A STAMP-Informed framework for classifying interorganizational risk management challenges in ports
Bibliographic record
Abstract
• Reveals systemic challenges undermining interorganizational risk management in ports. • Combines natural language processing with systems-theoretic analysis of challenges. • Applies the Systems-Theoretic Accident Model and Processes (STAMP) to risk management. • Offers a replicable framework to improve coordination and safety across organizations. Interorganizational Risk Management (IRM) is critical in port operations, where actors such as port authorities, shipping companies, and terminal operators must coordinate safety and risk governance across organizational boundaries. Traditional risk management approaches often neglect how failures emerge from systemic misalignments in control structures, feedback mechanisms, and interorganizational coordination. This study systematically classifies IRM challenges in port settings using the Systems-Theoretic Accident Model and Processes (STAMP) as an analytical lens to explore how such challenges are conceptualized in existing literature. Rather than modeling specific control systems, we map IRM challenges to STAMP components to identify commonly disrupted control functions. Based on a structured analysis of 50 peer-reviewed studies published between 2014 and 2024, we extract 233 quotes describing IRM challenges. Using a hybrid methodology that combines inductive coding, semantic clustering supported by natural language processing (NLP), and deductive mapping to STAMP, we identify 14 IRM challenge categories. These are grouped into three control layers: Strategic, Operative, and Adaptive. Each category is linked to specific STAMP control structure components to illustrate patterns in how interorganizational coordination is affected. Findings show that IRM challenges are concentrated in areas involving feedback loops, control logic, and constraints. This approach offers a novel system-theoretic classification of IRM challenges and contributes a transparent, replicable method for analyzing challenges in complex organizational networks. The paper also identifies future research opportunities, including expert validation, cross-regional comparison, and the use of organizational mechanisms to support IRM in ports.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".