Abstract
In this work we study a way to explore and extract more information from data sets with a hierarchical tree structure. We propose that any statistical study on this type of data should be made by group, after clustering. In this sense, the most adequate approach is to use the Mahalanobis-Wasserstein distance as a measure of similarity between the cases, to carry out clustering or unsupervised classification. This methodology allows for the clustering of cases, as well as the identification of their profiles, based on the distribution of all the variables that characterises each subject associated with each case. An application to a set of teenagers' interviews regarding their habits of communication is described. The interviewees answered several questions about the kind of contacts they had on their phone, Facebook, email or messenger as well as the frequency of communication between them. The results indicate that the methodology is adequate to cluster this kind of data sets, since it allows us to identify and characterise different profiles from the data. We compare the results obtained with this methodology with the ones obtained using the entire database, and we conclude that they may lead to different findings.

Abstract
Tourist behavior has always been a central issue in the tourism discourse. Research in this area has long focused on destination choices, especially those destinations which still attract visitors despite of their security circumstances. Most of the tourists who choose traveling to a less safe destination are looking for new adventures, thus unique holidays. However, with that need of experiencing new sensations, and having a non-standard experiences, tourists cannot deny the fact of being attentive and taking precautions while traveling to an unsafe destination. The purpose of this research relies on defining what kind of precautions the tourists take while traveling to a destination which can threaten their safety, and in which way those precautions contribute to increase the tourist satisfaction.

Abstract
This study aims to build and test a theoretical model of tourist nostalgia (nostalgia proneness and food nostalgia) and seeks to explore the gender differences regarding how tourists feel their nostalgia towards food, and if it impacts in the global experience of the event. Survey data were collected in a gastronomic event, from 400 visitors. Two research models grounded on gender-female and male-highlighted the predictive role of food. Surprisingly, the all-purpose nostalgic proneness construct had a very limited impact. The newly developed construct (food nostalgia) was able to capture complex multidimensional visitor's experiences in both male and female models, whereas the broadspectrum measure of nostalgia expressed a higher propensity of nostalgia feeling among men. The managerial implications comprise market segmentation strategy, the definition of specific nostalgia triggers associated with traditional food as attributes to promote the event, and a festivalscape environment designed to express those triggers. © 2018 Cognizant, LLC.

Abstract
Augmented Reality (AR) is emerging as a technology that is reshaping the current society, especially the fields of Business and Economics (B&E). Therefore, the scientific studies produced on AR call for an interdisciplinary systematic review of the knowledge generated to structure an organized framework. Three main questions are addressed: How has the production of AR scientific knowledge evolved? What user-related aspects does AR affect? Also, which set of subtopics is associated with each motivation to develop an AR solution? The content of 328 papers produced between 1997 and 2016 in the field of AR is analyzed, unveiling 58 coding categories. There are 13 digital media characteristics that assume instrumental roles in addressing four major motivations to develop AR solutions. Technological topics dominate the research focus over behavioral ones. The investigations on AR in mobile displays show the highest increase. This research identifies the main scientific topics that have led researchers' agenda. Consequently, they contributed to develop and to adopt AR solutions and to forecast its future application in the organizations' strategies.

Abstract
Applying ethical principles to research is essential to ensure both participants’ universal rights and data quality. From the ethical point of view, researching with children poses additional challenges in designing the research, collecting and analysing data. The ethical principles generally accepted in scientific research are complementary, yet presenting conflicts that must be anticipated and mitigated by the researcher. This article explores the application of ethical principles in research with children, considering the different stages of research and both quantitative and qualitative research, proposing a set of six ethical principles to be applied before, during, and after the collection of data. The text includes examples from research adopting a mixed-method approach which involved 779 participants aged 7 to 15 years old. The study demonstrates that there is a strong interdependence among ethical principles applicable to research with children, not devoid of contradictions. Even widely accepted principles such as informed consent are complex and multifaceted. Moreover, the adoption of mixed methodology, in this particular case, has proven to be able to create ethic synergies, making the research globally more balanced. © Springer International Publishing AG 2018.

Abstract
The economic impact of fraud is wide and fraud can be a critical problem when the prevention procedures are not robust. In this paper we create a model to detect fraudulent transactions, and then use a classification algorithm to assess if the agent is fraud prone or not. The model (BOND) is based on the analytics of an economic network of agents of three types: individuals, businesses and financial intermediaries. From the dataset of transactions, a sliding window of rows previously aggregated per agent has been used and machine learning (classification) algorithms have been applied. Results show that it is possible to predict the behavior of agents, based on previous transactions. © 2018, Springer International Publishing AG, part of Springer Nature.