Schedule delays of major projects: what should we do about it?

References

• Allen, M. (2017). The Sage encyclopedia of communication research methods. Los Angeles, CA: Sage.
   Google Scholar
• American Planning Association. (2005). Planners called to help end inaccuracies in public project revenue. http://www.planning.org/newsreleases/2005/ftp040705.htm
   Google Scholar
• Assaf, S., & Al-Hejji, S. (2006). Causes of delay in large construction projects. International Journal of Project Management, 24, 349–357.
   Google Scholar
• Awojobi, O., & Jenkins, G. (2016). Managing the cost overrun risks of hydroelectric dams: An application of reference class forecasting techniques. Renewable and Sustainable Energy Reviews, 64, 19–32.
   Web of Science ®Google Scholar
• Bajaj, N., Tyagi, A., & Agarwal, R. (2006). Software estimation: A fuzzy approach. ACM SIGSOFT Software Engineering Notes, 31, 1–5.
   Google Scholar
• Boltz, M., & Yum, Y. N. (2010). Temporal concepts and predicted duration judgments. Journal of Experimental Social Psychology, 46(6), 895–904.
   Web of Science ®Google Scholar
• Bordat, C., McCullouch, B., Labi, S., & Sinha, K. (2004). An analysis of cost overruns and time delays of INDOT projects. West Lafayette, IN: Purdue University.
   Google Scholar
• Bryde, D., Unterhitzenberger, C., & Joby, R. (2018). Conditions of success for earned value analysis in projects. International Journal of Project Management, 36, 474–484.
   Web of Science ®Google Scholar
• Connolly, T., & Dean, D. (1997). Decomposed versus holistic estimates of effort required for software writing tasks. Management Science, 43, 1029–1045.
   Web of Science ®Google Scholar
• Darrin, G., & Mervyn, L. (2002). Evaluating the risks of public private partnerships for infrastructure projects. International Journal of Project Management, 20(2), 107–118.
   Google Scholar
• Den Heijer, A. (2011). Managing the university campus: Information to support real estate decisions. Delft: Eburon Uitgeverij.
   Google Scholar
• Dunning, D., Meyerowitz, J., & Holzberg, A. (1989). Ambiguity and self-evaluation: The role of idiosyncratic trait definitions in self-serving assessments of ability”. Journal of Personality and Social Psychology, 57(6), 1082–1090.
   Web of Science ®Google Scholar
• Ellwood, P., Grimshaw, P., & Pandza, K. (2017). Accelerating the innovation process: A systematic review and realist synthesis of the research literature. International Journal of Management Review, 19(4), 510–530.
   Web of Science ®Google Scholar
• Fabricius, G., & Büttgen, M. (2015). Project managers’ overconfidence: How is risk reflected in anticipated project success? Business Research, 8, 239–263.
   Google Scholar
• Federal Highway Administration. (2014). Financial plans major project financial plan guidance. Washington, DC: Federal Highway Administration.
   Google Scholar
• Flyvbjerg, B. (2006). From nobel prize to project management: Getting risks right. Project Management Journal, 37(3), 5–15.
   Google Scholar
• Flyvbjerg, B. (2007). Cost overruns and demand shortfalls in urban rail and other infrastructure. Transportation Planning and Technology, 30(1), 9–30.
   Web of Science ®Google Scholar
• Flyvbjerg, B. (2011). Over budget, over time, over and over again. In P. Peter Morris (Ed.), The Oxford handbook of project management (pp. 321–344). Oxford: Oxford University Press.
   Google Scholar
• Flyvbjerg, B., Bruzelius, N., & Rothengatter, W. (2003). Megaprojects and risk: An anatomy of ambition. Cambridge: Cambridge University Press.
   Google Scholar
• Flyvbjerg, B., & COWI. (2004). Procedures for dealing with optimism bias in transport planning: Guidance document. London: UK Department for Transport.
   Google Scholar
• Flyvbjerg, B., Holm, M., & Buhl, S. (2002). Understand costs in public works projects: Error or lie? Journal of the American Planning Association, 68(3), 279–295.
   Web of Science ®Google Scholar
• Flyvbjerg, B., Holm, M., & Buhl, S. (2006). Inaccuracy in traffic forecasts. Transport Reviews, 26(1), 1–24.
   Web of Science ®Google Scholar
• Ford, A. (2002). Boom and bust in power plant construction: Lessons from the California electricity crisis. Journal of Industry, Competition and Trade, 2, 59–74.
   Google Scholar
• Gray, A., MacDonell, S., & Shepperd, M. (1999). Factors systematically associated with errors in subjective estimates of software development effort: The stability of expert judgment. Proceedings of the Sixth International Software Metrics Symposium. Boca Raton, FL. November 4th-6th.
   Google Scholar
• Gunsalus, C. (2012). The young professional’s survival guide: From crab fares to moral snares. Cambridge, MA: Harvard University Press.
   Google Scholar
• Halkjelsvik, T., & Jørgensen, M. (2012). From origami to software development: A review of studies on judgment-based predictions of performance time. Psychological Bulletin, 138(2), 238–271.
   PubMed Web of Science ®Google Scholar
• Heemstra, F., & Kusters, R. (1991). Function point analysis: Evaluation of a software cost estimation model. European Journal of Information Systems, 1, 229–237.
   Google Scholar
• Highways Agency. (2013). The project control framework handbook. London: Highway Agency.
   Google Scholar
• Hill, J., Thomas, L., & Allen, D. (2000). Experts’ estimates of task durations in software development projects. International Journal of Project Management, 18, 13–21.
   Google Scholar
• Jørgensen, M. (2004). Top-down and bottom-up expert estimation of software development effort. Information and Software Technology, 46(1), 3–16.
   Web of Science ®Google Scholar
• Kahneman, D., & Tversky, A. (1979). Prospect theory: Analysis of decisions under uncertainty. Econometrica, 47, 263–291.
   Web of Science ®Google Scholar
• Kitamura, R., Yoshii, T., & Yamamoto, T. (Eds.). (2009). Expanding sphere of travel behavior research: Selected papers from the 11th conference of the International Association for Travel Behavior Research. Bingley: Elsevier.
   Google Scholar
• Liu, H., Jiang, C., Liu, Y., Hertogh, M., & Lyu, X. (2018). Optimism bias evaluation and decision-making risk forecast on bridge project cost based on reference forecasting: Evidence from China. Sustainability, 10(11), 1–29.
   Web of Science ®Google Scholar
• Liu, L., Wehbe, G., & Sisovic, J. (2010). The accuracy of hybrid estimating approaches: A case study of an Australian state road and traffic authority. The Engineering Economist, 55, 225–245.
   Web of Science ®Google Scholar
• Lovallo, D., & Kahneman, D. (2003). Delusions of success: How optimism undermines executives’ decisions. Harvard Business Review, 81(7), 56–63.
   PubMed Web of Science ®Google Scholar
• MacDonald, M. (2002). Review of large public procurement in the UK. Surrey: Mott MacDonald.
   Google Scholar
• Makovšek, D., Tominc, P., & Logožar, K. (2012). A cost performance analysis of transport infrastructure construction in Slovenia. Transportation, 39(1), 197–214.
   Web of Science ®Google Scholar
• Miller, M., & Szimba, E. (2015). How to avoid unrealistic appraisal results?: A concept to reflect the occurrence of risk in the appraisal of transport infrastructure projects. Research in Transportation Economics, 49, 65–75.
   Web of Science ®Google Scholar
• Naderpajouh, N., & Hastak, M. (2014). Quantitative analysis of policies for governance of emergent dynamics in complex construction projects. Construction Management and Economics, 32(12), 1222–1237.
   Google Scholar
• Nijkamp, P., & Ubbels, B. (1999). How reliable are estimates of infrastructure costs?: A comparative analysis. International Journal of Transport Economics, 26(1), 23–53.
   Google Scholar
• Padalkar, M., & Gopinath, S. (2016). Six decades of project management research: Thematic trends and future opportunities. International Journal of Project Management, 34(7), 1305–1321.
   Web of Science ®Google Scholar
• Park, J. E. (2020). Curbing cost overruns in infrastructure investment: Has reference class forecasting delivered its promised success? Working Paper.
   Google Scholar
• Pollack, J., Helm, J., & Adler, D. (2018). What is the iron triangle, and how has it changed? International Journal of Managing Projects in Business, 11(2), 527–547.
   Web of Science ®Google Scholar
• Prater, J., Kirytopoulos, K., & Ma, T. (2017). Optimism bias within the project management context. International Journal of Managing Projects in Business, 10(2), 380–385.
   Web of Science ®Google Scholar
• Public Accounts Committee of the House of Commons. (1989). Improving the delivery of government IT projects. London: House of Common Stationary Office.
   Google Scholar
• Sanna, L., Parks, C., Chang, E., & Carter, S. (2005). The hourglass is half full or half empty: Temporal framing and the group planning fallacy. Group Dynamics: Theory, Research, and Practice, 9(3), 173–188.
   Web of Science ®Google Scholar
• Sauer, C., & Cuthbertson, C. (2003). The state of IT project management in the UK 2002–2003. Oxford: Sommerville.
   Google Scholar
• Sauer, C., Gemino, A., & Reich, B. (2007). The impact of size and volatility on IT project performance. Communications of the ACM, 50(11), 79–84.
   Web of Science ®Google Scholar
• Shmueli, O., Pliskin, N., & Fink, L. (2016). Can the outside view approach improve planning decisions in software development projects?. Information Systems Journal, 26(4), 395–418.
   Web of Science ®Google Scholar
• Siemiatycki, M. (2009). Academics and auditors: Comparing perspectives on transportation project cost overruns. Journal of Planning Education and Research, 29(2), 142–156.
   Web of Science ®Google Scholar
• Sovacool, B., & Cooper, C. (2013). The governance of energy megaprojects: Politics, hubris and energy security. Cheltenham: Edward Elgar.
   Google Scholar
• Sovacool, B., Gilbert, A., & Nugent, D. (2014). Risk, innovation, electricity infrastructure and construction cost overruns: Testing six hypotheses. Energy, 74, 906–917.
   Web of Science ®Google Scholar
• Standish Group. (2014). Chaos. https://www.projectsmart.co.uk/white-papers/chaos-report.pdf
   Google Scholar
• Zirger, B., & Hartley, J. (1994). A conceptual model of product development cycle time. Journal of Engineering and Technology Management, 11, 229–251.
   Web of Science ®Google Scholar