Computers in human behavior reports最新文献

{"title":"Context-dependent roles of familiarity, discipline, and personality in shaping social engagement in cooperative immersive learning","authors":"Lijie Zheng ,&nbsp;Shaoteng Ke ,&nbsp;Yuxin Zang ,&nbsp;Linjing Sun ,&nbsp;Matthew Pike ,&nbsp;Boon Giin Lee","doi":"10.1016/j.chbr.2026.101089","DOIUrl":"10.1016/j.chbr.2026.101089","url":null,"abstract":"<div><div>Collaborative learning represents a fundamental component of higher education, yet disparities in disciplinary perspectives, communication styles, and pre-existing social ties can lead to divergent interpretations of tasks and unbalanced contribution dynamics. Immersive virtual reality (IVR) offers innovative opportunities to mitigate these challenges. This study examines the combined influence of interpersonal familiarity, academic discipline, and personality trait composition on social interaction within a cooperative two-player IVR escape room game. A mixed methods approach, which combined quantitative questionnaires with qualitative observations and interviews, involved 108 students who formed 54 pairs. The analysis reveals a complex, context-dependent relationship between familiarity and participation. The study shows that although familiarity is generally associated with greater confidence and social interdependence, unfamiliar interdisciplinary pairs demonstrate significantly greater behavioral involvement, suggesting that unfamiliarity and cognitive divergence may be linked to more active communication and exploration. Furthermore, personality similarity was significantly associated with the satisfaction of unfamiliar pairs, and unfamiliar extrovert–extrovert pairs reported the highest motivational satisfaction. These findings challenge the assumption that familiarity is universally beneficial and offer actionable strategies for composing student groups to align with specific pedagogical objectives, such as pairing unfamiliar interdisciplinary students to promote exploration or familiar homogeneous groups to maximize efficiency. These findings provide an improved framework for collaboration, highlighting the need to align team composition with task structure in the design of cooperative IVR learning environments.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101089"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147995273","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}

{"title":"Towards an action-based taxonomy of employee cybersecurity behaviour in organisations (TECBO)","authors":"Benjamin Ambuehl ,&nbsp;Nico Ebert ,&nbsp;Tim Geppert ,&nbsp;Thierry Schaltegger ,&nbsp;Melanie Knieps ,&nbsp;Verena Zimmermann","doi":"10.1016/j.chbr.2026.101090","DOIUrl":"10.1016/j.chbr.2026.101090","url":null,"abstract":"<div><div>Organisational cybersecurity depends on countless employee cybersecurity behaviours (ECBs), such as <em>proper handling of phishing threats</em> or <em>safe browsing</em>. Hence, improving cybersecurity demands behaviour change towards security-enhancing ECBs, which itself – for effectiveness and efficiency – requires an in-depth understanding of target behaviours and their underlying characteristics. However, in the current literature, various notions of ECBs are used, and a consistent classification of ECBs is lacking, resulting in diverging research efforts. To overcome this gap, we transferred findings from behavioural science to cybersecurity and unifyingly define ECB as “<em>actions taken by individuals in organisations that influence the protection of information and information systems within the organisation</em>”. Further, based on existing classification approaches, we synthesized a new Taxonomy of Employee Cybersecurity Behaviour in Organisations (TECBO) that groups behaviours in five domains: 1) Using IT-Systems, 2) Communicating &amp; Exchanging Data, 3) Authenticating, Authorizing &amp; Controlling Access, 4) Maintaining &amp; Safeguarding IT-Systems, 5) Incident Reporting &amp; Handling. TECBO is the first taxonomy that is based on a foundational definition of ECB and uses underlying similarities of ECBs (e.g., behavioural characteristics on molecular level following Action Identification Theory) as classification criterion. When we used TECBO to categorize existing real-life examples of human cybersecurity behaviours from the security behaviour database (SebDB) and items of existing questionnaires, we found fair to substantial agreement amongst eight raters and acceptable inter-rater reliability. Consequently, TECBO provides a fundamentally new conceptualisation and a unifying classification structure to accelerate convergence in behavioural cybersecurity. Our results are of high ecological validity and demonstrate relevance for academia and industry alike.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101090"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147951442","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}

{"title":"Human-robot interaction based on artificial intelligence in clinical healthcare centers: A systematic review and meta-analysis","authors":"Mohammad Milad Shafaie ,&nbsp;Alireza Salehnia ,&nbsp;Negin Moslem ,&nbsp;Vahid Shafaie ,&nbsp;Majid Movahedi Rad","doi":"10.1016/j.chbr.2026.101080","DOIUrl":"10.1016/j.chbr.2026.101080","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) into human-robot interaction (HRI) in healthcare has fundamentally revolutionized the emotional, social, and cognitive interaction between humans and robotic systems. This systematic review examines how AI-powered healthcare bots affect patient trust, therapeutic alliance, and user bonding. A PRISMA-compliant literature search was conducted in five major databases: PubMed, Scopus, IEEE Xplore, Springer, and MDPI, covering studies published in English between 2010 and 2025. The inclusion criteria targeted experimental research, including evaluation studies, of AI-enhanced HRI in clinical and assistive fields. Reviews, non-experimental work, and studies without AI integration were excluded. Methodological quality and risk of bias were assessed using the revised Cochrane Risk of Bias tool for randomized trials (RoB 2), while robvis was used to generate visual summaries of the risk-of-bias assessments. Meta-analysis calculated Diagnostic Odds Ratios (95% CI) for reported diagnostic outcomes. Bibliometric visualization was performed using VOSviewer. The results show that visualized and emotionally intelligent robots outperformed virtual agents in delivering emotional security and therapeutic value. However, diagnostic accuracy was low (AUC≈0.39; pooled specificity = 0.53) with substantial heterogeneity (I² ˜ 68%), and meta-regression identified no significant moderators, leaving residual variability (τ² = 0.479). Gaps between user expectations and responsiveness limited engagement, and limited longitudinal designs restricted long-term insights. RoB 2 indicated moderate methodological variability. Despite these constraints, culturally adaptive robotic systems enhance clinical communication and patient trust, underscoring the importance of personalization and emotional intelligence in future AI healthcare robots.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101080"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802689","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}

{"title":"Perceived risks and benefits of Baby Tech in parenting among mothers and fathers in Japan","authors":"Ryuta Onishi","doi":"10.1016/j.chbr.2026.101081","DOIUrl":"10.1016/j.chbr.2026.101081","url":null,"abstract":"<div><div>Emerging technologies such as AI-enabled baby monitors and childcare robots (“Baby Tech”) are beginning to shape parenting; however, evidence on parents' evaluations of their benefits and risks remains limited outside Western contexts. This study examined Japanese fathers' and mothers’ perceived benefit and risk types of Baby Tech across four use domains (daily care, health management, behavioral management, and developmental support). Participants comprised 571 parents of children aged 0–3 years (286 fathers; 285 mothers) recruited from a nationwide online panel in March 2024. Within each domain, parents rated 10 benefit types and 10 risk types. We fitted cumulative-logit generalized estimating equation models to estimate population-averaged type profiles, accounting for intra-person correlations and covariates, and derived adjusted expected ratings. For perceived benefits, both mothers and fathers rated reduced parenting burden and reduced parenting anxiety relatively higher, whereas types related to interpersonal relationships and social interaction were rated lower. For perceived risks, overdependence on Baby Tech and system malfunctions were rated relatively higher. Group differences were limited overall, but mothers reported higher ratings than fathers for reduced parenting burden as a benefit and system malfunctions as a risk. These findings suggest that mothers and fathers share broadly similar evaluative patterns, while differing in the intensity of specific expectations and concerns. As Baby Tech becomes more widespread, challenges may arise, including overdependence on technology, tensions between partners regarding its use, and unexpected child health–related risks. Therefore, clarifying the concept of technology-enabled parenting literacy and developing practical guidelines for its implementation are warranted.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101081"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858446","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}

{"title":"Development and validation of the chilling effects in media scale","authors":"C.M. Segijn ,&nbsp;S.J. Opree ,&nbsp;J. Strycharz","doi":"10.1016/j.chbr.2026.101073","DOIUrl":"10.1016/j.chbr.2026.101073","url":null,"abstract":"<div><div>Chilling effects are inhibitions of legitimate behaviors and are an important ethical side effect of surveillance. With the centrality of media users’ data for digital communication it is important to gain insights into such ethical side effects, which requires a validated scale that can be used in future research. To develop and validate the Chilling Effects in Media (CEM) scale, we used a three-phased mixed-methods approach: (1) item generation using a literature review and qualitative interviews, (2) establishing content validity through experts interviews and a think-aloud study, and (3) establishing criterion and construct validity, and reliability through a pre-registered online panel survey (<em>N</em> = 415) in the context of corporate (e.g., personalized advertising) or government (e.g., security) surveillance. Factor analyses were conducted, resulting in three factors per context which individually and collectively scored adequately on reliability and validity. Between the two contexts, the factors were partially overlapping and partially distinct, showing that corporate and government surveillance result in unique chilling effects. In addition to the full-length scales per context (i.e., 16 items for corporate and 26 for government surveillance), shorter, more workable versions with respectively 8 and 9 were created too. These shorter versions perform just as well in terms of reliability and validity.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101073"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858430","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}

{"title":"Explaining in context: Perceived informativeness of Explainable Artificial Intelligence (XAI) in Arabic hate speech detection","authors":"Noor Al-Ansari,&nbsp;Dena Al-Thani,&nbsp;Mariam Bahameish","doi":"10.1016/j.chbr.2026.101083","DOIUrl":"10.1016/j.chbr.2026.101083","url":null,"abstract":"<div><div>Hate speech is pervasive across most social media platforms, profoundly impacting users' lives and wellbeing. To address this critical issue, researchers are exploring explainable AI (XAI), which helps users understand AI decision-making and outputs. To ensure its effectiveness, XAI must align with users’ expectations, often assessed using user-centred approaches from human-computer interaction (HCI). However, the integration of XAI in hate speech detection, particularly in Arabic contexts, remains underexplored. This study proposed and evaluated a high-fidelity prototype of an Arabic hate speech detection system with explainable features. The focus was on assessing the informativeness of different XAI representation methods and how Arabic-speaking users perceived them. Both subjective and objective measures were used, including the Social Media Activity Questionnaire (SMAQ), Social Networking Usage Questionnaire (SNUE), a Perceived Informativeness (PI) questionnaire, and eye-tracking to assess visual attention. Results showed that users perceived both textual and visual explanations—specifically visual saliency—as more informative than other XAI methods. Social media usage duration had no significant effect on perceived informativeness. While the combination of textual and visual explanations (presented as a pie chart) resulted in longer fixation durations, this did not necessarily translate into higher perceived informativeness. The extended fixation was likely due to the additional visual element rather than increased clarity. These findings highlight the importance of designing visually clear and focused XAI representation methods. Elements like text highlighting through saliency can improve user perception of informativeness more effectively than simply adding visual complexity.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101083"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858431","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}

{"title":"General Artificial Intelligence Attitude Scale (GAIA-15) – How to measure attitude toward an ever-changing object","authors":"Maike Hering,&nbsp;Nicolas Eric Neef,&nbsp;Sarah Zabel,&nbsp;Siegmar Otto","doi":"10.1016/j.chbr.2026.101096","DOIUrl":"10.1016/j.chbr.2026.101096","url":null,"abstract":"<div><div>The influence of Artificial Intelligence (AI) continues to grow and attitude toward AI is becoming an increasingly important scientific construct. However, to study AI attitude systematically, we must first operationalize it. We introduce the General Artificial Intelligence Attitude Scale (GAIA-15), a 15-item measure that assesses affective, behavioral, and cognitive aspects of AI attitude. For scale development, we conducted a review of existing scales and created new items. Then we used item response theory to select and integrate the best items into our GAIA-15 scale. Moreover, we provide the General Artificial Intelligence Attitude Item Pool (GAIA-I) from which researchers can draw customized scales. Providing the GAIA-I addresses a challenge linked to the assessment of AI attitude: unlike most attitude objects, AI is rapidly evolving and a measure of attitude toward AI must be adaptable to future developments of AI. The approach of an IRT-based item pool grants this flexibility. Using an online questionnaire, we validated both GAIA tools with a sample of 302 participants. We found evidence for convergent validity (alignment with established AI attitude scales), discriminant validity (distinction from the constructs “AI anxiety” and “innovativeness”), as well as predictive validity (relation to AI use in a follow-up survey).</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101096"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147951572","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}

{"title":"Peer pings and self-control: WhatsApp group norms, short-video algorithms, and risky adolescent behavior in Indonesia","authors":"Lidia Sandra","doi":"10.1016/j.chbr.2026.101087","DOIUrl":"10.1016/j.chbr.2026.101087","url":null,"abstract":"<div><div>Adolescent risky behavior remains a major public health concern increasingly shaped by digital social environments. This study examines how peer chat norms on WhatsApp and algorithmic exposure on short-video platforms jointly predict adolescents’ risky behaviors, and whether self-control moderates these effects. A cross-sectional multilevel survey was conducted with 1024 Indonesian students (aged 15–18) nested within 52 classes across Java and Bali. Participants completed validated measures of self-control, WhatsApp injunctive and descriptive norms, algorithmic exposure, and risky behavior, complemented by trace-linked notification density from smartphone screenshots. Multilevel structural equation modeling showed that WhatsApp injunctive norms significantly predicted risky behavior (β = .34, p &lt; 0.001), amplified by class-level norm climates (γ = .21, p = 0.003). Self-control reduced risk directly (β = −.28, p &lt; 0.001) and buffered peer-norm effects (β = −.11, p = 0.018). Algorithmic exposure independently predicted risk (β = .19, p &lt; 0.001) and strengthened peer-influence pathways (β = .14, p = 0.009). Notification density added unique variance beyond self-reported screen time. The findings highlight the psychosocial interplay between peer norms, self-regulation, and algorithmic mediation in digital contexts, suggesting intervention levers such as group norm charters, notification governance, and algorithmic literacy in schools.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101087"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147951571","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}

{"title":"Mindfulness, life skills, and internet time and addiction among high-achieving college students in China: The mediating role of self-control","authors":"Rong Tian ,&nbsp;Chien-Chung Huang ,&nbsp;Jeffrey Mertz","doi":"10.1016/j.chbr.2026.101095","DOIUrl":"10.1016/j.chbr.2026.101095","url":null,"abstract":"<div><h3>Background</h3><div>Internet time and addiction are key factors affecting mental health, academic performance, and social relationships for college students, presenting significant challenges in today's digital environment. This study investigates the roles of mindfulness and life skills in mitigating these risks, specifically examining whether self-control acts as a mediating mechanism linking these competencies to reduced problematic internet usage.</div></div><div><h3>Method</h3><div>A cross-sectional survey was administered to 218 first-year students at a highly selective institution in China, a member of Project 985. Participants completed validated measures assessing their levels of mindfulness, life skills, self-control, internet time and addiction. Structural Equation Modeling (SEM) was employed to test the direct and indirect effects of mindfulness and life skills on internet time and addiction via self-control, controlling for gender and academic discipline.</div></div><div><h3>Results</h3><div>Both mindfulness and life skills were significant positive predictors of self-control (beta = 0.51 and 0.22, respectively). In turn, self-control was a strong predictor of both internet time (beta = −0.34) and addiction (beta = −0.54). Crucially, self-control fully mediated the associations between mindfulness, life skills, and internet time and addiction, suggesting that these foundational competencies help students regulate digital engagement by enhancing their internal capacity for self-regulation. The mediation pathways demonstrated stability across gender and academic discipline.</div></div><div><h3>Conclusion</h3><div>Findings underscore the critical importance of fostering mindfulness and life skills to strengthen self-control, which serves as a protective factor against excessive internet time and addiction among high-achieving college students in China. University programs should prioritize interventions to cultivate these regulatory skills, directly supporting students' internet use and well-being in the digital age.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101095"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147951574","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}

{"title":"Decoding medical students’ attitudes toward ChatGPT: Psychometric evaluation of the Persian version of the attitudes toward ChatGPT questionnaire","authors":"Haniye Mastour ,&nbsp;Maryam Moghadasin ,&nbsp;S. Ayhan Caliskan ,&nbsp;Fatemeh Keshavarz ,&nbsp;Mohammad Mehdi Shadravan ,&nbsp;Somaye Sohrabi","doi":"10.1016/j.chbr.2026.101069","DOIUrl":"10.1016/j.chbr.2026.101069","url":null,"abstract":"<div><h3>Background</h3><div>The rapid emergence of generative AI like ChatGPT in medical education necessitates understanding student attitudes for its effective integration. This study aimed to translate, culturally adapt, and validate the Persian version of the Attitudes toward ChatGPT (CAS) scale to address this need.</div></div><div><h3>Methods</h3><div>A cross-sectional methodological study was conducted with 421 undergraduate medical students from Iranian medical universities. The 21-item CAS developed by Yu et al. (2024) was translated and adapted following Beaton's cross-cultural validation framework. Data were analyzed using exploratory (EFA) and confirmatory factor analyses (CFA) with maximum likelihood estimation. Internal consistency was examined using Cronbach's α and McDonald's ω; convergent and discriminant validity were assessed via composite reliability (CR), average variance extracted (AVE), and the Fornell–Larcker criterion.</div></div><div><h3>Results</h3><div>EFA supported the original five-factor structure—Tool, Tutor, Threat, Talk, and Trend—accounting for 44% of total variance (KMO = 0.81; RMSEA = 0.049). CFA confirmed acceptable model fit (χ²/df = 2.08; CFI = 0.922; TLI = 0.905; RMSEA = 0.051). Reliability was satisfactory across subscales (α = 0.63–0.82; ω = 0.67–0.84). Convergent validity was strong for Talk (AVE = 0.69) and Trend (AVE = 0.57), while discriminant validity was mostly supported, except for partial overlap between Tool and Tutor. One item (Q21) demonstrated a Heywood case and requires refinement.</div></div><div><h3>Conclusion</h3><div>The Persian version of the CAS demonstrates preliminary psychometric support for assessing medical students’ attitudes toward ChatGPT, with an interpretable five-factor structure and acceptable overall model fit. However, item-level instability, suboptimal convergent validity for several subscales, and conceptual overlap between the Tool and Tutor dimensions indicate that the scale requires further refinement. Accordingly, the Persian CAS should be considered a developing measurement framework, suitable for exploratory research and construct-level investigation, rather than a finalized instrument for applied or high-stakes educational use.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"22 ","pages":"Article 101069"},"PeriodicalIF":5.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802588","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}