Book contents
- Cambridge Handbook of Project Behavior
- Cambridge Handbook of Project Behavior
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- Part I Guiding Principles
- Part II Deviating from Plans
- Part III Practical Tips
- Chapter 13 Setting Realistic Project Goals
- Chapter 14 At the Brink of a Project
- Chapter 15 The Roles of Governance Mechanisms to Manage Misbehaviors in the Project Front-End
- Chapter 16 Coping with Changes to the Plan
- Chapter 17 Project Behavior, Disruptions, and the Antifragility Spectrum
- Chapter 18 Curbing Cost Increases and Benefit Shortfalls
- Afterword
- Index
- References
Chapter 14 - At the Brink of a Project
Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening
from Part III - Practical Tips
Published online by Cambridge University Press: aN Invalid Date NaN
Book contents
- Cambridge Handbook of Project Behavior
- Cambridge Handbook of Project Behavior
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- Part I Guiding Principles
- Part II Deviating from Plans
- Part III Practical Tips
- Chapter 13 Setting Realistic Project Goals
- Chapter 14 At the Brink of a Project
- Chapter 15 The Roles of Governance Mechanisms to Manage Misbehaviors in the Project Front-End
- Chapter 16 Coping with Changes to the Plan
- Chapter 17 Project Behavior, Disruptions, and the Antifragility Spectrum
- Chapter 18 Curbing Cost Increases and Benefit Shortfalls
- Afterword
- Index
- References
Summary
Traditional project management literature often portrays heuristics as flawed shortcuts leading to errors, advocating for rational, debiasing strategies to prevent cost overruns and benefit shortfalls. This is problematic as heuristics can be effective. Building on Gigerenzer’s concept of fast-and-frugal heuristics, this study examines the use of such smart heuristics by senior managers in a large engineering consultancy firm during the early bid/no-bid decision-making phase of infrastructure projects. Employing a qualitative method from the naturalistic decision-making program, the research uncovers a decision strategy termed "thresholding." This strategy distills extensive experience and interpretation of ambiguous information into binary decisions, effectively de-selecting projects that could be potentially disastrous. The approach also gives credence to agency, as it only deselects disasters but keeps many potential alternatives in the portfolio to mature into potentially ‘good projects’. At the same time, it addresses Flyvbjerg’s call for some scrutiny at the front end of projects to avoid catastrophic projects that start on the wrong premises. Our chapter adds to the debate on the Hiding Hand by not being concerned with the “hidden”, but instead, with what can be known in the early fuzzy front-end of projects.
Information
- Type
- Chapter
- Information
- Cambridge Handbook of Project Behavior , pp. 216 - 232Publisher: Cambridge University PressPrint publication year: 2025
References
Ahmad, I. (1990). Decision‐support system for modeling bid/no‐bid decision problem. Journal of Construction Engineering and Management, 116(4), 595–608.CrossRefGoogle Scholar
Albar, F. M., and Jetter, A. (2011). An investigation on Fast and Frugal Model for new project screening. PICMET: Portland International Center for Management of Engineering and Technology, Proceedings.Google Scholar
Bagies, A., and Fortune, C. (2006). Bid/no-bid decision modelling for construction projects (pp. 511–521). In Proceedings of the 22nd annual ARCOM conference. ARCOM.Google Scholar
Bartunek, J. M., and Louis, M. R. (1996). Insider/outsider team research. In Insider/outsider team research. SAGE.Google Scholar
Berg, N. (2014). Success from satisficing and imitation: Entrepreneurs’ location choice and implications of heuristics for local economic development. Journal of Business Research, 67(8), 1700–1709.10.1016/j.jbusres.2014.02.016CrossRefGoogle Scholar
de Araújo, M. C. B., Alencar, L. H., and de Miranda Mota, C. M. (2022). Classification model for bid/no‐bid decision in construction projects. International Transactions in Operational Research, 29(2), 1025–1047.10.1111/itor.13037CrossRefGoogle Scholar
Chen, Y. Q., Zhang, S. J., Liu, L. S., and Hu, J. (2015). Risk perception and propensity in bid/no-bid decision-making of construction projects. Engineering, Construction and Architectural Management, 22(1), 2–20.10.1108/ECAM-01-2013-0011CrossRefGoogle Scholar
Chisala, M. L. (2017). Quantitative bid or no-bid decision-support model for contractors. Journal of Construction Engineering and Management, 143(12), 04017088.10.1061/(ASCE)CO.1943-7862.0001407CrossRefGoogle Scholar
Crandall, B. W., and Hoffman, R. R. (2013). Cognitive task analysis (pp. 1–19). In Lee, J. D. and Kirlik, A. (eds.). The Oxford handbook of cognitive engineering. Oxford Univeristy Press.Google Scholar
Dhami, M. K. (2003). Psychological models of professional decision making. Psychological Science, 14, 175–180.CrossRefGoogle ScholarPubMed
Drew, D., Skitmore, M., and Lo, H. P. (2001). The effect of client and type and size of construction work on a contractor’s bidding strategy. Building and Environment, 36(3), 393–406.10.1016/S0360-1323(00)00009-3CrossRefGoogle Scholar
Egemen, M., and Mohamed, A. N. (2007). A framework for contractors to reach strategically correct bid/no bid and mark-up size decisions. Building and Environment, 42(3), 1373–1385.10.1016/j.buildenv.2005.11.016CrossRefGoogle Scholar
Eisenhardt, K. M. (1989). Building theories from case study research. The Academy of Management Review, 14(4), 532–550.10.2307/258557CrossRefGoogle Scholar
Eisenhardt, K. M., and Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32.CrossRefGoogle Scholar
Eriksson, T., and Kadefors, A. (2017). Organisational design and development in a large rail tunnel project: Influence of heuristics and mantras. International Journal of Project Management, in press. https://doi.org/10.1016/j.ijproman.2016.12.006.CrossRefGoogle Scholar
Flyvbjerg, B. (2008). Curbing optimism bias and strategic misrepresentation in planning: Reference class forecasting in practice. European Planning Studies, 16(1), 3–21. https://doi.org/10.1080/09654310701747936.CrossRefGoogle Scholar
Flyvbjerg, B. (2016). The fallacy of beneficial ignorance: A test of Hirschman’s Hiding Hand. World Development, 84, 176–189.10.1016/j.worlddev.2016.03.012CrossRefGoogle Scholar
Garcia-Retamero, R., and Dhami, M. K. (2009). Take-the-best in expert-novice decision strategies for residential burglary. Psychonomic Bulletin & Review, 16, 163–169.10.3758/PBR.16.1.163CrossRefGoogle ScholarPubMed
Gigerenzer, G., and Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143.10.1111/j.1756-8765.2008.01006.xCrossRefGoogle ScholarPubMed
Gigerenzer, G., and Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62(1), 451–482.10.1146/annurev-psych-120709-145346CrossRefGoogle ScholarPubMed
Gigerenzer, G., and Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669.10.1037/0033-295X.103.4.650CrossRefGoogle ScholarPubMed
Gigerenzer, G., Hertwig, R., and Pachur, T. (eds.). (2011). Heuristics: The foundations of adaptive behavior. Oxford University Press.10.1093/acprof:oso/9780199744282.001.0001CrossRefGoogle Scholar
Goldstein, D. G., and Gigerenzer, G. (2009). Fast and frugal forecasting. International Journal of Forecasting, 25(4), 760–772.10.1016/j.ijforecast.2009.05.010CrossRefGoogle Scholar
Gunduz, M., and Al-Ajji, I. (2022). Employment of CHAID and CRT decision tree algorithms to develop bid/no-bid decision-making models for contractors. Engineering, Construction and Architectural Management, 29(9), 3712–3736.10.1108/ECAM-01-2021-0042CrossRefGoogle Scholar
Han, S. H., and Diekmann, J. E. (2001). Making a risk-based bid decision for overseas construction projects. Construction Management and Economics, 19(8), 765–776.10.1080/01446190110072860CrossRefGoogle Scholar
Hartono, B., and Yap, C. M. (2011). Understanding risky bidding: A prospect-contingent perspective. Construction Management and Economics, 29(6), 579–593.CrossRefGoogle Scholar
Hoffman, R. (1998). Use of the critical decision method to elicit expert knowledge: A case study in the methodology of cognitive task analysis. Human Factors, 40(2), 158–170.10.1518/001872098779480442CrossRefGoogle Scholar
Ika, L. A. (2018). Beneficial or detrimental ignorance: The straw man fallacy of Flyvbjerg’s test of Hirschman’s Hiding Hand. World Development, 103, 369–382.10.1016/j.worlddev.2017.10.016CrossRefGoogle Scholar
Ika, L. A., Love, P. E. D., and Pinto, J. K. (2022). Moving beyond the Planning Fallacy: The emergence of a new principle of project behavior. IEEE Transactions on Engineering Management, 69(6), 3310–3325.10.1109/TEM.2020.3040526CrossRefGoogle Scholar
Kahneman, D., and Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 66(3), 497–527.Google Scholar
Knudsen, C. (2003). Pluralism, scientific progress, and the structure of organization theory (pp. 262–288). In Tsoukas, H. and Knudsen, C. (eds.). The Oxford handbook of organization theory. Oxford University Press.Google Scholar
Li, G., Zhang, G., Chen, C., and Martek, I. (2020). Empirical bid or no bid decision process in international construction projects: Structural equation modeling framework. Journal of Construction Engineering and Management, 146(6), 04020050.CrossRefGoogle Scholar
Lin, C.-T., and Chen, Y.-T. (2004). Bid/no-bid decision-making – a fuzzy linguistic approach. International Journal of Project Management, 22(7), 585–593.10.1016/j.ijproman.2004.01.005CrossRefGoogle Scholar
Lipshitz, R., Klein, G., and Orasanu, J. (2001). Focus article: Taking stock of naturalistic decision making. Journal of Behavioural Decision Making, 352, 331–352.Google Scholar
Love, P. E. D., Ika, L. A., and Pinto, J. K. (2022). Homo heuristicus: From risk management to managing uncertainty in large-scale infrastructure projects. IEEE Transactions on Engineering Management, 1–10.Google Scholar
Luan, S., Schooler, L., and Gigerenzer, G. (2011). A signal-detection analysis of fast-and-frugal trees. Psychological Review, 118(2), 316–338.10.1037/a0022684CrossRefGoogle ScholarPubMed
Mathews, S. (2010). Innovation portfolio architecture. Research Technology Management, 53(6), 30–40.10.1080/08956308.2010.11657660CrossRefGoogle Scholar
Marzouk, M., and Mohamed, E. (2018). Modeling bid/no bid decisions using fuzzy fault tree. Construction Innovation, 18(1), 90–108.10.1108/CI-11-2016-0060CrossRefGoogle Scholar
Maytorena, E., Winch, G., Freeman, J., and Kiely, T. (2007). The influence of experience and information search styles on project risk identification performance. IEEE Transactions on Engineering Management, 54(2), 315–326.10.1109/TEM.2007.893993CrossRefGoogle Scholar
Militello, L. G., and Hutton, R. J. (1998). Applied cognitive task analysis ( ACTA ): A practitioner’s toolkit for understanding cognitive task demands. Ergonomics, 41(11), 1618–1641.10.1080/001401398186108CrossRefGoogle ScholarPubMed
Pachur, T., and Bröder, A. (2013). Judgment: A cognitive processing perspective. Wiley Interdisciplinary Reviews: Cognitive Science, 4(6), 665–681.Google ScholarPubMed
Raasch, C., Lee, V., Spaeth, S., and Herstatt, C. (2013). The rise and fall of interdisciplinary research: The case of open source innovation. Research Policy, 42(5), 1138–1151.10.1016/j.respol.2013.01.010CrossRefGoogle Scholar
Rolstadås, A. a A., Pinto, J. K. b J. K., Falster, P. c P., Venkataraman, R. b R., Rolstadås, A. a A., Pinto, J. K. b J. K., Falster, P. c P., and Venkataraman, R. b R. (2015). Project decision chain. Project Management Journal, 46(4), 6–19.10.1002/pmj.21517CrossRefGoogle Scholar
Shanteau, J. (1992). Competence in experts: The role of task characteristics. Organizational Behavior and Human Decision Processes, 53, 252–266.10.1016/0749-5978(92)90064-ECrossRefGoogle Scholar
Stingl, V., and Geraldi, J. (2021). A research agenda for studying managerial decision making through the lenses of simple heuristics. Technological Forecasting and Social Change, in press.10.1016/j.techfore.2020.120367CrossRefGoogle Scholar
Stingl, V., and Geraldi, J. (2023). Imagining futures: Cognitive processes of desirable or undesirable project prospections. Technological Forecasting and Social Change, in press. https://doi.org/10.1016/j.techfore.2023.122701.CrossRefGoogle Scholar
Todd, P. M., and Gigerenzer, G. (2012). What is ecological rationality? (pp. 3–30). In Todd, P. M., Gigerenzer, G., and ABC Reserach Group (eds.). Ecological rationality: Intelligence in the world. Oxford University Press.Google Scholar
Todd, P. M., and Miller, G. F. (1999). From pride and prejudice to persuasion: Satisficing in mate search (pp. 287–308). In Gigerenzer, G., Todd, P. M., and The ABC Research Group (eds.). Simple heuristics that make us smart.Oxford University Press.Google Scholar
Tversky, A., and Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.10.1126/science.185.4157.1124CrossRefGoogle ScholarPubMed
Van Oorschot, K. E. a, Langerak, F. b, and Sengupta, K. c. (2011). Escalation, de-escalation, or reformulation: Effective interventions in delayed NPD projects. Journal of Product Innovation Management, 28(6), 848–867.10.1111/j.1540-5885.2011.00846.xCrossRefGoogle Scholar
Williams, T., and Samset, K. (2010). Issues in front-end decision making on projects. Project Management Journal, 43(2), 84–100.Google Scholar
Winch, G. M., and Maytorena, E. (2009). Making good sense: Assessing the quality of risky decision-making. Organization Studies, 30(2–3), 181–203.10.1177/0170840608101476CrossRefGoogle Scholar
Zaqout, I. S., Islam, M. S., Hadidi, L. A., and Skitmore, M. (2022). Modeling bidding decisions and bid markup size for construction projects: A fuzzy approach. Engineering Applications of Artificial Intelligence, 113, 104982.10.1016/j.engappai.2022.104982CrossRefGoogle Scholar
Accessibility standard: WCAG 2.0 A
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The PDF of this book
conforms to version 2.0 of the
Web Content Accessibility Guidelines
(WCAG), ensuring core accessibility principles are addressed and meets the basic
(A)
level of WCAG compliance, addressing essential accessibility barriers.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visualised data also available as non-graphical data
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
Visual Accessibility
Use of colour is not sole means of conveying information
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.