Abstract
Product inventory exerts two countervailing forces on the probability of purchase: More inventory on hand reduces the need to purchase; however, theory suggests higher levels of inventory can drive up consumption, thereby increasing the chance of purchase. Moreover, consumers have biased estimations of their own inventory—especially at high levels of inventory (Chandon and Wansink, 2006), which again suggests a positive relationship between inventory and purchase probability.
We model the negative (standard) and positive effects of inventory on the probability of purchase. The model is calibrated on ten product categories and fits better than the standard nested logit and an alternative developed by Ailawadi and Neslin (1998). The elasticity of purchase incidence with respect to inventory represents these opposing forces in an intuitive way, implying an inventory threshold below (above) which the net effect is positive (negative). Estimated thresholds are plausible across categories, with the food categories of hot dogs, ice cream and soft drinks showing the largest effects.
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Notes
See Chandon and Wansink (2002) for a rationale and empirical evidence.
This assumes that the parameters for these variables have the theoretically correct signs and are statistically different from zero, a condition which holds in all these studies.
This result is also consistent with the findings in Bell et al. (1999, p. 511).
Our use of secondary data makes it hard to speculate about precise reasons why more inventory leads to more consumption. Chandon and Wansink (2002), for example, provide a context for specific reasons and discuss what they term endogenous effects (more inventory leads to more consumption) and exogenous effects (households buy more in anticipation of some consumption-increasing event, such as a party). We refer the interested reader to their article for details.
The interested reader is referred to Chandon and Wansink (2006) for details.
The former variable for heterogeneity across households, the latter for heterogeneity across households and within households over time.
We set inventory to 0.01 in instances where our estimate of inventory hits zero. We checked the number of times this occurred for each category and found it to be very rare (less than 4% of observations for all categories). We re-estimated the models under a condition where these observations were ignored. That is, we stopped using observations for households once the estimate of inventory hit a very small but positive value and then only re-started using the particular household when inventory was again replenished (by the next purchase). This resulted in a small window of “inactivity” for the household. Under this condition our proposed model still fits the data better than the null model (all categories) and better than AN (all categories except sugar). The statistical significance of the results is unchanged and the quantitative effects virtually identical. We thank an anonymous reviewer for drawing our attention to this matter.
This control argument is also borne out by noting that within a household \({\it MCINV}^h_t\) and \(\log ({\it INV}^h_t)\) will be highly positively correlated by definition as \({\it MCINV}^h_t = {\it INV}^h_t - \overline{{\it INV}^h}\), however across all data points that pool household purchase histories, this correlation is greatly attenuated. We thank an anonymous reviewer for suggesting this elaboration.
Chandon and Wansink (2006) make some strides in this direction as they explore public policy implications of over-buying and spoilage induced by households’ inability to effectively estimate their own levels of inventory.
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Acknowledgments
We would like to thank three anonymous Marketing Letters reviewers for many constructive suggestions. In addition, Christophe Van den Bulte, Pierre Chandon, Lutz Hildebrandt and Dirk Temme provided helpful comments. This research was generously funded by the German Research Foundation (DFG) in the Sonderforschungsbereich 373 (National Research Center on the Quantification and Simulation of Economic Processes) and for the second author by the research grant #BO 1952/1-1 from the German Research Foundation.
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Bell, D.R., Boztuğ, Y. The positive and negative effects of inventory on category purchase: An empirical analysis. Market Lett 18, 1–14 (2007). https://doi.org/10.1007/s11002-006-9001-y
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DOI: https://doi.org/10.1007/s11002-006-9001-y