Understanding Consumer Reactions to Assortment Unavailability

common-method variance, we implemented more straightforward measures (Rossiter 2002). For
example, to measure store loyalty and brand loyalty, we used a behavioral measure (primary
store no/yes, primary brand no/yes) instead of a self-reported Likert-type item (e.g., “I consider
myself loyal to this brand”). To measure impulse buying, we asked if buying the product was
planned in advance (no/yes). For stockpiling, food experts (n = 15) rated each of the eight
product groups on the level of safety stock (low, medium, high) that consumers usually maintain
at home before they go to the supermarket to buy the product (e.g., Campo, Gijsbrechts, and
Nisol 2000; Narasimhan, Neslin, and Sen 1996). We also used objective criteria to measure
antecedents. For example, as an indication of the availability of alternative stores, we used the
number of supermarkets with a more or less similar merchandising strategy within a radius of
250 meters and/or 4 minutes of walking of the supermarket of interest. For other antecedents, we
used self-reported scales if there was no direct relation with the dependent variable. For example,
we used self-reported scales to measure shopping attitude, price consciousness, quality
consciousness, and general time constraints. In Appendix 2A, we provide an overview of the
explanatory variables, their measurement methods, and their sources.
2.4.5 Analysis
As already noted in our literature review, the cancellation and category switch OOS responses
are uncommon, which does not enable us to estimate parameters reliably for these choice
categories. Therefore, we added cancellation to the rather similar postponement category.
However, the category switch response is not similar to any of the other categories and therefore
is not considered in our model. As a consequence, our number of valid cases drops from 749 to
734. After this procedure, the dependent variable consists of four different choice categories: (1)
brand switch, (2) store switch, (3) item switch, and (4) postponement. Because these categories
are unordered, we use a multinomial logit model (Paap and Franses 2000; Guadagni and Little
1983), whose parameters are estimated using the statistical software package Limdep 7.0
(Greene 1998) for the maximum likelihood procedure, to test our hypotheses. We calculate the
marginal effects and their accompanying standard errors and significance levels (Campo,
Gijsbrechts, and Nisol 2000; Greene 1998), which show the effect and direction of a predictor
variable X on a choice category.
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