TY - CHAP
T1 - Influencing project success outcomes by utilising advanced statistical techniques and AI during the project initiating process
AU - Hayes, Jennifer
AU - Rahman, Azizur
AU - Mendis, Champake
N1 - Publisher Copyright:
© 2023 selection and editorial matter, Priyanka Harjule, Azizur Rahman, Basant Agarwal and Vinita Tiwari, individual chapters, the contributors.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Employing advanced statistical techniques to intelligent data analysis scenarios within the Retail IT project management industry against an underlying phenomenological Cynefin framework, this study proposes the use of the complementary techniques of factor, cluster, and regression analysis on a stratified random sampling of a large and historical dataset to measure and predict the optimal combination of variables leading to successful project outcomes. Statistical analysis techniques will enable inference from the data that is not otherwise directly measurable and where subjectivity is inherent in human analysis. Delivering statistical validation of the ability to identify Cynefin complexity category at Initiation and predict the optimal assignment of Project Manager and delivery methodology is expected to correlate to project success outcomes. Automating this data analysis in subsequent studies using a cognitive machine learning approach and Robotic Process Automation (RPA) tools will assist in providing recommendations for optimal alignment of complexity ranking, delivery methodology, and project manager skills prior to project commencement.
AB - Employing advanced statistical techniques to intelligent data analysis scenarios within the Retail IT project management industry against an underlying phenomenological Cynefin framework, this study proposes the use of the complementary techniques of factor, cluster, and regression analysis on a stratified random sampling of a large and historical dataset to measure and predict the optimal combination of variables leading to successful project outcomes. Statistical analysis techniques will enable inference from the data that is not otherwise directly measurable and where subjectivity is inherent in human analysis. Delivering statistical validation of the ability to identify Cynefin complexity category at Initiation and predict the optimal assignment of Project Manager and delivery methodology is expected to correlate to project success outcomes. Automating this data analysis in subsequent studies using a cognitive machine learning approach and Robotic Process Automation (RPA) tools will assist in providing recommendations for optimal alignment of complexity ranking, delivery methodology, and project manager skills prior to project commencement.
KW - intelligent data analysis
KW - Cynefin framework
KW - regression analysis
KW - optimal combination
KW - Robotic Process Automation
KW - Project Manager
UR - http://www.scopus.com/inward/record.url?scp=85162693365&partnerID=8YFLogxK
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UR - https://www.taylorfrancis.com/books/edit/10.1201/9781003253051/computational-statistical-methodologies-modeling-artificial-intelligence-priyanka-harjule-azizur-rahman-basant-agarwal-vinita-tiwari?refId=9ff6df33-3e02-4208-ab66-6c131d3f05a5&context=ubx
U2 - 10.1201/9781003253051-13
DO - 10.1201/9781003253051-13
M3 - Chapter (peer-reviewed)
SN - 9781032170800
SP - 254
EP - 272
BT - Computational statistical methodologies and modelling for Artificial Intelligence
A2 - Harjule, Priyanka
A2 - Rahman, Azizur
A2 - Agarwal, Basant
A2 - Tiwari, Vinita
PB - CRC Press
CY - Boca Raton
ER -