Industrial companies today must operate in an increasingly competitive world and need to rethink their organization accordingly. First, industrial strategies concerning technical management need to change and develop. If we consider the specific management of the product design process, managers use indicators during the lifecycle of the product concerning quality, cost and lead-times. For example, some new indicators concerning performance metrics related to cooperation issues among the design team are set up. Secondly, managers increasingly need to integrate the user in the early phases of the design process. The main benefit of this approach is that it allows designers to innovate more rapidly and robustly. The aim of this paper is to introduce a new approach making it possible to calculate a performance indicator concerning use design. The new indicator will help management drive the design process through the integration of use in the product, helping to enhance the usability of the future product, and consequently improve innovation.

We take the first steps towards a shot selection quality model in basketball that incorporates decisional cues that might be predictive, not only of proximal results (e.g., scoring), but also of distal results (e.g., winning/losing the match). 2976 jump-shots from 50 Euroleague matches were sampled, following systematic observation guidelines. The decisional cues under scrutiny were shooting opposition, distance and lateral angle, disposition to offensive rebound and disposition to defensive balance at the moment of shooting. A first set of regressions between decisional cues and proximal results showed higher opposition and distance to decrease the probability of scoring (OR = .81; p < .001 and OR = .89; p = .013); a better disposition towards rebound to increase the chances of catching rebound (OR = 1.57; p < .001); and better defensive balance disposition to decrease the probability of a fast break (OR = 1.27; p < .036). A second set of regressions between proximal and distal results showed shooting and offensive rebound effectiveness to predict total points scored (β = .62; p < .001 and β = .32; p < .001) and game result (winning/losing the game; OR = 1.12; p < .001 and OR = 1.05; p = .021). Finally, an analysis of the impact of decisional cues on distal results showed a positive relationship between likelihood of winning and average team’s disposition to offensive rebound (OR = 1.18; p = .018). These results cast light on the actual weights (validities) of the different cues involved in predicting outcomes of shooting decisions. This evidence could help coaches provide objective feedback about players’ shooting performance beyond hit percentages.