IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v492y2018icp418-430.html
   My bibliography  Save this article

Word-of-mouth dynamics with information seeking: Information is not (only) epidemics

Author

Listed:
  • Thiriot, Samuel

Abstract

Word-of-mouth is known to determine the success or failure of innovations (Rogers, 2003) and facilitate the diffusion of products (Katz and Lazarsfeld, 1955). Word-of-mouth is made of both individuals seeking out information and/or pro-actively spreading information (Gilly et al., 1998; Rogers, 2003). Information seeking is considered as a step mandatory for individuals to retrieve the expert knowledge necessary for them to understand the benefits of an innovation or decide to buy a product (Arndt, 1967; Rogers, 2003). Yet the role of information seeking in the word-of-mouth dynamics was not investigated in computational models. Here we study in which conditions word-of-mouth enables the population to retrieve the initial expertise scattered in the population. We design a computational model in which awareness and expert knowledge are both represented, and study the joint dynamics of information seeking and proactive transmission of information. Simulation experiments highlight the apparition of cascades of awareness, cascades of expertise and chains of information retrieval. We find that different strategies should be used depending on the initial proportion of expertise (disruptive innovations, incremental innovations or products belonging to well-known categories). Surprisingly, when there is too much expertise in the population prior the advertisement campaign, word-of-mouth is less efficient in the retrieval of this expertise than when less expertise is initially present. Our results suggest that information seeking plays a key role in the dynamics of word-of-mouth, which can therefore not be reduced solely to the epidemic aspect.

Suggested Citation

  • Thiriot, Samuel, 2018. "Word-of-mouth dynamics with information seeking: Information is not (only) epidemics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 418-430.
  • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:418-430
    DOI: 10.1016/j.physa.2017.09.056
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117309482
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2017.09.056?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Xiaoyang & He, Daobing & Yang, Linfeng & Liu, Chao, 2019. "A novel negative feedback information dissemination model based on online social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 371-389.

    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:eee:phsmap:v:492:y:2018:i:c:p:418-430. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.