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Tradition And Fashion In Consumer Choice: Bagging The Scottish Munros

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  • Alex Bentley
  • Paul Ormerod

Abstract

Evidence is growing that in many markets consumers select not simply on the basis of the perceived attributes of products, but their preferences are modified by the behaviour of others. Economists have paid relatively little attention to such markets. We consider evidence from the activity of hill‐walking. The Munros are a list of Scottish hills over 3000 feet in height. We analyse completions of both the Munros themselves, and the Munro Tops, a difficult and time‐consuming extension of the Munros. The classic Bass diffusion model incorporates the imitation of others as a part of the behavioural rules used by consumers in making choice. We extend the Bass model to be able to apply it to long‐term case studies where substantial changes over time in the population making choices at any given point have to be taken into account. The Munros are a particular illustration of this, but the extension can be used in other situations where such population changes are important. Our results show that Top completions are dominated by the ‘fashion’ component, suggesting there was a cohort among which Tops completions became fashionable, but which has not been sustained. Both our extended model and the standard model can account for this. In contrast, our extension is needed to explain why Munro completions have remained close to their peak level for a decade now, a fact for which the standard Bass model is unable to account.

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  • Alex Bentley & Paul Ormerod, 2009. "Tradition And Fashion In Consumer Choice: Bagging The Scottish Munros," Scottish Journal of Political Economy, Scottish Economic Society, vol. 56(3), pages 371-381, July.
  • Handle: RePEc:bla:scotjp:v:56:y:2009:i:3:p:371-381
    DOI: 10.1111/j.1467-9485.2009.00489.x
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    1. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    2. 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.
    3. George J. Stigler, 1961. "The Economics of Information," Journal of Political Economy, University of Chicago Press, vol. 69(3), pages 213-213.
    4. 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.
    5. David C. Schmittlein & Vijay Mahajan, 1982. "Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 1(1), pages 57-78.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
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    Cited by:

    1. Jason Potts & Stuart Cunningham & John Hartley & Paul Ormerod, 2008. "Social network markets: a new definition of the creative industries," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 32(3), pages 167-185, September.
    2. Bentley, R. Alexander & Ormerod, Paul, 2010. "A rapid method for assessing social versus independent interest in health issues: A case study of 'bird flu' and 'swine flu'," Social Science & Medicine, Elsevier, vol. 71(3), pages 482-485, August.
    3. Salva Duran-Nebreda & Michael J. O’Brien & R. Alexander Bentley & Sergi Valverde, 2022. "Dilution of expertise in the rise and fall of collective innovation," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.

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