Forecasting the success of megaprojects with Judgmental methods

Litsiou, K 2021, Forecasting the success of megaprojects with Judgmental methods , PhD thesis, University of Salford.

[img] PDF
Restricted to Repository staff only until 5 November 2021.

Download (2MB) | Request a copy

Abstract

Forecasting the success of megaprojects is a very difficult and important task because of the complexity of such projects, as well as the large capital investment that is required for the completion of these projects. One could argue that forecasting is not needed in this context: the master Gantt chart of the tasks with assigned person-hours plus the respective Bill of Materials should suffice for an accurate estimation of the duration and cost of a project. If that was the case then every project would finish on time and on budget – but this is far from true as the numerous examples attest: HS2, Channel Tunnel, major IT public projects in NHS, to name a few. In this research, we employ judgmental forecasting methods to predict the success of megaprojects in as series of forecasting experiments. In the first experiment,the participants forecast for one megaproject ('space exploration') with Unaided Judgment (UJ), Structured Analogies (SA) and Interaction Groups (IG) with IG showing the best results since IG>SA>SA. In the second experiment, we use a second megaproject ('a major recreational facility in the very city centre of a major cosmopolis') and see separately the success in terms of excesses in the budget and the duration of the project. Furthermore, the participants forecast the extent to which the socio-economic benefits are realised. We do analyse three different stakeholder perspectives: that of the a) project manager, b) funder(s), and c) the public. We do control for two levels of expertise – novices, and semi-experts, and the participants use UJ, SA, IG and Delphi (D) as well, resulting IG>D>SA>UJ. In the third and final experiment, we qualitatively explore the use of scenarios in forecasting the success of megaprojects.

Item Type: Thesis (PhD)
Contributors: Polychronakis, Y (Supervisor) and Sapountzis, S (Supervisor)
Schools: Schools > Salford Business School
Funders: Manchester Metropolitan Business School
Depositing User: Konstantia Litsiou
Date Deposited: 05 Oct 2021 14:18
Last Modified: 05 Oct 2021 14:18
URI: http://usir.salford.ac.uk/id/eprint/61802

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year