IDEAS home Printed from https://ideas.repec.org/p/wrk/warwec/941.html
   My bibliography  Save this paper

Word of Mouth Advertising, Credibility and Learning in Networks

Author

Listed:
  • Chatterjee, Kalyan

    (Department of Economics, The Pennsylvania State University)

  • Dutta, Bhaskar

    (Department of Economics, University of Warwick)

Abstract

Social networks representing the pattern of social interactions - who talks to or who observes whom- play a crucial role as a medium for the spread of information, ideas, diseases, products. Someone in the population may struck with an infection or may adopt a new technology, and it can then either die out quickly or spread throughout the population, depending possibly on the location of the initial appearance, the structure of the network - for instance, how dense it is. The dynamics of adoption -the extent to which individuals are in uenced by their neighbours, the impact of "word of-mouth" communication- also plays a role in determining the speed of diffusion. Given the large range of contexts in which social learning is important, it is not surprising that researchers from various disciplines have studied processes of diffusion from a variety of perspectives.

Suggested Citation

  • Chatterjee, Kalyan & Dutta, Bhaskar, 2010. "Word of Mouth Advertising, Credibility and Learning in Networks," The Warwick Economics Research Paper Series (TWERPS) 941, University of Warwick, Department of Economics.
  • Handle: RePEc:wrk:warwec:941
    as

    Download full text from publisher

    File URL: https://warwick.ac.uk/fac/soc/economics/research/workingpapers/2010/twerp_941.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Glenn Ellison & Drew Fudenberg, 1995. "Word-of-Mouth Communication and Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 93-125.
    2. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    3. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
    4. Ellison, Glenn, 1993. "Learning, Local Interaction, and Coordination," Econometrica, Econometric Society, vol. 61(5), pages 1047-1071, September.
    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. Jeffry Jacob & Abdul Munasib, 2020. "Do social networks promote homeownership?," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 67(2), pages 189-230, June.
    2. Kimberley Scharf, 2014. "Private Provision Of Public Goods And Information Diffusion In Social Groups," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(4), pages 1019-1042, November.

    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. Kalyan Chatterjee & Bhaskar Dutta, 2016. "Credibility And Strategic Learning In Networks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(3), pages 759-786, August.
    2. Jonas Hedlund & Carlos Oyarzun, 2018. "Imitation in heterogeneous populations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(4), pages 937-973, June.
    3. Sobel, Joel, 2000. "Economists' Models of Learning," Journal of Economic Theory, Elsevier, vol. 94(2), pages 241-261, October.
    4. Michel Grabisch & Agnieszka Rusinowska, 2016. "Determining influential models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01318081, HAL.
    5. Grabisch, Michel & Rusinowska, Agnieszka, 2013. "A model of influence based on aggregation functions," Mathematical Social Sciences, Elsevier, vol. 66(3), pages 316-330.
    6. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    7. Edward Cartwright, 2002. "Learning to play approximate Nash equilibria in games with many players," Levine's Working Paper Archive 506439000000000070, David K. Levine.
    8. Zhu, Z.;, 2023. "The Value of Patients: Heterogenous Physician Learning and Generic Drug Diffusion," Health, Econometrics and Data Group (HEDG) Working Papers 23/12, HEDG, c/o Department of Economics, University of York.
    9. Fudenberg, Drew & Imhof, Lorens A., 2006. "Imitation processes with small mutations," Journal of Economic Theory, Elsevier, vol. 131(1), pages 251-262, November.
    10. Goyal, Sanjeev, 2003. "Learning in Networks: a survey," Economics Discussion Papers 9983, University of Essex, Department of Economics.
    11. Michel Grabisch & Agnieszka Rusinowska, 2016. "Determining models of influence," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 26(2), pages 69-85.
    12. Tsakas Nikolas, 2014. "Imitating the Most Successful Neighbor in Social Networks," Review of Network Economics, De Gruyter, vol. 12(4), pages 403-435, February.
    13. Lamberson PJ, 2010. "Social Learning in Social Networks," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 10(1), pages 1-33, August.
    14. Michel Grabisch & Agnieszka Rusinowska, 2010. "Iterating influence between players in a social network," Post-Print halshs-00543840, HAL.
    15. Sanjeev Goyal & Andrea Galeotti, 2007. "A Theory of Strategic Diffusion," Working Papers 2007.70, Fondazione Eni Enrico Mattei.
    16. Alós-Ferrer, Carlos & Weidenholzer, Simon, 2008. "Contagion and efficiency," Journal of Economic Theory, Elsevier, vol. 143(1), pages 251-274, November.
    17. Andrea Galeotti & Sanjeev Goyal, 2009. "Influencing the influencers: a theory of strategic diffusion," RAND Journal of Economics, RAND Corporation, vol. 40(3), pages 509-532, September.
    18. Guo, Xiaoli & Ryvkin, Dmitry, 2022. "When is intergroup herding beneficial?," Mathematical Social Sciences, Elsevier, vol. 120(C), pages 66-77.
    19. Bala, V. & Goyal, S., 1997. "Self-Organization in Communication Networks," Econometric Institute Research Papers EI 9713-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. Lelarge, Marc, 2012. "Diffusion and cascading behavior in random networks," Games and Economic Behavior, Elsevier, vol. 75(2), pages 752-775.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:wrk:warwec:941. 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: Margaret Nash (email available below). General contact details of provider: https://edirc.repec.org/data/dewaruk.html .

    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.