IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v342y2024i3d10.1007_s10479-023-05594-x.html
   My bibliography  Save this article

Importance-performance analysis to develop product/service improvement strategies through online reviews with reliability

Author

Listed:
  • Xingli Wu

    (Sichuan University)

  • Huchang Liao

    (Zhejiang Gongshang University)

  • Chonghui Zhang

    (Zhejiang Gongshang University)

Abstract

Online reviews are important data for developing product/service improvement strategies. Relevant studies treated different online reviews as equally important, and the validity of the results was vulnerable to unreliable online reviews. To solve this challenge, this study proposes an importance-performance analysis model that considers the reliability of online reviews. First, the reliability degree of online reviews is defined based on the quality and timeliness of online reviews and the credibility of reviewers. To estimate the importance of product/service attributes from online reviews, a preference learning model is designed based on the reliability degrees of online reviews, where the online reviews with higher reliability have a greater impact on the learning results. In addition, the attribute performance is determined by aggregating the satisfaction of online reviews for the attribute. Finally, we verify the practicability of the proposed importance-performance analysis model by a case study on four five-star hotels.

Suggested Citation

  • Xingli Wu & Huchang Liao & Chonghui Zhang, 2024. "Importance-performance analysis to develop product/service improvement strategies through online reviews with reliability," Annals of Operations Research, Springer, vol. 342(3), pages 1905-1924, November.
    • Handle: RePEc:spr:annopr:v:342:y:2024:i:3:d:10.1007_s10479-023-05594-x
    DOI: 10.1007/s10479-023-05594-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-023-05594-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-023-05594-x?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. Phadermrod, Boonyarat & Crowder, Richard M. & Wills, Gary B., 2019. "Importance-Performance Analysis based SWOT analysis," International Journal of Information Management, Elsevier, vol. 44(C), pages 194-203.
    2. Keeney,Ralph L. & Raiffa,Howard, 1993. "Decisions with Multiple Objectives," Cambridge Books, Cambridge University Press, number 9780521438834, November.
    3. Wieland, M.L. & Wu, S.T. & Kaggal, V.C. & Yawn, B.P., 2013. "Tracking health disparities through natural-language processing," American Journal of Public Health, American Public Health Association, vol. 103(3), pages 448-449.
    4. Yolande Piris & Anne-Cécile Gay, 2021. "Customer satisfaction and natural language processing," Post-Print hal-03110702, HAL.
    5. Chen, Jinyan & Becken, Susanne & Stantic, Bela, 2022. "Assessing destination satisfaction by social media: An innovative approach using Importance-Performance Analysis," Annals of Tourism Research, Elsevier, vol. 93(C).
    6. Piris, Yolande & Gay, Anne-Cécile, 2021. "Customer satisfaction and natural language processing," Journal of Business Research, Elsevier, vol. 124(C), pages 264-271.
    7. Wieland, M.L. & Wu, S.T. & Kaggal, V.C. & Yawn, B.P., 2013. "Tracking health disparities through natural-language processing," American Journal of Public Health, American Public Health Association, vol. 103(3), pages 448-449.
    8. Grigoroudis, E. & Siskos, Y., 2002. "Preference disaggregation for measuring and analysing customer satisfaction: The MUSA method," European Journal of Operational Research, Elsevier, vol. 143(1), pages 148-170, November.
    9. Gabriela D. Oliveira & Luis C. Dias, 2020. "The potential learning effect of a MCDA approach on consumer preferences for alternative fuel vehicles," Annals of Operations Research, Springer, vol. 293(2), pages 767-787, October.
    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. Luis C. Dias & Gabriela D. Oliveira & Paula Sarabando, 2021. "Choice-based preference disaggregation concerning vehicle technologies," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 177-200, March.
    2. J-B Yang & D-L Xu & X Xie & A K Maddulapalli, 2011. "Multicriteria evidential reasoning decision modelling and analysis—prioritizing voices of customer," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1638-1654, September.
    3. Nilashi, Mehrbakhsh & Abumalloh, Rabab Ali & Samad, Sarminah & Alrizq, Mesfer & Alyami, Sultan & Abosaq, Hamad & Alghamdi, Abdullah & Akib, Noor Adelyna Mohammed, 2022. "Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques," Technology in Society, Elsevier, vol. 71(C).
    4. Baehre, Sven & O'Dwyer, Michele & O'Malley, Lisa & Story, Vicky M, 2022. "Customer mindset metrics: A systematic evaluation of the net promoter score (NPS) vs. alternative calculation methods," Journal of Business Research, Elsevier, vol. 149(C), pages 353-362.
    5. Wu, Jie & Zhao, Narisa & Yang, Tong, 2024. "Wisdom of crowds: SWOT analysis based on hybrid text mining methods using online reviews," Journal of Business Research, Elsevier, vol. 171(C).
    6. Gabriela D. Oliveira & Luis C. Dias, 2020. "The potential learning effect of a MCDA approach on consumer preferences for alternative fuel vehicles," Annals of Operations Research, Springer, vol. 293(2), pages 767-787, October.
    7. Villarroel Ordenes, Francisco & Silipo, Rosaria, 2021. "Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications," Journal of Business Research, Elsevier, vol. 137(C), pages 393-410.
    8. Gül Yazıcı & Tuğçe Ozansoy Çadırcı, 2024. "Creating meaningful insights from customer reviews: a methodological comparison of topic modeling algorithms and their use in marketing research," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 865-887, December.
    9. Christopher Gerling & Stefan Lessmann, 2024. "Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap Analysis," Papers 2411.14463, arXiv.org.
    10. KARRI PASANEN & MIKKO KURTTILA & JOUNI PYKÄlÄINEN & JYRKI KANGAS & PEKKA LESKINEN, 2005. "Mesta — Non-Industrial Private Forest Owners' Decision-Support Environment For The Evaluation Of Alternative Forest Plans Over The Internet," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(04), pages 601-620.
    11. Gerd Gigerenzer, 1997. "Bounded Rationality: Models of Fast and Frugal Inference," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 133(II), pages 201-218, June.
    12. Shuang Liu & Kirsten Maclean & Cathy Robinson, 2019. "A cost-effective framework to prioritise stakeholder participation options," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 221-241, November.
    13. Smith, Chris M. & Shaw, Duncan, 2019. "The characteristics of problem structuring methods: A literature review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 403-416.
    14. Chorus, Caspar & van Cranenburgh, Sander & Daniel, Aemiro Melkamu & Sandorf, Erlend Dancke & Sobhani, Anae & Szép, Teodóra, 2021. "Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 28-44.
    15. Perrels, Adriaan & Molarius, Riitta & Porthin, Markus & Rosqvist, Tony, 2008. "Testing a Flood Protection Case by Means of a Group Decision Support System," Discussion Papers 449, VATT Institute for Economic Research.
    16. Ahrens, Heinz & Kantelhardt, Jochen, 2007. "Integrating Ecological And Economic Aspects In Land Use Concepts: Some Conclusions From A Regional Land Use Concept For Bayerisches Donauried," 81st Annual Conference, April 2-4, 2007, Reading University, UK 7986, Agricultural Economics Society.
    17. Baudry, Gino & Delrue, Florian & Legrand, Jack & Pruvost, Jérémy & Vallée, Thomas, 2017. "The challenge of measuring biofuel sustainability: A stakeholder-driven approach applied to the French case," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 933-947.
    18. Lupo, Toni, 2015. "Fuzzy ServPerf model combined with ELECTRE III to comparatively evaluate service quality of international airports in Sicily," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 249-259.
    19. Guo, Mengzhuo & Zhang, Qingpeng & Liao, Xiuwu & Chen, Frank Youhua & Zeng, Daniel Dajun, 2021. "A hybrid machine learning framework for analyzing human decision-making through learning preferences," Omega, Elsevier, vol. 101(C).
    20. Yuval Rottenstreich & Alex Markle & Johannes Müller-Trede, 2023. "Risky Sure Things," Management Science, INFORMS, vol. 69(8), pages 4707-4720, August.

    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:spr:annopr:v:342:y:2024:i:3:d:10.1007_s10479-023-05594-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.