Human Factors and Ergonomics in Manufacturing & Service Industries最新文献

{"title":"A Psychophysiological Model Based on Machine Learning Algorithms for Evaluating Commercial Airline Pilots' Mental Workload in Flight-Simulation Context","authors":"Lei Wang,&nbsp;Shan Gao,&nbsp;Nan Zhang","doi":"10.1002/hfm.70019","DOIUrl":"https://doi.org/10.1002/hfm.70019","url":null,"abstract":"<div>\u0000 \u0000 <p>Pilot errors account for the majority of flight accidents, many of which are influenced by mental workload. This study introduces a data-driven psychophysiological model, utilizing machine learning algorithms, to evaluate pilots' mental workload. We conducted a flight-simulation experiment involving twenty commercial airline pilots, assessing their mental workload under varying levels of task demand and visibility conditions. Psychophysiological responses were recorded, and machine learning algorithms were employed to analyze the data. To evaluate model performance, we used a leave-one-subject-out cross-validation method and calculated area under the curve values. Our findings indicate that psychophysiological metrics vary in their sensitivity to changes in pilots' mental workload. Notably, the Gradient Boosting Decision Tree algorithm demonstrated the highest classification performance under high-visibility conditions, while the Gaussian Naive Bayes algorithm excelled under low-visibility conditions. These results suggest pilots' mental workload can be effectively identified through psychophysiological metrics combined with machine learning algorithms. Furthermore, visibility conditions may influence the model's classification performance. This model offers a complementary approach to the subjective evaluation currently used by flight instructors to assess pilots' mental workload management capabilities during flight training and certification. It also provides a data-driven tool aligned with evidence-based training principles, enhancing the evaluation of pilots' mental workload management capabilities in flight scenarios.</p>\u0000 </div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Silence Is Deafening: Exploring the Impacts of Serious Incidents on Practitioners Across the Outdoor and Adventure Programs Work System","authors":"Clare Dallat,&nbsp;Denise Mitten,&nbsp;Stuart Slay,&nbsp;Virginia Mitchell,&nbsp;Deb Ajango","doi":"10.1002/hfm.70017","DOIUrl":"https://doi.org/10.1002/hfm.70017","url":null,"abstract":"<p>Findings from studies within safety-critical domains such as healthcare confirm that professionals can experience emotional distress, often long-lasting, from their involvement in serious incidents. Known as “second victims,” these professionals commonly report reactions such as fear, guilt, shame, self-doubt, anger, and disappointment. However, little is currently known regarding the impact of these events on the multiple stakeholders situated further across the work system (e.g., the initial call receiver in the office, managers, coordinators, recruitment, training, and executive staff). This article reports on a study investigating the psychological, emotional and relational impact of serious incidents on practitioners situated across organizational hierarchies within the global outdoor and adventure programs sector. A total of 147 respondents reported 171 incidents, 73 of which were fatal. Respondents occupied a range of roles during these incidents, including instructor, coordinator, managers, and senior directors. Findings reveal that individuals across a wide range of organizational roles—including those not physically present at the incident scene—reported a range of personal and professional psychological, emotional and relational impacts. The most common effects included hypervigilance upon returning to work and negative impacts on personal relationships, experienced by over half of the respondents. These findings have important implications for leaders in safety-critical domains, highlighting the need for whole-of-work system post-incident responses that actively support the well-being of all involved, regardless of their role or proximity to the incident.</p>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hfm.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Luddites? Counterproductive Work Behavior and Its Correlates, Including Work Characteristics, Stress at Work, and Job Satisfaction Among Employees Working With Industrial and Collaborative Robots","authors":"Anita Pollak,&nbsp;Elżbieta Biolik,&nbsp;Agata Chudzicka-Czupała","doi":"10.1002/hfm.70016","DOIUrl":"https://doi.org/10.1002/hfm.70016","url":null,"abstract":"<div>\u0000 \u0000 <p>Human-robot interaction (HRI) is integral to Industry 4.0, yet its psychological aspects remain insufficiently explored. In particular, relatively little is known about differences in the organizational and individual factors contributing to counterproductive work behaviors (CWB) among employees working with industrial robots and collaborative robots (cobots). This deficiency highlights the need to deepen our understanding of socially undesirable organizational behaviors that might occur in HRI and their potential correlates to better align with the human-centered focus of the evolving Industry 5.0. The first aim of our study was to investigate whether work characteristics, job satisfaction, and stress at work are related to CWB (overall and in two dimensions—sabotage and withdrawal) in the total sample and the two subgroups, including industrial robot operators and cobot operators. Based on the stressor-emotion model of CWB, we expected that these organizational and individual factors would contribute to CWB among employees working with robots. The second aim of our study was to examine differences in CWB and its potential correlates (i.e., work characteristics, work stress, and job satisfaction) among industrial robot operators and cobot operators. We found no significant correlation between work stress and CWB, including its dimensions of sabotage and withdrawal. However, our results showed that cobot operators reported more favorable physical work conditions and ergonomics, lower work stress, higher job satisfaction, and greater overall CWB compared to industrial robot operators. These results underscore the distinctive psychological dynamics in HRI among industrial robot operators and cobot operators, which might contribute to differences in CWB in both groups of employees. Our study also suggests potential directions for future research on the predictors of CWB and moderators and mediators of the stress-CWB relationship in robotic work environments.</p></div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Hospital Built Environment Hazards Using HART and FRAM Frameworks: A Clinical Simulation Study","authors":"Natália Ransolin,&nbsp;Colleen Cheek,&nbsp;Matthew Wooler,&nbsp;Nick Towle,&nbsp;Robyn Clay-Williams","doi":"10.1002/hfm.70015","DOIUrl":"https://doi.org/10.1002/hfm.70015","url":null,"abstract":"<p>In Situ simulations of work in health settings have been adopted to proactively identify hazards and manage risks related to the built environment (BE). In particular, video-recorded simulations allow repeated reviews and debriefings of scenarios. This study evaluated BE hazards influencing clinical performance and patient outcomes based on video In Situ simulation of emergency scenarios before BE occupation. Four retrospective In Situ simulation and debriefing sessions of two emergency scenarios (ventricular fibrillation and acute myocardial infarction) were conducted with 12 medical and nursing staff participants in a hitherto new resuscitation bay of an Emergency Department (ED) in Australia. Data were analyzed according to the Hazard Assessment Remediation Tool (HART) and Functional Resonance Analysis Method (FRAM) by independent clinical and HFE researchers. Each 10-s video fragment was associated with FRAM functions (i.e., tasks performed), aspects (e.g., input, output, preconditions), agents and variabilities of FRAM outputs to identify latent and active hazards according to the HART categories (i.e., slip/trip/fall/injury risk; impaired access to patient or equipment; obstructed path; poor visibility; and infection risk). HART categories were used to identify BE latent and active hazards, translated into potential and actual variabilities of the FRAM function outputs that arise from the suboptimal BE conditions. FRAM models of each emergency scenario were developed, 45 BE hazards were identified and 18 recommendations to the ED BE were linked to the precondition aspects of FRAM functions as a strategy to mitigate the output variabilities. Our two key contributions were (1) combining FRAM and HART as a methodology; and (2) using clinical simulations to identify BE hazards.</p>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hfm.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Function Allocation Strategy for Human–Machine Systems in Armored Vehicles Based on Evolutionary Game Theory and System Dynamics","authors":"Qingyang Huang,&nbsp;Yuning Wei,&nbsp;Jingyuan Zhang,&nbsp;Xiucheng Xu,&nbsp;Mingyang Guo,&nbsp;Fang Xie,&nbsp;Xiaoping Jin","doi":"10.1002/hfm.70014","DOIUrl":"https://doi.org/10.1002/hfm.70014","url":null,"abstract":"<div>\u0000 \u0000 <p>The complex battlefield conditions can cause unreasonable function allocations of the human–machine system in armored vehicles, decreasing the combat safety and efficiency. Aiming to optimize the function allocation in typical combat tasks, this study proposes a cooperation strategy by integrating evolutionary game theory with system dynamics. Taking the crew and the automated system as different players in the evolutionary game model, the payment matrix is established. The evolutionary stable strategies of the replicator dynamic system are discussed under different evolution routes, revealing the time-variant dynamic features of the human–machine evolutionary game. Moreover, the system dynamics model is built to explain the internal interaction behavior and mechanism of the human–machine system. The simulation results indicate that the game with different initial system states can converge to different equilibrium points. The analysis of evolutionary processes with different model parameters demonstrates that the game strategies are more sensitive to the cost of an increase in mental workload and the payoff of an increase in trust and decision accuracy. With the adoption of the proposed function allocation strategy, the mental workload coefficient decreases by 36.09%, while the trust level and the decision accuracy increase by 33.59% and 38.83%, respectively. The proposed strategy highlights the significant impact of mental workload, trust, and decision accuracy on game approaches, and explains the internal interaction behavior and mechanism between evolutionary game strategies and the dynamics of the human–machine system. This study can provide a theoretical reference and modeling approach for human–machine cooperation in armored vehicles.</p>\u0000 </div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Error Identification and Risk Prioritization in Human–Robot Collaboration in Manufacturing","authors":"Li Liu,&nbsp;Shixiong Sheng,&nbsp;Jiansi Li,&nbsp;Siu Shing Man","doi":"10.1002/hfm.70012","DOIUrl":"https://doi.org/10.1002/hfm.70012","url":null,"abstract":"<div>\u0000 \u0000 <p>Human error recognition and subsequent prioritization are the most important tasks in the human–robot reliability analysis. This study aims to address the issue of human error in human–robot collaboration (HRC) by developing a model for identifying and assessing risks. First, the key tasks performed by operators during HRC were identified using the hierarchical task analysis, and a cognitive model was built based on information processing theory. This model breaks down the collaboration process into stages and identifies potential human errors at each step. Next, failure modes and effects analysis and evidence reasoning were applied to quantify the risk levels of these errors. Finally, the risks associated with human errors were measured, ranked, and compared with existing studies, and recommendations were made. The findings showed that the leading causes of safety risks in HRC are fatigue, illegal operations, error operations, misjudgments, and misperception. The perception stage of the process was found to carry the highest risk level, which means operators are more likely to make errors during the perception stage than during decision or execution, largely due to factors such as fatigue, distraction, and misperception. These results provide important theoretical support for improving safety in HRC and offer practical suggestions for refining risk management strategies in HRC systems.</p>\u0000 </div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Injury-Related Acute Pain in Elite Sport: A Model of Athletes' Pain Management Strategies","authors":"Anne-Claire Macquet,&nbsp;Marion Trousselard","doi":"10.1002/hfm.70013","DOIUrl":"https://doi.org/10.1002/hfm.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>Acute pain from injury is a major concern in sport and public health. Athletes are known to have higher pain tolerance than the general population, suggesting that they may manage pain differently. This study aims to elicit the “how” and “why” of acute pain management strategies when coping with sport injuries. A thematic analysis of qualitative data involved 12 injured elite athletes (EAs) to ascertain their drug and nondrug acute pain management strategies. Results showed 14 strategies relating to two functions: problem-focused and emotion-focused. Six problem-focused strategies aimed to change the relationship between an individual and his/her environment: (a) modulating activity; (b) seeking instrumental support; (c) goal setting; (d) encouraging oneself; (e) taking painkillers; and (f) reframing the perception of pain. Eight emotion-focused strategies aimed to manage emotional distress: (a) diverting attention; (b) acceptance; (c) relativising pain; (d) repeating negative thoughts; (e) ignoring pain; (f) seeking emotional support; (g) hoping and praying; and (h) self-blame. EAs were shown to favor three strategies: modulating activity, diverting attention, and seeking instrumental support. Results showed a repertoire of acute pain management strategies and the function each strategy served. The variety of pain management strategies reinforces the coping functions (Lazarus and Folkman 1984) and suggests newly-identified flexibility in coping with acute pain extending beyond previous results on chronic pain. Results enriched the pain management approach concerning nondrug-strategies for patients in acute pain. They could offer new perspectives on human factors in health to improve acute pain management in the general population.</p>\u0000 </div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pilot Fatigue Prediction Method Based on Dynamic Bayesian Networks","authors":"Yao Zhou,&nbsp;Dengkai Chen,&nbsp;Jianghao Xiao,&nbsp;Yao Xiao,&nbsp;Yihui Lu,&nbsp;Youyi Zhang","doi":"10.1002/hfm.70011","DOIUrl":"https://doi.org/10.1002/hfm.70011","url":null,"abstract":"<div>\u0000 \u0000 <p>Pilots of long-haul aircraft face a variety of challenges, including unstable flight environments, confined and narrow cockpit spaces, complex human–machine system operations, multiple tasks, and long-haul flight times. This study analyzed the factors leading to pilot fatigue from four aspects (human, machine, environment, task) and predicted the fatigue risk of long-haul flights using a dynamic Bayesian networks method. First, we identified factors related to fatigue during long-haul flights from four aspects: human, machine, environment, and task, and established an index system containing 20 fatigue risk factors. Second, 10 experts in the field of aviation evaluated these factors within the fatigue risk system to derive the prior probabilities for the dynamic Bayesian networks on pilot fatigue on long-haul flights. Finally, we introduced the Noisy-OR model to derive the conditional probabilities and calculated the posterior probabilities using the dynamic Bayesian networks. We validated the proposed method with a real case study, and the results showed that this method can predict fatigue during long-haul flights.</p></div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Reliability, Its Framing and Error Bias on Trust in Human-Vehicle Collaboration","authors":"Jue Li,&nbsp;Yilu Ye,&nbsp;Long Liu,&nbsp;Andreas Butz","doi":"10.1002/hfm.70008","DOIUrl":"https://doi.org/10.1002/hfm.70008","url":null,"abstract":"<p>System reliability promotes trust, but may also impair human monitoring performance and in turn affects trust. This effect varies across different errors. This study examined the effect of automation reliability (100%, 75%, and 50%) and its framing (negative and positive description of reliability), and error bias (false alarm and miss) on user trust and its related factors in the automated driving system (ADS). Each participant completed 16 trials with human-vehicle collaboration task in a static driving simulator. The results showed that ADS with higher reliability positively impact user trust, but negatively impact situation awareness. Users' trust was higher in false alarm (FA) events than in miss events, but task success and situation awareness were higher in miss events. This study revealed an unusual negative correlation between trust and situational awareness in human-vehicle collaboration and provided possible insights into the internal factors of error bias in automation. Our finding has implications for reliability disclosure strategies and trust calibration.</p>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hfm.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Optimization of the an Health Tourism Organization: An Integrated Approach Based on Resilience Engineering and Emotional Intelligence","authors":"Homayoun Oghbatalab,&nbsp;Mahdi Hamid,&nbsp;Behnaz Salimi,&nbsp;Masoud Rabbani","doi":"10.1002/hfm.70003","DOIUrl":"https://doi.org/10.1002/hfm.70003","url":null,"abstract":"<div>\u0000 \u0000 <p>Every year, health tourism organizations (HTOs) provide numerous patients worldwide with specialized or less costly medical services. Any crisis or uncertainty can have detrimental effects on the performance of these organizations, the behavior and emotions of staff, as well as the economy of the host country. Therefore, it is essential to evaluate the resilience of these organizations and the behavior of their staff in facing challenges and crises. In this study, we assessed the performance of an HTO in Tehran using resilience engineering and emotional intelligence indicators. The required data were collected using standard questionnaires, and data envelopment analysis was employed to study the performance of this HTO. Sensitivity analysis was applied to evaluate the performance of the HTO concerning each indicator. Finally, the strengths, weaknesses, opportunities, and threats matrix was applied to propose strategies aimed at enhancing the performance of the studied HTO.</p></div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}