While the assumption of utility-maximizing consumers has been challenged for decades, empirical applications of alternative choice rules are still very recent. We add to this growing body of literature by proposing a model based on Simon's idea of a "satisficing" decision maker. In contrast to previous models (including recent models implementing alternative choice rules), satisficing depends on the order in which alternatives are evaluated. We therefore conduct a visual conjoint experiment to collect search and choice data. We model search and choice jointly and allow for interdependence between them. The choice rule incorporates a conjunctive rule and, contrary to most previous models, does not rely on compensatory tradeoffs at all. The results strongly support the proposed model. For instance, we find that search is indeed influenced by product evaluations. More importantly, the model results strongly support the satisficing stopping rule. Finally, we perform a holdout prediction task and find that the proposed model outperforms a standard multinomial logit model.
This section gives details on data preparation with regard to product prices, missing and otherwise excluded data, and promotions, as well as on summary statistics of the data. Daily Product PricesFor each day, we observe the number of units sold as well as the dollar revenue per product and store. Thus, we calculate prices as dollar revenue divided by the number of units sold.For days with no units sold, this is undefined. However, since prices rarely change in the course of the time-series for a given product at a given store, we can fill in the missing price data with great confidence. As prices do not change at all for some products at some stores, we normalize the log(P RICE svt ) by subtracting its mean for each store-product time series to avoid the problem of separate identification of β 0sv and β 1sv (equation (4) in the main paper) in those cases. Missing and Excluded DataWe are missing data for a total of 329 individual store-product-day points (distributed over 15 of the 169 store-product time-series). In addition to this, we also exclude some of the data for the following reason. The model assumes that the shipments follow a so-called pull strategy, i.e. products are ordered when inventory runs low. This is a reasonable assumption for most of the data. However, for part of the data, the shipments are the result of a push strategy, i.e. the national headquarters decided to send shipments irrespective of current inventory levels, presumably for a promotion. In particular, this happens for five products (across all ten stores) towards the end of the time series. During this period, shipment volumes increase significantly (up to 63 cases per shipment), and consequently occur less frequently. The rare shipments in this period occur on the same day for all stores. Since the shipment model informs the timing and amount of shrinkage by linking the probability of 2 orders to the unobserved inventory and shrinkage, the independence of the shipments from inventory in this period defeats the purpose of modeling the order process. Thus, we restrict ourselves to the more common case of a pull strategy and exclude the data from the period of the push strategy. For two products, this affects the last month of the data; for the other three, we must disregard the last two months. In total, this affects 2,704 store-product-day combinations, leaving us with data on 27,556 store-product-day combinations. SeasonalitySEASON t is a sine curve with wavelength of one year fitted to the average sales per day across all stores and products (excluding Thanksgiving Day as well as the five products for which we excluded the end of the time-series, as they would have biased the estimate for seasonality due to its extraordinarily low and extraordinarily high sales, respectively).Like log(P RICE svt ), the mean of log(SEASON t ) is normalized to 0; log(SEASON t ) then ranges from -.11 to .22. Figure 1 shows the average sales per day with the fitted sine curve.The weekend spikes in sales amount are obvious, as is the expected low numb...
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