Recently there has been an increasing demand for technologies (automated and intelligent machines) that brings benefits to organizations and society. Similar to the widespread use of personal computers in the past, today’s needs are towards facilitating human-machine technology appropriation, especially in highly risky and regulated industries like robotics, manufacturing, automation, military, finance, or healthcare. In this context, trust can be used as a critical element to instruct how human-machine interaction should occur. Considering the context-dependency and multidimensional trust, this study seeks to find a way to measure the effects of perceived trust in a collaborative robot (cobot), regardless of its literal credibility as a real person. This article aims at translating, adapting, and validating a Human-Computer Trust Scale (HCTM) in human-robot interaction (HRI) context and its application to cobots. The Human-Robot Interaction Trust Scale (HRITS) involved 239 participants and included eleven items. The 2nd order CFA with a general factor called “trust” have proven to be empirically robust ( ; ; ; and ) [ ; , and ]; results indicated a good measurement of the general factor trust, and the model satisfied the criteria for measure trust. An analysis of the differences in perceptions of trust by gender was conducted using a -test. This analysis showed that statistical differences by gender exist ( ). This study’s results allowed for a better understanding of trust in HRI, specifically regarding cobots. The validation of a Portuguese scale for trust assessment in HRI can give a valuable contribution to designing collaborative environments between humans and robots.

Collaborative robots (cobots) could help humans in tasks that are mundane, dangerous or where direct human contact carries risk. Yet, the collaboration between humans and robots is severely limited by the aspects of the safety and comfort of human operators. In this paper, we outline the use of extended reality (XR) as a way to test and develop collaboration with robots. We focus on virtual reality (VR) in simulating collaboration scenarios and the use of cobot digital twins. This is specifically useful in situations that are difficult or even impossible to safely test in real life, such as dangerous scenarios. We describe using XR simulations as a means to evaluate collaboration with robots without putting humans at harm. We show how an XR setting enables combining human behavioral data, subjective self-reports, and biosignals signifying human comfort, stress and cognitive load during collaboration. Several works demonstrate XR can be used to train human operators and provide them with augmented reality (AR) interfaces to enhance their performance with robots. We also provide a first attempt at what could become the basis for a human–robot collaboration testing framework, specifically for designing and testing factors affecting human–robot collaboration. The use of XR has the potential to change the way we design and test cobots, and train cobot operators, in a range of applications: from industry, through healthcare, to space operations.

Based on emotional labor theory, we aim to study the relationships between the dimensions of emotional labor (requirements and strategies), work engagement, and occupational identity in Portuguese police officers. Therefore, we intend to explore the possible effects of emotional labor both on work engagement and occupational identity, as well as ways of preventing and/or mitigating the impact of these relationships. We identified a gap in the studies on this subject in Portugal, particularly in the context of police professionals. Thus, a sample of 924 Portuguese police officers of the Public Security Police (PSP) was asked to answer a set of questionnaires: the Emotional Labour Scale, the Emotion Work Requirements Scale, the Utrecht Work Engagement Scale, and the Social Identity Scale. The data obtained was analyzed using correlation and multiple linear regression. Overall, the results revealed relationships between the emotional demands (i.e., suppression of negative emotions and expression of positive emotions) and strategies (i.e., deep and surface acting) of emotional labor and work engagement as well as occupational identity. We discuss these results and propose directions for future research, given the richness of the subject.

Collaborative robots are an indispensable element of both industry 4.0 and industry 5.0, the latter of which gives special emphasis to the human facet of the human-robot collaboration. To facilitate such an interaction, attention should be given to the design of the cobot, including its interface, which enables communication with the user. Programming through the interface and performing a task with the robotic device are responsible for the user experience (UX), which comprises both pragmatic and hedonic aspects. In order to design the most positive experience for users, their perspectives must be considered, which is achieved through the identification of UX goals. In this respect, a systematic review was conducted to revise the UX goals present in the literature. The following seven UX goals were identified: safety, relationship, usability, inspiration, flexibility, efficiency, and accomplishment. These findings represent the first systematic categorization of UX goals for the specific design of cobots, that should empirically be tested.