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

We aimed to provide causal evidence on the contradictory effects of projection-based intelligent assistance systems (IASs) for nine motivational work characteristics (MWCs). IASs are increasingly implemented in assembly to counteract rising cognitive workload due to individualized manufacturing processes. However, how IASs enhance or restrict MWCs is largely unknown. We conducted two studies with experimental vignette methodology. In Study 1 (N₁ = 169 German employees), we manipulated an assembly workplace (with IAS vs. without IAS) and tested whether findings indicating only positive effects of IASs in the support of a simple assembly process can be transferred to more complex assembly processes. In Study 2 (N₂ = 176 German employees), we manipulated again the assembly workplace (with IAS vs. without IAS) and in addition the dynamic of product changes (task rotation after 1 h vs. no task rotation). Analyzing the data with SPSS 27, we found increased feedback from job and information processing and decreased work scheduling, decision-making, and work methods autonomy when working with IAS. In Study 2, we did not find the main or interaction effects of task rotation on MWCs. Our experimental evidence suggests that working with IASs represents a double-edged sword regarding MWCs and that the effect of task rotation is limited. Hence, our results provide vital theoretical implications for a much-needed work design theory that delineates how new technologies shape work design and practical implications for modern assembly.

Artificial intelligence (AI) virtual assistants are rapidly growing, permeating people's daily lives and work. However, some trust and risk issues prevent the acceptance and use of AI virtual assistants by users. Thus, understanding the roles of trust and perceived risk in user acceptance of AI virtual assistants is crucial. This study develops a comprehensive research model based on unified theory of acceptance and use of technology (UTAUT) to explain user acceptance of AI virtual assistants. This model extends UTAUT by adding users' perception of trust and risk. The research model and hypotheses are validated through structural equation modeling with a sample of 926 AI virtual assistant users. Results show that gender is significantly related to behavioral intention to use, education is positively related to trust and behavioral intention to use, and usage experience is positively related to attitude toward using. UTAUT variables, including performance expectancy, effort expectancy, social influence, and facilitating conditions, are positively related to behavioral intention to use AI virtual assistant. Trust and perceived risk respectively have positive and negative effects on attitude toward using and behavioral intention to use AI virtual assistants. Trust and perceived risk play equally important roles in explaining user acceptance of AI virtual assistants. Theoretical and practical implications of the current AI virtual assistant acceptance model and directions for future research are discussed.

Aggressive driving significantly impacts traffic safety, and heavy-duty vehicle drivers are more liable for causing serious crashes. This paper analyzes drivers' aggressive driving behavior from the vehicle type perspective and identifies the influencing factors of aggressive driving behavior through artificial neural network (ANN) and Shapley additive explanations (SHAPs). Using Kaggle's open-source aggressive driving data, we establish an ANN model to identify driving styles, where road conditions, environmental conditions, and vehicle parameters are independent variables and driving style is a dependent variable. The following measurements, including accuracy, recall, precision, and F1 score, are used to evaluate the model's performance, and the neural network got 85.33%, 82.32%, 84.16%, and 0.8308, respectively. To illustrate the influence of independent variables, the SHAP algorithm is used to analyze the model's feature importance. It was found that illumination and weather conditions influenced the model's performance along with the vehicle length. The number of lanes relates to driving style, and there were more aggressive driving behaviors on two-lane roads than on single-lane roads. Besides, heavy-duty vehicle drivers were more likely to drive aggressively in wet road conditions and indulge in aggressive driving behaviors at night. Particularly, drivers of heavy-duty vehicles were more likely to drive aggressively, provided that the vehicle in front was also a heavy-duty vehicle. These findings inform heavy-duty vehicle drivers to reduce aggressive driving behavior. The information is suitable for inclusion in driver education programs, thus improving traffic safety.

Aerial refueling is an extender of air combat capability that has received widespread attention with the development of the military field, particularly the mental workload of the pilot performing the aerial refueling task, as it is the key to the aerial refueling success rate. Therefore, this study analyzes the behavior of receiver aircraft pilots from rendezvous to the separation phase and constructs a mental workload model to improve the aerial refueling success rate. First, the time-channel-action unit network was constructed based on Petri net and multiple resource theory (MRT). Second, the mental workload evaluation model was constructed from three dimensions: conventional resource occupation, additional resource occupation, and time occupation. Finally, a simulated experiment was conducted, and the results showed that the mental workload obtained by the model constructed in this study exhibits a high positive correlation with subjective mental workload, pupil diameter, and blink rate, surpassing the accuracy of the traditional MRT model.

The emergency department (ED) is at the forefront of the chemical, biological, radiological, and nuclear (CBRN) response. This study adopted a multilevel systems approach using the human factors/ergonomics (HFE) method of hierarchical task analysis (HTA) with document analyses of CBRN plans to represent work-as-Imagined. Work-as-imagined was compared with data from semistructured interviews using prompt cards for CBRN scenarios (n = 57) representing work-as-done. The aim was to provide methodological evidence for the use of HTA with an empirical synthesis of the ED in response to CBRN events. HTA was the preferred systems mapping tool because it aligns with a systems thinking approach, allows multiple-level comparisons, highlights variability, and has an established usability track record. This study demonstrates the usability of HTA in the context of the ED responding to a CBRN event. The findings for core CBRN concepts included (1) liaise and communicate, (2) isolate and contain, and (3) personal protective equipment.

In digital interactive interfaces with high visual workloads, it is important for operators to allocate their limited attentional resources appropriately to ensure efficient information collection. The salience, effort, expectancy, value (SEEV) model, which combines top-down and bottom-up attention mechanisms for predicting attention allocation, has been validated in research areas such as piloting, driving, and surgical operations. However, the validity of the SEEV model in the field of robotic arm teleoperation has not yet been thoroughly studied. The primary purpose of this study was to confirm the feasibility of the SEEV model for operator visual attention allocation prediction in a robotic arm teleoperation scenario. The improved ITTI algorithm, distance-measuring tool, Delphi method, and lowest ordinal algorithm were adopted to qualify the four factors of the SEEV model, which also contributed to salience and expectancy quantification methods. Accordingly, an attention allocation prediction model in a robotic arm teleoperation scene was constructed. To verify the validity of the prediction model, 20 participants were recruited to control the robotic arm using V-REP simulation software, and their fixation durations were recorded using an eye tracker as an attention allocation indicator. Participants controlled the robotic arm according to the experimental requirements and operational tasks, such as grasping and placing the target. The results demonstrated that the theoretical data based on the SEEV prediction model are significantly related to the proportion of fixation durations. The experiment verifies the suitability of the SEEV prediction model, and it is anticipated to be utilized in the optimization of interactive interfaces for robotic arm teleoperation.

With the rapid development of online car-hailing, the related crashes have become a key issue with public concern. Identifying and predicting aggressive driving behaviors is critical to reduce traffic crashes. In this study, we propose a method to recognize aggressive driving behavior based on association classification, with multisource features being employed, including driver emotion, vehicle kinematic characteristics, and road environment. The model performs best in a 10-fold cross-test when the minimum support and minimum confidence are set as 0.01 and 0.8, respectively. Besides, we also compare the performance of aggressive driving behavior recognition classifiers constructed using association classification with other rule-based classification methods, including ID3, C4.5, CART, and Random Forest. The results show that association classification performs better than other classification competitors. Thirty-six if–then rules generated by the association classification are used to analyze the influencing factors and associated mechanisms of aggressive driving behavior. It is found that aggressive driving behavior is highly correlated with driver anger and disgust emotions. Aggressive driving behavior is more likely to occur when no passengers are in the car than the case with passengers. Driver entertainment behavior and passenger interference also affect driving behavior. Moreover, drivers are prone to aggressive driving when making a U-turn. This research not only proposed a new identification method for aggressive driving behavior but also provided a comprehensive understanding of the associated influencing factors which thus benefit the further research and development of safety assistance driving devices.

Industrial collaborative robots have become increasingly important in recent years due to their ability to work safely and efficiently alongside humans. As a result, there is a growing need for evaluation standards to ensure the quality of collaborative robots. However, existing studies only consider system-centered and technical aspects of collaborative robots, and there is a lack of research on user-centered quality evaluation. In this study, we identified 21 user requirements based on a user-centered design framework and confirmed the limitations of existing quality standards by reviewing the standard clauses for collaborative robots. It was found that user needs to be related to performance, safety, and even usability and enjoyment are already being expressed according to the user-centered design framework, but the quality standards for these needs only present design principles or do not consider them at all. This study provides information on the quality attributes that need to be fulfilled to satisfy user requirements and suggests the need and direction for further research on the user-centered evaluation of collaborative robots. Accordingly, the user's perception and experience of collaborative robots are expected to improve.

A number of studies analyze the link between the presence of psychosocial risk factors and work-related musculoskeletal disorders. The increase in job insecurity (JI) has resulted in a growing focus on its likely role as a risk factor within occupational health. Accordingly, the aim of this research was to carry out a systematic review of studies that include JI among the relevant risk factors, specifically drawing data from this variable to observe the significance of its association with musculoskeletal disorders (MSDs). For this purpose, a literature search was carried out: from a sample of 859 studies found and 23 were selected after applying the eligibility criteria. Fifteen studies (65.2% of the selection) presented statistically significant results regarding the link between JI and MSDs: the upper limbs and back were the body areas most affected by this association. In sum, JI should be considered a potential precursor of MSDs. Therefore, further study on this psychosocial risk and its association with these types of pathologies is necessary.

This commentary paper will describe how the discipline of human factors and ergonomics (HFE) can help to close the gender data gap, which is prevalent across many domains and arises due to a lack of data capturing female metrics and viewpoints. HFE is a domain-independent discipline that seeks to understand human performance and well-being with respect to the interactions that humans engage in and the environments that they inhabit. HFE therefore presents an opportunity to understand how gender influences human performance, effective design, social interactions, and environmental factors. This paper argues that a sociotechnical systems approach is essential when reviewing equality, diversity, and inclusivity issues, without which attempts to close the gender data gap will not go far enough. Following the sociotechnical systems approach in HFE, the micro-, meso-, and macro-levels of system design with respect to closing the gender data gap are reviewed. We discuss these issues in relation to a case study example of a crash test dummy. A checklist approach for researchers is presented, which identifies key questions that prompt where gender should be considered in the research process across these levels of sociotechnical systems.