Understanding Consumer Reactions to Assortment Unavailability

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changes in the price level do not influence any relative comparison across brands. We recognize that estimates for the development of category sales in the control stores will be affected by promotions, so to integrate for the presence of promotions, we construct a promotional indicator. Because we know that promotions occur in all stores at the same time, we base the promotional indicator on the total sales across all stores. To identify the weeks in which a promotion of some sort took place, we estimate a model with a cubic spline function for total sales across all stores. We assume that a promotion occurred for each observation with a large positive error. We then reestimate the same model, which now includes the promotion indicator, to identify those promotions that had a smaller impact. 4.5 Empirical results 4.5.1 Analysis 1: Total category sales We first focus on the weekly total category sales for each store, which can be directly obtained from the database by simple aggregation. In Figure 4.2, we show time series plots for the category sales in each store, which demonstrate a slight decrease in sales for all four stores. This overall decrease in detergent sales cannot be attributed to the delisting because, in the control stores, the number of available items remained constant. To assess the actual effect of the assortment reduction, we must compare the changes in the test stores to changes in the control stores. In Table 4.4, we provide the parameter estimates for Equations 2a and b, with which we model the total category sales per store. As regressors, we include the promotional indicator to control for promotional effects, which will lead to a better fit in the models and thus a smaller residual variation. We also include a dummy variable to correct for an influential outlier that corresponds to a week of extremely low reported sales in one of the stores. The retailer informed us that this was due to an error in the data collection system and that the actual sales were higher but that the exact figures were unknown. Although the stores were selected in advance for their similarities in detergent shelf metrics, the estimated store intercepts show some differences in baseline sales across the four stores, which may be explained by the unique characteristics and environment of each store. The most interesting results appear in the final lines of Table 4.4, which display the estimated function value of f(t|γ) and g(t|θ) at the chosen knot points, as well as the associated standard errors. The results clearly show that the effect changes over time and 105
that a model in which the effect of the delisting is captured by a single dummy variable is therefore not valid. In Figure 4.3, we depict the same values together with the interpolated values with a 95% confidence interval to indicate the uncertainty in these estimates. The function value of the spline at any point in time is a linear combination of the parameters. The confidence intervals therefore can be obtained easily from the covariance matrix of the parameter estimates. Figure 4.2: Total weekly category sales per store 10000 7500 5000 2500 10000 7500 5000 2500 Test store 1 Test store 2 2003 2003 10000 7500 5000 2500 106 Control store 1 10000 Control store 2 7500 5000 2500 Notes: Vertical lines indicate start of the delisting in the test stores. 2003 2003
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LAURENS SLOOT Understanding Consume
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UNDERSTANDING CONSUMER REACTIONS TO
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PREFACE (IN DUTCH) Begin 2001 schre
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TABLE OF CONTENTS Preface (in Dutch
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3.7 Study 2 74 3.8 Hypotheses 74 3.
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CHAPTER 1: INTRODUCTION AND OVERVIE
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Most supply chain-oriented reductio
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400 square meters) are disappearing
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more. However, Aldi is known to be
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antecedents, such as the brand equi
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of assortment reduction responses a
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assortment benefits and assortment
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2.1 Introduction Out-of-stock (OOS)
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other explanatory studies, we study
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Studies that apply exploratory desi
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Table 2.2: Methodological overview
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Situation-related variables. Situat
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As noted, consumers generally prefe
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2.3.3 The interaction of hedonic le
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For example, supermarkets are usual
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were selected for further analyses.
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example, a key utilitarian benefit
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The mathematical formulation of the
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Table 2.6: Correlation matrix depen
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correlation between the hedonic lev
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2.6 Discussion In this study, we in
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Following the literature on promoti
Page 56 and 57: consider this finding when they att
Page 58 and 59: Appendix 2A: Overview and definitio
Page 60 and 61: CHAPTER 3 UNDERSTANDING THE IMPACT
Page 62 and 63: the Dutch food retailer Edah delist
Page 64 and 65: FMI (1993) conducts natural experim
Page 66 and 67: customers no longer or less frequen
Page 68 and 69: extent, comparable to the tendency
Page 70 and 71: Figure 3.1: Graphical display of as
Page 72 and 73: consumers evaluate assortments that
Page 74 and 75: Figure 3.3: Interaction effect of a
Page 76 and 77: SSI (H1). We expect that the brand
Page 78 and 79: 3.8.4 Store-related antecedents Fin
Page 80 and 81: among the 10 specified product grou
Page 82 and 83: Retail assortment-related anteceden
Page 84 and 85: Table 3.4: Average, standard deviat
Page 86 and 87: 3.10.2 Store switching intentions W
Page 88 and 89: effects indicated in Study 1 (β =
Page 90 and 91: 3.12 Managerial implications Retail
Page 92 and 93: Appendix 3A: Overview and definitio
Page 94 and 95: 4.1 Introduction Since the early 19
Page 96 and 97: changes in assortment perceptions (
Page 98 and 99: Negative sales effects may occur be
Page 100 and 101: therefore might question the existe
Page 102 and 103: Table 4.2: Overview of separate ana
Page 104 and 105: analogously. The resulting complete
Page 108 and 109: Table 4.4: Estimated parameters for
Page 110 and 111: Table 4.5: Estimated parameters for
Page 112 and 113: infrequently in the sample stores a
Page 114 and 115: Measurement. Following Hoch, Bradlo
Page 116 and 117: 4.7 Discussion In this collaborativ
Page 118 and 119: into consequences at the customer l
Page 120 and 121: eduction occurs, and the type of st
Page 122 and 123: Manufacturers in hedonic product gr
Page 124 and 125: 2. When retailers set priorities fo
Page 126 and 127: References Aaker, David A. (1991),
Page 128 and 129: Broniarczyk, Susan M. and Wayne D.
Page 130 and 131: Franses, Philip Hans and Richard Pa
Page 132 and 133: Narasimhan, Chakravartthi, Scott A.
Page 134 and 135: Van Trijp, Hans C.M., Wayne D. Hoye
Page 136 and 137: de rol van het assortiment bij het
Page 138 and 139: die het out-of-stock niveau binnen
Page 140 and 141: Campbell, R.A.J., Rethinking Risk i
Page 142 and 143: Langen, P.W. de, The Performance of
Page 144 and 145: Slager, A.M.H., Banking across Bord
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