IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v21y2021i4d10.1007_s10660-020-09401-8.html
   My bibliography  Save this article

Economical user-generated content (UGC) marketing for online stores based on a fine-grained joint model of the consumer purchase decision process

Author

Listed:
  • S. G. Li

    (Shanghai University)

  • Y. Q. Zhang

    (Shanghai University)

  • Z. X. Yu

    (East China University of Science and Technology)

  • F. Liu

    (Shanghai University)

Abstract

User-generated content (UGC) is influential in reducing customer perceived risk and determining online store sales. E-sellers spend huge costs and efforts to improve UGC for it serves as a convenient and persuasive alternative for marketing and advertising purposes. Considering that consumers may set lower and/or upper limits (i.e., psychological thresholds) in which the good is expected to be, and purchase decisions are considered as a multi-stage decision process, yet models in previous research cannot uncover this decision-making process. Therefore, exploring the impact of UGC at each decision-making stage and detecting the psychological thresholds on various aspects of UGC (i.e., the fine-grained effects of UGC) contribute to optimizing the UGC with the best cost to boost sales. To this end, a fine-grained joint two-stage decision model, zero-inflated negative binomial regression (ZINB-P) model is proposed to support economical UGC marketing. Specifically, we compile a factors system composed of various types of aggregate-level statistics of UGC, which can impact risk perception. Afterward, change point analysis is used to find multi-level consumer psychological thresholds on UGC factors and consumers’ risk perception model is constructed to measure purchasing probabilities in the first decision-making stage. On the basis of consumers’ risk perception model, the ZINB-P model is built to fully capture the fine-grained effects of UGC factors on each stage of the consumer purchase decision. It integrates two stages of consumer decision: the consumer risk perception and non-compensatory choice in the first stage, and the second compensatory stage. A genetic algorithm is constructed to jointly estimate the parameters in ZINB-P model. Finally, an experiment on a kind of fresh produce from Taobao.com evidences the precision of our model. We demonstrate how our model can provide with economical UGC marketing strategies using a decision support table, in which some scenarios are identified. E-sellers can use this table to find the scenarios they are located in and identify the critical UGC factors that impede the sales in each scenario, and thus economical UGC marketing strategies can be obtained by improving these critical UGC factors.

Suggested Citation

  • S. G. Li & Y. Q. Zhang & Z. X. Yu & F. Liu, 2021. "Economical user-generated content (UGC) marketing for online stores based on a fine-grained joint model of the consumer purchase decision process," Electronic Commerce Research, Springer, vol. 21(4), pages 1083-1112, December.
    • Handle: RePEc:spr:elcore:v:21:y:2021:i:4:d:10.1007_s10660-020-09401-8
    DOI: 10.1007/s10660-020-09401-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09401-8
    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/s10660-020-09401-8?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. Gobinda Roy & Biplab Datta & Rituparna Basu, 2017. "Effect of eWOM Valence on Online Retail Sales," Global Business Review, International Management Institute, vol. 18(1), pages 198-209, February.
    2. Peter S. Fader & Russell S. Winer, 2012. "Introduction to the Special Issue on the Emergence and Impact of User-Generated Content," Marketing Science, INFORMS, vol. 31(3), pages 369-371, May.
    3. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    4. Garbarino, Ellen & Strahilevitz, Michal, 2004. "Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation," Journal of Business Research, Elsevier, vol. 57(7), pages 768-775, July.
    5. Drakopoulos, S A, 1992. "Psychological Thresholds, Demand and Price Rigidity," The Manchester School of Economic & Social Studies, University of Manchester, vol. 60(2), pages 152-168, June.
    6. Jianan Wu & Arvind Rangaswamy, 2003. "A Fuzzy Set Model of Search and Consideration with an Application to an Online Market," Marketing Science, INFORMS, vol. 22(3), pages 411-434, March.
    7. Macdonald, Emma K. & Sharp, Byron M., 2000. "Brand Awareness Effects on Consumer Decision Making for a Common, Repeat Purchase Product:: A Replication," Journal of Business Research, Elsevier, vol. 48(1), pages 5-15, April.
    8. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    9. Stuart Dillon & John Buchanan & Kholoud Al-Otaibi, 2014. "Perceived Risk and Online Shopping Intention: A Study Across Gender and Product Type," International Journal of E-Business Research (IJEBR), IGI Global, vol. 10(4), pages 17-38, October.
    10. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-754, July/Aug..
    11. Ashish Sen & S. Srivastava, 1975. "On tests for detecting change in mean when variance is unknown," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 27(1), pages 479-486, December.
    12. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
    13. 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.
    14. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    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. Isaac Kofi Mensah & Guohua Zeng & Chuanyong Luo, 2023. "Determinants of Social Commerce Purchase and Recommendation Intentions Within the Context of Swift Guanxi Among Chinese College Students," SAGE Open, , vol. 13(2), pages 21582440231, June.
    2. Shimi Naurin Ahmad & Michel Laroche, 2023. "Extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 662-676, December.
    3. Vincenzo Basile & Amélia Brandão & Mariana Ferreira, 2024. "Does user-generated content influence value co-creation in the context of luxury fashion brand communities? Matching inclusivity and exclusivity," Italian Journal of Marketing, Springer, vol. 2024(4), pages 419-444, December.

    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. Kordrostami, Elika & Rahmani, Vahid, 2020. "Investigating conflicting online review information:evidence from Amazon.com," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    2. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    3. Bill Russell & Dooruj Rambaccussing, 2019. "Breaks and the statistical process of inflation: the case of estimating the ‘modern’ long-run Phillips curve," Empirical Economics, Springer, vol. 56(5), pages 1455-1475, May.
    4. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    5. Hsiang-Ming Lee & Tsai Chen & Ya-Hui Hsu & Yu-Chi Wu, 2018. "Effect Of Complementary Product Fit And Brand Awareness On Brand Attitude After M&As: Word Of Mouth As A Moderator," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 12(1), pages 51-67.
    6. Amin Ansary & Nik M. Hazrul Nik Hashim, 2018. "Brand image and equity: the mediating role of brand equity drivers and moderating effects of product type and word of mouth," Review of Managerial Science, Springer, vol. 12(4), pages 969-1002, October.
    7. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    8. Maria Antonietta Raimondo & Stefania Farace & Gaetano Nino Miceli, 2018. "User-Generated Systems of Signs and Meanings in Product Customization: Taxonomies and Research Directions," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2018(2), pages 61-83.
    9. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    10. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    11. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    12. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    13. Currim, Imran S. & Mintz, Ofer & Siddarth, S., 2015. "Information Accessed or Information Available? The Impact on Consumer Preferences Inferred at a Durable Product E-commerce Website," Journal of Interactive Marketing, Elsevier, vol. 29(C), pages 11-25.
    14. Christof Naumzik & Stefan Feuerriegel & Markus Weinmann, 2022. "I Will Survive: Predicting Business Failures from Customer Ratings," Marketing Science, INFORMS, vol. 41(1), pages 188-207, January.
    15. Boegershausen, Johannes & Datta, Hannes & Borah, Abhishek & Stephen, Andrew, 2022. "Fields of Gold: Web Scraping and APIs for Impactful Marketing Insights," Other publications TiSEM 5f1ed70a-48c3-422c-bc10-0, Tilburg University, School of Economics and Management.
    16. Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    17. Peters, Kay & Chen, Yubo & Kaplan, Andreas M. & Ognibeni, Björn & Pauwels, Koen, 2013. "Social Media Metrics — A Framework and Guidelines for Managing Social Media," Journal of Interactive Marketing, Elsevier, vol. 27(4), pages 281-298.
    18. Lifang Peng & Qinyu Liao & Xiaorong Wang & Xuanfang He, 2016. "Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites," Electronic Commerce Research, Springer, vol. 16(2), pages 145-169, June.
    19. Arijit Bhattacharya & Manjari Srivastava, 2020. "A Framework of Online Customer Experience: An Indian Perspective," Global Business Review, International Management Institute, vol. 21(3), pages 800-817, June.
    20. Baolong Ying & Qijing Yan & Zehua Chen & Jinchao Du, 2024. "A sequential feature selection approach to change point detection in mean-shift change point models," Statistical Papers, Springer, vol. 65(6), pages 3893-3915, 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:elcore:v:21:y:2021:i:4:d:10.1007_s10660-020-09401-8. 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.