Exploring the effects of automation malfunction on team communication and coordination in ships' engine rooms
Bibliographic record
Abstract
Abstract Automation malfunctions within complex socio‐technical systems reserve the potential to significantly affect human performance. In the context of maritime operations, varying consequences of automation malfunction on human performance can be observed. This study introduced a two‐step research framework to examine the repercussions of such malfunctions, particularly those related to communication and coordination among human teams in ship engine rooms. Initially, a qualitative semi‐structured interview was conducted with seven professional marine engineers to explore the potential impact of hypothetical automation malfunction on team communication. Subsequently, a quantitative survey involving 32 professional marine engineers employed coordination demand analysis (CDA) to scrutinize changes in team coordination resulting from malfunction. The findings indicate that an automation malfunction within an engine room can precipitate an abrupt overload of the socio‐technical system. This can significantly increase communication frequency among engineers, particularly in relation to the physical and organizational aspects of the environment. Furthermore, the study highlights the influence of disparate levels of expertise among team members on coordination demands. A positive correlation was discovered between differences in expertise and increased coordination demands within a team. These insights underscore the necessity for future research on human–automation interaction, specifically focusing on individual differences and nontechnical 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".