A Systematic Literature Review of Cognitive Biases in Workplace Decision-Making
Bibliographic record
Abstract
In this paper, a systematic literature review is performed to identify heuristics and biases of decision-making for employees in the workplace. The research starts by utilizing existing literature reviews until 2022 and then conducts its literature review to bridge the gap to 2025. The literature review is conducted with the help of methods from Kitchenham (2004) and Nightingale (2009). The databases EBSCOhost, Scopus, and Web of Science were used for searching related literature. A precise keyword string is used to search, as well as various filtering, in order to get peer-reviewed journal articles. Initially, 221 articles were found and reviewed, and 70 were included in the literature review. The literature review shows an overwhelming amount of studies in investment and finance settings. However, it further indicates a lack of studies in other areas, especially in the workplace setting, such as in Singapore. Furthermore, it overviews the most prominent biases and recommends that further studies in other settings could utilize similar biases. The biases were overconfidence bias, herding bias, and decision avoidance bias. Thus, further research into other fields and regions could utilize these biases to get new insights into these topics.
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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 | 0.042 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.040 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".