Computers in Human Behavior最新文献

Anti-stigma campaigns increasingly leverage personal narratives of celebrities or public figures through social media, yet little is known about what makes social media an effective storytelling tool. This study empirically tests their mental health stories in different media formats using a multiple message design. Through a pilot study, four celebrities were selected: Kristen Bell, Mariah Carey, Dwayne Johnson, and Kevin Love, who have disclosed their mental health struggles. An online between-subjects experiment was conducted with 389 participants who were randomly assigned to one of three conditions (social media vs. online article vs. no-message control). Results showed that participants who read the stories in a social media format felt a greater social presence, as if they were engaging in a real conversation with the celebrity, compared to those who read the same stories in an online article format. This, in turn, facilitated a stronger sense of transportation into the story, ultimately leading to persuasive outcomes such as positive attitudes toward help-seeking and increased intentions to seek both professional and informal help. Further analyses revealed that the effects of social presence and transportation on attitudes toward help-seeking were more pronounced among those with greater parasocial intimacy with the celebrity. The findings provide empirical support for the effectiveness of using social media as a storytelling channel for anti-stigma campaigns.

Context

The Internet of Things (IoT) is playing an important role in the development of cars, smart homes and schools. Schools are using IoT technology to enhance student learning, digitize traditional learning and improve academic performance. IoT is rapidly replacing the traditional school system with a modern, smart and safe school system. Several modern education systems are presented in the literature.

Problem statement

The difficulty of choosing a smart and safe school system while evaluating the respective merits of different school systems has long been a concern for educators.

Method

In this study we proposed a hybrid multi-criteria decision-making method (MCDM) to address the selection Problem. To rank the different smart school systems and evaluate the value of the required parameter, two MCDM-based methods are used: Entropy and TOPSIS (Technique for Preference Ordering by Similarity to an Ideal Solution).

Results

Twelve beneficial features were considered that represent the fundamental positive characteristics required to create an intelligent education system. Ten different educational systems are considered as alternatives in order to carry out an evaluation. Entropy is used to measure the importance of each criterion and TOPSIS is used to rank the school systems. In this paper, we propose how the concepts of Entropy and TOPSIS can be used to address the challenge of selecting a safe and smart school system based on IoT. Our investigation selected the IoT-based school system that was found to be the smartest and safest out of a set of schools. Finally, we discuss the evaluation of the proposed research.

The widespread use of Artificial Intelligence (AI) in language education contexts has motivated several scholars around the world to uncover the advantages and disadvantages of AI and AI-powered instruments in different language classrooms. Yet, as the review of earlier investigations revealed, few inquiries have been carried out to divulge the pros and cons of leveraging AI in EFL classes. To narrow this gap, using the phenomenological approach, this inquiry investigated the opportunities and challenges of implementing AI in EFL classes from the perspective of Chinese EFL students. To do so, through the criterion sampling technique, a total of 45 EFL students was recruited from different educational institutions in China. To collect the dataset, participants were asked to complete an open-ended questionnaire. For the sake of triangulation, among the 45 participants, 15 were randomly selected to engage in a follow-up interview session. With the aid of MAXQDA software (version 2023), participants’ perceptions of AI opportunities and challenges were carefully analyzed. Overall, the analysis findings uncovered that leveraging AI in EFL classes can bring numerous opportunities for EFL students, including individualized learning, timely and immediate feedback, rich educational resources, and an interactive learning atmosphere. However, as demonstrated by the analysis outcomes, implementing AI in EFL courses may also face students with a range of challenges and problems. The research outcomes would be of great help to teachers and educational leaders in mitigating the challenges of leveraging AI in language classrooms.

The pervasive use of artificial intelligence (AI) among educational stakeholders has inevitably led to discussion around its influence on learning as well as users' continuance adoption. There is a dearth of studies that contextualize the use of AI in the English-speaking learning setting, and even fewer studies examined the roles of learners' emotions generated from AI usage in influencing their speaking practice. The current study was designed based on the Expectation Confirmation Model to investigate the impact of perceived enjoyment, willingness to communicate with AI, and innovativeness on influencing EFL learners' continuance learning intention with AI-enhanced tools, such as EAP Talk. Results from the structural equation modelling suggested EFL EAP Talk users perceived positively in AI affordances and wiliness to communicate with AI significantly influenced perceived usefulness and satisfaction, which further impacted their intentions to continue the usage. Moreover, the enjoyment they perceived is significantly associated with continuance intention, while the role of innovation was not suggested. The research model explained 88% of the variance for continuance behavioral intention. This study provides suggestions for policymakers, AI designers, and course instructors to innovate learning methods and strengthen supervision to enhance students’ language learning.

The present experimental research was an endeavor to assess the role of AI-driven chatbots in fostering Chinese EFL students' academic engagement. To do so, a sample of 113 EFL students was chosen from a national university in central China. Following that, through a random sampling method, students were allocated to the experimental and control groups. The experimental group (N = 57) was instructed through three AI-driven chatbots, whereas the control group (N = 56) received regular instructions without using AI-driven chatbots. To evaluate participants' level of academic engagement, a self-report scale was administered to them before and after the intervention. The study results indicated that the AI-driven chatbots positively influenced the academic engagement of students in Chinese EFL classrooms. Put it another way, the study outcomes revealed that AI-driven chatbots served as an important role in fostering Chinese EFL students’ behavioral, cognitive, and emotional engagement. The results of this intervention study may be illuminating for all language teachers working in L2 instructional contexts. Concerning the outcomes of this inquiry, AI-driven chatbots could be of great help to language teachers in enhancing the academic engagement of their students.

Beautifying augmented reality face filters (AR-FF) have been linked to reduced body satisfaction, particularly in young women. Social comparison theory has been used to explain this negative effect through upward social comparisons against the beautified version of oneself. However, downward comparisons have received little attention, though their investigation could have critical theoretical and practical implications. This proof-of-concept study evaluated whether the type of face filter has an effect on body satisfaction in young female students (N = 76) by comparing uglifying (downwards) and beautifying (upwards) face filters in a between-subjects experiment. Additionally, the moderating roles of self-esteem and self-identification with a filtered depiction were investigated. Exposure to beautifying face filters decreased body satisfaction, while no significant effect was detected for uglifying face filters. Furthermore, the effect of beautifying face filters was moderated by self-esteem but not self-identification, while there were no interactions for uglifying face filters. Possible implications and future research opportunities are discussed.

Conversational AI (CAI) systems which encompass voice- and text-based assistants are on the rise and have been largely integrated into people’s everyday lives. Despite their widespread adoption, users voice concerns regarding privacy, security and trust in these systems. However, the composition of these perceptions, their impact on technology adoption and usage and the relationship between privacy, security and trust perceptions in the CAI context remain open research challenges. This study contributes to the field by conducting a Systematic Literature Review and offers insights into the current state of research on privacy, security and trust perceptions in the context of CAI systems. The review covers application fields and user groups and sheds light on empirical methods and tools used for assessment. Moreover, it provides insights into the reliability and validity of privacy, security and trust scales, as well as extensively investigating the subconstructs of each item as well as additional concepts which are concurrently collected. We point out that the perceptions of trust, privacy and security overlap based on the subconstructs we identified. While the majority of studies investigate one of these concepts, only a few studies were found exploring privacy, security and trust perceptions jointly. Our research aims to inform on directions to develop and use reliable scales for users’ privacy, security and trust perceptions and contribute to the development of trustworthy CAI systems.

Objective

This study aims to critically analyze and synthesize mediating models that elucidate the complex interplay of variables associated with Gaming Disorder (GD), spanning affective, cognitive, and executive domains, to highlight significant mediation effects.

Methods

We reviewed 171 studies, involving 185,991 participants, exploring GD as the dependent variable and psychosocial variables as mediators. We utilized standard psychometric scales and reported effect sizes (e.g., Pearson correlation coefficients). Extensive database searches, from inception to August 10, 2023, included MEDLINE, PsycINFO, EMBASE, PubMed, Web of Science, CNKI, and Wanfang Data. Publication bias assessments and study quality evaluations were conducted. Pooled mediating effect sizes were determined using one-stage meta-analytic structural equation modeling (MASEM).

Results

We identified 13 crucial models through MASEM, with the impulsivity-delay discounting model displaying the most significant effect sizes, underscoring its pivotal role in elucidating the dynamics of GD. Eight models, including emotional problems-escapism models, underscore GD's multifaceted nature driven by mood-modifying and needs-fulfilling mechanisms. Among cognitive dimensions, avatar identification-oriented models emerged as significant mediators, emphasizing the importance of beliefs regarding in-game characters. Sensitivity analysis confirmed the robustness of results against outliers and publication bias.

Conclusion

The synthesized models shed light on the mechanisms underpinning GD, showcasing the dynamic interplay among affective, cognitive, and executive factors. Strategies including reducing delay discounting, addressing emotional underpinnings, and reshaping thoughts tied to avatars and gaming behaviors hold promise for effective GD intervention. However, limitations, including reliance on cross-sectional data and limited studies for mediational models, warrant consideration.

Problematic media use (PMU) tends to be related to significant social, emotional, and behavioral problems throughout life. Little research, however, has examined the development of PMU during early childhood, where media habits begin to form. The current longitudinal study examines the growth of PMU across early childhood (between 2.5 and 5.5 years of age) with a focus on predictors and outcomes (social, emotional, and parenting) of the intercept and slope of PMU over time. Participants (N = 269 children and their parents; 4.9% Asian American, 8.2% Black, 21% Hispanic or Latino, 63% White, 7% Mixed or Other race) completed questionnaires assessing PMU, media parenting, parental warmth and connection, and child social and emotional problems. Results indicated slight overall increases in PMU from ages 2.5 to 5.5. Emotional reactivity, aggression, and overall TV time predicted initial levels of PMU. Additionally, initial levels of PMU were related to social and emotional problems four years later. An increasing trajectory of PMU was associated with later aggression in childhood, even when controlling for initial levels of aggression. Implications for parents and prevention efforts aimed at reducing PMU are discussed.

This study delves into the complex world of gaming metaverses to explore the psychological factors shaping user experience and engagement. Amidst increasing interest in virtual gaming worlds, it examines the roles of presence, seamlessness, and psychological responses in influencing user satisfaction and continuance intention. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research analyzes responses from 273 gaming metaverse users. The results highlight that presence significantly boosts vicarious satisfaction, escapism, and self-expression. While seamlessness enhances vicarious satisfaction, it doesn't markedly affect escapism or self-expression. Vicarious satisfaction contributes to hedonic benefits, albeit not to immersion. Both escapism and self-expression positively influence hedonic benefits and immersion. Hedonic benefits positively impact immersiona, satisfaction, and continuance intention. Immersion directly affects satisfaction and indirectly influences continuance intention through satisfaction. These findings challenge established ideas in gaming psychology and provide actionable insights for metaverse developers, emphasizing the importance of balanced game design for optimal user engagement and satisfaction. The research lays the groundwork for further studies, particularly in qualitative analysis and specific gaming genres.