IDEAS home Printed from https://ideas.repec.org/a/eee/joreco/v82y2025ics0969698924004314.html
   My bibliography  Save this article

Examining product quality and competitiveness via online reviews: An integrated approach of importance performance competitor analysis and Kano model

Author

Listed:
  • Kim, Su-Ah
  • Park, Sohyun
  • Kwak, Minjung
  • Kang, Changmuk

Abstract

Understanding and enhancing product quality and competitiveness are vital in today's market. By leveraging the significant potential of online reviews for market-wide consumer research and strategic quality enhancement, this study introduces a novel review analytics approach that combines Importance Performance Competitor Analysis (IPCA) with the Kano model to evaluate and improve product attributes. The proposed Kano-Weighted IPCA (KWIPCA) framework assesses the importance and performance of product attributes by independently weighting positive and negative reviews according to the principles of the Kano model, while also considering multiple competitors in the market. The final output is a user-friendly, four-quadrant matrix that provides comprehensive insights into the market importance of attributes, their relative achievement, and competitors' performance distribution. This KWIPCA matrix enables the evaluation of a product's strengths and weaknesses relative to competitors, identification of critical areas needing immediate improvement, and offers strategic guidance for enhancing overall customer satisfaction. To demonstrate and validate the KWIPCA framework, a case study was conducted using 8567 reviews of 22 five-star hotels in Seoul, South Korea. The results confirm the KWIPCA's effectiveness in providing actionable insights and competitive benchmarking.

Suggested Citation

  • Kim, Su-Ah & Park, Sohyun & Kwak, Minjung & Kang, Changmuk, 2025. "Examining product quality and competitiveness via online reviews: An integrated approach of importance performance competitor analysis and Kano model," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:joreco:v:82:y:2025:i:c:s0969698924004314
    DOI: 10.1016/j.jretconser.2024.104135
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969698924004314
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretconser.2024.104135?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. Seungju Nam & Hyun Cheol Lee, 2019. "A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service," Sustainability, MDPI, vol. 11(21), pages 1-24, November.
    2. Zhang, Dianfeng & Shen, Zifan & Li, Yanlai, 2023. "Requirement analysis and service optimization of multiple category fresh products in online retailing using importance-Kano analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    3. Anning Wang & Qiang Zhang & Shuangyao Zhao & Xiaonong Lu & Zhanglin Peng, 2020. "A review-driven customer preference measurement model for product improvement: sentiment-based importance–performance analysis," Information Systems and e-Business Management, Springer, vol. 18(1), pages 61-88, March.
    4. 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.
    5. 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).
    6. Zhang, Lei & Xuan, Yan & Li, Ziqi & Gao, Pengfei & Zheng, Yu, 2024. "How to obtain customer requirements for each stage of the product life cycle from online reviews: Using mobile phones as an example," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    7. Martí Bigorra, Anna & Isaksson, Ove & Karlberg, Magnus, 2019. "Aspect-based Kano categorization," International Journal of Information Management, Elsevier, vol. 46(C), pages 163-172.
    8. 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).
    9. Hao Yang & Yunxiang Huo & Ruoyu Jia & Feng Sha & Naiqi Hu & Linglan Yu & Yueran Wang, 2022. "Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
    10. Jiacong Wu & Yu Wang & Ru Zhang & Jing Cai, 2018. "An Approach to Discovering Product/Service Improvement Priorities: Using Dynamic Importance-Performance Analysis," Sustainability, MDPI, vol. 10(10), pages 1-26, October.
    11. Kumar, Avinash & Chakraborty, Shibashish & Bala, Pradip Kumar, 2023. "Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    12. Junegak Joung & Harrison M. Kim, 2022. "Explainable neural network-based approach to Kano categorisation of product features from online reviews," International Journal of Production Research, Taylor & Francis Journals, vol. 60(23), pages 7053-7073, December.
    13. Ma, Bohao & Wong, Yiik Diew & Teo, Chee-Chong & Wang, Ziyan, 2024. "Enhance understandings of Online Food Delivery's service quality with online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    14. Jeong, Byeongki & Yoon, Janghyeok & Lee, Jae-Min, 2019. "Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis," International Journal of Information Management, Elsevier, vol. 48(C), pages 280-290.
    15. Tontini, Gerson, 2016. "Identifying opportunities for improvement in online shopping sites," Journal of Retailing and Consumer Services, Elsevier, vol. 31(C), pages 228-238.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Siling Yang & Hao Chen & Puwei Zhang & Tengfei Zhao & Yang Zhang, 2025. "Evaluating the Value Relationships of Stakeholders in Urban Land Redevelopment: A Study Based on Stakeholder Value Network and Adversarial Interpretive Structure Modeling," Sustainability, MDPI, vol. 17(10), pages 1-23, May.

    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. Ray, Rajeev Kumar & Singh, Amit, 2025. "From online reviews to smartwatch recommendation: An integrated aspect-based sentiment analysis framework," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    2. Wang, Zhenfei & Pan, Younghwan, 2025. "From popularity to pitfalls: Diagnosing consumer issues and engineering solutions for an ink-inspired mobile theme system," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    3. Wang, Xiaoli & Zhang, Chenxi & Xu, Zeshui, 2024. "A product recommendation model based on online reviews: Improving PageRank algorithm considering attribute weights," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    4. Sharan Srinivas & Surya Ramachandiran, 2024. "Passenger intelligence as a competitive opportunity: unsupervised text analytics for discovering airline-specific insights from online reviews," Annals of Operations Research, Springer, vol. 333(2), pages 1045-1075, February.
    5. Zeyu Chen & Stephanie Hui-Wen Chuah & Kandappan Balasubramanian, 2025. "Be Smart, but Not Humanless? Prioritizing the Improvement of Service Attributes in Smart Hotels Based on an Online Reviews-Driven Method," Sustainability, MDPI, vol. 17(9), pages 1-22, April.
    6. Hanyang Luo & Wugang Song & Wanhua Zhou & Xudong Lin & Sumin Yu, 2023. "An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content," Sustainability, MDPI, vol. 15(18), pages 1-29, September.
    7. Wu, Xingli & Liao, Huchang, 2025. "Determining investment allocation strategies to improve consumer satisfaction based on a preference learning model," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    8. Kumar, Anand & Bala, Pradip Kumar & Chakraborty, Shibashish & Behera, Rajat Kumar, 2024. "Exploring antecedents impacting user satisfaction with voice assistant app: A text mining-based analysis on Alexa services," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    9. 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).
    10. Yi, Jisu & Oh, Yun Kyung & Kim, Jung-Min, 2025. "Unveiling the drivers of satisfaction in mobile trading: Contextual mining of retail investor experience through BERTopic and generative AI," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    11. Wang, Binni & Wang, Pong & Tu, Yiliu, 2021. "Customer satisfaction service match and service quality-based blockchain cloud manufacturing," International Journal of Production Economics, Elsevier, vol. 240(C).
    12. Xue Gong & Zhuen Guo & Zhongqu Xie, 2022. "Using Kansei Engineering for the Design Thinking Framework: Bamboo Pen Holder Product Design," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    13. Nasrabadi, Mohamadreza Azar & Beauregard, Yvan & Ekhlassi, Amir, 2024. "The implication of user-generated content in new product development process: A systematic literature review and future research agenda," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    14. Nasiri, Mohammad Sadegh & Shokouhyar, Sajjad, 2021. "Actual consumers' response to purchase refurbished smartphones: Exploring perceived value from product reviews in online retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    15. Jabbour Al Maalouf, Nada & Sayegh, Elie & Makhoul, Wissam & Sarkis, Nada, 2025. "Consumers' attitudes and purchase intentions toward food ordering via online platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    16. Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
    17. Maria Rostasova & Anna Padourova & Tatiana Corejova, 2020. "KANO model as a tool of effective customer satisfaction diagnostics of postal services," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 811-828, December.
    18. Liu, Xiao & Li, Ming-Yang, 2024. "Sustainable service product design method: Focus on customer demands and triple bottom line," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    19. Basu, Bibaswan & Chakraborty, Debarun & Kumar Kar, Arpan, 2024. "How can we improve buyer experiences for the operations of e-grocery platforms? Towards a unified framework for digital service operations," International Journal of Production Economics, Elsevier, vol. 278(C).
    20. 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).

    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:joreco:v:82:y:2025:i:c:s0969698924004314. 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: https://www.journals.elsevier.com/journal-of-retailing-and-consumer-services .

    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.