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New products, Project management, Regression analysis, Decision making


Research in psychology is increasingly interested in decision-makers' use of heuristics or rules of thumb because they have accuracies close to more complex decision rules and seem particularly useful in difficult decision-making contexts when uncertainty is high and speed is of the essence. One particularly difficult decision setting is the fuzzy front-end of new product development because a large number of product ideas need to be screened to identify the few that should be developed further. This process is currently poorly supported through decision tools and mainly occurs on the basis of managerial “gut-feel”. This study explores managerial “gut-feel” by investigating the performance of simple project screening heuristics: two so-called Fast and Frugal (F&F) heuristics, Take-the-Best and Tallying, and three logistic regression models with 3, 5, and 7 decision variables are used to screen a simulated dataset of 52 projects. Each model's ability to recognize successful projects and correctly reject poor projects is compared against the predictions of the other decision models. The results how that the logistic regression models outperform the F&F models in overall prediction quality and in the ability to predict project failure. However, the Tallying model has an overall performance that is close to the logistic regression and both F&F models are better at predicting success than the logistic regression model. Furthermore, the regression model that only takes 3 decision variables into consideration performs better than the regression models with 5 and all 7 decision variables. This indicates that a simple “less is more” decision approach, which is the basis of managerial “gut-feel”, can be a successful strategy for front-end screening.


This is the publisher's final pdf. Copyright 2011 by PICMET; permission to reprint received from PICMET.

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