Abstract
Engineering has long been influenced by the intuition of its practitioners (Lucietto & Cai Shi, 2022), which enables swift conclusions without extensive analytical reasoning (Elbanna, 2016). While effective in dynamic contexts, intuitive approaches are susceptible to perceptual errors and unconscious cognitive biases (Marcovici & Blume-Marcovici, 2013). Experts in practice acknowledge this limitation (Miskioğlu et al., 2023). Integrating intuitive and analytical thinking is essential for effective decision-making (Calabretta et al., 2016; Szanto, 2022), yet practical strategies for achieving this balance in engineering management remain unexplored.
Inspired by the role of checklists in promoting rational thinking in fields such as medicine and aviation (Marcovici & Blume-Marcovici, 2013), this study investigates the potential of a theoretically grounded decision-making guideline to harmonize intuition and analysis. Using the System Decision Process (SDP) framework (Parnell et al., 2010a), the study examines the empirical grounding of a proposed guideline by addressing two research questions: (RQ1) What are the key steps or stages involved in making a decision? and (RQ2) what are the influences at each stage of the decision-making process that could impact the quality of the decision?
A descriptive cross-sectional survey was conducted to quantify the engagement of engineering managers in decision-making relative to the SDP framework, using a five-point Likert scale. Findings indicated peak engagement during the solution implementation phase (e.g. monitoring: M=4.81 92% “Every time”). In contrast, engagement was noticeably weaker in upstream processes, particularly in research & stakeholders (M=3.5), Value modelling (M=3.38), idea generation (M=3.35), and alternative generation & improvement (M=3.35). This observed pattern raises questions about whether foundational choices receive sufficient scrutiny, which could introduce vulnerability into project outcomes.
Analysis of contextual factors derived from literature that affect decision-making yielded descriptive insights. However, low subscale reliability prevented inferential analysis and highlighted a need for psychometric refinement. Despite this limitation, preliminary empirical support for the SDP framework suggests that the proposed guideline holds promise as a conceptual tool. Future research should strengthen its empirical grounding and evaluate its efficacy across diverse engineering management contexts.