IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v150y2022icp676-689.html
   My bibliography  Save this article

Text analysis of online customer reviews for products in the FCB quadrants: Procedure, outcomes, and implications

Author

Listed:
  • Kim, Taeyong
  • Hwang, Seungsoo
  • Kim, Minkyung

Abstract

The present researchers performed a study to present a detailed procedure of full-scale text analyses of online customer reviews (OCRs) and display an exemplary trial of machine-learning-based generation of “selling phrases” from a subset of the same database. As an effort to overcome the limitations of past studies, the present study expanded the scope of target products from one or a few to 16, based on the classic and long-held product classification scheme of the FCB Grid, to obtain broad understanding of the general market. The results are thought to have significant practical values for marketers, consumers, and marketplace platforms. Of particular note, the total of 48 word clouds yielded results that are overall consistent with what the FCB Grid suggests, and the phrases generated through the machine-learning algorithm were considered to have potential to supplement or substitute for the earlier step(s) of the “attributes-consequences-values” chain in the popular Means-End Chain (MEC) model. Also discussed extensively are the immense value of OCR data, as they are more “real,” “natural,” “fair,” and “live” than those acquired from laboratories or through formal surveys or interviews, and the possibility that the data could be utilized for revisiting and reconfirming existing beliefs in marketing research.

Suggested Citation

  • Kim, Taeyong & Hwang, Seungsoo & Kim, Minkyung, 2022. "Text analysis of online customer reviews for products in the FCB quadrants: Procedure, outcomes, and implications," Journal of Business Research, Elsevier, vol. 150(C), pages 676-689.
    • Handle: RePEc:eee:jbrese:v:150:y:2022:i:c:p:676-689
    DOI: 10.1016/j.jbusres.2022.05.077
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296322005227
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2022.05.077?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ruiz-Mafe, Carla & Chatzipanagiotou, Kalliopi & Curras-Perez, Rafael, 2018. "The role of emotions and conflicting online reviews on consumers' purchase intentions," Journal of Business Research, Elsevier, vol. 89(C), pages 336-344.
    2. Jonah Berger & Alan T. Sorensen & Scott J. Rasmussen, 2010. "Positive Effects of Negative Publicity: When Negative Reviews Increase Sales," Marketing Science, INFORMS, vol. 29(5), pages 815-827, 09-10.
    3. Kristine de Valck & Roberts V. Kozinets & Andrea C. Wojnicki & Sarah J.S. Wilner, 2010. "Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities," Post-Print hal-00458424, HAL.
    4. Loureiro, Sandra M.C. & Cavallero, Luisa & Miranda, Francisco Javier, 2018. "Fashion brands on retail websites: Customer performance expectancy and e-word-of-mouth," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 131-141.
    5. Botschen, Gunther & Hemetsberger, Andrea, 1998. "Diagnosing Means-End Structures to Determine the Degree of Potential Marketing Program Standardization," Journal of Business Research, Elsevier, vol. 42(2), pages 151-159, June.
    6. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Grenoble Ecole de Management (Post-Print) halshs-01923243, HAL.
    7. Kostyra, Daniel S. & Reiner, Jochen & Natter, Martin & Klapper, Daniel, 2016. "Decomposing the effects of online customer reviews on brand, price, and product attributes," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 11-26.
    8. Martin, William C. & Lueg, Jason E., 2013. "Modeling word-of-mouth usage," Journal of Business Research, Elsevier, vol. 66(7), pages 801-808.
    9. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    10. Nath, Prithwiraj & Devlin, James & Reid, Veronica, 2018. "The effects of online reviews on service expectations: Do cultural value orientations matter?," Journal of Business Research, Elsevier, vol. 90(C), pages 123-133.
    11. Zhu, Yongmin & Liu, Miaomiao & Zeng, Xiaohua & Huang, Pei, 2020. "The effects of prior reviews on perceived review helpfulness: A configuration perspective," Journal of Business Research, Elsevier, vol. 110(C), pages 484-494.
    12. Kichan Nam & Jeff Baker & Norita Ahmad & Jahyun Goo, 2020. "Dissatisfaction, Disconfirmation, and Distrust: an Empirical Examination of Value Co-Destruction through Negative Electronic Word-of-Mouth (eWOM)," Information Systems Frontiers, Springer, vol. 22(1), pages 113-130, February.
    13. Tata, Sai Vijay & Prashar, Sanjeev & Gupta, Sumeet, 2020. "An examination of the role of review valence and review source in varying consumption contexts on purchase decision," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    14. En-Gir Kim & Se-Hak Chun, 2019. "Analyzing Online Car Reviews Using Text Mining," Sustainability, MDPI, vol. 11(6), pages 1-22, March.
    15. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Post-Print hal-03511272, HAL.
    16. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 22(5), pages 1203-1226, October.
    17. Sparks, Beverley A. & Browning, Victoria, 2011. "The impact of online reviews on hotel booking intentions and perception of trust," Tourism Management, Elsevier, vol. 32(6), pages 1310-1323.
    18. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Post-Print halshs-01923243, HAL.
    19. Fong, John & Burton, Suzan, 2008. "A cross-cultural comparison of electronic word-of-mouth and country-of-origin effects," Journal of Business Research, Elsevier, vol. 61(3), pages 233-242, March.
    20. Kim, Junyong & Gupta, Pranjal, 2012. "Emotional expressions in online user reviews: How they influence consumers' product evaluations," Journal of Business Research, Elsevier, vol. 65(7), pages 985-992.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    2. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 22(5), pages 1203-1226, October.
    3. Nath, Prithwiraj & Devlin, James & Reid, Veronica, 2018. "The effects of online reviews on service expectations: Do cultural value orientations matter?," Journal of Business Research, Elsevier, vol. 90(C), pages 123-133.
    4. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 0. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    5. Cai, Xiaowei & Cebollada, Javier & Cortiñas, Mónica, 2023. "Impact of seller- and buyer-created content on product sales in the electronic commerce platform: The role of informativeness, readability, multimedia richness, and extreme valence," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    6. Filieri, Raffaele & Lin, Zhibin & Pino, Giovanni & Alguezaui, Salma & Inversini, Alessandro, 2021. "The role of visual cues in eWOM on consumers’ behavioral intention and decisions," Journal of Business Research, Elsevier, vol. 135(C), pages 663-675.
    7. Yi Luo & Xiaowei Xu, 2019. "Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    8. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    9. Surachartkumtonkun, Jiraporn (Nui) & Grace, Debra & Ross, Mitchell, 2021. "Unfair customer reviews: Third-party perceptions and managerial responses," Journal of Business Research, Elsevier, vol. 132(C), pages 631-640.
    10. Jin Li & Yulan Zhang & Jianping Li & Jiangze Du, 2023. "The Role of Sentiment Tendency in Affecting Review Helpfulness for Durable Products: Nonlinearity and Complementarity," Information Systems Frontiers, Springer, vol. 25(4), pages 1459-1477, August.
    11. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    12. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    13. Colmekcioglu, Nazan & Marvi, Reza & Foroudi, Pantea & Okumus, Fevzi, 2022. "Generation, susceptibility, and response regarding negativity: An in-depth analysis on negative online reviews," Journal of Business Research, Elsevier, vol. 153(C), pages 235-250.
    14. Osterbrink Lars & Alpar Paul & Seher Alexander, 2020. "Influence of Images in Online Reviews for Search Goods on Helpfulness," Review of Marketing Science, De Gruyter, vol. 18(1), pages 43-73, September.
    15. Hou, Lei, 2022. "Network versus content: The effectiveness in identifying opinion leaders in an online social network with empirical evaluation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    16. Rita, Paulo & Moro, Sérgio & Cavalcanti, Gabriel, 2022. "The impact of COVID-19 on tourism: Analysis of online reviews in the airlines sector," Journal of Air Transport Management, Elsevier, vol. 104(C).
    17. Yi Feng & Yunqiang Yin & Dujuan Wang & Lalitha Dhamotharan & Joshua Ignatius & Ajay Kumar, 2023. "Diabetic patient review helpfulness: unpacking online drug treatment reviews by text analytics and design science approach," Annals of Operations Research, Springer, vol. 328(1), pages 387-418, September.
    18. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    19. Sangjae Lee & Kun Chang Lee & Joon Yeon Choeh, 2020. "Using Bayesian Network to Predict Online Review Helpfulness," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    20. Román, Sergio & Riquelme, Isabel P. & Iacobucci, Dawn, 2024. "Antecedents and consequences of perceived helpfulness of extremely positive and exaggerated reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:150:y:2022:i:c:p:676-689. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.