023. "Can users embed their user experience in user-generated images? Evidence from JD.com," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    11. Kejia Chen & Jian Jin & Zheng Zhao & Ping Ji, 2022. "Understanding customer regional differences from online opinions: a hierarchical Bayesian approach," Electronic Commerce Research, Springer, vol. 22(2), pages 377-403, June.

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