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Quantifying Success Factors for IT Projects-An Expert-Based Bayesian Model
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0003-2017-7914
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0003-3089-3885
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
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2014 (English)In: Information systems management, ISSN 1058-0530, E-ISSN 1934-8703, Vol. 31, no 1, 21-36 p.Article in journal (Refereed) Published
Abstract [en]

Large investments are made annually to develop and maintain IT systems. Successful outcome of IT projects is therefore crucial for the economy. Yet, many IT projects fail completely or are delayed or over budget, or they end up with less functionality than planned. This article describes a Bayesian decision-support model. The model is based on expert elicited data from 51 experts. Using this model, the effect management decisions have upon projects can be estimated beforehand, thus providing decision support for the improvement of IT project performance.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2014. Vol. 31, no 1, 21-36 p.
Keyword [en]
IT project success factors, decision support, Bayesian networks, Noisy-OR, expert elicitation
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-129084DOI: 10.1080/10580530.2014.854033ISI: 000329911500003Scopus ID: 2-s2.0-84892973418OAI: oai:DiVA.org:kth-129084DiVA: diva2:649950
Note

QC 20140131. Updated from accepted to published.

Available from: 2013-09-19 Created: 2013-09-19 Last updated: 2017-12-06Bibliographically approved

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fulltext(745 kB)486 downloads
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Franke, UlrikLagerström, Robert

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