Search Results (451)

Understanding the social drivers of pro-environmental behavior in urban forests and green spaces is critical for addressing sustainability challenges. Subjective norms serve as a key pathway through which social expectations influence individuals’ behavioral intentions. Despite mixed findings in the literature regarding the impact of subjective norms on individuals’ intentions, there is a research gap about the determinants of this construct. This study was conducted to explore how social expectations shape perceived subjective norms among visitors of urban forests. A theoretical model was developed with subjective norms at its center, incorporating their predictors including social identity, media influence, interpersonal influence, and institutional trust, personal norms as a mediator, and behavioral intention as the outcome variable. Using structural equation modeling, data was collected and analyzed from a sample of visitors of urban forests in Tehran, Iran. The results revealed that subjective norms play a central mediating role in linking external social factors to behavioral intention. Social identity emerged as the strongest predictor of subjective norms, followed by media and interpersonal influence, while institutional trust had no significant effect. Subjective norms significantly influenced both personal norms and intentions, and personal norms also directly predicted intention. The model explained 50.9% of the variance in subjective norms and 39.0% in behavioral intention, highlighting its relatively high explanatory power. These findings underscore the importance of social context and internalized norms in shaping sustainable behavior. Policy and managerial implications suggest that strategies should prioritize community-based identity reinforcement, media engagement, and peer influence over top-down institutional messaging. This study contributes to environmental psychology and the behavior change literature by offering an integrated, empirically validated model. It also provides practical guidance for designing interventions that target both social and moral dimensions of environmental action. Full article

This study integrates the Theory of Planned Behavior and the Theory of Consumption Values through a mixed-methods approach (structured interview and structural equation model) to investigate cross-platform disposal behaviors for idle clothing on social media and second-hand platform ecosystems. The study reconstructs traditional theoretical variables: psychological motivation dimension (platform-enabled green attitude, social circle environmental demonstration, and cross-platform behavioral control) and perceived value dimension (functional integration value perception, socialized emotional empowerment, and community identity value). Key findings: Cross-platform behavioral control is the strongest predictor of behavioral intention. In the value dimension, emotional value has the strongest direct impact on disposal intentions, functional integration is key to enhancing behavioral control, and community identity value most significantly impacts the platform-enabled green attitude and the social circle environmental demonstration. Finally, proposing a governance framework of “technological empowerment–emotional resonance–identity motivation”, offering theoretical foundations for optimizing platform interoperability and formulating digital environmental policies. Full article

Background/Objectives: Social worry may appear in preadolescence, causing it to persist in the future, such that adolescents become more avoidant of situations in which they are evaluated by others. Many previous studies have shown that approximately 4% of preadolescent children suffer from high levels of social worry, which may lead to other problems in the future. It is important to take necessary precautions to explore this worry and take the necessary measures to address it. This study aimed to do so by extracting the psychometric properties of the Social Worries Questionnaire (SWQ) for preadolescence in Arab countries. Methods: The sample consisted of 269 children aged 8–12 years. The SWQ, Spence Child Anxiety Scale (SCAS), Child Abuse Self-Reporting Scale (CASRS-12), and Arabic version of the Children’s Depression Inventory (AVCDI) were administered via various social media. Results: Confirmatory factor analysis was used, and the one-factor model showed that the scale consists of 14 items. The results also indicated evidence of discriminant validity, and that the internal consistency was acceptable. For reliability, the test–retest results showed that the value was appropriate. Conclusions: This scale could be valuable in detecting worry in preadolescent children and providing them with therapeutic and psychological services. Full article

Background/Objectives: In an increasingly pervasive digital environment, trait boredom has been identified as a key psychological factor in the onset and maintenance of problematic digital technology use. This systematic review aims to investigate the role of trait boredom in digital behavioral addictions, including problematic smartphone use, Internet and social media overuse, and gaming addiction, through theoretical models such as the I-PACE model and the Compensatory Internet Use Theory (CIUT). Methods: A systematic literature search was conducted across multiple scientific databases (PsycINFO, Web of Science, PubMed, and Scopus), yielding a total of 4603 records. Following the PRISMA guidelines after duplicate removal and screening based on title and abstract, 152 articles were assessed for full-text eligibility, and 28 studies met the predefined inclusion and exclusion criteria and were included in the final review. Results: Findings reveal that trait boredom functions as both a direct and indirect factor in problematic technology use. It serves as a mediator and moderator in the relationship between psychological vulnerabilities (e.g., depression, alexithymia, vulnerable narcissism) and dysfunctional digital behaviors. Furthermore, as an independent variable, it has an influence on technological variables through Fear of Missing Out (FoMO), loneliness, low self-regulation, and dysfunctional metacognitions, while protective factors such as mindfulness and attentional control mitigate its impact. Conclusions: Boredom represents a central psychological lever for understanding behavioral addictions in the digital age and should be considered a key target in preventive and therapeutic interventions focused on enhancing self-regulation and meaningful engagement with free time. Full article

The extensive use of digital media among adolescents has raised concerns about its impact on emotional development and mental health. Understanding the psychological factors behind problematic digital media use is essential for effective prevention. This study examined whether the relationships between emotion regulation (negative and positive), compulsive use, cognitive preoccupation, and negative outcomes linked to digital media are consistent across sex and academic year. We used a cross-sectional design with 2357 adolescents (12–16 years old) from Compulsory Secondary Education. Participants completed validated self-report questionnaires assessing problematic digital media use, and associated consequences in four domains: internet, video games, social networking, and messaging. Four structural equation models (SEMs), each focused on a media type, tested whether these relationships remained stable across sex and academic year. All models showed good fit, and differences between groups were minimal, supporting valid comparisons. Results confirm that emotion regulation difficulties and problematic digital media use are consistently associated with negative outcomes in all adolescents, regardless of sex or academic level. Preventive strategies targeting emotional regulation and digital media behaviors may be broadly applied to reduce emotional and functional problems related to excessive media use. Full article

This study investigates the interplay between adaptive online learning, students’ digital intelligence, sustainable learning, and digital mental well-being among female university students in Saudi Arabia. In response to the growing reliance on digital platforms in higher education, a structured questionnaire was distributed via social media to capture student perceptions of their online learning experiences. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the analysis revealed that while adaptive online learning is a critical enabler, its influence is most effective when mediated by students’ digital intelligence. The findings highlighted that students with higher digital intelligence are more likely to engage in sustainable learning practices and maintain better mental well-being in digital environments. Furthermore, innovative teaching practices were shown to strengthen these relationships, underscoring the importance of interactive and adaptive pedagogies. This research contributes to the growing discourse on digital education by emphasizing the importance of indirect pathways and learner-centred dynamics in shaping positive educational and psychological outcomes. This study offers practical and theoretical implications for educators, institutions, and policymakers aiming to create inclusive, resilient, and psychologically supportive digital learning environments. Future research is encouraged to examine these relationships across different cultural and institutional contexts and explore the longitudinal impacts of digital learning strategies. Full article

The Harry Potter fandom community around the world prefers a universe of wizards and witches that includes all people, but also has concerns about the author’s perspective regarding gender identity. This disjunction paralyzes the cultural reader with moral confusion, which is a danger to their emotional investment in the text. Although scholars have analyzed this phenomenon using fragmented prisms, such as social media activism, cognitive engagement, translation, pedagogy, and fan creativity, there is no unifying model that can be used to understand why reading pleasure endures. This article aims to fill this gap by examining these strands of research in a divergent manner, adopting a convergent mixed-methods study approach. Based on neurocognitive (EEG) values, cross-cultural focus groups, social media analysis, and corpus linguistics, we outline the terrain of reader coping mechanisms. We identify separate fan fractions and examine the corresponding practices. The results are summarized by proposing a model called the MDRL (Moral dissonance repair loop) which is a theoretical model that shows how translation smoothing, pedagogical reframing and fan-based re-moralization interact with one another in creating a system that enables the reader to be collectively able to obtain their relations with the text back to a manageable point and continue being engaged. This model makes a theoretical contribution to new areas in the study of fans, moral psychology, and cognitive literature. Full article

This investigation examines the psychological mechanisms governing human–virtual influencer relationships and their consequential impact on environmentally-conscious consumer behavior within digital marketing ecosystems. Employing theoretical frameworks from computer-mediated communication and social psychology, this study scrutinizes how algorithmically generated social media personalities cultivate para-social relationships that drive sustainable consumption patterns. The research operationalizes five core virtual influencer characteristics—expertise, similarity, attractiveness, familiarity, and para-social interaction—as predictive variables influencing relationship commitment and subsequent eco-conscious brand engagement. Consumer innovativeness functions as a moderating variable within this theoretical model. The data collection encompassed 677 respondents demonstrating active engagement with sustainability-focused virtual influencer content, analyzed through structural equation modeling (EQS 6.4) and the PLS-SEM methodology (SmartPLS 4.0). The empirical analysis reveals significant positive correlations between virtual influencer characteristics and relationship commitment, with similarity and attractiveness demonstrating the strongest predictive validity. Relationship commitment emerged as a significant mediator influencing sustainable brand attitudes, which subsequently affected purchase intentions for environmentally responsible products. Consumer innovativeness demonstrated positive moderating effects across all virtual influencer characteristics, with particularly robust effects observed for attractiveness and para-social interaction within sustainable brand contexts. This research advances the human–AI interaction literature by elucidating the psychological mechanisms through which virtual influencers facilitate consumer relationship formation and drive behavioral outcomes toward sustainable consumption practices. The findings provide empirically validated strategic frameworks for marketers developing virtual influencer campaigns that promote environmental stewardship, emphasizing the cultivation of perceived similarity and attractiveness while incorporating audience innovativeness as a critical segmentation variable in sustainable marketing initiatives. Full article

Statistical data demonstrate a concurrent rise in smoking prevalence and mental disorders, such as depression and anxiety, which may be attributed to contemporary lifestyle factors, including social media and recent global events. This indicates a potential correlation between these trends, as individuals with mental disorders may engage in smoking as a form of self-medication to alleviate anxiety. However, smoking is harmful and increases the risk of many diseases. Therefore, smoking cessation strategies are increasingly being considered. Nicotine is a naturally produced alkaloid in plants that makes smoking so addictive. Unfortunately, the public’s lack of understanding of the effects of nicotine leads to misleading claims in the public and media about its biological effects. Thus, current narrative literature review is focused on the examination of the biological effects of organic nicotine from various angles, considering the psychological aspects of addiction and the immune system. Analysis of recent data showed that nicotine not only causes addiction but also has therapeutic benefits in certain diseases (depression, anxiety, schizophrenia) and has anti-inflammatory properties (autoimmunity, neurodegenerative diseases), and a deeper understanding and a broader approach to its effects is needed. Full article

Identifying when and which adolescents are at increased risk of developing problematic social media use (PSMU) is critical for effective prevention and early intervention. Previous research has examined risk and protective factors using theory-driven (confirmatory-explanatory) approaches, such as regression models. However, few studies have simultaneously considered personal, peer, and parent characteristics to assess their relative contributions, and none have explored how these factors are structurally interrelated using data-driven (inductive–exploratory) approaches. To address these gaps, this study combines logistic regression and psychological network analysis to examine which personal, parent, and peer factors are most relevant in identifying at-risk/problematic social media use among adolescents. Using three waves of data analyzed cross-sectionally from N = 2441 secondary school students, adolescents were classified as normative (0–1 symptoms) or at-risk/problematic (2+ symptoms) users based on the Social Media Disorder Scale. Logistic regression showed that fear of missing out, impulsivity, depressive symptoms, intensity of meeting with friends, and reactive parental rules uniquely predicted at-risk/problematic use. Psychological network analysis identified self-esteem, attention problems, impulsivity, depressive symptoms, and life satisfaction as central, highly interconnected nodes. These findings show that theory- and data-driven approaches illuminate different aspects of PSMU risk, and that network analysis can generate novel hypotheses about underlying processes. Full article

The rapid development of online media has significantly facilitated the public’s information consumption, knowledge acquisition, and opinion exchange. However, it has also led to more violent conflicts in online discussions. Therefore, controversy detection becomes important for computational and social sciences. Previous research on detection methods has primarily focused on larger datasets and more complex computational models but has rarely examined the underlying mechanisms of conflict, particularly the psychological motivations behind them. In this paper, we propose a lightweight and language-independent method for controversy detection by introducing two novel psychological features: ascending gradient (AG) and tier ascending gradient (TAG). These features capture psychological signals in user interactions—specifically, the patterns where controversial comments generate disproportionate replies or replies outperform parent comments in likes. We develop these features based on the theory of the backfire effect in ideological conflict and demonstrate their consistent effectiveness across models and platforms. Compared with structural, interaction, and text-based features, AG and TAG show higher importance scores and better generalizability. Extensive experiments on Chinese and English platforms (Reddit, Toutiao, and Sina) confirm the robustness of our features across languages and algorithms. Moreover, the features exhibit strong performance even when applied to early-stage data or limited “one-page” scenarios, supporting their utility for early controversy detection. Our work highlights a new psychological perspective on conflict behavior in online discussions and bridges behavioral patterns and computational modeling. Full article

Background/Objectives: Adolescent levels of psychological distress are strongly influenced by community, individual, social, and family factors. Family functioning, social media use, and community disorder have shown high predictive value for psychological distress during this critical stage of development. However, these relationships are not always direct and are often mediated by individual-level variables, such as intolerance of uncertainty. Adolescent psychological well-being is not solely determined by contextual factors; the coping skills developed during this critical stage also play a significant role. Our study aims to analyze how these factors are directly and indirectly related by developing a predictive model of psychological distress in adolescents. Methods: The study included 908 adolescents (46.9% female) aged between 14 and 21 years (M = 16.29, SD = 1.5). Participants completed self-administered questionnaires in a school setting. Structural equation modeling was used to estimate total, direct, and indirect effects. Results: The model showed a good fit to the data. Social media disorder and family functioning showed statistically significant direct and indirect effects on psychological distress. Social media disorder was associated with higher psychological distress, while positive family functioning was protective. Community social disorder was only indirectly linked to higher psychological distress through the increase of intolerance of uncertainty. Conclusions: Intolerance of uncertainty is a critical predictor of adolescent distress, often overlooked despite its significant mediating role. Direct effects of family functioning and social media use also strongly influence distress levels. Impaired family functioning and community disorder interact bidirectionally, creating a cycle that exacerbates distress. Adolescents in these contexts face compounded negative effects from these reinforcing environments. Full article

Objectives: Persistent symptoms following COVID-19 may adversely impact the general mental health of people with chronic pain, and psychological flexibility may buffer these impacts. However, it remains unclear whether such lasting implications of COVID-19 differ between people with and without chronic pain. This study investigated the relationships between persistent symptoms post-COVID-19, psychological flexibility, and general mental health among people with and without persistent pain during the COVID-19 pandemic in the UK. Methods: A total of 204 adults living in the UK were recruited via social media and completed an online survey, including measures of persistent symptoms, depression (Patient Health Questionnaire-9), anxiety (General Anxiety Disorder-7), insomnia (the Insomnia Severity Index), and psychological flexibility (the Multidimensional Psychological Flexibility Inventory), and were included in the analyses. Results: Participants with persistent pain (n = 70) experienced more-persistent symptoms, poorer general mental health, and a higher level of psychological inflexibility compared with participants without persistent pain (n = 133). Overall, the relationships between persistent physical symptoms, general mental health, and psychological (in)flexibility showed similar patterns in the two groups. Participants with more-persistent physical symptoms experienced significantly poorer general mental health. Furthermore, people with higher levels of psychological inflexibility reported worse general mental health. There was little evidence that psychological (in)flexibility could “buffer” the association between persistent physical symptoms and general mental health. Conclusions: People with chronic pain appear more vulnerable to persistent symptoms and reduced general mental health compared with people without pain. Treatments that reduce psychological inflexibility, such as ACT, may improve outcomes for people with persistent symptoms post-COVID-19. Full article

This study examines the scientific output on older adults’ use of social media by examining 2695 documents from the Web of Science database. These publications cover various areas, such as behavioral sciences, psychology, healthcare sciences, and computer sciences, and highlight institutions such as the University of Seville and the University of Michigan. The analysis was extended from 1995 to 2023, identifying 2012 as the year with the most significant increase. The United States led with 757 documents, followed by Spain with 511. Prominent authors include Jia Wu, Howard Giles, and Itai Himelboim. The study also highlighted frequent keywords such as “social networks, communication, and media”. A total of 41 countries were mentioned, and journals such as Profesional de la Información and Comunicar were analyzed. The historical perspective reveals theoretical connections between health, communication, and social networks in the elderly. Despite various challenges, such as the digital divide, the positive impact of social networks on the elderly stands out, providing a training demand for media education as an essential for all. Full article

In today’s digital era, social media platforms like Instagram have become deeply embedded in daily life, generating billions of content items each day. This vast stream of publicly accessible data presents a unique opportunity for researchers to gain insights into human behaviour and personality. However, leveraging such unstructured and highly variable data for psychological analysis introduces significant challenges, including data sparsity, noise, and ethical considerations around privacy. This study addresses these challenges by exploring the potential of machine learning to infer personality traits from Instagram content. Motivated by the growing demand for scalable, non-intrusive methods of psychological assessment, we developed a personality prediction system combining convolutional neural networks (CNNs) and random forest (RF) algorithms. Our model is grounded in the Big Five Personality framework, which includes Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Using data collected with informed consent from 941 participants, we extracted visual features from their Instagram images using two pretrained CNNs, which were then used to train five RF models, each targeting a specific trait. The proposed system achieved an average mean absolute error of 0.1867 across all traits. Compared to the PAN-2015 benchmark, our method demonstrated competitive performance. These results highlight that using social media data for personality prediction offers potential applications in personalized content delivery, mental health monitoring, and human–computer interactions. Full article