Well-being is a multidimensional construct that affects various areas of a person’s life. In the university context, a student’s well-being can influence not only their academic and professional success but also the future development of society. This study aimed to evaluate how the interactions of time perspective (assessed by the Stanford Time Perspective Inventory—Short Form), self-efficacy (assessed by the General Self-efficacy Scale), self-regulation (assessed by the Self-regulated Knowledge Scale—University), and drop-out intention (assessed by the Intention to Drop-Out Scale) affect students’ perceptions of current and future well-being (assessed by the I COPPE Scale). Using a cross-sectional design, 192 students attending the University of Cagliari (Italy) were evaluated. A partial least squares structural equation modelling (PLS-SEM) analysis was performed to examine the relationships among well-being and all the variables examined herein. The empirical findings highlighted the direct and indirect effects of the studied variables on students’ current and future well-being.

This work aimed to validate the use of the Smartphone Distraction Scale (SDS) in Italy. The SDS was devised to assess distraction related to smartphone use in adult populations. A cross-sectional study was conducted among n = 609 adults (females = 76.4%; mean age = 30.26; SD age = 9.90). An assessment of the factorial structure of the Italian version was carried out using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The four factors identified by Throuvala and colleagues were confirmed (i.e., attention impulsiveness, online vigilance, multitasking and emotion regulation). Reliability was assessed using Cronbach’s alpha coefficient (0.703–0.889). The scale’s scores showed significant linear correlations with validated instruments, including the Mobile Phone Problematic Use Scale (MPPUS)and the Cognitive Failures Questionnaire (CFQ). A multivariate analysis of variance showed significant differences in the means among participants belonging to different age groups (born before 1995 vs. born after 1996). In summary, the good psychometric properties observed led us to assume that this instrument can be applied and used in Italian studies to assess the cognitive dimension of distraction related to the use of smartphones.

The rapid diffusion of Artificial Intelligence (AI) is transforming job characteristics, raising important questions about how to implement these technologies in organizations in ways that support employee well-being and performance. Drawing on the High-Involvement Management framework, this study examined employee-centered Artificial Intelligence implementation (ECAII) practices (defined as transparent communication, consultation, and training initiatives) as strategic levers to foster positive employee outcomes during Artificial Intelligence-driven transformations. Survey data were collected from 168 Italian white-collar employees who actively used Artificial Intelligence in their work. Structural equation modeling was employed to test direct and indirect relationships among employee-centered Artificial Intelligence implementation practices, work meaningfulness, job satisfaction, and job performance, as well as the moderating role of personal attitudes toward AI. Results showed that employee-centered Artificial Intelligence implementation practices had significant direct effects on both job satisfaction and performance, as well as indirect effects through work meaningfulness. Latent moderated mediation analyses further revealed that these indirect effects were stronger among employees with more positive attitudes toward Artificial Intelligence. Overall, the findings highlighted the importance of employee-centered strategies for enhancing meaningfulness and fostering positive outcomes during technological change. This study contributed to Human Resource Management (HRM) and meaningful work research by extending classic theoretical frameworks to Artificial Intelligence-enabled workplaces. Furthermore, from a practical perspective, our findings provided valuable guidance for organizations by highlighting the importance of transparent communication, employee involvement, and targeted training in reducing uncertainty and helping employees perceive their roles as relevant during the implementation of Artificial Intelligence.

The adoption of artificial intelligence (AI) is reshaping organizational practices, yet workers’ attitudes remain crucial for its successful integration. This study examines how perceived organizational ethical culture, organizational innovativeness, and job performance influence workers’ attitudes towards AI. A survey was administered to 356 workers across diverse sectors, with analyses focusing on 154 participants who reported prior AI use. Measures included the Attitudes Towards Artificial Intelligence at Work (AAAW), Corporate Ethical Virtues (CEV), Inventory of Organizational Innovativeness (IOI), and an adapted version of the In-Role Behaviour Scale. Hierarchical regression analyses revealed that ethical culture dimensions, particularly Clarity and Feasibility, significantly predicted attitudes towards AI, such as anxiety and job insecurity, with Feasibility also associated with the attribution of human-like traits to AI. Supportability, reflecting a cooperative work environment, was linked to lower perceptions of AI human-likeness and adaptability. Among innovation dimensions, only Raising Projects, the active encouragement of employees’ ideas, was positively related to perceptions of AI adaptability, highlighting the importance of participatory innovation practices over abstract signals. Most importantly, perceived job performance improvements through AI predicted more positive attitudes, including greater perceived quality, utility, and reduced anxiety. Overall, this study contributes to the growing literature on AI in organizations by offering an exploratory yet integrative framework that captures the multifaceted nature of AI acceptance in the workplace.