IDEAS home Printed from https://ideas.repec.org/a/eee/joreco/v90y2026ics0969698925004564.html

A dual-orientation framework integrating explicit and silent information for Mobile game improvement and publicity: An asymmetry and term-level CR analysis based on online reviews

Author

Listed:
  • Liu, Yanze
  • You, Tian-Hui
  • Cao, Bing-Bing
  • Zou, Junrong

Abstract

This study proposes a dual-orientation framework for mining customer requirements (CRs) from online reviews to support product improvement and publicity in mobile games. Existing research often overlooks silent information embedded in unmentioned CRs, limiting the effectiveness of strategic decision-making. Our framework integrates explicit and silent information through term-level asymmetry analysis. Specifically, BERTopic and BW-CNN are used to extract CRs and sentiments, while SHAP quantifies their influence on satisfaction. A four-quadrant perception model is developed to conduct asymmetry analysis of silent information, and the Kano model is applied to perform asymmetry analysis of explicit feedback. Finally, LIME-based term contribution analysis integrates these perspectives to evaluate the priorities of CR improvement and publicity. Experiments on 72,000 reviews from eight mobile games demonstrate the effectiveness of the proposed method in providing a comprehensive understanding of customer perceptions and supporting more targeted product improvement and publicity strategies.

Suggested Citation

  • Liu, Yanze & You, Tian-Hui & Cao, Bing-Bing & Zou, Junrong, 2026. "A dual-orientation framework integrating explicit and silent information for Mobile game improvement and publicity: An asymmetry and term-level CR analysis based on online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:joreco:v:90:y:2026:i:c:s0969698925004564
    DOI: 10.1016/j.jretconser.2025.104677
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969698925004564
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretconser.2025.104677?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Qiang & Liu, Meng & Chen, Zhen-Song & Yan, Wan-Mei & Jiang, Wen-Hui & Deveci, Muhammet, 2025. "How to sustainably improve passenger satisfaction of high-speed rail in China? A text mining and product service system integrated approach," Transport Policy, Elsevier, vol. 168(C), pages 244-262.
    2. 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).
    3. Son, Youngdoo & Kim, Wonjoon, 2023. "Development of methodology for classification of user experience (UX) in online customer review," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    4. Pham, Thi Tuan Linh & Liao, Gen-Yih & Shih, Sheng-Pao & Cheng, T.C.E. & Teng, Ching-I, 2024. "How gaming team participation fosters consumers’ social networks, communication and commitment," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    5. 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.
    6. 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).
    7. Zheng, Lu & Sun, Lin & He, Zhen & He, Shuguang, 2025. "Dynamic product quality improvement using social media data and competitor-based Kano model," International Journal of Production Economics, Elsevier, vol. 285(C).
    8. Huiya Xu & Ha-young Song, 2024. "Key Factors Influencing Chinese Consumers’ Demand for Naturally Dyed Garments: Data Analysis through KJ Method and KANO Model," Sustainability, MDPI, vol. 16(3), pages 1-28, January.
    9. Cao, Wenjie & Wang, Xinjian & Feng, Yuanjun & Zhou, Jingen & Yang, Zaili, 2026. "Improving maritime accident severity prediction accuracy: A holistic machine learning framework with data balancing and explainability techniques," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
    10. Li, An-Da & He, Zhen & Wang, Qing & Zhang, Yang & Ma, Yanhui, 2025. "A multi-objective evolutionary algorithm with mutual-information-guided improvement phase for feature selection in complex manufacturing processes," European Journal of Operational Research, Elsevier, vol. 323(3), pages 952-965.
    11. Zhang, Dianfeng & Liu, Jinfa & Hou, Xuefeng & Sun, Jiaqi & Zeng, Fu, 2025. "Research on requirement analysis and service improvement of online retail photovoltaic modules using the interval importance-Kano model," Applied Energy, Elsevier, vol. 385(C).
    12. Mueller, Alexander & Kuester, Sabine & von Janda, Sergej, 2025. "Socially (un)acceptable errors of AI: Consumer perceptions of different AI-induced errors," Journal of Business Research, Elsevier, vol. 201(C).
    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. Wu, Xingli & Liao, Huchang, 2025. "A preference learning method to estimate consumer preferences from online reviews," Journal of Business Research, Elsevier, vol. 201(C).
    2. Brand, Benedikt M. & Rese, Alexandra, 2026. "Introducing the adaptive “Dual Response†Kano method – conceptualization and empirical application," Journal of Retailing and Consumer Services, Elsevier, vol. 89(PA).
    3. 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).
    4. 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).
    5. Liu, Xiao & Li, Ming-Yang & Yuan, Duo-Ning, 2026. "Analyzing consumer satisfaction using Interpretive Structural Modeling driven by online reviews: An integrated approach," Journal of Business Research, Elsevier, vol. 205(C).
    6. 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.
    7. 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).
    8. Guo, Jian & Hu, Zhaolin & Tian, Bin & Wei, Jinxiang, 2025. "Modeling and optimizing routing problems with customer satisfaction under stochastic travel times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).
    9. 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).
    10. Ming-Tsang Lu & Hsi-Peng Lu & Chiao-Shan Chen, 2022. "Exploring the Key Priority Development Projects of Smart Transportation for Sustainability: Using Kano Model," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    11. Lionel Nicod & Élodie Mallor & Sylvie Llosa, 2023. "L’influence de l’aide à participer en magasin sur la satisfaction client : une approche par le modèle tétraclasse," Post-Print hal-04311121, HAL.
    12. Wen-Kuo Chen & Dalianus Riantama & Long-Sheng Chen, 2020. "Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    13. Won, Jisoo & Son, Jiyeong & Kwak, Minjung, 2025. "Review lifecycle analytics and importance–obsolescence analysis: A data-driven design approach to product circularity," Journal of Retailing and Consumer Services, Elsevier, vol. 85(C).
    14. Junegak Joung & Ki-Hun Kim & Kwangsoo Kim, 2021. "Data-Driven Approach to Dual Service Failure Monitoring From Negative Online Reviews: Managerial Perspective," SAGE Open, , vol. 11(1), pages 21582440209, January.
    15. Zhen He & Lu Zheng & Shuguang He, 2023. "A novel approach for product competitive analysis based on online reviews," Electronic Commerce Research, Springer, vol. 23(4), pages 2259-2290, December.
    16. Yuan Yuan & Tianhui You & Tian’ai Xu & Xun Yu, 2022. "Customer-Oriented Strategic Planning for Hotel Competitiveness Improvement Based on Online Reviews," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    17. Qiuying Chen & Shangyue Xu & Ronghui Liu & Qingquan Jiang, 2023. "Exploring the Discrepancy between Projected and Perceived Destination Images: A Cross-Cultural and Sustainable Analysis Using LDA Modeling," Sustainability, MDPI, vol. 15(12), pages 1-31, June.
    18. Pu, Zhongmin & Xu, Zeshui & Zhang, Chenxi & Zeng, Xiao-Jun & Gan, Weidong, 2025. "An online review-driven two-stage hotel recommendation model considering customers’ risk attitudes and personalized preferences," Omega, Elsevier, vol. 131(C).
    19. Li, Hui & Xiao, Jinli & Gan, Langxiong & Liu, Kang, 2026. "Prediction of navigation aid malfunction based on hash chain-optimized FP-growth and gradient boosting random forest," Reliability Engineering and System Safety, Elsevier, vol. 269(C).
    20. Li-Chen Cheng & Hsin-Yi Huang & Yu-Wei Huang, 2025. "Multi-task Chinese aspect-based sentiment analysis framework for service improvement: a case study on BNB reviews," Electronic Commerce Research, Springer, vol. 25(6), pages 4765-4798, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:90:y:2026:i:c:s0969698925004564. 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.