IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v25y2025i4d10.1007_s10660-023-09753-x.html
   My bibliography  Save this article

Lean persuasive design of electronic word-of-mouth (e-WOM) indexes for e-commerce stores based on fogg behavior model

Author

Listed:
  • Shugang Li

    (Shanghai University)

  • Fang Liu

    (Shanghai University)

  • Yuqi Zhang

    (Shanghai University)

  • Zhaoxu Yu

    (East China University of Science and Technology)

Abstract

Modeling the persuasiveness of electronic word-of-mouth (e-WOM) indexes helps e-sellers to implement lean persuasive design and shape consumers’ behaviors. This paper develops a quantitative and flexible Fogg Behavior Model for Consumer Purchase Decision-making (FBMCPD) to finely depict the non-linear and the threshold effect of the persuasiveness of e-WOM indexes during the three-stage consumers’ decision-making process. The FBMCPD captures the characteristics of decision-making in each stage including the Halo effect and loss aversion, by introducing various non-linear functions. A hybrid genetic algorithm–particle swarm optimization (GA-PSO) algorithm is proposed to find the model that fits best. Based on the FBMCPD, the four hierarchies of index importance are constructed and the lean improvement curves are plotted, providing guidelines for lean e-WOM indexes persuasive design for online stores. Using data from Taobao.com, the experiment results show that FBMCPD performs better in describing consumers’ purchase behavior and improving e-WOM indexes’ persuasive design.

Suggested Citation

  • Shugang Li & Fang Liu & Yuqi Zhang & Zhaoxu Yu, 2025. "Lean persuasive design of electronic word-of-mouth (e-WOM) indexes for e-commerce stores based on fogg behavior model," Electronic Commerce Research, Springer, vol. 25(4), pages 2463-2502, August.
    • Handle: RePEc:spr:elcore:v:25:y:2025:i:4:d:10.1007_s10660-023-09753-x
    DOI: 10.1007/s10660-023-09753-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-023-09753-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/s10660-023-09753-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

    for a different version of it.

    References listed on IDEAS

    as
    1. Reimer, Thomas & Benkenstein, Martin, 2016. "When good WOM hurts and bad WOM gains: The effect of untrustworthy online reviews," Journal of Business Research, Elsevier, vol. 69(12), pages 5993-6001.
    2. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    3. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    4. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
    5. Mastrobuoni, Giovanni & Peracchi, Franco & Tetenov, Aleksey, 2014. "Price as a Signal of Product Quality: Some Experimental Evidence," Journal of Wine Economics, Cambridge University Press, vol. 9(2), pages 135-152, August.
    6. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    7. Eline Jongmans & Florence Jeannot & Lan Liang & Maud Dampérat, 2022. "Impact of website visual design on user experience and website evaluation: The sequential mediating roles of usability and pleasure," Post-Print halshs-04159555, HAL.
    8. Alain Yee Loong Chong & Eugene Ch’ng & Martin J. Liu & Boying Li, 2017. "Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5142-5156, September.
    9. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2019. "Modeling Consumer Footprints on Search Engines: An Interplay with Social Media," Management Science, INFORMS, vol. 65(3), pages 1363-1385, March.
    10. repec:hal:journl:hal-04004419 is not listed on IDEAS
    11. 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.
    12. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
    13. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
    14. Zhen Li & Aoi Shimizu, 2018. "Impact of Online Customer Reviews on Sales Outcomes: An Empirical Study Based on Prospect Theory," The Review of Socionetwork Strategies, Springer, vol. 12(2), pages 135-151, December.
    15. Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
    16. Jyrki Wallenius & James S. Dyer & Peter C. Fishburn & Ralph E. Steuer & Stanley Zionts & Kalyanmoy Deb, 2008. "Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead," Management Science, INFORMS, vol. 54(7), pages 1336-1349, July.
    17. B.H. Schmitt & L. Zarantonello, 2013. "Consumer experience and experiential marketing: a critical review," Post-Print hal-00847411, HAL.
    18. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    19. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
    20. Vlachos, Ilias & Bogdanovic, Aleksandra, 2013. "Lean thinking in the European hotel industry," Tourism Management, Elsevier, vol. 36(C), pages 354-363.
    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. Ao Shen & Peng Wang & Yongyuan Ma, 2022. "When crowding‐in and when crowding‐out? The boundary conditions on the relationship between negative online reviews and online sales," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2016-2032, September.
    2. Mardumyan, Anna & Siret, Iris, 2023. "When review verification does more harm than good: How certified reviews determine customer–brand relationship quality," Journal of Business Research, Elsevier, vol. 160(C).
    3. Hsing Kenneth Cheng & D. Daniel Sokol & Xinyu Zang, 2024. "The rise of empirical online platform research in the new millennium," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 416-451, March.
    4. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    5. Román, Sergio & Riquelme, Isabel P. & Iacobucci, Dawn, 2024. "Antecedents and consequences of perceived helpfulness of extremely positive and exaggerated reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    6. Román, Sergio & Riquelme, Isabel P. & Iacobucci, Dawn, 2023. "Fake or credible? Antecedents and consequences of perceived credibility in exaggerated online reviews," Journal of Business Research, Elsevier, vol. 156(C).
    7. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    8. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    9. Wei Zhou & Zidong Wang, 2020. "Competing for Search Traffic in Query Markets: Entry Strategy, Platform Design, and Entrepreneurship," Working Papers 20-12, NET Institute.
    10. Agnieszka Zablocki & Bodo Schlegelmilch & Michael J. Houston, 2019. "How valence, volume and variance of online reviews influence brand attitudes," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 61-77, June.
    11. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    12. Lijia Ma & Xingchen Xu & Yong Tan, 2024. "Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines," Papers 2402.19421, arXiv.org.
    13. Rahul Kumar & Shubhadeep Mukherjee & Nripendra P. Rana, 2024. "Exploring Latent Characteristics of Fake Reviews and Their Intermediary Role in Persuading Buying Decisions," Information Systems Frontiers, Springer, vol. 26(3), pages 1091-1108, June.
    14. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    15. Mingyang Zhang & Heyan Xu & Ning Ma & Xinglin Pan, 2022. "Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    16. Lu Wei & Shufan Ma & Maoze Wang, 2025. "Understanding the information characteristics of consumers’ online reviews: the evidence from Chinese online apparel shopping," Electronic Commerce Research, Springer, vol. 25(4), pages 3071-3097, August.
    17. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    18. Pyle, Martin A. & Smith, Andrew N. & Chevtchouk, Yanina, 2021. "In eWOM we trust: Using naïve theories to understand consumer trust in a complex eWOM marketspace," Journal of Business Research, Elsevier, vol. 122(C), pages 145-158.
    19. Yang, Liu & Dong, Shaozeng, 2018. "Rebate strategy to stimulate online customer reviews," International Journal of Production Economics, Elsevier, vol. 204(C), pages 99-107.
    20. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).

    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:spr:elcore:v:25:y:2025:i:4:d:10.1007_s10660-023-09753-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.