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Quantifying uncertainty and equivocality in engineering projects

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  • Andrew Chang
  • Chih-Chiang Tien

Abstract

Engineering work is intangible and difficult to measure and manage. Engineering project tasks entail various degrees of uncertainty and equivocality (U&E). Quantifying U&E of project tasks helps understand and manage engineering work. This study developed an assessing questionnaire from five sources of U&E and tested it on a large subway design project. Questions were answered by nine discipline managers of 58 tasks and the quantified U&E scores reflect reality. It was found that uncertainty is higher than equivocality for most tasks. The U&E reduction test shows that task U&E decrease over one year and different reduction patterns exist. Task-possessed information contributes the largest decrease. This means information from planning, past projects or experiences is very important to performing new projects. The developed questionnaire can be a useful tool to help managers better understand and plan project tasks.

Suggested Citation

  • Andrew Chang & Chih-Chiang Tien, 2006. "Quantifying uncertainty and equivocality in engineering projects," Construction Management and Economics, Taylor & Francis Journals, vol. 24(2), pages 171-184.
    • Handle: RePEc:taf:conmgt:v:24:y:2006:i:2:p:171-184
    DOI: 10.1080/01446190500310353
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    References listed on IDEAS

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    1. Anna Dubois & Lars-Erik Gadde, 2002. "The construction industry as a loosely coupled system: implications for productivity and innovation," Construction Management and Economics, Taylor & Francis Journals, vol. 20(7), pages 621-631.
    2. Raymond E. Levitt & Jan Thomsen & Tore R. Christiansen & John C. Kunz & Yan Jin & Clifford Nass, 1999. "Simulating Project Work Processes and Organizations: Toward a Micro-Contingency Theory of Organizational Design," Management Science, INFORMS, vol. 45(11), pages 1479-1495, November.
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    Cited by:

    1. Sarah Zelt & Jan Recker & Theresa Schmiedel & Jan vom Brocke, 2018. "Development and validation of an instrument to measure and manage organizational process variety," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-25, October.
    2. Madduma Kaluge Chamitha Sanjani Wijewickrama & Nicholas Chileshe & Raufdeen Rameezdeen & Jose Jorge Ochoa, 2022. "Information Processing for Quality Assurance in Reverse Logistics Supply Chains: An Organizational Information Processing Theory Perspective," Sustainability, MDPI, vol. 14(9), pages 1-31, May.

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