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The changes that are constantly occurring in the labour sector have led organisations and companies to move towards digital transformation. This process was accelerated by the COVID-19 pandemic and conducted to a massive recourse to the practice of remote working, which in this study is understood as the term for the way of performing work outside the usual workplace and with the support of ICT. Currently, there are no flexible scales in the literature that allow measuring the benefits and disadvantages of remote working with a single instrument. Thus, the distinction between the positive and negative consequences of working remotely, substantiated by a solid literature, provides a framework for a systematical understanding of the issue. The aim of the present study is to develop and validate a scale on remote working benefits and disadvantages (RW-B&D scale). For this end, a preliminary Exploratory Factor Analysis (EFA) with 304 participants, a tailored EFA with a sample of 301 workers and a Confirmatory Factor Analysis (CFA) with 677 workers were conducted. Participants were all Italian employees who worked remotely during the period of the COVID-19 health emergency. Data were collected between October 2020 and April 2021. The psychometric robustness of the model was assessed through bootstrap validation (5000 resamples), fit indices testing and measurement of factorial invariance. The statistical analyses demonstrated the bifactorial nature of the scale, supporting the research hypothesis. The model showed good fit indices, bootstrap validation reported statistically significant saturations, good reliability indices, and convergent and discriminant validity. Measurement invariance was tested for gender and organisational sector. The results suggested that the novel scale facilitates the quantitative measurement of the benefits and disadvantages associated with remote working in empirical terms. For this reason, it could be a streamlined and psychometrically valid instrument to identify the potential difficulties arising from remote working and, at the same time, the positive aspects that can be implemented to improve organisational well-being.

The topic of community resilience attracts as much academic research as it does social media. Understanding the drivers of change and community adaptation in the face of critical events is a key clue to governance actions and local measures. However, both academia and the media often provide partial definitions of community resilience. Beginning with an integration of theory-driven and data-driven knowledge, the study aims to uncover and operationalize the building blocks of community resilience potential within a measurement tool. An assessment study, conducted on 1278 participants from diverse communities statistically supported a broad, inclusive model: Community Resilience Potential is composed of four main constituents (social capital, community competence, structural-functional potential, socio-economic potential). The Confirmatory Composite Analysis formalized for Partial Least-Squares Structural Equation Modeling revealed its good psychometric properties and measurement invariance. Although the study has limitations, it provides researchers with a valuable, theoretically grounded, widely-applicable tool for the investigation of the community resilience potential.

Backgrounds: Among the problematic online behaviours, online grooming, that is, a manipulative process leading to sexual solicitation between an adult and a minor, is receiving increasing attention. However, most of the studies come from qualitative data, confirming that a formalized measurement tool is still missing. Moreover, online grooming measurement mostly relies on its latest, advanced stages, when the abuse and the actual exchange of sexual material has already occurred and reported to the authorities. The aim of the present study is to develop and validate a measurement instrument grasping the earlier stages of the phenomenon. Methods: The psychometric properties of the Online Grooming Risk (henceforth, OGR) scale were assessed in a validation study conducted on 316 adolescents, and its location within a network of psychological constructs relevant to the literature was examined with a non-parametric approach, the Structural Equation Modeling based on Partial Least Squares. Results: Results confirmed reliability and validity of the OGR scale. Moreover, its association with both protective (e.g., a good family support) and risk antecedents (e.g., low self-esteem), as well as with the development of clearly sexualized references, seems also confirmed. Conclusions: The OGR tool configures itself as a useful resource in perspective of an early detection of online grooming. The study also highlights the protective role of family support and low self-esteem as a significant risk factor., Mediterranean Journal of Clinical Psychology, Vol 10, No 1 (2022)

Many data science competitions occur in the context of soccer match prediction. The Kaggle European Soccer (KES) database, one of the biggest soccer datasets available on Kaggle, includes information about soccer players and matches from season 2009 to 2015 in 10 different European countries. For what concerns players’ performance indicators, sofifa experts’ of Electronic Arts Sports are considered the leading authority: they state that specific abilities make up broader dimensions, each of which reflects a more general performance ability. In other words, players’ performance attibutes (variables) of the KES database can be summarized into fewer performance composite indicators, useful for predictive modeling. Assuming experts’ classifications solidity, Carpita et al. (Stat Model 19(1):74–101, 2019c) recently underlined the importance of variables transformation and information about players’ role in building these indicators. However, previous works focused on clustering matches rather than players’ attributes (e.g., investigating the role of seasonality in successful vs dropping performance; Wibowo in Commun Sci Technol 1(1), 2016), thus leaving the statistical examination of experts’ groupings a still unexplored territory. The present work aims at shedding light on this aspect through the Cluster of variables around Latent Variables approach: this clustering method makes latent components simultaneously shine from variable groupings. This procedure might finetune the recently developed role-based players’ performance indicators and improve predictive modeling of match outcomes.

In this paper, data concerning MILANO EXPO2015 is collected from the official twitter page of the event before and after its opening. In order to extract a semi-supervised ontology and to evaluate the global sentiment around the event, a variety of language processing techniques has been applied on the collected “tweets”: Latent Semantic Analysis, sentiment polarity tracking, along with gap analysis has allowed the semantic evaluation of users’ opinions. Moreover, the generalized cross entropy approach has been applied for the first time on web data, adding prior information on the effect of semantic classes on the global sentiment, improving accuracy and adding detail to the analysis.

Drawing and adapting the Job Demands-Resources Model to the academic context, the study aimed at exploring how students manage academic study demands by seeking challenges and adopting their human (e.g. meta-competencies) and social capital (e.g. networking) to improve their academic performance. 152 undergraduate psychology students were asked to fill out an online anonymous questionnaire, whose reliability was assessed. Analyses through four latent variables (seeking challenges, meta-competencies, networking and academic performance) were carried out through PLS-SEM, a non-parametrical approach to Structural Equation Modelling, while Bootstrap validation with n = 200 strengthened model validity. Results showed a direct, positive, significant structural relationship between students’ seeking challenges, meta-competencies and networking; moreover, the additional, indirect, presence of human capital arises in the relationship between seeking challenges and academic performance

Impairments in mentalizing abilities are thought to account for the high aggressive tendencies observed among individuals with pathological personality. However, the question of whether mentalizing impairments may mediate the pathways by which pathological personality leads to aggression has not yet been answered. This study first investigated the psychometric proprieties of the Italian version of the Reflective Functioning Questionnaire (RFQ). Then, we tested the mediating role of mentalizing in the relationship between the three pathological personality domains and aggressiveness. The study was conducted on a sample of 327 participants includinggroup of violent offenders (n=118) and a group of community participants (n=209). All subjects fulfilled the RFQ, the Personality Inventory for DSM-5 (PID-5) and the Aggression Questionnaire (AQ). Partial Least Squares–Path Modelling with higher-order construct definition was used. Mentalizing capacities were shown to significantly mediate the pathways leading some pathological personality traits to aggression. Data supported the factorial structure of the RFQ found in the original validation study. Results also support the existence of a second-order variable, mentalizing, resulting from the convergence of hypomentalizing and hypermentalizing, Psychology Hub, Vol 38, No 1 (2021)

This study explores a big and open database of soccer leagues in 10 European countries. Data related to players, teams and matches covering seven seasons (from 2009/2010 to 2015/2016) were retrieved from Kaggle, an online platform in which big data are available for predictive modelling and analytics competition among data scientists. Based on both preliminary data analysis, experts’ evaluation and players’ position on the football pitch, role-based indicators of teams’ performance have been built and used to estimate the win probability of the home team with the binomial logistic regression (BLR) model that has been extended including the ELO rating predictor and two random effects due to the hierarchical structure of the dataset.The predictive power of the BLR model and its extensions has been compared with the one of other statistical modelling approaches (Random Forest, Neural Network, k-NN, Naïve Bayes). Results showed that role-based indicators substantially improved the performance of all the models used in both this work and in previous works available on Kaggle. The base BLR model increased prediction accuracy by 10 percentage points, and showed the importance of defence performances, especially in the last seasons. Inclusion of both ELO rating predictor and the random effects did not substantially improve prediction, as the simpler BLR model performed equally good. With respect to the other models, only Naïve Bayes showed more balanced results in predicting both win and no-win of the home team.