PROJECT TITLE :

SVM-Based Techniques for Predicting Cross-Functional Team Performance: Using Team Trust as a Predictor

ABSTRACT:

Due to the characteristics of cross-useful groups, trust is crucial for cross-functional groups to reinforce performance. However, as a important factor, trust had been neglected in previous team performance models. In this paper, we have a tendency to investigate whether trust will be used as a predictor of cross-functional team performance by proposing a prediction model. The inputs of the model are both team structural and contextual (SC) factors, and project method (PP) factors, that are two major sources that kind team trust. The output of the model is totally different levels of team performance, that consists of internal performance and external performance. The support vector machine techniques are used to determine the model. Results show that prediction accuracy is high (84.ninety five%) when using each SC and PP factors as inputs, while PP factors have better prediction accuracy than SC factors on team performance and internal performance. It is instructed that team trust will be used as a smart predictor of cross-functional team performance. In observe, this paper presents a better understanding of the connection between trust and performance in cross-useful teams, and so, enhances practitioners' managerial skills. It additionally offers reference for managers to dynamically management and predict team performance throughout project period.