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

{"title":"Towards a Criteria-Based Approach to Selecting Human-AI Interaction Mode","authors":"Jessica Irons,&nbsp;Patrick Cooper,&nbsp;Melanie McGrath,&nbsp;Shahroz Tariq,&nbsp;Andreas Duenser","doi":"10.1002/hfm.70022","DOIUrl":"https://doi.org/10.1002/hfm.70022","url":null,"abstract":"<p>Artificial intelligence (AI) tools are now prevalent in many knowledge work industries. As AI becomes more capable and interactive, there is a growing need for guidance on how to employ AI most effectively. The A²C framework (Tariq, Chhetri, Nepal &amp; Paris, 2024) distinguishes three decision-making modes for engaging AI: automation (AI completes a task, including decision/action), augmentation (AI supports human to decide) and collaboration (iterative interaction between human and AI). However, selecting the appropriate mode for a specific application is not always straightforward. The goal of the present study was to compile and trial a simple set of criteria to support recommendations about appropriate AI mode for a given application. Drawing on human factors and computer science literature, we identified key criteria related to elements of the task, worker experience and support needs. From these criteria we built a scoring rubric with recommendation for A²C AI mode. As a preliminary test of this approach, we applied the criteria to cognitive task analysis (CTA) outputs from three case studies within the science domain—genome annotation, biological collections curation and protein crystallization—which provided insights into worker decision points, challenges and expert strategies. This paper describes the method for connecting CTA to A²C, reflecting on the challenges and future directions.</p>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hfm.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657578","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":"Introducing the Revamped PLI: A Versatile Tool for Efficient Workplace Risk Assessment","authors":"Aswin Ramaswamy Govindan,&nbsp;Jiale Zhu,&nbsp;Xinming Li","doi":"10.1002/hfm.70018","DOIUrl":"https://doi.org/10.1002/hfm.70018","url":null,"abstract":"<p>Currently, practitioners face the challenge of selecting assessment tools based on self-report, observational measurements, or direct measurements while considering time and budget constraints. This selection process can be time-consuming and discouraging for practitioners, potentially deterring risk assessments. As a tool designed for lumbar load assessment self-reports, the Physical Load Index (PLI) accommodates all data collection methods, providing an index with three primary input factors (postures, repetition/frequency, and force/load), which gives it the potential to be compatible with all data collection methods and incorporate a comprehensive set of risk factors. However, the inherent subjectivity involved in self-reporting and the lack of risk categories hinder it development as a versatile assessment tool. This study proposes a Revamped PLI, comprising: (1) The illustration of objective data collection for postures, weights, and frequencies. (2) The elimination of impractical body postures through overlap analysis. (3) The creation of five risk categories based on the score range. Subsequently, the comparison based on 92 industrial tasks confirms its reliable risk assessment by comparing it with REBA. The Revamped PLI simplifies tool selection and effectively facilitates the reduction of ergonomic risks in industries.</p>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hfm.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524599","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":"Evaluating the Impact of Mind-Wandering on Driller's Situation Awareness Using Wearable Eye-Tracking Technology","authors":"Su Hao,&nbsp;Fan Siping,&nbsp;Jiang Jiaxin,&nbsp;Wang Jian,&nbsp;Xie Ruiying,&nbsp;Xu Lifei,&nbsp;Wang Xiaoqin,&nbsp;Qing Xin,&nbsp;Zeng Yuxi,&nbsp;Shen Liaoyuan","doi":"10.1002/hfm.70020","DOIUrl":"https://doi.org/10.1002/hfm.70020","url":null,"abstract":"<div>\u0000 \u0000 <p>Drilling accidents often occur due to inadequate situational awareness of drilling workers. The prolonged and tedious monitoring of drilling parameters may result in drilling workers experiencing mind-wandering. To explore this problem, this study employed wearable eye tracking technology to assess the mind-wandering status of drilling workers during work finishing (WF) and investigate the influence of mind-wandering on the situational awareness of these workers. A simulation of monitoring drilling parameters was conducted in the laboratory by 18 drillers, who were asked to fill out situational awareness questionnaires using the global assessment of situational awareness (SAGAT) and had their behavioral and eye movement data collected during the task. The eye movement results revealed that drilling workers experienced mind-wandering during the WF, as evidenced by a significant increase in fixation duration and count, along with a decrease in pupil diameter. This led to a decrease in situational awareness scores and a notable increase in reaction times, indicating that mind-wandering had an impact on the situational awareness of drilling workers. In addition, the eye movement areas of interest (AOIs) were further analyzed to explore drilling worker attention allocation. This study showcases the potential of using wearable eye-tracking technology to identify mind-wandering in drilling workers who are responsible for monitoring drilling parameters. By gaining a deeper insight into the eye movement patterns associated with mind-wandering in drilling workers, we can establish a strong basis for creating interventions that enhance their situational awareness.</p>\u0000 </div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482045","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":"Assessment of Team Performance Based on Teamwork Characteristics in Collaborative Teams","authors":"Yuqing Dang,&nbsp;Xiaoru Wanyan,&nbsp;Shuang Liu,&nbsp;Yuchen Min,&nbsp;Zhen Liao,&nbsp;Tuoyang Zhou,&nbsp;Ning Li","doi":"10.1002/hfm.70021","DOIUrl":"https://doi.org/10.1002/hfm.70021","url":null,"abstract":"<div>\u0000 \u0000 <p>Reliable assessment of team performance based on teamwork characteristics is crucial for optimizing maritime teamwork and ensuring maritime safety. However, the relationship between teamwork characteristics and team performance remains unclear. This study explored teamwork characteristics that contribute to good team performance, and constructed a team performance assessment model based on teamwork characteristics. A total of 84 qualified participants were recruited and divided into 21 teams to perform underwater collaborative tasks on a maritime simulator. Eye movement, communication, scale, and team performance data were collected and divided into input features and outcome features. Based on input features, important teamwork characteristics were obtained, showing that teams with good team performance presented adequate situation awareness for judgment and decision, excellent task management, appropriate decision making, and efficient communication. In addition, machine learning algorithms were applied to train classification models, and the support vector machine (SVM) exhibited the best results, with an accuracy of 83.3%. The team performance assessment model offers an effective auxiliary tool for maritime training to evaluate training effects as well as create more efficient training programs, and is beneficial for optimizing teamwork skills, improving team performance, and ensuring maritime safety.</p>\u0000 </div>","PeriodicalId":55048,"journal":{"name":"Human Factors and Ergonomics in Manufacturing & Service Industries","volume":"35 4","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482043","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 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}