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The shape of Word-of-Mouth response function

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  • Park, Sang-June
  • Lee, Yeong-Ran
  • Borle, Sharad

Abstract

A consumer may not be affected by all positive recommenders due to limitations of cognitive capacity. This limitation (of cognitive capacity) results in two different response functions for the size of positive recommenders: One is an S-shaped function which assumes that the second and third sources (recommenders) have greater additional impact than the first source, and the other is a concave-shaped function which assumes that the first source (recommender) is more influential than the second and the third sources. In this paper we operationalize volume of Word-of-Mouth as the total number of positive Word-of-Mouth senders and using two conjoint studies empirically investigate whether the relationship between the volume of Word-of-Mouth and its impact follows a concave-shaped function or an S-shaped function. The two conjoint studies support the concave-shaped response for the volume of Word-of-Mouth.

Suggested Citation

  • Park, Sang-June & Lee, Yeong-Ran & Borle, Sharad, 2018. "The shape of Word-of-Mouth response function," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 304-309.
  • Handle: RePEc:eee:tefoso:v:127:y:2018:i:c:p:304-309
    DOI: 10.1016/j.techfore.2017.10.006
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    as
    1. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    2. Mizerski, Richard W, 1982. "An Attribution Explanation of the Disproportionate Influence of Unfavorable Information," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(3), pages 301-310, December.
    3. Raghuram Iyengar & Christophe Van den Bulte & Jae Young Lee, 2015. "Social Contagion in New Product Trial and Repeat," Marketing Science, INFORMS, vol. 34(3), pages 408-429, May.
    4. Vijay Mahajan & Eitan Muller & Roger A. Kerin, 1984. "Introduction Strategy for New Products with Positive and Negative Word-of-Mouth," Management Science, INFORMS, vol. 30(12), pages 1389-1404, December.
    5. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
    6. 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.
    7. Matthew O. Jackson, 2014. "Networks in the Understanding of Economic Behaviors," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 3-22, Fall.
    8. Ramya Neelamegham & Pradeep Chintagunta, 1999. "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets," Marketing Science, INFORMS, vol. 18(2), pages 115-136.
    9. Frenzen, Jonathan & Nakamoto, Kent, 1993. "Structure, Cooperation, and the Flow of Market Information," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(3), pages 360-375, December.
    10. Park, Sang-June & Choi, Sungchul, 2016. "Valuation of adopters based on the Bass model for a new product," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 63-69.
    11. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    12. Vijay Mahajan & Eitan Muller, 1986. "Advertising Pulsing Policies for Generating Awareness for New Products," Marketing Science, INFORMS, vol. 5(2), pages 89-106.
    13. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    14. Kucher, Eckhard & Hilleke, Klaus, 1993. "Value pricing through conjoint measurement: A practical approach," European Management Journal, Elsevier, vol. 11(3), pages 283-290, September.
    15. Bearden, William O & Netemeyer, Richard G & Teel, Jesse E, 1989. "Measurement of Consumer Susceptibility to Interpersonal Influence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(4), pages 473-481, March.
    16. Vijay Mahajan & Eitan Muller, 1986. "Reply—Reflections on Advertising Pulsing Policies for Generating Awareness for New Products," Marketing Science, INFORMS, vol. 5(2), pages 110-111.
    17. Karniouchina, Ekaterina V. & Moore, William L. & van der Rhee, Bo & Verma, Rohit, 2009. "Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research," European Journal of Operational Research, Elsevier, vol. 197(1), pages 340-348, August.
    18. Herr, Paul M & Kardes, Frank R & Kim, John, 1991. "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 454-462, March.
    19. Christophe Van den Bulte & Gary L. Lilien, 1997. "Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models," Marketing Science, INFORMS, vol. 16(4), pages 338-353.
    20. 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.
    21. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
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