Project Managers’ Cognitive Style in Decision Making: A Perspective from Construction Industry
Bibliographic record
Abstract
A large and growing body of literature focuses on the project manager’s technical issues while ignoring theso-called soft project management. This study proposes that there is a need to extend management practices forproject management from a human-related factor by incorporating the cognitive styles in the decision-makingprocess towards the accomplishment of a successful project. The study aims to introduce the concept ofcognitive styles in decision making by project managers. Furthermore, it takes up a discussion on the definitionand types and roles of cognitive styles by linking these styles with project manager’s decision making behaviour.A questionnaire-based survey using Cognitive Style Instrument (CoSI), covering 110 project managers in theconstruction industry, was used to determine the cognitive styles among Malaysian project managers. The paperprovides the empirical findings that reveal that Malaysian project managers used Cognitive Style on a daily basisin their project environment. Planning Style was the most preferred style, followed by Knowing Style andCreating Style. This study is significant both for researchers and practitioners to shed light upon the ways inwhich project managers organise and process information and make judgements from a psychologicalperspective. Moreover, this study contributes more generally to the evolving understanding of the humanintellect process in project management. This paper introduces the concept of Cognitive Styles as an importanthuman-related factor for project managers, contributing to the body of project manager’s soft skills.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".