Structuring the Decision-Making Process Using Quantitative Options Valuation

This study focuses on enhancing decision-making processes in the construction industry by investigating quantitative decision-making models. The construction industry is known for its diverse projects and inherent risks. Effective decision-making is crucial for project success, but it faces challenges due to various factors. The research explores biases and heuristics in decision-making, specifically in entrepreneurial and managerial contexts, with a focus on two biases: overconfidence and representativeness. Data collection involved surveys administered to entrepreneurs and managers in prominent industrial sectors. The surveys measured the levels of overconfidence and representativeness in decision-making. Additionally, the study examined commonly used decision-making models in construction, including multi-criteria decision analysis, decision support systems, decision trees, and mathematical optimization techniques. The objective was to gain insights into applying quantitative models and improve the understanding of decision-making processes in construction projects. The survey achieved a response rate of 54%, and participating managers were categorized based on their two-digit Standard Industrial Classification (SIC) codes, specifically in the 1300, 3400, 3500, 3600, and 3800 categories. Rigorous statistical analyses were conducted to evaluate potential response bias. Comparing usable responses to non-respondents using chi-square tests, no significant evidence of bias was found (χ^2 (4) = 3.973, p = .59). Moreover, a further analysis explored potential response bias across the broader set of five two-digit SIC categories, and again, no significant evidence of bias was observed (χ^2 (5) = 1.782, p = .878). The findings of this study contribute to the improvement of decision-making in construction projects and provide valuable insights into the practical application of quantitative models. By addressing biases and exploring effective decision-making approaches, this research aims to enhance project success within the complex construction industry.