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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.

In sports, studying player performances is a key issue since it provides a guideline for strategic choices and helps teams in the complex procedure of buying and selling of players. In this paper we aim at investigating the ability of various composite indicators to define a measurement structure for the global soccer performance. We rely on data provided by the EA Sports experts, who are the ultimate authority on soccer performance measurement: they periodically produce a set of players’ attributes that make up the broader, theoretical performance dimensions. Considering the potential of clustering techniques to confirm or disconfirm the experts’ assumptions in terms of aggregations between indicators, 29 players’ performance attributes or variables (from the FIFA19 version of the videogame, that is, sofifa) have been considered and processed with three different techniques: the Cluster of variables around latent variables (CLV), the Principal covariates regression (PCovR) and Bayesian model-based clustering (B-MBC). The three procedures yielded clusters that differed from experts’ classification. In order to identify the most appropriate measurement structure, the resulting clusters have been embedded into Structural equation models with partial least squares (PLS-SEMs) with a Higher-Order Component (that is, the overall soccer performance). The statistically derived composite indicators have been compared with those of experts’ classification. Results support the concurrent validity of composite indicators derived through the statistical methods: overall, they show that, in the lack of expert judgement, composite indicators, as well as the resulting PLS-SEM models, are a viable alternative given their greater correlation to players’ economic value and salary.

Background According to more recent approaches on problematic internet use (PIU), using the internet can be seen as a way of compensating for psychosocial malaise. Taking semiotic cultural psychology theory as its theoretical framework, this study examines the role of affect-laden assumptions concerning the world, known as latent dimensions of sense (LDSs), in promoting (or not) adaptive responses, including internet use as a maladaptive strategy against problems and difficulties. Aims To test a theoretical model in which PIU is predicted by LDSs through the mediation of high levels of psychosocial malaise. Method We measured PIU (using the Generalized Problematic Internet Use Scale 2), LDSs (View of Context questionnaire), negative affect (Positive and Negative Affect Schedule), social anxiety (Interaction Anxiousness Scale) and loneliness (Italian Loneliness Scale) in 764 Italian adolescents (mean age 15.05 years, s.d. = 1.152 years). LDSs were detected using a multiple correspondence analysis; after confirmatory composite analysis, partial least squares structural equation modelling with higher-order components was performed to test the mediation model. Results The results show a relationship between LDSs corresponding to an extreme negative evaluation of the sociocultural context, experienced as absolutely unreliable, and PIU through the mediation of psychosocial malaise (95% CI 0.101– 0.171; P = 0.000). Conclusions Overall, the findings suggest that PIU might be a way of compensating for unpleasant states in a context perceived in an extremely negative and homogenising way, i.e. as totally lacking resources and trustworthy people.

The structure of knowledge about Structural Equation Modelling (SEM) based on the Partial Least Squares (PLS) estimator has been analysed by systematic and reproducible bibliometric citation analysis. This contribution aims to create a dynamic picture of the PLS-SEM research activity to support scholars with an enhanced understanding of the history, the present and the future directions of this fascinating modelling approach. Analysis was conducted using the Bibliometrix packageR with documents extracted (n = 3,854) from the Web of Science (WoS) database by Clarivate. Hence, we find seminal papers in the context of PLS-SEM as well as the diffusion and use in different research domains, suggesting new directions of applications. We also identify the collaboration networks involving authors and countries to highlight the new potential for cooperation from a co-authorship and international project standpoint. Furthermore, the dynamics of the sources indicate the interest of journals in this field in a dissemination role, which can assist authors in selecting a suitable publisher. Finally, the historiographic overview shows the dominant topics and the possible evolution in the citation analysis from the theoretical and application angles.