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Entropy 2015, 17(5), 2834-2852; doi:10.3390/e17052834

A Novel Risk Metric for Staff Turnover in a Software Project Based on Information Entropy

School of Information, Yunnan University of Finance and Economics, Kunming 650221, China
School of Software, Yunnan University, Kunming 650091, China 
Academic Editor: Raúl Alcaraz Martínez
Received: 12 March 2015 / Revised: 20 April 2015 / Accepted: 27 April 2015 / Published: 4 May 2015
(This article belongs to the Section Information Theory)
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Staff turnover in a software project is a significant risk that can result in project failure. Despite the urgency of this issue, however, relevant studies are limited and are mostly qualitative; quantitative studies are extremely rare. This paper proposes a novel risk metric for staff turnover in a software project based on the information entropy theory. To address the gaps of existing studies, five aspects are considered, namely, staff turnover probability, turnover type, staff level, software project complexity, and staff order degree. This paper develops a method of calculating staff turnover risk probability in a software project based on the field, equity, and goal congruence theories. The proposed method prevents the probability of subjective estimation. It is more objective and comprehensive and superior than existing research. This paper not only presents a detailed operable model, but also theoretically demonstrates the scientificity and rationality of the research. The case study performed in this study indicates that the approach is reasonable, effective, and feasible. View Full-Text
Keywords: staff turnover; risk metric; software project; information entropy staff turnover; risk metric; software project; information entropy

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jiang, R. A Novel Risk Metric for Staff Turnover in a Software Project Based on Information Entropy. Entropy 2015, 17, 2834-2852.

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