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Erschienen in: International Journal of Machine Learning and Cybernetics 2/2018

12.05.2015 | Original Article

Integration of fuzzy DEMATEL and FMCDM approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome

verfasst von: Jagadeesh Gopal, Arun Kumar Sangaiah, Anirban Basu, Xiao Zhi Gao

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2018

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Abstract

The offshore/on-site teams’ knowledge transfer effectiveness is one of the key predictors for evaluating the outcome of global software development (GSD) project. This study aims at proposing an assessment framework that can be used to evaluate the GSD teams’ (offshore/on-site) knowledge transfer effectiveness in the following criteria: knowledge, team, technology, and organization factors. For evaluating the teams’ knowledge transfer effectiveness in GSD projects, we have integrated the fuzzy decision making trial and evaluation laboratory (DEMATEL) and fuzzy multi-criteria decision making (FMCDM) approaches. Moreover, the proposed DEMATEL-FMCDM methodology is used to determine priority weights of each evaluation criterion and rate knowledge transfer effectiveness on the GSD project outcome from software service outsourcing perspective into two dimensions: product success, (schedule, cost improvement, etc.) and service quality (personal satisfaction, and successful collaboration). The proposed methodology is used for the prediction of a possible rating mechanism for determining the GSD project outcome through knowledge transfer criterions of GSD teams. It also facilitates the determination of knowledge transfer effectiveness of GSD teams’ towards the success of the GSD project outcome and initiates actions to improve the project outcome in GSD environment. This study demonstrates a survey research method that addresses 35 knowledge transfer factors assessed by 70 software professionals at Inowits software organization in India.

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Metadaten
Titel
Integration of fuzzy DEMATEL and FMCDM approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome
verfasst von
Jagadeesh Gopal
Arun Kumar Sangaiah
Anirban Basu
Xiao Zhi Gao
Publikationsdatum
12.05.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0370-5

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