A Methodology for Evaluating How Product Characteristics Impact Choice in Retail Settings with Many Zero Observations: An Application to Restaurant Wine Purchase

Article excerpt

An approach is developed to examine the impact of product characteristics on choice using a quantity-dependent hedonic model with retail panel data. Since panel data for individual products from retail settings can include a large number of zero sales, a modification of the zero-inflated Poisson (ZIP) regression model is proposed for estimation. Results for this model compare favorably to results for alternative hurdle and negative binomial models. An application of this methodology to restaurant wine sales produces useful results regarding sensory characteristics, price, and origin-varietal information.

Key words: hedonic, restaurant, sensory, wine, zero-inflated Poisson (ZIP)


As competition for food markets becomes more intense and food producers look for ways to encourage consumer preference for their products, it is useful to develop methods for understanding the impact of product characteristics on consumer choice. Experimental work and hedonic price analysis both provide some information on consumer choice but cannot address all questions of interest. Such approaches can also produce results which conflict with observed choice behavior in actual retail situations. In experimental work, this can occur because subjects pay closer attention to the object of a study than they would in actual retail settings, thus inflating apparent preference effects. On the other hand, typical applications of hedonic models may have more to do with production costs than with consumer valuation. Further, both approaches tend to limit the descriptive factors that can be examined. The complementary approach of modeling observed retail sales data therefore has the potential to greatly add to our understanding. In this study, a methodology is developed which allows examination of the impact from descriptive information on product choice using a hedonic model with data from a restaurant or retail store.

There are a number of analytical and methodological considerations when using such retail data. First, since price is generally exogenous in these settings, a hedonic quantity model rather than price model is appropriate. Second, labeling, signage, and promotional activities may all be relevant when preparing for data collection. Third, although panel data allow all these pieces of information to be used in examining demand, many zero quantity observations can result. Finally, product choice data can give rise to many forms of response variable, including continuous and categorical data. Here, however, the focus is count data, and so the econometric model selected for analysis must also handle this feature.

These considerations are explored by examining the impact of sensory and other characteristics on wine selection in a restaurant setting. Because the data have many zero observations, a modification of the zero-inflated Poisson (ZIP) model is developed for estimation, and found to provide a better fit to the data than alternatives such as hurdle and negative binomial models.

The next two sections provide background on the theoretical underpinnings of a hedonic quantity approach and a review of economic literature relevant to wine characteristics and quality. This is followed by a discussion of the motivation for modeling wine demand at the restaurant level, and consideration of those factors that can be examined more fully at this level of aggregation. The next section provides a description of the methods and data for this type of analysis and gives details regarding the specific data used. In the remaining sections, the empirical model, results, and conclusions are presented.

Theoretical Model

A principal feature of the approach used here is the underlying quantity-dependent hedonic model. The hedonic approach was originally designed with price as the dependent variable, and assumed that price contains the information inherent in consumer valuation of product attributes. …