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How biased household inventory estimates distort shopping and storage decisions

Author: Chandon, Pierre ; Wansink, BrianINSEAD Area: MarketingIn: Journal of Marketing, vol. 70, no. 4, October 2006 Language: EnglishDescription: p. 118-135.Type of document: INSEAD ArticleNote: Please ask us for this itemAbstract: The authors developed a model of how consumers estimate the level of product inventory in their household. Two laboratory experiments and two field studies involving 29 product categories show that: 1) consumers anchor their estimates on their average inventory and fail to adjust sufficiently, 2) adjustments follow an inelastic psychophysical power function, leading to overestimations of low levels of inventory and underestimations of high levels, and 3) adjustments are more elastic - and thus more accurate - when inventory is salient. Contrary to the assumptions of practitioners and academic modellers, these inventory estimates - not actual inventory levels - drive subsequent purchase incidence. Simulation results further show that biased estimates increase overstocking and spoilage among stockout-averse consumers but increase stockouts and unmet demand among overstocking-averse consumers. By predicting the magnitude - and not just the direction - of estimation biases, the model and results offer new insights for accelerating the consumption of health foods and for improving the targeting of stockpiling - inducing sales promotions.
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The authors developed a model of how consumers estimate the level of product inventory in their household. Two laboratory experiments and two field studies involving 29 product categories show that: 1) consumers anchor their estimates on their average inventory and fail to adjust sufficiently, 2) adjustments follow an inelastic psychophysical power function, leading to overestimations of low levels of inventory and underestimations of high levels, and 3) adjustments are more elastic - and thus more accurate - when inventory is salient. Contrary to the assumptions of practitioners and academic modellers, these inventory estimates - not actual inventory levels - drive subsequent purchase incidence. Simulation results further show that biased estimates increase overstocking and spoilage among stockout-averse consumers but increase stockouts and unmet demand among overstocking-averse consumers. By predicting the magnitude - and not just the direction - of estimation biases, the model and results offer new insights for accelerating the consumption of health foods and for improving the targeting of stockpiling - inducing sales promotions.

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