Understanding how people behave when facing hazardous situations, how intrinsic and extrinsic factors influence the risk taking (RT) decision making process and to what extent it is possible to modify their reactions externally, are questions that have long interested academics and society in general. In the spheres, among others, of Occupational Safety and Health (OSH), the military, finance and sociology, this topic has multidisciplinary implications because we all constantly face RT situations. Researchers have hitherto assessed RT profiles by conducting questionnaires prior to and after the presentation of stimuli; however, this can lead to the production of biased, non-realistic, RT profiles. This is due to the reflexive nature of choosing an answer in a questionnaire, which is remote from the reactive, emotional and impulsive decision making processes inherent to real, risky situations. One way to address this question is to exploit VR capabilities to generate immersive environments that recreate realistic seeming but simulated hazardous situations. We propose VR as the next-generation tool to study RT processes, taking advantage of the big four families of metrics which can provide objective assessment methods with high ecological validity: the real-world risks approach (high presence VR environments triggering real-world reactions), embodied interactions (more natural interactions eliciting more natural behaviors), stealth assessment (unnoticed real-time assessments offering efficient behavioral metrics) and physiological real-time measurement (physiological signals avoiding subjective bias). Additionally, VR can provide an invaluable tool, after the assessment phase, to train in skills related to RT due to its transferability to real-world situations.

Risk taking (RT) is an essential component in decision-making process that depicts the propensity to make risky decisions. RT assessment has traditionally focused on self-report questionnaires. These classical tools have shown clear distance from real-life responses. Behavioral tasks assess human behavior with more fidelity, but still show some limitations related to transferability. A way to overcome these constraints is to take advantage from virtual reality (VR), to recreate real-simulated situations that might arise from performance-based assessments, supporting RT research. This article presents results of a pilot study in which 41 individuals explored a gamified VR environment: the Spheres & Shield Maze Task (SSMT). By eliciting implicit behavioral measures, we found relationships between scores obtained in the SSMT and self-reported risk-related constructs, as engagement in risky behaviors and marijuana consumption. We conclude that decontextualized Virtual Reality Serious Games are appropriate to assess RT, since they could be used as a cross-disciplinary tool to assess individuals' capabilities under the stealth assessment paradigm.

The validity of environmental simulations depends on their capacity to replicate responses produced in physical environments. However, very few studies validate navigation differences in immersive virtual environments, even though these can radically condition space perception and therefore alter the various evoked responses. The objective of this paper is to validate environmental simulations using 3D environments and head-mounted display devices, at behavioural level through navigation. A comparison is undertaken between the free exploration of an art exhibition in a physical museum and a simulation of the same experience. As a first perception validation, the virtual museum shows a high degree of presence. Movement patterns in both ‘museums’ show close similarities, and present significant differences at the beginning of the exploration in terms of the percentage of area explored and the time taken to undertake the tours. Therefore, the results show there are significant time-dependent differences in navigation patterns during the first 2 minutes of the tours. Subsequently, there are no significant differences in navigation in physical and virtual museums. These findings support the use of immersive virtual environments as empirical tools in human behavioural research at navigation level. Research highlights The latest generation HMDs show a high degree of presence. There are significant differences in navigation patterns during the first 2 minutes of a tour. Adaptation time need to be considered in future research. Training rooms need to be realistic, to avoid the ‘wow’ effect in the main experiment. Results support the use of Virtual Reality and the latest HMDs as empirical tools in human behavioural research at navigation level.

Background: The significance of national tourism in the global data highlights the importance of studying the characteristics of Spanish tourists that show interest in visiting Valencia (Spain). Personality traits might influence tourism behavior, and their importance has scarcely been addressed in the prior tourism literature. Objectives: We aimed to identify the touristic profiles of national tourists based on their lifestyles and to analyze the influence of personality traits in tourism segmentation. Methodology: 329 individuals participated in this study, they responded questionnaires about sociodemography, personality, lifestyle and a 3-item questionnaire developed by the authors. We performed analysis to obtain profiles by lifestyle, we carried out tests to study differences in personality traits among profiles and we analyzed the effects of the responses to the author-developed questionnaire and the demographic characteristics of the subjects on their cluster membership. Results: The results show that this market can be segmented into four clusters. We found significant statistical differences in personality traits among profiles. In addition, the authors present an author-designed questionnaire that, together with demographic variables, is able to predict participants' profiles. Conclusion:The results suggest that lifestyle is an appropriate indicator for this market segmentation and the analysis of its relationship with personality provides a deep comprehension of the resulting profiles. In addition, the profile prediction by the responses to the author-developed questionnaire constitutes a new basis for tourism segmentation, as these predictors might be used as "quick touristic classifiers". Implications or recommendations: The study of decision-making processes in tourism allows researchers and sellers to predict tourist behaviors and adapt offers to tourists' preferences and interests.

Risk taking (RT) measurement constitutes a challenge for researchers and practitioners and has been addressed from different perspectives. Personality traits and temperamental aspects such as sensation seeking and impulsivity influence the individual’s approach to RT, prompting risk-seeking or risk-aversion behaviors. Virtual reality has emerged as a suitable tool for RT measurement, since it enables the exposure of a person to realistic risks, allowing embodied interactions, the application of stealth assessment techniques and physiological real-time measurement. In this article, we present the assessment on decision making in risk environments (AEMIN) tool, as an enhanced version of the spheres and shield maze task, a previous tool developed by the authors. The main aim of this article is to study whether it is possible is to discriminate participants with high versus low scores in the measures of personality, sensation seeking and impulsivity, through their behaviors and physiological responses during playing AEMIN. Applying machine learning methods to the dataset we explored: (a) if through these data it is possible to discriminate between the two populations in each variable; and (b) which parameters better discriminate between the two populations in each variable. The results support the use of AEMIN as an ecological assessment tool to measure RT, since it brings to light behaviors that allow to classify the subjects into high/low risk-related psychological constructs. Regarding physiological measures, galvanic skin response seems to be less salient in prediction models.