Kansei Engineering (KE) has shown its prominent applicability in service design and development, focusing on translating and interpreting customers’ emotional needs (Kansei) into service characteristics. It is critical and promising as the services sector has grown faster than the manufacturing sector in developing economies in the past three decades. It accounted for an average of 55% of GDP in some developing economies. KE’s flexibility in collaborating with other methods and covering various service settings shows its unique superiority. However, there is criticism of the collected Kansei’s validity and the proposed solution’s robustness. It might be potentially caused by the dynamics of customer emotional needs and various service settings. As a result, Kansei is found to be somewhat fuzzy, unclear, and ambiguous. Hence, a more structured KE methodology incorporating the Kansei text mining process for robust service design is proposed. Kansei text mining approach will extract and summarize service attributes and their corresponding affective responses based on the online product descriptions and customer reviews. The Taguchi method will support the robustness of the proposed improvement strategy. An empirical study of a zoo as a tourism attraction service and its practical implication is discussed and validated in the proposed integrative framework.

Improving traffic safety requires a better knowledge of cognitive Science, especially of the cognitive Ergonomics of road infrastructure and the vehicle–human interface. Driving is a complex task that involves different cognitive modules that have to coordinate simultaneously. Perception, Language, Memory and Mental Representation, Learning, Emotion and Motivation, Attention, Executive Functions, Thinking and Reasoning or Motor Programming should be better understood in order to adapt traffic infrastructure and interfaces to the human information processing. In this work, we review the importance of these cognitive modes in traffic safety. A holistic exam of all cognitive processes related to driving and road safety is recommended to be taken by all governments and in all countries. In this sense, systematic research in drivers’ evaluation and its link to automobile accidents should be implemented. Driver assistance systems can assist to drivers but they cannot substitute the human processing.

The topic of an autonomous system initiating trust repair has generated intense interest from researchers and has led to a stream of empirical works studying the impact of different trust repair strategies. Unfortunately, there does not seem to be a clear pattern of results or systematicity in the experimental manipulations. This is likely due to a lack of a coherent model or theoretical framework of trust repair. We present a possible theoretical model that may explain and predict how different trust repair strategies may work with different autonomous systems, in different situations, and with different people. We have adapted and applied a well-established social cognition theory that has successfully explained and predicted complex attitudinal and behavioural phenomena. The model suggests that significant variance in trust repair results may be partly due to individual differences (e.g. motivation, cognitive abilities), which have not been extensively examined in the literature, and confounded or uncontrolled study parameters (e.g. timing of trust measurement, repair frequency, workload). We hope that this theoretical model stimulates discussion toward a more theory-driven trust repair research agenda to understand basic underlying mechanisms.

Theories and models are central to Human Factors/Ergonomics (HFE) sciences for producing new knowledge, pushing the boundaries of the field, and providing a basis for designing systems that can improve human performance. Despite the key role, there has been less attention to what constitutes a good theory/model and how to examine the relative worth of different theories/models. This study aims to bridge this gap by (1) proposing a set of criteria for evaluating models in HFE, (2) employing a methodological approach to utilize the proposed criteria, and (3) evaluating the existing models of trust in automation (TiA) according to the proposed criteria. The resulting work provides a reference guide for researchers to examine the existing models’ performance and to make meaningful comparisons between TiA models. The results also shed light on the differences among TiA models in satisfying the criteria. While conceptual models offer valuable insights into identifying the causal factors, their limitation in operationalization poses a major challenge in terms of testability and empirical validity. On the other hand, although more readily testable and possessing higher predictive power, computational models are confined to capturing only partial causal factors and have reduced explanatory power capacity. The study concludes with recommendations that in order to advance as a scientific discipline, HFE should adopt modelling approaches that can help us understand the complexities of human performance in dynamic sociotechnical systems.

This study presents a holistic model based on Fuzzy Bayesian Network-Human Factor Analysis and Classification System (FBN-HFACS) to analyze contributing factors in the pandemic, Covid 19, risk management under uncertainty. The model contains three main phases include employing a) HFACS to systematically identify influencing factors based on validation using content validity indicators, b) Fuzzy Set Theory to obtain the prior probability distribution of contributing factors in pandemic risk and address the epistemic uncertainty and subjectivity, and finally, c) Bayesian network to develop causality model of the risk, probabilistic inferences and handle parameter and model uncertainties. The Ratio of Variation (RoV), as BN-driven importance measures, is utilized to conduct sensitivity analysis and explore the most critical factors that yield effective safety countermeasures. The model is tested to investigate four large manufacturing industries in South Khorasan (Iran). It provided a deep understanding of influencing human and organizational factors and captured dependencies among those factors, while quantitative finding paves a way to efficiently make risk-based decisions to deal with the pandemic risks under uncertainty.

Trust is perhaps one of the most important constructs when trying to understand human interactions with systems. As such, there has been a considerable amount of research done on factors such as individual differences in trust, environmental factors affecting trust, and even ‘contagion’ effects of trust. However, there still exists many inconsistencies in the literature that need to be resolved. This manuscript attempts to highlight some of these inconsistencies. This is done to highlight research questions which must be answered by the field. Prioritizing these questions will mitigate the risk of current research efforts being wasted. We used Hoff and Bashir’s (2015) model consisting of learned trust, situational trust, and dispositional trust to structure our review wherein we identified research gaps and discrepancies in the trust in systems literature. Our review contains 17 research questions that will need to be answered before researchers understand how users form trust in systems. Finally, we propose 3 questions that researchers should ask in every study which may help mitigate some of these issues in the future.

Musculoskeletal Disorders (MSDs) have a significant impact on people’s lives as well as their workplaces, organizations, families, society, and national economy. Therefore, the main objective of this study is to investigate the impacts of different risk factors in developing MSD problems. Structural equation modelling has been used to examine the effects of different risk factors on developing MSD problems. Five hypotheses were developed for workplace, personal, biomechanical, psychosocial, and organisational risk factors to examine the positive relation with MSD problems generation. Results showed that biomechanical risk factors, including repetitive motion, vibration, force, posture, and deviation from neutral body alignment, have significant impacts on the development of MSD problems. Similar results were found for workplace, personal, psychosocial, and organisational risk factors. Therefore, either the single risk factor or collectively contributes significantly to MSD problems generation. Decision-makers can use this study to analyse the impacts of different factors on the generation of MSD problems within their industries or organizations. To the best of the author’s knowledge, this study is the first and foremost approach to determine the impacts of the critical risk factors on developing MSD problems through an organised and scientific approach.

Educational institutions are now concentrating to develop new initiative methods for recognizing the ability of the students through evaluation of the student’s answer sheets. The manual evaluation of the answer sheet is a burdensome process for the tutors, which consumes more time and increases stress of the tutors. Hence, the advanced method is required for the automatic assessment of the student’s mark sheet which saves the time of the tutors and meets the demand of the educational institution. In this research, the student performance is evaluated through the MCQ test using the neural networks in the VLab platform. The computational complexity and the overfitting issues are greatly reduced by the feature extraction process through Term Frequency-Inverse Document Frequency (TF-IDF) technique. The effectiveness of the proposed method is manifested through the comparative analysis. The accuracy, precision, and recall attained by the proposed student’s performance prediction based on Neural Network while considering training percentage are found to be 0.9416, 0.9364, and 0.9502 respectively. The accuracy, precision, and recall attained by the proposed student’s performance prediction based on Neural Network, while considering the K-fold value are found to be 0.9475, 0.9474, 0.9538, respectively.

To reduce ergonomic risks for their workforce, many industries in Thailand seek assistance from professional ergonomists. However, most academic programs are delivered by instructors with limited human factor and ergonomics (HFE) background, who incorporate either physical or cognitive parts of HFE. To reliably assess and design systems according to HFE principles and standards, programs should be provided by creditable HFE professionals and based on holistic HFE knowledge. The objective of the present study was to initiate a transformation of the professional development in Thailand. The process included a consolidation of the details of HFE education through questionnaires, and identification of requirements from industries through a focus group interview. The results showed a prevalent lack of holistic considerations of HFE knowledge and a primary focus on physical ergonomics. Problems with lack of resources and basic knowledge in design were also reflected by concerns from the industry regarding limited experience, design competency and use of objective methodologies of HFE practitioners. This information was subsequently used to constitute the development of preliminary competencies and a pilot certification system. The proposed competencies and system were then disseminated and additional requirements that need to be incorporated into the professional HFE system were identified.

During the transition period when connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) coexist on the roadway, miscommunication and improper interactions may lead to accidents due to lack of awareness of each other’s intentions. The most promising approach to this problem is to view roadway transportation as a cyber-physical-social system consisting of CAV, HDV, and infrastructure subsystems. Although adaptations of infrastructure are as critical as the technological advances of vehicles, the role of infrastructure in CAV and HDV interactions has not been fully acknowledged. We consider the roadway transportation system from the system-of-systems perspective, taking a human-centred approach that integrates the behaviours of human drivers and CAVs with the design and enhancement of transportation infrastructure. We provide an overview of prior studies regarding information-processing and communication of the subsystems. Interactions between HDVs and infrastructure are summarised by human driving behaviours and HDV crash analysis. Interactions between HDVs and CAVs focus on how they perceive and predict actions of each other. Interactions between CAVs and infrastructure are characterised by possible adaptations of infrastructure to support CAV navigation. Lastly, we propose a human-centred framework to provide guidance for research on and design of next-generation transportation infrastructure with CAVs and HDVs.