IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v13y2023i1p21582440231156897.html
   My bibliography  Save this article

Consumers’ Concerns Regarding Product Quality: Evidence From Chinese Online Reviews

Author

Listed:
  • Xian Wang
  • Huixian Li
  • Qingyi Wang
  • Alison Noble

Abstract

Customers’ concerns regarding product quality (CPQs), as expressed in online reviews, provide future customers with information that may influence their own purchase decisions. Recent research attaches considerable importance to consumers’ emotions embedded in their language style, based on function words, but rarely touches on their CPQs, which are also embedded in their language style. Therefore, this study aims to examine the CPQs of Chinese women’s clothes buyers. We build a corpus of 32,667 words from the online reviews of women’s clothes buyers on the Chinese website Taobao. A novel language inquiry word count dictionary for topic types related to product quality is built, which is then imported into a language style matching algorithm which assesses the synchrony of the language style. Our results show that buyers’ CPQs are diverse and concentrated in scope; consumers’ different expressions in online reviews are dependent on the sequence and impact factor of CPQs embedded in their language style. This study’s findings offer companies valuable insights to identify customers’ CPQs based on their language style, which may allow them to devise more effective promotion strategies and product designs.

Suggested Citation

  • Xian Wang & Huixian Li & Qingyi Wang & Alison Noble, 2023. "Consumers’ Concerns Regarding Product Quality: Evidence From Chinese Online Reviews," SAGE Open, , vol. 13(1), pages 21582440231, March.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:1:p:21582440231156897
    DOI: 10.1177/21582440231156897
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440231156897
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440231156897?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
    ---><---

    References listed on IDEAS

    as
    1. Antioco, Michael & Coussement, Kristof, 2018. "Misreading of consumer dissatisfaction in online product reviews: Writing style as a cause for bias," International Journal of Information Management, Elsevier, vol. 38(1), pages 301-310.
    2. Liu, Stephanie Q. & Bogicevic, Vanja & Mattila, Anna S., 2018. "Circular vs. angular servicescape: “Shaping” customer response to a fast service encounter pace," Journal of Business Research, Elsevier, vol. 89(C), pages 47-56.
    3. Wang, Fang & Karimi, Sahar, 2019. "This product works well (for me): The impact of first-person singular pronouns on online review helpfulness," Journal of Business Research, Elsevier, vol. 104(C), pages 283-294.
    4. Guo, Junpeng & Wang, Xiaopan & Wu, Yi, 2020. "Positive emotion bias: Role of emotional content from online customer reviews in purchase decisions," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    5. Michael Antioco & Kristof Coussement, 2018. "Misreading of consumer dissatisfaction in online product reviews: Writing style as a cause for bias," Post-Print hal-02572087, HAL.
    6. Prentice, Catherine & Wang, Xuequn & Loureiro, Sandra Maria Correia, 2019. "The influence of brand experience and service quality on customer engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 50-59.
    7. Srivastava, Vartika & Kalro, Arti D., 2019. "Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 33-50.
    8. Cheng, Yi-Hsiu & Ho, Hui-Yi, 2015. "Social influence's impact on reader perceptions of online reviews," Journal of Business Research, Elsevier, vol. 68(4), pages 883-887.
    9. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    10. 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.
    11. Prentice, Catherine & Han, Xiao Yun & Hua, Lian-Lian & Hu, Lin, 2019. "The influence of identity-driven customer engagement on purchase intention," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 339-347.
    12. Paul A. Pavlou & Angelika Dimoka, 2006. "The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation," Information Systems Research, INFORMS, vol. 17(4), pages 392-414, December.
    13. Ann Kronrod & Shai Danziger, 2013. ""Wii Will Rock You!" The Use and Effect of Figurative Language in Consumer Reviews of Hedonic and Utilitarian Consumption," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(4), pages 726-739.
    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. Lien Thi Kim Nguyen & Hao-Hsuan Chung & Kristine Velasquez Tuliao & Tom M. Y. Lin, 2020. "Using XGBoost and Skip-Gram Model to Predict Online Review Popularity," SAGE Open, , vol. 10(4), pages 21582440209, December.
    3. Lutz, Bernhard & Pröllochs, Nicolas & Neumann, Dirk, 2022. "Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentation," Journal of Business Research, Elsevier, vol. 144(C), pages 888-901.
    4. Guha Majumder, Madhumita & Dutta Gupta, Sangita & Paul, Justin, 2022. "Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis," Journal of Business Research, Elsevier, vol. 150(C), pages 147-164.
    5. 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).
    6. Meek, Stephanie & Wilk, Violetta & Lambert, Claire, 2021. "A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews," Journal of Business Research, Elsevier, vol. 125(C), pages 354-367.
    7. 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.
    8. Wang, Fang & Du, Zhao & Wang, Shan, 2023. "Information multidimensionality in online customer reviews," Journal of Business Research, Elsevier, vol. 159(C).
    9. Bi, Sheng & Liu, Zhiying & Usman, Khalid, 2017. "The influence of online information on investing decisions of reward-based crowdfunding," Journal of Business Research, Elsevier, vol. 71(C), pages 10-18.
    10. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    11. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    12. Yanni Ping & Chelsey Hill & Yun Zhu & Jorge Fresneda, 2023. "Antecedents and consequences of the key opinion leader status: an econometric and machine learning approach," Electronic Commerce Research, Springer, vol. 23(3), pages 1459-1484, September.
    13. Munzel, Andreas, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 96-108.
    14. Ravula, Prashanth & Bhatnagar, Amit & Gauri, Dinesh K, 2023. "Role of gender in the creation and persuasiveness of online reviews," Journal of Business Research, Elsevier, vol. 154(C).
    15. Srivastava, Vartika & Kalro, Arti D., 2019. "Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 33-50.
    16. Yi, Jisu & Oh, Yun Kyung, 2022. "The informational value of multi-attribute online consumer reviews: A text mining approach," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    17. 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.
    18. Wu, Xiaoyue & Jin, Liyin & Xu, Qian, 2021. "Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews," Journal of Retailing, Elsevier, vol. 97(2), pages 238-250.
    19. Miyea Kim & Jeongsoo Han & Mina Jun, 2020. "Do same-level review ratings have the same level of review helpfulness? The role of information diagnosticity in online reviews," Information Technology & Tourism, Springer, vol. 22(4), pages 563-591, December.
    20. 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.

    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:sae:sagope:v:13:y:2023:i:1:p:21582440231156897. 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: SAGE Publications (email available below). General contact details of provider: .

    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.