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Erschienen in: Neural Computing and Applications 1/2017

07.09.2015 | Original Article

An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome

verfasst von: Arun Kumar Sangaiah, Jagadeesh Gopal, Anirban Basu, Prabhakar Rontala Subramaniam

Erschienen in: Neural Computing and Applications | Ausgabe 1/2017

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Abstract

The offshore/onsite teams’ knowledge transfer (KT) effectiveness is one of the key determinants for achieving the outcome of global software development (GSD) projects. In this study, the significance of offshore/onsite teams (GSD teams) KT effectiveness in GSD projects is measured through various factors: knowledge, team, technology, and organization factors. Moreover, the assessment framework for the integration of knowledge, team, technology, and organization factors for evaluating KT effectiveness in GSD projects has not been adequately available in the existing literature. For this motivation, the main objective of this study is to propose the assessment framework to evaluate offshore/onsite teams KT effectiveness with reference to GSD project outcome. For evaluating KT effectiveness of GSD teams, we have integrated three Fuzzy Multi-Criteria Decision Making (FMCDM) methodologies: (a) Fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), (b) Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and (c) Elimination Et Choix Traduisant la REaite (ELECTRE). Further, the hybridization of fuzzy DEMATEL, TOPSIS, and ELECTRE has not available in the existing literature. Based on this research gap, we have integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating KT effectiveness of offshore/onsite teams in the context of GSD project outcome. Subsequently, the applicability and capability of proposed framework has been validated by software experts at Inowits Software Organization in India.

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Metadaten
Titel
An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome
verfasst von
Arun Kumar Sangaiah
Jagadeesh Gopal
Anirban Basu
Prabhakar Rontala Subramaniam
Publikationsdatum
07.09.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2040-7

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