Computers in Human Behavior最新文献

This cross-sectional study explored the impact of Adverse Childhood Experiences (ACEs) on Short-form Video Addiction (SVA) and investigated the potential mediating roles of resilience and life satisfaction. Participants comprised 11425 Chinese college students (M_age = 20.31; 47.72% female). We employed multiple logistic regression analysis to examine the ACEs¹-SVA² connection and constructed a structural equation model to analyze the relationships among the variables. First, the multiple logistic regression analysis revealed a significant increase in the likelihood of SVA² as the number of ACEs¹ increased (OR: 2.40, 95% CI: 2.001–2.888; OR: 4.68, 95% CI: 3.467–6.325). A strong linear association was observed between child abuse/neglect, violence outside the family, and SVA (OR: 1.49, 95% CI: 1.388–1.604; OR: 1.30, 95% CI: 1.170–1.449). Furthermore, significant differences were found based on sex (OR: 1.46, 95% CI: 1.345–1.595), grade (OR: 1.89, 95% CI: 1.509–2.367), and major (OR: 1.47, 95% CI: 1.266–1.699). Second, the serial mediation model suggested that resilience and life satisfaction may serially mediate the relationship between ACEs¹ and SVA² (β = 0.009, SE = 0.001, 95% CI [0.006, 0.011]). These findings imply that promoting resilience and life satisfaction might mitigate the impact of ACEs¹ on SVA². Finally, we discuss the practical implications and limitations of the study.

As cultural products, games frequently integrate cultural elements, which can be classified as indexical elements or iconic elements based on their degree of connection to real-world cultures. Indexical elements are more directly tied to specific cultural references, while iconic elements are more symbolic. This study explores how these elements influence game evaluations through secondary data analysis and three experiments. Our findings reveal that indexical elements indexical elements enhance perceived cultural authenticity more than iconic elements, but do not necessarily lead to better game evaluations. Conversely, iconic elements positively impact perceived effort, which contributes to better game evaluations. Furthermore, for players with strong authenticity-seeking motivations, indexical elements improve game evaluations by increasing perceived cultural authenticity. This research contributes to the theoretical understanding of cultural element integration in game design, while providing actionable insights for game developers and cultural product firms.

Mobilephone distraction in learning is a widely observed phenomenon due to dividing and switching attention between learning activities (e.g., attending lectures and reading textbooks) and off-task activities (e.g., texting and chatting). In the past 30 years, hundreds of empirical studies have examined this distraction and generated abundant but inconclusive findings. Built on two influential meta-analyses (Liu et al., 2017; Kates et al., 2018), this meta-analysis aimed to systematically synthesize evidence from 27 randomized controlled experiments, with a total of 55 effect sizes and 2245 participants, and precisely estimate the effect of mobilephone distraction on students' immediate recall scores for the first time. It is concluded that mobilephone distraction (1) causes an overall negative medium-sized effect on immediate recall (Hedges'g = −0.65, 95% CI [-0.81, −0.49]), (2) has a negative nearly-large-sized effect on lecture recall (Hedges's g = −0.70, 95% CI [-.86, -.54]), (3) is significantly moderated by gender but not by the 10 other moderators related to study features and demographical variables, and (4) is not distorted by publication bias, outlier studies, and missing data. These findings and future studies are discussed.

The habitual use of electronic devices is commonly observed in populations on the autism spectrum. However, few reviews have examined the extent to which such use becomes problematic and/or disordered (e.g., gaming disorder) in these populations. This review was designed to critically evaluate the literature on autism in relation to gaming disorder (GD) and so-called internet addiction (IA). A systematic search of five databases was conducted, which identified 31 studies. Study quality was generally moderate, with the main weakness across studies tending to relate to psychometrics. A total of 78,442 participants were included across the studies of GD and 14,474 participants in studies of IA. The results showed that, although these conditions appear to be overrepresented in autistic populations, there are no true prevalence studies due to methodological limitations. The frequency rates reported in survey and clinical studies were highly variable and exceeded 20% in some studies. Variables that predict greater risk of problems include being male; adolescent; co-occurring ADHD symptoms; lack of parental rules around devices; parent-child conflict; and high parental stress. There is a need for clinical studies that differentiate excessive gaming and internet use behaviors as either related to impaired control (i.e., addiction) or features of autism (i.e., restricted interests) and determine how these profiles affect overall functioning. Research in this area requires more sophisticated measurement approaches to avoid misclassification of clinical issues that implicate digital technology.

Understanding human activities in daily life is of utmost importance, especially in the context of personalized and adaptive ubiquitous learning. Although existing HAR systems perform well-identifying activities based on their inter-spatial and temporal relationships, they lack in identifying the importance of accurately detecting postural transitions that not only enhance the activity recognition rate and reduced the error rate but also provides added motivation to explore and develop hybrid models. It's in this context we propose an ensemble approach of 1D-CNN and LSTM for the task of postural transition recognition, facilitated by wireless computing and wearable sensors. The proliferation of achieving ubiquitous learning will ultimately lead to the creation of adaptive devices enabled by various data analysis and relation learning techniques. Our approach is one of the methods that can be incorporated to enable seamless learning and acquire correlations with adaptive learning techniques. The experimental results on testing datasets including newly produced HAPT (Human Activities and Postural Transitions) show better classification accuracy than existing state-of-the-art HAR approaches (97.84% for transitional activities and 99.04% for dynamic human activities) indicating the capability of the model in ubiquitous learning scenarios and personalized and adaptive human learning environments.

Building on impression formation theories and the stereotype content model, this study examined age bias in LinkedIn screening, which is an understudied topic despite the aging workforce. An experimental study among 366 HR professionals considered the effects of LinkedIn recommendations (warmth/competence) and recruiters' age stereotypes on older applicants' job suitability ratings. First, we investigated and found that LinkedIn screening is prone to bias against older applicants. Furthermore, although having a recommendation on LinkedIn stressing competence or warmth is beneficial for both younger and older applicants, younger applicants benefited more from a recommendation reflecting their competence compared to older applicants. Second, recruiters' positive stereotypes regarding older workers' competence positively influenced job suitability ratings of older job applicants. This positive effect of recruiters' stereotypes was not affected by counter-stereotypical information emphasized through a recommendation. Understanding how applicants' LinkedIn profile affect recruiters’ hiring outcomes might help organizations to develop policies for fair selection procedures.

Online gaming is quickly gaining popularity, but the effects of game streaming and social connections on player loyalty remain under-explored. This study aimed to investigate the effects of watching game streaming and game-related social connections on player loyalty in MOBA games based on social cognitive theory and social capital theory. Game streaming watching was hypothesized to have a positive effect on personal skills and cooperative game knowledge, and game-related social connections had a positive effect on cooperative game knowledge. In addition, the study examined the effects of these factors on player loyalty. The study was conducted by constructing structural equation modeling and administering questionnaires to 415 MOBA gamers. Specifically, watching game streams significantly improves individual skill and cooperative game knowledge, which in turn positively affects game loyalty. Game-related social connections also significantly increase cooperative game knowledge and directly contribute to higher game loyalty. Furthermore, both individual skill and game cooperation knowledge positively mediated the relationship between game streaming watch and game loyalty. This study provides valuable insights for stakeholders and game industry practitioners by elucidating the mechanisms behind MOBA game loyalty, and promotes greater engagement among MOBA gamers.

Despite the wealth of literature vested in the association between social media use and vulnerability to fake news, it remains underexplored how and what kinds of social media usage contribute to fake news susceptibility. To fill this research gap, we draw upon the emergent scholarship of News-Finds-Me and propose a new conceptual model to examine fake news vulnerability and engagement in digital worlds. Drawing upon an online national sample in the US (N = 1014), results corroborated the prevalence of the News-Finds-Me perception, a social media-derived news attainment pattern that propels users to misconceive knowledgeability, over-depend on intimate peers and algorithms, and disengage from active news learning. Furthermore, evidence showed that News-Finds-Me perceptions make individuals more likely to believe and share fake news by creating a biased mentality that one is fake-news-proof while others are fake-news-impressionable. Such an asymmetric cognitive fallacy is called Third-Person Perception in literature. Our findings elucidate that the widely noted social media empowerment hypothesis might be double-sided. While social media can facilitate the dissemination and diversification of knowledge, they may also foster a sense of illusioned knowledgeability and overconfidence. This, in turn, could impede users from being adequately informed.

Social network data, such as Twitter/X, is of Big Social Data type. Big social data describes people's social behaviors and interactions. They have high business value for decision-making in organizations. However, because of the anonymous nature of social network users, their credibility is ambiguous. Credibility expresses the accuracy and value of big social data. Despite extensive research on the credibility of big social data, most methods have not paid sufficient attention to the important dimensions of their assessment, including user expertise based on topic, selecting social network features, and labeling them. Furthermore, these methods cannot manage the time, high volume, and speed of big social data. To address these issues, this paper presents a novel model for assessing the credibility of Twitter/X users by integrating Twitter/X with Google Scholar. The model automatically defines users' credibility labels using Google Scholar. Machine learning feature selection methods also select features that affect the credibility of Twitter/X users based on the topic. This study uses Google Scholar and the BerTopic algorithm for effective topic modeling on Twitter/X. The model considers unrelated data management, dynamic user credibility, and organizing activities based on the Big Data lifecycle. Finally, using Linear Regression, Support Vector Regression, K-Nearest Neighbor, Random Forest, Classification and Regression Trees algorithms, the model predicts the credibility of Twitter/X users and proves that it performed better than similar models through Classification and Regression Trees. In addition, the model is generalizable for all organizational purposes due to the integration of heterogeneous resources and feature selection methods.