Human Behavior and Emerging Technologies最新文献

Older adults are increasingly relying on digital services for health, finance, and social communication. Confidence in internet use or internet use self-efficacy plays a key role in determining whether and how older adults access these services. However, existing measurement tools have limited relevance for older adults, particularly in low- and middle-income countries such as Thailand. This study is aimed at developing a culturally appropriate Internet Use Self-Efficacy Scale (IUSES) for older adults in Thailand and evaluating its psychometric properties among this population. An 8-item IUSES was developed based on Bandura′s social cognitive theory and a review of related literature. Content validity was ensured through expert consultation and pilot testing. A cross-sectional survey was conducted with 687 older adults from Northeastern Thailand. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and reliability tests were employed to evaluate the psychometric quality. EFA supported a unidimensional structure with high factor loadings (0.798–0.864), explaining 70.32% of the variance. CFA confirmed a good model fit (χ²/df = 2.700, RMSEA = 0.065, CFI = 0.980, TLI = 0.949, and SRMR = 0.033). The composite reliability was acceptable (CR = 0.790), though average variance extracted was slightly below threshold (0.333). All items were statistically significant (p < 0.001). In conclusion, the IUSES is a valid and reliable tool for assessing internet use self-efficacy in older Thai adults. It can support digital inclusion strategies and future interventions. Further cross-cultural validation is recommended.

The political body has long stood at the center of debates on sovereignty, discipline, and biopolitics. In the digital era, algorithmic governance extends these dynamics by continuously classifying, monitoring, and regulating human life. This article examines how state and corporate systems—China’s Social Credit System (SCS), India’s Aadhaar, U.S. predictive policing, and Amazon’s workplace surveillance—deploy legitimizing discourses of trust, modernization, efficiency, and integrity to normalize surveillance. Using Critical Discourse Analysis (CDA), the study demonstrates how these narratives translate into behavioral mechanisms of compliance, avoidance, and resistance, revealing governance as both a structural imposition and a lived practice negotiated by individuals. The comparative perspective highlights that, while China’s SCS represents the most integrated model of algorithmic governance, parallel strategies of legitimization are evident across democratic and corporate contexts. To conceptualize this convergence, the paper introduces the notion of the algorithmically mediated political body, which captures how disciplinary and biopolitical logics are inscribed onto everyday practices through digital infrastructures. By synthesizing Foucauldian biopolitics with Zuboff’s surveillance capitalism, the study offers a theoretical contribution, showing how algorithmic dispositifs merge population-level regulation with the commodification of behavioral data. The findings underscore that algorithmic governance is not confined to authoritarian regimes but constitutes a global pattern with profound implications for autonomy, inclusion, and resistance in the digital age. While the study includes a comparative discussion of Aadhaar, predictive policing, and Amazon’s workplace surveillance, primary data collection and discourse analysis were conducted solely for China’s SCS, with other cases examined through secondary academic and policy sources.

Although physical activity (PA) is an established pastime, digital gaming (DG) has created a new social world for young people. Scales to measure digital gaming relationships (DGRs) and physical activity relationships (PARs) are in their infancy. Therefore, the aim of this study was to investigate the properties and differences of the DGR and PAR scales used by adolescents. A convenience sample of high school students was recruited for the study. Participants completed the same questionnaire twice, with a 3-week interval between administrations. After merging and matching the two sets of responses, data from 116 students were analysed using intraclass correlation coefficients to assess intrarater reliability and principal component analyses to evaluate convergent validity. T-tests were used to determine gender differences in the DGR dimensions and linear associations with gaming and PA behaviour. After determining reliable items, five dimensions were found (competitiveness, social aspects, self-development, mental health and functional features). Cronbach′s alphas for each dimension ranged from 0.74 to 0.94. Scores from males in four of the five dimensions were statistically significantly higher than females, and there were linear associations with gaming behaviour in four dimensions. To conclude, the DGR and PAR scales seem to be suitable for use by adolescents in surveys.

Various methods have been proposed to measure a person’s sexual preference, but most of these methods have not been evaluated on a yet unseen sample of participants. We examine here the importance of using an unseen sample with the recently introduced mouse tracking method, which has been previously shown to produce reliable group differences in trajectories. To this end, we applied machine learning on data from a mouse tracking task with 52 self-declared heterosexual male and female participants, who either responded according to their sexual preference or were asked to ‘fake’ their response and always provide the opposite response from their preference. While performance was high when the model was applied to the same data it was trained on (AUC = 0.91), substantially lower classification performance was found on unseen data (AUC = 0.69). Our results show that statistically reliable group differences do not automatically lead to reliable predictions in unseen data. Before a method can be used in clinical practice, it should therefore first be tested on unseen data.

There is growing consensus on the potential of digital health technology to improve individual health and well-being outcomes. Existing research examining digital health technology adoption among underserved populations has largely relied upon theoretical lenses with an emphasis on perceived benefits and costs at the preadoption or initial use stage. This study demonstrates an alternative approach, relying on basic psychological needs as an explanatory framework to explore how and why older adults integrate digital health technologies into their everyday lives in a sustained manner. A whole-of-community approach was taken to examine this question, with 17 focus groups held with 107 older adults, volunteers, and the people who work with them. Overall, we found that the integration of digital health technology into the day-to-day life of older adults is contingent on the different ways in which it satisfies or frustrates the basic needs of competence, autonomy, and relatedness. The social environment, user interface design choices, reward, and feedback systems were found to both satisfy and frustrate needs. Researchers and designers intending to study or implement digital health technology for older adults ought to consider how different implementation decisions impact psychological needs.

The increasing presence of technology in our lives has led to the reliance on systems that are difficult to understand. As individuals entrust more complex tasks to technology, understanding the factors that influence users′ trust in them becomes essential. Nevertheless, current trust–assessment methods are often limited in scope or validity. This article builds on previous research and aims to deepen our understanding of the factors that affect users′ trust in technology. The first objective is to enhance the human–computer trust scale (HCTS), an existing trust assessment instrument, by evaluating the validity of a new component that we believe can better capture the sociotechnical nature of current systems. Second is to explore whether culture plays a moderating role in trust assessment by comparing studies in Brazil (N = 133) and Singapore (N = 108). We present the procedure followed for the studies and the results of the partial least squares structural equation modeling (PLS-SEM) analysis used to assess our hypotheses. The results demonstrate that a new component proved valid in both cases and supports the moderating effect of culture. This article contributes to the human–computer interaction literature by advancing toward a more reliable tool for assessing trust in technology and highlights the influence of culture on user trust, which should inform trust assessment and design decisions.

This study examines trust in AI chatbot therapists within the context of Yang and Wibowo’s conceptual framework on user trust in generative artificial intelligence (GenAI), which addresses factors, components, and outcomes of trust. Drawing on insights from secondary school teachers (n = 29) and students (n = 69), the research evaluates the opportunities and challenges of integrating GenAI into mental health support services in school settings. Qualitative data analysis was conducted using deductive coding guided by Yang and Wibowo’s framework on user trust in AI, followed by inductive coding to capture themes that fell outside the predefined categories. Participants appreciated the accessibility, predictability, and nonjudgmental nature of AI, highlighting its potential to enhance accessibility to these services. However, concerns about data security, emotional detachment, and the inability of AI to address complex emotional issues limited deeper trust. The perceived ability, integrity, and benevolence of AI emerged as critical, with participants achieving cognitive trust in AI to complete basic tasks but affective and long-term trust remaining elusive. Findings highlight the societal and educational implications of AI adoption for mental health, emphasizing the need for enhanced emotional intelligence of AI tools, transparent data governance, and the positioning of AI as a complementary tool to human therapy. This research offers practical insights for integrating AI responsibly in mental health services while addressing broader societal challenges.