IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v18y2020i1d10.1007_s10257-020-00463-7.html
   My bibliography  Save this article

A review-driven customer preference measurement model for product improvement: sentiment-based importance–performance analysis

Author

Listed:
  • Anning Wang

    (Hefei University of Technology
    Ministry of Education)

  • Qiang Zhang

    (Hefei University of Technology
    Ministry of Education)

  • Shuangyao Zhao

    (Hefei University of Technology
    Ministry of Education)

  • Xiaonong Lu

    (Hefei University of Technology
    Ministry of Education)

  • Zhanglin Peng

    (Hefei University of Technology
    Ministry of Education)

Abstract

An increasing number of people use social media to share their consumption experiences. Publicly available online reviews have become a significant source of information, which manufacturers use to better understand customer needs and preferences. To facilitate product improvement, this study first considers the inconsistencies between the numerical product ratings and the textual product reviews to establish the inconsistent ordered choice model (IOCM) for measuring customer preferences with regard to product features. The IOCM model effectively reduces the negative impact of inconsistent reviews on the quality of the customer preference measurement model. On the basis of customer preferences obtained from the IOCM model, we then develop a sentiment-based importance–performance analysis (SIPA) model to analyze the categorization of product features for guiding product development. Compared with the original IPA model, the proposed SIPA model in this paper introduces sentiment-importance into the IPA model that makes the product improvement more adaptive to customer preferences. Finally, we empirically evaluate the effectiveness of our proposed IOCM model and illustrate the utility of our proposed SIPA model.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:infsem:v:18:y:2020:i:1:d:10.1007_s10257-020-00463-7
    DOI: 10.1007/s10257-020-00463-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-020-00463-7
    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/s10257-020-00463-7?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. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    3. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    4. 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.
    5. Yi-Chun (Chad) Ho & Junjie Wu & Yong Tan, 2017. "Disconfirmation Effect on Online Rating Behavior: A Structural Model," Information Systems Research, INFORMS, vol. 28(3), pages 626-642, September.
    6. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    7. Halme, Merja & Kallio, Markku, 2011. "Estimation methods for choice-based conjoint analysis of consumer preferences," European Journal of Operational Research, Elsevier, vol. 214(1), pages 160-167, October.
    8. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.
    9. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    10. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    11. Chen, Chun-Chih & Chuang, Ming-Chuen, 2008. "Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design," International Journal of Production Economics, Elsevier, vol. 114(2), pages 667-681, August.
    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. Grazyna Rosa, 2021. "Customer Preferences with Regard to Correspondence from an Energy Company," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 43-55.

    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. 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.
    2. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2018. "User-Generated Content and Competing Firms’ Product Design," Management Science, INFORMS, vol. 64(10), pages 4608-4628, October.
    3. Kick, Markus, 2015. "Social Media Research: A Narrative Review," EconStor Preprints 182506, ZBW - Leibniz Information Centre for Economics.
    4. Peiyu Chen & Lorin M. Hitt & Yili Hong & Shinyi Wu, 2021. "Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application," Information Systems Research, INFORMS, vol. 32(4), pages 1470-1489, December.
    5. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2014. "Online Product Reviews: Implications for Retailers and Competing Manufacturers," Information Systems Research, INFORMS, vol. 25(1), pages 93-110, March.
    6. Li, Yiming & Li, Gang & Tayi, Giri Kumar & Cheng, T.C.E., 2019. "Omni-channel retailing: Do offline retailers benefit from online reviews?," International Journal of Production Economics, Elsevier, vol. 218(C), pages 43-61.
    7. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    8. Heeseung Andrew Lee & Angela Aerry Choi & Tianshu Sun & Wonseok Oh, 2021. "Reviewing Before Reading? An Empirical Investigation of Book-Consumption Patterns and Their Effects on Reviews and Sales," Information Systems Research, INFORMS, vol. 32(4), pages 1368-1389, December.
    9. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.
    10. Christoph Schneider & Markus Weinmann & Peter N.C. Mohr & Jan vom Brocke, 2021. "When the Stars Shine Too Bright: The Influence of Multidimensional Ratings on Online Consumer Ratings," Management Science, INFORMS, vol. 67(6), pages 3871-3898, June.
    11. Yabing Jiang & Hong Guo, 2015. "Design of Consumer Review Systems and Product Pricing," Information Systems Research, INFORMS, vol. 26(4), pages 714-730, December.
    12. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    13. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    14. 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.
    15. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    16. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    17. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers Dissertations 13, Paderborn University, Faculty of Business Administration and Economics.
    18. Hongpeng Wang & Rong Du & Jin Li & Weiguo Fan, 2020. "Subdivided or aggregated online review systems: Which is better for online takeaway vendors?," Electronic Commerce Research, Springer, vol. 20(4), pages 915-944, December.
    19. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers CIE 84, Paderborn University, CIE Center for International Economics.
    20. Foster, Joshua, 2022. "How rating mechanisms shape user search, quality inference and engagement in online platforms: Experimental evidence," Journal of Business Research, Elsevier, vol. 142(C), pages 791-807.

    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:infsem:v:18:y:2020:i:1:d:10.1007_s10257-020-00463-7. 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.