IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p16510-d999096.html
   My bibliography  Save this article

Research on the Evolution of Consumers’ Purchase Intention Based on Online Reviews and Opinion Dynamics

Author

Listed:
  • Na Zhang

    (Department of Engineering and Technology, School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Ping Yu

    (Department of Engineering and Technology, School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Yupeng Li

    (Department of Engineering and Technology, School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Wei Gao

    (Department of Engineering and Technology, School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Due to the development of the e-commerce platform and the internet technology, the inclination of consumers for online shopping is shooting up. To lure consumers and gratify consumers, it’s necessary for enterprise to explore and excavate the purchase intention evolution mechanism so that enterprises can customize the marketing strategies and get consumers to purchase products. Previous studies have shown that consumers’ purchase intention is influenced significantly by online reviews. However, the mechanism by which consumers’ real purchase intentions change when they refer to online reviews is unclear. In fact, the process that consumers browse online reviews is truly an opinion interaction process between recipients (consumers who buy goods) and reviewers (consumers who post online reviews). Interaction between opinions may lead to changes in consumers’ purchase intentions. Therefore, an opinion dynamics model, the Deffuant–Weisbuch (D-W) model, is introduced and improved to explore the dynamic evolution of consumers’ purchase intention. Firstly, online reviews are executed. Then, fuzzy quantification of sentimental opinion values is performed through trapezoidal fuzzy numbers. Secondly, the improved D-W model is constructed considering the influence of the personality of recipients and the professionalism of reviewers on opinion interaction and the “negative bias” mechanism. Finally, a case study is constructed with online reviews of a cell phone by using the above method. In addition, sensitivity analyses are conducted for the personality coefficient of recipients, professionalism of reviewers, and size of heterogeneous consumers, respectively, through which, the validity of the proposed method is expounded. This study not only contributes to an in-depth discussion about the influencing factors of purchase intention, but also provides references for enterprises to better utilize online reviews to promote products and attract consumers.

Suggested Citation

  • Na Zhang & Ping Yu & Yupeng Li & Wei Gao, 2022. "Research on the Evolution of Consumers’ Purchase Intention Based on Online Reviews and Opinion Dynamics," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16510-:d:999096
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/16510/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/24/16510/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Prateep Puengwattanapong & Adisorn Leelasantitham, 2022. "A Holistic Perspective Model of Plenary Online Consumer Behaviors for Sustainable Guidelines of the Electronic Business Platforms," Sustainability, MDPI, vol. 14(10), pages 1-38, May.
    2. Plotkina, Daria & Munzel, Andreas, 2016. "Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product," Journal of Retailing and Consumer Services, Elsevier, vol. 29(C), pages 1-11.
    3. Daria Plotkina & Andreas Munzel, 2016. "Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product," Post-Print hal-02423571, HAL.
    4. Mehrbakhsh Nilashi & Abbas Mardani & Huchang Liao & Hossein Ahmadi & Azizah Abdul Manaf & Wafa Almukadi, 2019. "A Hybrid Method with TOPSIS and Machine Learning Techniques for Sustainable Development of Green Hotels Considering Online Reviews," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
    5. Yuan Luo & William K. Thompson & Timothy M. Herr & Zexian Zeng & Mark A. Berendsen & Siddhartha R. Jonnalagadda & Matthew B. Carson & Justin Starren, 2017. "Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review," Drug Safety, Springer, vol. 40(11), pages 1075-1089, November.
    6. Qiang Yan & Simin Zhou & Xiaoyan Zhang & Ye Li, 2019. "A System Dynamics Model of Online Stores’ Sales: Positive and Negative E-WOM and Promotion Perspective," Sustainability, MDPI, vol. 11(21), pages 1-13, October.
    7. Ketron, Seth, 2017. "Investigating the effect of quality of grammar and mechanics (QGAM) in online reviews: The mediating role of reviewer crediblity," Journal of Business Research, Elsevier, vol. 81(C), pages 51-59.
    8. Wenshuai Wu & Gang Kou, 2016. "A group consensus model for evaluating real estate investment alternatives," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-10, December.
    9. Yingxue Xia & Hong-Youl Ha, 2022. "Do Online Reviews Encourage Customers to Write Online Reviews? A Longitudinal Study," Sustainability, MDPI, vol. 14(8), pages 1-12, April.
    10. Jooa Baek & Jaeseok Lee, 2021. "A Conceptual Framework on Reconceptualizing Customer Share of Wallet (SOW): As a Perspective of Dynamic Process in the Hospitality Consumption Context," Sustainability, MDPI, vol. 13(3), pages 1-11, January.
    11. Daria Plotkina & Andreas Munzel, 2016. "Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product," Post-Print halshs-01522518, HAL.
    12. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
    13. Bai, Yanzhuang & Li, Tingwu & Zheng, Chundong, 2022. "Is there any value in the online reviews of remedial satisfied customers? An empirical study in the hospitality industry," Journal of Retailing and Consumer Services, Elsevier, vol. 64(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. 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.
    2. Plotkina, Daria & Munzel, Andreas & Pallud, Jessie, 2020. "Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews," Journal of Business Research, Elsevier, vol. 109(C), pages 511-523.
    3. Kaushik, Kapil & Mishra, Rajhans & Rana, Nripendra P. & Dwivedi, Yogesh K., 2018. "Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 21-32.
    4. Hu, Xin & He, Liuyi & Liu, Junjun, 2022. "Status reinforcing: Unintended rating bias on online shopping platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    5. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 22(5), pages 1203-1226, October.
    6. Eslami, Seyed Pouyan & Ghasemaghaei, Maryam, 2018. "Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 74-80.
    7. Abdul Kadir Othman & Lailatul Faizah Abu Hassan & Muhammad Iskandar Hamzah & Amirun Razin Razali & Mohamad Amir Shah Saim & Mohd Safwan Ramli & Muhammad Amir Osman & Mohamad Amirul Anbia Azhar, 2019. "The Influence of Social Commerce Factors on Customer Intention to Purchase," Asian Themes in Social Sciences Research, Knowledge Press, vol. 3(1), pages 1-10.
    8. Ismagilova, Elvira & Dwivedi, Yogesh K. & Slade, Emma, 2020. "Perceived helpfulness of eWOM: Emotions, fairness and rationality," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    9. Zarco, Carmen & Giráldez-Cru, Jesús & Cordón, Oscar & Liébana-Cabanillas, Francisco, 2024. "A comprehensive view of biometric payment in retailing: A complete study from user to expert," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    10. Wu, Jia-Jhou & Chang, Sue-Ting, 2020. "Exploring customer sentiment regarding online retail services: A topic-based approach," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    11. Petrescu, Maria & O’Leary, Kathleen & Goldring, Deborah & Ben Mrad, Selima, 2018. "Incentivized reviews: Promising the moon for a few stars," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 288-295.
    12. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 0. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    13. Könsgen, Raoul & Schaarschmidt, Mario & Ivens, Stefan & Munzel, Andreas, 2018. "Finding Meaning in Contradiction on Employee Review Sites — Effects of Discrepant Online Reviews on Job Application Intentions," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 165-177.
    14. Perez, Dikla & Stockheim, Inbal & Baratz, Guy, 2022. "Complimentary competition: The impact of positive competitor reviews on review credibility and consumer purchase intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    15. Qiuxue Luo & Mayuree Suacamram, 2022. "Product Innovation and National Image of Chinese Products in the Eyes of Thai People," SAGE Open, , vol. 12(1), pages 21582440221, March.
    16. Ayat Zaki Ahmed & Manuel Rodríguez Díaz, 2022. "A Methodology for Machine-Learning Content Analysis to Define the Key Labels in the Titles of Online Customer Reviews with the Rating Evaluation," Sustainability, MDPI, vol. 14(15), pages 1-31, July.
    17. Krishnamurthy, Anup & Kumar, S. Ramesh, 2018. "Electronic word-of-mouth and the brand image: Exploring the moderating role of involvement through a consumer expectations lens," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 149-156.
    18. Juan Carlos Martín & Veronika Rudchenko & María-Victoria Sánchez-Rebull, 2020. "The Role of Nationality and Hotel Class on Guests’ Satisfaction. A Fuzzy-TOPSIS Approach Applied in Saint Petersburg," Administrative Sciences, MDPI, vol. 10(3), pages 1-24, September.
    19. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    20. Park, Sangwon & Nicolau, Juan L., 2017. "Effects of general and particular online hotel ratings," Annals of Tourism Research, Elsevier, vol. 62(C), pages 114-116.

    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:gam:jsusta:v:14:y:2022:i:24:p:16510-:d:999096. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.