Peter Stüttgen


PhD Student in Marketing

Tepper School of Business

5000 Forbes Avenue

Carnegie Mellon University

Pittsburgh, PA 15213

Office: Tepper 312 (GSIA)

Phone: (412) 268-5355


Curriculum Vitae

Areas of Interest

Non-Compensatory Choice Models

Empirical Models of Consumer Behavior

Bayesian Statistics

Working Papers

A Satisficing Choice Model

(with Peter Boatwright and Robert T. Monroe, under review at Marketing Science)

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 these 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. We find that search is indeed influenced by product evaluations. More importantly, the model results strongly support the satisficing stopping rule. Finally, we discuss the different nature of choice predictions for the satisficing model and for a standard choice model and show how the satisficing model results in predictions that are more useful to retailers

Identifying Stockouts and Shrinkage at the Micro-Level

(with Peter Boatwright and Joseph B. Kadane, under review at the Journal of the American Statistical Association)

We present a model to estimate the occurrence of stockouts and shrinkage (product loss) at the daily level using data that is readily available to suppliers. This allows the suppliers to monitor the retail outlets if the incentives to avoid stockouts are not perfectly aligned, without having to physically check for stockouts in the stores. The model is estimated using data provided by a supplier to a national grocery chain. We find that the average stockout rates vary widely between stores, identifying two stores with stockout rates twice as high as for most other stores. Thus, the model identifies stores that may have management issues. Similarly, we find that the amount of shrinkage varies significantly across stores, where the maximum estimated shrinkage rate in our data is 20 times larger than the minimum estimated shrinkage rate. Moreover, the model can distinguish between store stockouts (i.e., zero inventory in the store) vs. shelf stockouts (i.e., an empty shelf, but some inventory in other parts of the store). Finally, we find that both the stockout rate and the ratio of store stockouts to shelf stockouts are positively related to a measure of relative variability of sales, and that the percentage of expected sales lost due to a stockout is significant (ranging from 39% to 98%).

Adding Significance to the Implicit Association Test

(with Joachim Vosgerau, Claude Messner, and Peter Boatwright, invited for resubmission at the Journal of Personality and Social Psychology)

The Implicit Association Test has become one of the most widely used tools in psychology and related research areas. The IAT's validity and reliability, however, are still debated. We argue that the IAT's reliability, and thus its validity, strongly depends on the particular application (i.e., which attitudes are measured, which stimuli are used, and the sample). Thus, whether a given application for a given sample will achieve sufficient reliability cannot be answered a priori. Using extensive simulations, we demonstrate an easily calculated post-hoc method based on standard significance tests that enables researchers to test whether a given application reached sufficient reliability levels. Applying this straightforward method can thus enhance confidence in the results of a given IAT. In an empirical test, we manipulate the sources of error in a given IAT experimentally and show that our method is sensitive to otherwise unobservable sources of error.