Objectives This study aims to characterize the working environment, stress levels, and psychological detachment of employees working from home during the COVID-19 pandemic in the Philippines and investigate their relationship to productivity and musculoskeletal symptoms. Methods Structural equation modeling was used to examine the direct effect of workstation characteristics, stress, and musculoskeletal symptoms to productivity and the indirect effect of psychological detachment to productivity. Data were gathered from a survey of employees working from home during the pandemic from different industries (n = 352). Multigroup analysis was also conducted to determine the effect of age, having a spouse, and having children less than 18 years old, to the model. Results Ergonomic suitability of the workstation (WES) has a significant effect on musculoskeletal symptoms (MSS) (β = −0.31, SE = 0.06; p < .001). Both workstation suitability (β = −0.24, SE = 0.03; p < .001) and workstation ergonomic suitability (β = −0.18, SE = 0.01; p < .01) inversely affect STR. Psychological detachment has a significant inverse effect on stress (β = −0.31, SE = 0.07; p < .001) and stress has a significant negative effect on productivity (β = −0.13, SE = 0.09; p = .03). Multigroup analyses showed that stress significantly affected the productivity of those without spouses and young employees. Conclusion Workstation suitability helps improve the productivity of people working from home while stress negatively affects it. Workstation ergonomic suitability and musculoskeletal symptoms have no significant effect.

Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, the field grew, and development flourished. However, concerns have been expressed that the techniques are limited in terms of to whom they are applicable and how their effect can be leveraged. Currently, most XAI techniques have been designed by developers. Though needed and valuable, XAI is more critical for an end-user, considering transparency cleaves on trust and adoption. This study aims to understand and conceptualize an end-user-centric XAI to fill in the lack of end-user understanding. Considering recent findings of related studies, this study focuses on design conceptualization and affective analysis. Data from 202 participants were collected from an online survey to identify the vital XAI design components and testbed experimentation to explore the affective and trust change per design configuration. The results show that affective is a viable trust calibration route for XAI. In terms of design, explanation form, communication style, and presence of supplementary information are the components users look for in an effective XAI. Lastly, anxiety about AI, incidental emotion, perceived AI reliability, and experience using the system are significant moderators of the trust calibration process for an end-user.

There was a shift in sustainability consumption in the last decade that stimulated new strategies for ecological friendly industries and new product innovations. Environmental labeling is a marketing technique used to inform consumers that a company has employed a process to protect the environment. However, uncertainty remains concerning how eco-labels influence consumers. Buying green products can elicit emotion in consumers. When consumers buy eco-products, they feel that they are helping save the environment. Products provide certain emotional benefits and therefore affect mood and behavior. This study aims to examine how consumers who differ in environmental attitudes respond to eco-labels. Aside from this, it wants to determine the intensity and type of emotions elicited by these kinds of products based on a certain set of pre-purchase emotions. These emotions are still unknown. Also, it proposes the Green Emotion Model (GEM) 2.0 that correlates environmental attitudes, visual attention towards these eco-labels, emotion and the desirability of purchasing a product. This framework proposes that the environmental attitudes and awareness of consumers are crucial for them to look for this eco-label on a product. These environmental labels should be able to capture the attention of consumers and thus will provoke positive emotions and lead to the purchase of an eco-product.

In the air traffic management domain, a set of performance-shaping factors are defined to characterize how such factors influence the overall performance of air traffic controllers. While it is worth understanding the nature of these factors, including their priority for policymaking and strategy implementation, no research in the extant literature has conducted an in-depth investigation of this matter. Therefore, this paper aims to explore the performance-shaping factors of air traffic controllers by using fuzzy DEMATEL and fuzzy BWM—such methodologies in the classification of factors as well as their corresponding priority vector. As an illustration, a case study in the Mactan Control Tower of the Civil Aviation Authority of the Philippines (CAAP) is conducted. The key results of the hybrid methodology showed that causal factors are ‘situation awareness,’ ‘communication,’ ‘teamwork,’ ‘vigilance,’ ‘and ‘attention.’ Effect factors are ‘workload,’ ‘trust,’ ‘fatigue,’ and ‘stress.’ Furthermore, ‘communication’ is found to be of the highest priority among other factors. The results can provide relevant insights to the decision-makers of air traffic management in formulating programs and strategies related to the improvement of air traffic controllers’ performance. Note, however, that the study is limited to identifying the inherent characteristics of the factors and their priority ranking; specific plans of action for improving the performance of air traffic controllers are not provided. As a future research direction, the inputs obtained in this paper can pave the way to a more in-depth analysis of improving the performance of air traffic controllers.

Medical device (MD) developers currently use the Technology Readiness Levels (TRLs) to determine the status of technology. However, the broad definition of each level makes it difficult to ascertain readiness of MDs. The TRL is also indifferent to the strict regulatory requirements for MDs, requirements for human systems integration, and market competition. This paper proposes a technology readiness framework for communicating and planning the development of Class III MDs called the Medical Device Readiness Levels (MDRLs). Five exit criteria were staged in a meaningful sequence within the framework: safety, clinical effectiveness, usability, comfort, and affective response. It also incorporates the stakeholders’ perspectives, mindful of the users’ varying needs in manipulating the MD. The usefulness of the framework was affirmed by professionals involved in MD development.

To illustrate how fatigue affects the workload, situation awareness, and control strategy of air traffic controllers.As air traffic control becomes more complex and demanding due to the increasing ...