Does social media addiction impair the well-being of non-clinical individuals? Despite the Internet being able to be considered as a promoting factor for individual empowerment, previous literature suggests that the current massive availability of Information and Communication Technologies (ICT) may be dangerous for users’ well-being. This article discusses the relationship between the most used social media addiction measures (i.e., the Bergen Facebook Addiction Scale—BFAS, the Bergen Social Media Addiction Scale—BSMAS) and well-being. A systematic review considering all the publications indexed by PsycInfo, PsycArticles, PubMed, Science Direct, Sociological Abstracts, Academic Search Complete, and Google Scholar databases was performed to collect the data. Ten of 635 studies were included in the qualitative synthesis. Overall, most of the included works captured a negative but small relationship between BFAS/BSMAS and well-being, across multiple definitions and measurement.

Facial emotion recognition is a crucial process for many applications and is still unresolved. Historically, emotional recognition has usually been achieved through artificial intelligence techniques such as Convolutional Neural Networks. However, this approach is quite expensive in terms of computational power and complexity. To alleviate this problem, we propose a lightweight CNN for facial emotion recognition, called Custom Lightweight CNN-based Model (CLCM), based on the well-known MobileNetV2 architecture. Performance was evaluated on four public datasets, FER-2013, RAF-DB, AffectNet, and CK+, where seven facial emotions were detected. The CLCM model was compared to the well-known MobileNetV2 and ShuffleNetV2 architectures. The CLCM performance results were close to or better than the more complex models. Specifically, in the FER-2013 dataset, CLCM achieved an accuracy of 63%, while MobileNetV2 was 58%, and ShuffleNetV2 65%. In the RAF-DB dataset, CLCM showed 84%, MobileNetV2 73%, and ShuffleNetV2 80%. Lastly, in the AffectNet dataset, MobileNetV2 and ShuffleNetV2 showed an accuracy of 57%, while CLCM showed 54%. The results obtained from this study establish CLCM as one of the efficient models in FER. Although CLCM is a smaller model in terms of parameters (2.3 Million) compared to MobileNetV2 (3.5 Million) and ShuffleNetV2 (3.9 Million), it showed good results in almost all analyses. The reduced CLCM’s computational power allows its application in enhanced human-computer interaction, affective computing, and personalized user experience, especially for real-world scenarios such as psychological and medical assessment, automotive (real-time driver emotional state), and vulnerable individual care where limited resource systems are commonly employed and real-time and reliable response are strongly recommended.

Phubbing could be defined as a new form of addiction; however, checking the phone and ignoring the speaker could also be linked to the increased availability of virtual social environments. We developed a multidimensional model for phubbing considering psychological dimensions and information and communication technology related habits. We collected data through online questionnaires and surveys. The best model obtained from our data was constituted by Information and Communication Technologies’ (ICTs) usage behaviours, Trait Anxiety, Virtual Sense of Community and Neuroticism. Finally, our study confirmed a strong connection between phubbing and online addiction behaviours.

Nowadays, video games are part of our everyday life, and the number of players is increasing each day passing by. Thus, understanding what motivations drive people to play video games is becoming a very important topic for researchers. That is why this systematic review had the objective to summarize the existing literature about gaming motivation by including papers that used a validated tool to do so while excluding those that did address just the psychopathological aspect of gaming. The systematic review was carried out through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRSIMA). A total of 53 papers were included in this systematic review, and the findings revealed that nonaddicted players and addicted players seem both to play for social, achievement, and competition motivations. Male players appeared more oriented to play to compete with others, while female players seemed to use games for relationship and social reasons. Gaming motivation was stronger in younger people.

Innovative behavior is necessary to combat the 21st century’s sustainability challenges, as well as to ensure organizations’ longevity and success. Personality traits, such as extraversion, are strongly related to innovative behavior; nevertheless, such traits are not increasable through specific training. Intrapreneurial self-capital is a promising preventive resource to enhance people’s capability to cope with innovations. On this basis, this study analyzed, in an explorative way, the relationship between extraversion, intrapreneurial self-capital, and innovative behavior, using a sample of 120 Italian workers. A mediation model was used to assess the effects of extraversion on innovative behavior and innovative implementation behavior (outcome variables) through intrapreneurial self-capital (conceived as an intervening mediator variable). The mediation analysis highlighted that intrapreneurial self-capital is correlated with both workers’ innovative behavior and innovative implementation behavior. Therefore, implementing dedicated training on intrapreneurial self-capital could help organizations better address sustainability issues and achieve the Sustainable Development Goals introduced by the United Nations.

A systematic review of the literature investigating the effects of the COVID-19 pandemic on psychological and psychosocial factors was completed. Published literature was examined using electronic databases to search psychosocial factors such as beliefs and media persuasion, social support, coping, risk perception, and compliance and social distancing; and psychological factors as anxiety, stress, depression, and other consequences of COVID-19 that impacted mental health among the pandemic. A total of 294 papers referring to the first wave of the COVID-19 pandemic (December 2019–June 2020) were selected for the review. The findings suggested a general deterioration of mental health, delineating a sort of “psychological COVID-19 syndrome”, characterized by increased anxiety, stress, and depression, and decreased well-being and sleep quality. The COVID-19 effect on the psychological dimensions of interest was not the same for everyone. Indeed, some socio-demographic variables exacerbated mental health repercussions that occurred due to the pandemic. In particular, healthcare workers and young women (especially those in postpartum condition) with low income and low levels of education have been shown to be the least resilient to the consequences of the pandemic.