I know what you did last weekend- or do I? Introducing mental anchoring to the demand for sport
AbstractFootball matches are by no means homogenous goods. Rather, there are big differences in single match quality, which is ex-ante unobservable to consumers. We argue that quality uncertainty leads consumers to search for quality proxies which are observable in advance. Aggregate demand functions are shown to depend merely on prices, ex-ante quality perception and stochastic influence factors. Following the work by Kahneman, Tversky and Slovic, we suggest that consumer behaviour is to some extent driven by mental anchoring. Therefore, the usual approach to rely on absolute measures only, seems doubtful. The main focus of our empirical analysis is to introduce relative quality measures, which are based on different anchor levels. Besides seasonal-dynamic and seasonal-static anchors, this specification allows us to include absolute quality proxies as a special case. Applying median regression on a sample from over 2000 individual matches in the German Bundesliga, we find evidence for mental anchoring in the demand for sport. Our results indicate that consumers tend to compare current values for quality proxies to last season’s indicator values instead of last match’s indicator values.
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Bibliographic InfoPaper provided by University of Zurich, Center for Research in Sports Administration (CRSA) in its series Working Papers with number 0008.
Length: 34 pages
Date of creation: 2006
Date of revision:
Other versions of this item:
- Men-Andri Benz & Leif Brandes & Egon Franck, 2006. "I know what you did last weekend- or do I? Introducing mental anchoring to the demand for sport," Working Papers 0047, University of Zurich, Institute for Strategy and Business Economics (ISU).
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
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