Human Behavior and Emerging Technologies最新文献

Purpose: The main purpose of this research paper is to explore the factors affecting Islamic Fintech adoption based on digital accounting practices. The paper examines the relationship among technology integration, organizational culture, regulatory support, tech advancements, innovation readiness, and Islamic Fintech adoption.

Design/Methodology/Approach: Employing a quantitative methodology, the study administered an 18-item questionnaire. A total of 232 participants aged 18 or older, who were aware of Islamic Fintech, were selected through social media and other relevant channels.

Findings: The outcomes show that tech integration has significant influences on Fintech adoption, with the impact of technology, advancement, and innovative technologies restructuring the financial environment. Second, innovation readiness also plays a crucial role, especially in highly educated and talented individuals working in innovative and dynamic environments. Third, organizational culture also mediates the relationship between IR and Fintech adoption, which means that the practices an organization has in relation to information sharing, knowledge sharing, and experimentation will impact how an employee behaves. Fourth, regulatory support mediates the relationship with advancement in tech, finding the importance of government regulation and support that provides legitimacy and structure with regulation. However, it does not significantly mediate the relationship between tech in structural integration and Fintech adoption.

Originality/Value: The study’s originality lies in its nuanced analysis of how regulatory support may mediate or facilitate tech integration and Islamic Fintech adoption within an authoritarian domain, drawing attention to the subtle dynamics of regulatory change. It offers vital insights about which aspects of regulation might expedite and spur innovation in Islamic Fintech while cautioning about the many challenges that continue to beset this domain. The study reaffirms the reality that proper knowledge of formal and informal regulatory structures and government relations is crucial to organizations, policymakers, and stakeholders to be able to address in dealing with these new regulatory settings.

Implication: In Jordan, rapid digitalization requires the implementation of the ISO 20376 Standard to propel Islamic Fintech solutions to new heights. It focuses on developing an innovation-fit culture in institutions, considering Jordan’s conservative, individualistic culture. Regulatory frameworks must facilitate technology adoption while adhering to Islamic financial principles, and there must be a sustainable financial sector. Policymakers and stakeholders must close theoretical gaps and practical challenges to establish an environment conducive to innovation in Islamic Fintech, driving the sector’s growth, flexibility, and excellence.

The growth in interest has shown great promise in game-based learning, with particular potential found in healthcare for first aid training. This study indicates that the issues with conventional first aid training, such as with nonexperts and youth, can be resolved by games. The present study is aimed at designing, implementing, and evaluating a gamified first aid training system using the First Aid Serious Game Design Model (FASGDM) to enhance user motivation, knowledge retention, and practical application of first aid techniques. Feedback was incorporated in the design process to make sure the refinement of low-fidelity and high-fidelity prototypes to ensure usability and accuracy with an interrater reliability (IRR) coefficient of 90%, confirming the gamified system’s effectiveness and reliability. A quasiexperimental study was conducted with 40 students aged 12–15 years, divided into a control group (n = 20) who received traditional lessons and an experimental group (n = 20) trained using the gamified prototype. Pretest results showed equal performance in both groups, while posttest results revealed significant improvement in the experimental group’s performance through statistical analysis t = −23.77, df = 24, p < 0.05. The gamified approach increased engagement, improved knowledge retention, and increased learner’s confidence in applying first aid skills in real-life scenarios, contributing to improved public health outcomes.

Digital technologies are an integral part of everyday life, making a balanced digital life essential to avoid their negative impact on well-being. This study is aimed at validating the Digital Life Balance (DLB) Scale in Urdu and examining its relationship with social media addiction, Internet addiction, and life satisfaction among Urdu-speaking individuals. A sample of 332 participants (Mage: 26.75 years; SD = 7.12; 41.9% female) completed the DLB Scale along with the Social Media Addiction Scale (SMA), Internet Addiction Test (IAT), and Satisfaction With Life Scale (SWLS). The results demonstrated satisfactory to acceptable internal consistency for the DLB Scale (α = 0.72), consistent with previous validations. Confirmatory factor analysis supported the unidimensionality of the scale. Correlation analysis revealed that higher DLB is positively associated with life satisfaction and negatively correlated with social media and internet addiction, particularly with avoidance behaviors and problematic social media use. Stepwise regression identified life satisfaction as the strongest predictor of DLB, followed by internet avoidance and problematic social media use. These findings underscore the importance of balancing digital and nondigital activities for maintaining psychological well-being. The study highlights the need for culturally adapted tools to assess digital behaviors and provides critical insights for developing interventions aimed at promoting digital well-being in diverse populations.

Deepfakes refer to a wide range of computer-generated synthetic media, in which a person’s appearance or likeness is altered to resemble that of another. This systematic review is aimed at providing an overview of the existing research into people’s ability to detect deepfakes. Five databases (IEEE, ProQuest, PubMed, Web of Science, and Scopus) were searched up to December 2023. Studies were included if they (1) were an original study; (2) were reported in English; (3) examined people’s detection of deepfakes; (4) examined the influence of an intervention, strategy, or variable on deepfake detection; and (5) reported relevant data needed to evaluate detection accuracy. Forty independent studies from 30 unique records were included in the review. Results were narratively summarized, with key findings organized based on the review’s research questions. Studies used different performance measures, making it difficult to compare results across the literature. Detection accuracy varies widely, with some studies showing humans outperforming AI models and others indicating the opposite. Detection performance is also influenced by person-level (e.g., cognitive ability, analytical thinking) and stimuli-level factors (e.g., quality of deepfake, familiarity with the subject). Interventions to improve people’s deepfake detection yielded mixed results. Humans and AI-based detection models focus on different aspects when detecting, suggesting a potential for human–AI collaboration. The findings highlight the complex interplay of factors influencing human deepfake detection and the need for further research to develop effective strategies for deepfake detection.

When a person chooses to interact with their phone instead of their partner (e.g., technoference, phubbing), it may diminish interactional quality, relationship satisfaction, and well-being. However, much of the research on technology use in relationships has utilized self-reports. We extend prior work by objectively measuring smartphone use in a sample of 247 adult participants (75% women; mean age = 30.87 years) to better understand the extent of use around one’s partner and the connection between this use and relational and personal well-being. Participants completed an online baseline survey and 8 days of phone tracking and nightly time diaries. On average, participants used their smartphone during 27% of their time around their partner; 86% used their phone every day at least some around their partner. Linear regression modeling revealed that phone use around partner (not total daily phone use) predicted lower relationship satisfaction and coparenting quality, although effects were only significant for women. We also found that phone habits in general (i.e., both phone use around partner and total phone use) predicted greater depression and lower life satisfaction, with effects trending toward being stronger for women. Overall, our results suggest that one’s own phone use is connected—especially for women—to one’s own relational and personal well-being. Our objective phone use and daily diary methods offer one potential model for studying the nuances of technoference and its effects on relational and personal well-being. Future research should continue to explore both objective and subjective measures of device use within couples and families.

Telepractice in neuropsychology has become increasingly prevalent in recent years due to its ability to provide accessible and convenient care to patients regardless of their location. However, the validation of many neuropsychological tools for distance assessments remains limited, and there is a particular lack of remotely administered assessment tests with alternate forms, which are crucial for monitoring symptoms and performance in clinical contexts and for minimizing practice effects in research practice. Consequently, the present study was aimed at evaluating the consistency of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) scores across videoconference and face-to-face administrations and to examine whether the scores obtained via videoconference support interpretations similar to those obtained via face-to-face administration. A total of 185 participants aged between 20 and 79 years (M = 46.24, SD = 19.63) underwent RBANS testing twice: once in person using the standard pen-and-paper modality and once remotely via videoconference, using Alternate Forms A and B to mitigate the learning effects. Results from the linear mixed models revealed no significant differences between remote and face-to-face administrations based on the modality of administration (p > 0.05). Bayes factors supported the null hypothesis, suggesting that RBANS performance is consistent across the two modalities of administration. However, discrepancies were observed in certain subtests between alternate forms of the RBANS, highlighting the need for standardization. In conclusion, findings suggested that the same norms that are used to interpret the RBANS scores obtained via face-to-face administration may be employed when administered remotely through videoconferencing. Accordingly, the study provides valuable insights into the feasibility of remote neuropsychological assessment and underscores the potential utility of videoconference technology in clinical and research settings.

Emotion recognition in conversations (ERC) is of high importance, especially when it relates with human behavior assessment. Nevertheless, ERC so far has mainly focused on the identification of each interlocutor’s emotions. Here, for the first time, we consider the concept of emotion climate (EC), that is, the emotion reciprocally established by the peers during a naturalistic conversation, and we introduce machine learning (ML) models that efficiently perform emotion climate recognition (ECR). The latter is explored in the cases where the EC is (a) perceived within a conversational group, (b) conveyed from interlocutors involved in a conversation to the external observers, and (c) felt by the external observer. Features from conversational speech and affect dynamics (AD) data (n = 4685), drawn from three open datasets (i.e., K-EmoCon, IEMOCAP, and SEWA), were inputted to the ML-based ECR, achieving maximum accuracy of 96% and 83% in the K-EmoCon and IEMOCAP datasets, respectively. Cross-lingual validation was performed on SEWA dataset, justifying the generalization potential of the proposed approach. These results show that efficient ML-based ECR can identify how the EC is jointly built, perceived, and felt by others, providing a new approach in assessing emotional aspects in naturalistic conversations.

The immersion of artificial intelligence (AI) in higher education presents significant challenges and opportunities. This study examines the acceptance of AI as a teaching strategy among university teachers, following the extended UTAUT2 model with the inclusion of the teacher skills and knowledge for technology integration (SKTI) construct. Employing a quantitative cross-sectional research design, data were collected from 318 university teachers with prior experience using AI as a learning strategy through nonprobabilistic convenience sampling across 10 universities in northern Peru. Participants completed an online survey, and data were analyzed using descriptive statistics, Kruskal–Wallis tests with Dunn’s post hoc comparisons, and partial least squares structural equation modeling (PLS-SEM). The results showed that performance expectancy (β = 0.129^∗∗), hedonic motivation (β = 0.167^∗∗), habit (β = 0.405^∗∗∗), and SKTI (β = 0.263^∗∗∗) had a positive influence on the behavioral intention to adopt AI as a teaching strategy. Additionally, behavioral intention (β = 0.303^∗∗∗), facilitating conditions (β = 0.115^∗), and habit (β = 0.464^∗∗) determine the behavioral use of AI by teachers. The Kruskal–Wallis test revealed significant differences among age groups in the performance expectancy, social influence, habit, and behavioral intention constructs, with the 37- to 48-year-old age group showing higher average ranks. The discussion highlights that these findings suggest a positive adoption of AI among teachers, driven by individual and contextual factors, and challenges assumptions about the relevance of certain constructs in this specific context. In conclusion, this study represents a significant advancement in understanding the adoption of AI in university teaching and provides valuable guidance for practical implementation efforts.

Cervical cancer presents a significant global health challenge, affecting patients and healthcare systems. Early identification and accurate prediction of risk factors are essential for reducing incidence and improving patient outcomes. This study focuses on predicting indicators and diagnosing cervical cancer using a comprehensive dataset that includes demographic information, lifestyle factors, and medical histories. We developed a predictive model to aid early diagnosis and identify key risk factors. The dataset consists of four cervical cancer tests—Hinselmann, Schiller, cytology, and biopsy—with 858 participants and 30 features. We addressed 22.14% of missing values using the MICE iterative imputer and balanced the data through the synthetic minority oversampling technique (SMOTE). We applied five machine learning algorithms: random forest (RF), linear regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). The SpFSR technique was utilized to enhance feature selection, assessing how a subset of features could maintain high accuracy compared to the full model. Our findings showed that selecting fewer features, such as half or even a quarter of the variables, still yielded strong results, emphasizing the importance of careful feature selection in cervical cancer prediction. The RF algorithm achieved the highest accuracy, with 99% using the full feature set and 98% with a reduced set of five features. Notably, diagnosis and hormonal contraceptives were identified as significant predictors. Hormonal contraceptives, which can affect cervical health, are linked to increased risks of HPV infection and cervical cancer. This study highlights the role of SpFSR in improving prediction models and suggests that external validation is necessary to confirm our findings in diverse populations. Further research should explore additional datasets and variables not covered in this study, as well as the model’s practical applicability in clinical settings.

Behavioral science confronts the issue of how people’s behaviors differ from what they intend to do. However, current models, such as the theory of planned behavior, are insufficient to account for contextual influences and interdisciplinary effects, especially in the case of modern social phenomena. The majority of studies concentrate on single domains (e.g., health and consumer behavior) and employ manual coding schemes, overlooking essential thematic relationships. This research highlights the necessity for integrative frameworks that attempt to analyze why intentions fail to be realized in complex settings such as climate change and digitalization. The primary objectives of this research are to identify and operate dominant and emerging thematic trends in intention–behavior literature in a time series from 1979 to 2025 and to analyze and investigate the effects of publication index status and citation patterns on scholarly impact. This study uses structural topic modeling (STM) alongside bibliometric analyses to identify themes and correlations in intention–behavior research. STM employs generalized linear models to include document-level metadata, allowing for the discovery of related topics and the key factors influencing the development of the literature. Data collection was initially performed on February 20, 2025, through the Web of Science database, using studies that were identified following PRISMA guidelines, reviewed, and considered relevant. The initial records numbered 5350. Significant thematic trends were found to define, and key psychological mechanisms to explain the intention–behavior gap were identified. The study also found that the determinants of publication index status and citation trends play important roles in establishing the discipline’s fate and the impact of intention–behavior literature. Based on these findings, the study highlights how strong thematic links in intention–behavior research can inform cross-domain interventions—such as integrating physical activity and organic food campaigns or leveraging sustainable tourism to promote ethical consumption—by targeting shared psychological drivers like health identity and self-image. In future research, the intention–behavior gap should be investigated across different disciplines and contexts and with longitudinal and experimental designs to take advantage of the psychological and contextual factors that affect behavior.