Pub Date : 2025-11-20DOI: 10.1016/j.chbr.2025.100877
Suzanne van de Groep , Ilse H. van de Groep , Eveline A. Crone
Adolescence and emerging adulthood are key periods for extending prosocial behaviors into broader societal contexts, including online environments. In a sample of 998 adolescents and young adults (ages 12–24; 59 % female), this study examined age- and gender-related patterns of online emotional support and online activism using the extended Online Prosocial Behavior Scale (OPBS-E). The OPBS-E showed good reliability over 6 months and convergent validity with offline prosocial behaviors. Findings revealed that online emotional support was associated with generosity toward friends in an economic game, while online activism was linked to higher compulsive social media use. Females reported more online emotional support, and males more online activism. Age patterns indicated that emotional support was higher for females than males in early adolescence and young adulthood, but similar across genders in mid-late adolescence. Online activism was more frequent in adolescence and declined into emerging adulthood, independent of gender. These findings highlight distinct age and gender patterns in online prosocial behaviors and contribute new insights into how these behaviors evolve during the socially formative years of adolescence and early adulthood.
{"title":"Online prosocial behaviors in adolescence and young adulthood: Differential age and gender patterns for online emotional support and online activism","authors":"Suzanne van de Groep , Ilse H. van de Groep , Eveline A. Crone","doi":"10.1016/j.chbr.2025.100877","DOIUrl":"10.1016/j.chbr.2025.100877","url":null,"abstract":"<div><div>Adolescence and emerging adulthood are key periods for extending prosocial behaviors into broader societal contexts, including online environments. In a sample of 998 adolescents and young adults (ages 12–24; 59 % female), this study examined age- and gender-related patterns of online emotional support and online activism using the extended Online Prosocial Behavior Scale (OPBS-E). The OPBS-E showed good reliability over 6 months and convergent validity with offline prosocial behaviors. Findings revealed that online emotional support was associated with generosity toward friends in an economic game, while online activism was linked to higher compulsive social media use. Females reported more online emotional support, and males more online activism. Age patterns indicated that emotional support was higher for females than males in early adolescence and young adulthood, but similar across genders in mid-late adolescence. Online activism was more frequent in adolescence and declined into emerging adulthood, independent of gender. These findings highlight distinct age and gender patterns in online prosocial behaviors and contribute new insights into how these behaviors evolve during the socially formative years of adolescence and early adulthood.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100877"},"PeriodicalIF":5.8,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.chbr.2025.100870
Anne Zola , Harlym K. Pike , Renee Engeln
The online body positivity movement focuses on representing and supporting those with marginalized bodies, particularly fat women and women of color. Despite the popularity of body-positive posts on Instagram, no research has examined how the race and body size of women featured in the posts affects users’ reactions. Across four experiments (total n = 2113), young women (aged 18–30) in the U.S. were randomly assigned to rate a body positivity Instagram post featuring either a Black or White model who was either fat or thin. Study 1 indicated participants preferred body positivity posts featuring women with marginalized bodies (i.e., Black and/or fat). We replicated these findings with a new sample (Study 2), a new set of images (Study 3), and with a sample of Black and White women to examine the effects of participant race on reactions to the posts (Study 4). Results suggested that in the context of body positivity posts, women preferred posts featuring women with marginalized bodies over posts featuring thin, White women. Despite the proliferation of anti-Black and anti-fat attitudes in online spaces, these studies suggest women prefer to see body positivity posts that center women with marginalized bodies.
{"title":"Women's reactions to body positivity posts vary by posters' race and body size","authors":"Anne Zola , Harlym K. Pike , Renee Engeln","doi":"10.1016/j.chbr.2025.100870","DOIUrl":"10.1016/j.chbr.2025.100870","url":null,"abstract":"<div><div>The online body positivity movement focuses on representing and supporting those with marginalized bodies, particularly fat women and women of color. Despite the popularity of body-positive posts on Instagram, no research has examined how the race and body size of women featured in the posts affects users’ reactions. Across four experiments (total <em>n</em> = 2113), young women (aged 18–30) in the U.S. were randomly assigned to rate a body positivity Instagram post featuring either a Black or White model who was either fat or thin. Study 1 indicated participants preferred body positivity posts featuring women with marginalized bodies (i.e., Black and/or fat). We replicated these findings with a new sample (Study 2), a new set of images (Study 3), and with a sample of Black and White women to examine the effects of participant race on reactions to the posts (Study 4). Results suggested that in the context of body positivity posts, women preferred posts featuring women with marginalized bodies over posts featuring thin, White women. Despite the proliferation of anti-Black and anti-fat attitudes in online spaces, these studies suggest women prefer to see body positivity posts that center women with marginalized bodies.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100870"},"PeriodicalIF":5.8,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.chbr.2025.100882
Conrado Eiroa-Solans , Michael Inzlicht
Scalable interventions promoting sustained behavioral change are crucial for addressing societal issues, yet traditional approaches often require intensive one-on-one therapy. We developed and tested Intrinsic AI, a motivational interviewing chatbot built on GPT-4 and tuned using self-determination theory principles, to increase prosocial behavior. In a preregistered randomized controlled trial (N = 237), participants either engaged in a 15-min conversation with Intrinsic AI about becoming more prosocial or talked freely with an unmodified version of GPT-4. We measured changes in motivation using validated self-report scales and assessed prosocial behavior through an effort-based decision-making task where participants chose between exerting cognitive effort for themselves versus charity. Compared to controls, participants who interacted with Intrinsic AI showed greater increases in motivational readiness as assessed by the motivational interviewing ruler, reporting that becoming prosocial was more important to them, that they felt more confident in their ability to change, and that they were more ready to begin. However, this motivational gain did not persist at 24-h follow-up, translate into trait level changes in motivation, or influence prosocial effort in a behavioral task. Our findings demonstrate that theoretically grounded AI chatbots can effectively increase short-term prosocial motivation and suggest that a single brief interaction may be insufficient for creating lasting motivational change or impact actual prosocial behavior. This work provides a proof-of-concept for automated motivational interviewing while highlighting the need for more sustained AI-human interactions to achieve durable behavioral change.
{"title":"From extrinsic to intrinsic motivation: Testing an AI-powered motivational interviewing system to foster prosocial motivation","authors":"Conrado Eiroa-Solans , Michael Inzlicht","doi":"10.1016/j.chbr.2025.100882","DOIUrl":"10.1016/j.chbr.2025.100882","url":null,"abstract":"<div><div>Scalable interventions promoting sustained behavioral change are crucial for addressing societal issues, yet traditional approaches often require intensive one-on-one therapy. We developed and tested Intrinsic AI, a motivational interviewing chatbot built on GPT-4 and tuned using self-determination theory principles, to increase prosocial behavior. In a preregistered randomized controlled trial (N = 237), participants either engaged in a 15-min conversation with Intrinsic AI about becoming more prosocial or talked freely with an unmodified version of GPT-4. We measured changes in motivation using validated self-report scales and assessed prosocial behavior through an effort-based decision-making task where participants chose between exerting cognitive effort for themselves versus charity. Compared to controls, participants who interacted with Intrinsic AI showed greater increases in motivational readiness as assessed by the motivational interviewing ruler, reporting that becoming prosocial was more important to them, that they felt more confident in their ability to change, and that they were more ready to begin. However, this motivational gain did not persist at 24-h follow-up, translate into trait level changes in motivation, or influence prosocial effort in a behavioral task. Our findings demonstrate that theoretically grounded AI chatbots can effectively increase short-term prosocial motivation and suggest that a single brief interaction may be insufficient for creating lasting motivational change or impact actual prosocial behavior. This work provides a proof-of-concept for automated motivational interviewing while highlighting the need for more sustained AI-human interactions to achieve durable behavioral change.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100882"},"PeriodicalIF":5.8,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-17DOI: 10.1016/j.chbr.2025.100878
Clemens Koob
While research has examined how social media brand post characteristics drive consumer engagement, the underlying content preferences of consumers remain underexplored. This study investigates these preferences, their heterogeneity, and contextual factors shaping preference variation using best-worst scaling. Data were collected via an online survey. The sample comprised 515 consumers in Germany, Austria, and Switzerland. BWS responses were analyzed using counting analysis combined with multinomial and mixed logit models to quantify relative preferences and heterogeneity, latent class analysis to identify distinct consumer segments, and a random forest machine learning algorithm to assess contextual influences on segment membership. Analyses revealed a clear hierarchy: shopping-related use value was most preferred, followed by daily inspirations and brand/product information, while social value ranked lowest. Four distinct consumer segments emerged: Information Hunters, Entertainment Enthusiasts, Shopping and Inspiration Seekers, and Brand Post Omnivores. Age, personality traits, and the primary social media platform were strong predictors of segment membership. The results enrich our understanding of the motivational mechanisms underlying consumer brand post engagement and offer guidance for designing user-centric brand post content, helping organizations align their strategies with user preferences to enhance both interaction quality and content effectiveness.
{"title":"Unveiling consumers’ brand post content preferences: a best-worst scaling approach","authors":"Clemens Koob","doi":"10.1016/j.chbr.2025.100878","DOIUrl":"10.1016/j.chbr.2025.100878","url":null,"abstract":"<div><div>While research has examined how social media brand post characteristics drive consumer engagement, the underlying content preferences of consumers remain underexplored. This study investigates these preferences, their heterogeneity, and contextual factors shaping preference variation using best-worst scaling. Data were collected via an online survey. The sample comprised 515 consumers in Germany, Austria, and Switzerland. BWS responses were analyzed using counting analysis combined with multinomial and mixed logit models to quantify relative preferences and heterogeneity, latent class analysis to identify distinct consumer segments, and a random forest machine learning algorithm to assess contextual influences on segment membership. Analyses revealed a clear hierarchy: shopping-related use value was most preferred, followed by daily inspirations and brand/product information, while social value ranked lowest. Four distinct consumer segments emerged: Information Hunters, Entertainment Enthusiasts, Shopping and Inspiration Seekers, and Brand Post Omnivores. Age, personality traits, and the primary social media platform were strong predictors of segment membership. The results enrich our understanding of the motivational mechanisms underlying consumer brand post engagement and offer guidance for designing user-centric brand post content, helping organizations align their strategies with user preferences to enhance both interaction quality and content effectiveness.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"21 ","pages":"Article 100878"},"PeriodicalIF":5.8,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-17DOI: 10.1016/j.chbr.2025.100876
Moritz Vogel , Jürgen Buder
Recent research has established an uncongeniality bias (a preference to reply to attitudinally uncongenial social media content) that runs counter to the notion that individuals prefer like-minded content. Two preregistered studies (N = 222, N = 227) explored this uncongeniality bias by investigating the subjective attitudinal conflict that individuals report when reading congenial and uncongenial comments. Results from Study 1 suggest that low cognitive conflict is associated with a congeniality bias (b = -0.33, 95 % CI = [-0.41, -0.25]) whereas high affective conflict is associated with an uncongeniality bias (b = 0.22, 95 % CI = [0.14, 0.29]). Key findings from Study 2 show that the uncongeniality bias is moderated by emotion reactivity, suggesting that individuals who experience strong and persistent emotions are more likely to reply to uncongenial comments. Moreover, results provide tentative evidence that the congeniality bias can be linked to “cold cognition” whereas the uncongeniality bias is associated with “hot cognition”. This may explain the heated nature of discussions in comment sections.
最近的研究发现了一种不一致的偏见（偏好回复态度不一致的社交媒体内容），这与人们更喜欢志同道合的内容的观念背道而驰。两项预先注册的研究（N = 222, N = 227）通过调查个人在阅读意气相投和意气相投评论时报告的主观态度冲突，探讨了这种非意气相投偏见。研究1的结果表明，低认知冲突与亲和性偏差相关（b = -0.33, 95% CI =[-0.41, -0.25]），而高情感冲突与非亲和性偏差相关（b = 0.22, 95% CI =[0.14, 0.29]）。研究2的主要发现表明，情绪反应会缓和不和谐偏见，这表明经历强烈和持续情绪的个体更有可能对不和谐的评论做出回应。此外，研究结果还提供了初步证据，证明亲和性偏差与“冷认知”有关，而不亲和性偏差与“热认知”有关。这也许可以解释评论区讨论的激烈性质。
{"title":"Dissecting cognitive, affective, and behavioral facets of attitudinal conflict in selective exposure and selective response","authors":"Moritz Vogel , Jürgen Buder","doi":"10.1016/j.chbr.2025.100876","DOIUrl":"10.1016/j.chbr.2025.100876","url":null,"abstract":"<div><div>Recent research has established an uncongeniality bias (a preference to reply to attitudinally uncongenial social media content) that runs counter to the notion that individuals prefer like-minded content. Two preregistered studies (<em>N</em> = 222, <em>N</em> = 227) explored this uncongeniality bias by investigating the subjective attitudinal conflict that individuals report when reading congenial and uncongenial comments. Results from Study 1 suggest that low cognitive conflict is associated with a congeniality bias (<em>b</em> = -0.33, 95 % CI = [-0.41, -0.25]) whereas high affective conflict is associated with an uncongeniality bias (<em>b</em> = 0.22, 95 % CI = [0.14, 0.29]). Key findings from Study 2 show that the uncongeniality bias is moderated by emotion reactivity, suggesting that individuals who experience strong and persistent emotions are more likely to reply to uncongenial comments. Moreover, results provide tentative evidence that the congeniality bias can be linked to “cold cognition” whereas the uncongeniality bias is associated with “hot cognition”. This may explain the heated nature of discussions in comment sections.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"20 ","pages":"Article 100876"},"PeriodicalIF":5.8,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.chbr.2025.100872
Rosa Lilia Segundo Díaz , Sevda Ece Kizilkilic , Wim Ramakers , Dominique Hansen , Paul Dendale , Karin Coninx
Data-driven personas are increasingly used to inform design decisions. Various methods are published to produce personas based on data collected from projects of different types and scales, each with a specific focus. This study aims to create a set of personas using data collected from a prior randomised controlled trial (RCT), which will be instrumental in designing future eHealth applications to support individuals with cardiovascular disease (CVD). Our method followed five phases for designing personas: (Phase I) expert analysis and variable selection, (Phase II) clustering, (Phase III) expert validation, (Phase IV) persona optimisation, and (Phase V) final check. To ensure that personas accurately reflected the patients, we employed the k-prototype algorithm to cluster mixed data and we focused on validation with colleagues, including medical colleagues, physiotherapists, a psychologist and Human-Computer Interaction (HCI) experts. Seven different personas resulted from the clustering. A validation step involved a multidisciplinary team that assessed the personas’ realism, giving an average rating of 8.0 out of 10. Based on their feedback, three of the personas were slightly updated. The final descriptions of all seven personas incorporated the clustered data and the proposed changes after the validation. We concluded that data-driven approaches and expert-based refinement to develop personas is an effective method for understanding the target population. This study highlighted the importance of validation, revealing that creating personas cannot be fully automated, as this may result in losing essential characteristics that only experts can identify. Future research includes demonstrating the practical use of personas.
{"title":"Integrating data-driven methods and expert knowledge to develop personas: Balancing automation and multi-disciplinary validation","authors":"Rosa Lilia Segundo Díaz , Sevda Ece Kizilkilic , Wim Ramakers , Dominique Hansen , Paul Dendale , Karin Coninx","doi":"10.1016/j.chbr.2025.100872","DOIUrl":"10.1016/j.chbr.2025.100872","url":null,"abstract":"<div><div>Data-driven personas are increasingly used to inform design decisions. Various methods are published to produce personas based on data collected from projects of different types and scales, each with a specific focus. This study aims to create a set of personas using data collected from a prior randomised controlled trial (RCT), which will be instrumental in designing future eHealth applications to support individuals with cardiovascular disease (CVD). Our method followed five phases for designing personas: (Phase I) expert analysis and variable selection, (Phase II) clustering, (Phase III) expert validation, (Phase IV) persona optimisation, and (Phase V) final check. To ensure that personas accurately reflected the patients, we employed the k-prototype algorithm to cluster mixed data and we focused on validation with colleagues, including medical colleagues, physiotherapists, a psychologist and Human-Computer Interaction (HCI) experts. Seven different personas resulted from the clustering. A validation step involved a multidisciplinary team that assessed the personas’ realism, giving an average rating of 8.0 out of 10. Based on their feedback, three of the personas were slightly updated. The final descriptions of all seven personas incorporated the clustered data and the proposed changes after the validation. We concluded that data-driven approaches and expert-based refinement to develop personas is an effective method for understanding the target population. This study highlighted the importance of validation, revealing that creating personas cannot be fully automated, as this may result in losing essential characteristics that only experts can identify. Future research includes demonstrating the practical use of personas.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"20 ","pages":"Article 100872"},"PeriodicalIF":5.8,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.chbr.2025.100864
Dao Van Le , Tung Bui , Teck Lee Yap
This study examines the impact of Artificial Intelligence (AI) applications on the subjective well-being of the unemployed, analyzing a data set comprising 179,504 global citizens spanning from 1981 to 2022. Employing a high-dimensional fixed-effect estimator, we assess how different intensities and scales of AI deployment across various national contexts correlate with life satisfaction and happiness. Our findings indicate that rapid and unprepared AI integrations tend to amplify the negative effects of unemployment on subjective well-being and exacerbate inequalities, particularly among the most vulnerable populations. Conversely, in the context where AI applications are well-planned and gradual, the negative impacts are mitigated, which suggests the importance of considering the social and regulatory contexts of AI applications. Furthermore, our results suggest that cautious and thoughtful AI applications can potentially cushion vulnerable populations from the adverse impacts of job displacement. Moreover, enhancing public engagement and transparency in AI policies can contribute to reducing the socio-economic divides exacerbated by rapid technological changes. This study underscores the necessity for policymaking frameworks that foster equitable AI applications and integration with socio-economic development, ensuring that advancements in AI do not widen existing social disparities but rather promote social inclusivity and well-being.
{"title":"Beyond automation: Understanding unemployment in the AI Epoch from a global viewpoint","authors":"Dao Van Le , Tung Bui , Teck Lee Yap","doi":"10.1016/j.chbr.2025.100864","DOIUrl":"10.1016/j.chbr.2025.100864","url":null,"abstract":"<div><div>This study examines the impact of Artificial Intelligence (AI) applications on the subjective well-being of the unemployed, analyzing a data set comprising 179,504 global citizens spanning from 1981 to 2022. Employing a high-dimensional fixed-effect estimator, we assess how different intensities and scales of AI deployment across various national contexts correlate with life satisfaction and happiness. Our findings indicate that rapid and unprepared AI integrations tend to amplify the negative effects of unemployment on subjective well-being and exacerbate inequalities, particularly among the most vulnerable populations. Conversely, in the context where AI applications are well-planned and gradual, the negative impacts are mitigated, which suggests the importance of considering the social and regulatory contexts of AI applications. Furthermore, our results suggest that cautious and thoughtful AI applications can potentially cushion vulnerable populations from the adverse impacts of job displacement. Moreover, enhancing public engagement and transparency in AI policies can contribute to reducing the socio-economic divides exacerbated by rapid technological changes. This study underscores the necessity for policymaking frameworks that foster equitable AI applications and integration with socio-economic development, ensuring that advancements in AI do not widen existing social disparities but rather promote social inclusivity and well-being.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"20 ","pages":"Article 100864"},"PeriodicalIF":5.8,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1016/j.chbr.2025.100871
Anca Velicu , Gyöngyvér Erika Tőkés
This study examines whether sharenting—parents' and caregivers' practices of sharing children's personal information online without their consent—increases the risk of cybervictimization among Romanian children aged 12–17. Using data from the EU Kids Online study 2020, we analyze sharenting as a predictor of general cybervictimization, cyberhate, and personal data misuse in two different settings. First, we examine sharenting's impact on cybervictimization in isolation. Second, theoretically framed by the social ecological model, we consider its impact when controlling for victim-related factors that can also predict cybervictimization.
In the first analysis, our results indicate that sharenting, when examined independently, significantly predicts all three forms of cybervictimization. In the second analysis, when controlling for other individual-level factors, sharenting remains a significant predictor for cyberhate victimization and personal data misuse victimization, but not for general cybervictimization. Specifically, we found that older children with a lower level of digital skills are more likely to be victims of cyberhate when subjected to sharenting. We also found that emotionally vulnerable children who are exposed by parents through sharenting are at increased risk for personal data misuse victimization. General cybervictimization is predicted only by individual-level factors—such as having emotional difficulties, a tendency for self-disclosure, and lack of privacy concern—when controlling for sharenting.
Our findings confirm sharenting's impact on children's online safety by establishing statistically significant connections between this parental practice and specific victimization outcomes in exposed children. Importantly, the study shows how sharenting may disproportionately affect already vulnerable children.
{"title":"Understanding sharenting as a risk factor for three forms of cybervictimization in children: Evidence from Romania","authors":"Anca Velicu , Gyöngyvér Erika Tőkés","doi":"10.1016/j.chbr.2025.100871","DOIUrl":"10.1016/j.chbr.2025.100871","url":null,"abstract":"<div><div>This study examines whether sharenting—parents' and caregivers' practices of sharing children's personal information online without their consent—increases the risk of cybervictimization among Romanian children aged 12–17. Using data from the EU Kids Online study 2020, we analyze sharenting as a predictor of general cybervictimization, cyberhate, and personal data misuse in two different settings. First, we examine sharenting's impact on cybervictimization in isolation. Second, theoretically framed by the social ecological model, we consider its impact when controlling for victim-related factors that can also predict cybervictimization.</div><div>In the first analysis, our results indicate that sharenting, when examined independently, significantly predicts all three forms of cybervictimization. In the second analysis, when controlling for other individual-level factors, sharenting remains a significant predictor for cyberhate victimization and personal data misuse victimization, but not for general cybervictimization. Specifically, we found that older children with a lower level of digital skills are more likely to be victims of cyberhate when subjected to sharenting. We also found that emotionally vulnerable children who are exposed by parents through sharenting are at increased risk for personal data misuse victimization. General cybervictimization is predicted only by individual-level factors—such as having emotional difficulties, a tendency for self-disclosure, and lack of privacy concern—when controlling for sharenting.</div><div>Our findings confirm sharenting's impact on children's online safety by establishing statistically significant connections between this parental practice and specific victimization outcomes in exposed children. Importantly, the study shows how sharenting may disproportionately affect already vulnerable children.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"20 ","pages":"Article 100871"},"PeriodicalIF":5.8,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As artificial intelligence (AI) becomes increasingly integrated into healthcare, assessing medical students’ confidence in using AI tools is essential. This study aimed to translate, adapt, and psychometrically validate the Persian version of the Artificial Intelligence Self-Efficacy Scale (AISES) among Iranian medical sciences students, ensuring its suitability for assessing AI-specific self-efficacy in this population.
Methods
A cross-sectional study was conducted in late 2023 using a convenience sample of 501 students recruited via an online survey. Content and face validity were evaluated both qualitatively and quantitatively. Construct validity was examined using Exploratory and Confirmatory Factor Analysis (EFA and CFA). Item Response Theory (IRT) with the Graded Response Model was applied to assess item-level discrimination. Convergent and discriminant validity were tested using the Average Variance Extracted (AVE) and the Fornell–Larcker criterion. Reliability was assessed with Cronbach's alpha, McDonald's omega, and Composite Reliability.
Results
EFA supported a four-factor structure consistent with the original AISES, explaining 77.27 % of the variance. CFA confirmed model fit (CFI = 0.944, RMSEA = 0.057). IRT results showed high item discrimination (α = 1.06–3.78) and logically ordered thresholds. The Test Information Function showed the highest precision in the lower-than-average range of AISE. All reliability coefficients exceeded 0.80. AVE values confirmed convergent validity, and discriminant validity was supported by the Fornell-Larcker criterion.
Discussion
The Persian AISES is a valid, reliable, and culturally adapted tool for assessing AI self-efficacy in Iranian students. Its ability to identify low-confidence learners supports targeted curriculum design, early intervention, and more equitable AI-based learning.
{"title":"Psychometric properties of the Persian version of the artificial intelligence self-efficacy scale (AISES) in medical sciences students","authors":"Neda Gilani , Ahmad Pourabbas , Gholamali Dehghani , Zahra Parsian","doi":"10.1016/j.chbr.2025.100858","DOIUrl":"10.1016/j.chbr.2025.100858","url":null,"abstract":"<div><h3>Introduction</h3><div>As artificial intelligence (AI) becomes increasingly integrated into healthcare, assessing medical students’ confidence in using AI tools is essential. This study aimed to translate, adapt, and psychometrically validate the Persian version of the Artificial Intelligence Self-Efficacy Scale (AISES) among Iranian medical sciences students, ensuring its suitability for assessing AI-specific self-efficacy in this population.</div></div><div><h3>Methods</h3><div>A cross-sectional study was conducted in late 2023 using a convenience sample of 501 students recruited via an online survey. Content and face validity were evaluated both qualitatively and quantitatively. Construct validity was examined using Exploratory and Confirmatory Factor Analysis (EFA and CFA). Item Response Theory (IRT) with the Graded Response Model was applied to assess item-level discrimination. Convergent and discriminant validity were tested using the Average Variance Extracted (AVE) and the Fornell–Larcker criterion. Reliability was assessed with Cronbach's alpha, McDonald's omega, and Composite Reliability.</div></div><div><h3>Results</h3><div>EFA supported a four-factor structure consistent with the original AISES, explaining 77.27 % of the variance. CFA confirmed model fit (CFI = 0.944, RMSEA = 0.057). IRT results showed high item discrimination (α = 1.06–3.78) and logically ordered thresholds. The Test Information Function showed the highest precision in the lower-than-average range of AISE. All reliability coefficients exceeded 0.80. AVE values confirmed convergent validity, and discriminant validity was supported by the Fornell-Larcker criterion.</div></div><div><h3>Discussion</h3><div>The Persian AISES is a valid, reliable, and culturally adapted tool for assessing AI self-efficacy in Iranian students. Its ability to identify low-confidence learners supports targeted curriculum design, early intervention, and more equitable AI-based learning.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"20 ","pages":"Article 100858"},"PeriodicalIF":5.8,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1016/j.chbr.2025.100865
Alessandro Piedimonte , Valeria Volpino , Andrea Bottino , Francesco Strada , Fabio Cielo , Francesco Campaci , Giorgia Cecconato , Elisa Carlino
The sense of embodiment — the subjective experience of owning and controlling one’s body — is crucial for self-awareness. Virtual Reality (VR) allows controlled manipulation of visuomotor synchrony to investigate embodiment. This study investigates how temporal discrepancies between real and avatar movements affect subjective embodiment and the Readiness Potential (RP), a neurophysiological marker of motor preparation. Using a VR “reach and press” task, participants () performed movements under three delay conditions (200, 400, 600 ms) and one condition with no added delay (NA-delay), while EEG (64-channel) recorded RP in anterior-frontal and central regions, and subjective embodiment was assessed via questionnaire. A control group performed the NA-delay condition in a real setting. Results showed that embodiment decreased with increasing delay (significant at 400 ms, 600 ms). RP peaks also diminished, particularly frontally, suggesting a shift from motor preparation to cognitive processes like error monitoring. sLORETA implicated dorsal anterior cingulate and prefrontal cortices in monitoring user–avatar discrepancies. These findings highlight RP as an objective biomarker for embodiment in VR. This offers significant implications for human–computer Interaction, providing a continuous, objective measure to improve user agency in VR, enhance neurorehabilitation therapies, optimize avatar design, and advance brain–computer interface systems.
化身感——拥有和控制自己身体的主观体验——对自我意识至关重要。虚拟现实（VR）允许对视觉运动同步的控制操作来研究体现。本研究探讨了真实运动和虚拟运动之间的时间差异如何影响主观体现和准备电位（RP），一个运动准备的神经生理标记。采用虚拟现实“伸手按”任务，25名参与者分别在200、400、600 ms 3种延迟条件和na -延迟条件下进行运动，同时通过脑电图（64通道）记录前额叶和中央区域的RP，并通过问卷评估主观体现。对照组在真实环境中进行na延迟条件。结果表明，体现随延迟的增加而降低（在400 ms、600 ms时显著）。RP峰值也减少了，尤其是前额，这表明从运动准备到错误监测等认知过程的转变。sLORETA涉及背前扣带和前额叶皮层监测用户-化身差异。这些发现突出了RP作为VR体现的客观生物标志物。这对人机交互具有重要意义，为改善虚拟现实中的用户代理、加强神经康复治疗、优化化身设计和推进脑机接口系统提供了持续、客观的措施。
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