Human Behavior and Emerging Technologies最新文献

This study examines determinants of smart city technologies adoption in the United Arab Emirates′ regional cities by extending the Unified Theory of Acceptance and Use of Technology (UTAUT). The pertinent literature has been thoroughly examined. Trust has been integrated as a central mediating construct, and personal innovativeness and financial cost were also added to the UTAUT original model. In addition, a cross-sectional survey of 224 IT professionals was used to collect quantitative data from the regional cities of UAE. The model under research was evaluated and confirmed using a structural equation modeling approach. Reliability and validity met conventional thresholds, and a bootstrapped mediation test was applied. The empirical research findings showed that performance expectancy, effort expectancy, social influence, facilitating conditions, personal innovativeness, and financial cost each positively influence trust and trust in turn significantly predicts adoption attention. Further, the model explains 58.2% of the variance in trust and 27.9% in adoption intention. Besides, mediation analysis indicates that trust mediates the impacts of the six antecedents on adoption intention, highlighting trust as the principal mechanism translating into functional, social, and economic perception into adoption behavior. The findings also suggest that policymakers and government officials should prioritize trust-building measures, for instance, transparent data practices, clear value-for-money communication, and sustained user support and training alongside continued investments in enabling infrastructure. Finally, the study contributes an empirically by validating a trust-centered extension of UTAUT tailored to the government-led smart city context in emerging urban settings.

The insurance industry faces unprecedented challenges as digital transformation accelerates while regulatory frameworks struggle to keep pace with technological innovation, creating significant risks that require new models of public–private cooperation. This study examines key factors driving effective public–private cooperation in insurance regulation during digital transformation, developing an integrated theoretical framework that combines new public management principles, trust–commitment theory, and information systems participation theory. Using structural equation modeling with data from 546 stakeholders across multiple jurisdictions, we identify critical pathways through which efficiency considerations, accountability mechanisms, change agent activities, and open data initiatives influence collaborative governance outcomes. Analysis reveals three transformative insights that reshape understanding of collaborative governance in digital regulatory environments. First, relational factors serve as essential mediators between technological capabilities and collaborative outcomes, with relationship commitment, principled engagement, and trust collectively explaining nearly half of the variance in public–private cooperation effectiveness. Second, an efficiency–relationship paradox emerges where efficiency pressures simultaneously improve engagement processes while potentially undermining long-term commitment formation, challenging traditional assumptions about efficiency-focused governance approaches. Third, digital enablers function as relationship catalysts rather than mere operational tools, with change agents and open data initiatives proving crucial for trust development and sustained collaboration. The research provides actionable guidance for policymakers implementing AI governance frameworks while advancing theoretical understanding of collaborative governance in digital regulatory environments. Findings demonstrate that technological solutions alone prove insufficient for effective digital governance, requiring explicit integration of relationship-building mechanisms to achieve sustainable public–private cooperation. These contributions prove particularly timely as insurance ecosystems worldwide experience simultaneous technological revolution and intensified regulatory scrutiny.

This research examines the role of digital social responsibility (DSR) in fostering sustainable economic development in Iraq. Using a mixed-methods approach that included a large-scale survey and supplementary expert interviews, we model the observable relationship between DSR awareness, trust in digital companies, the priority given to data protection, and sustainable economic behavior in digitally active Iraqis. The regression model explained over 80% of the variance in sustainable economic behavior (R² ≈ 0.81). The qualitative results underscored that most decision-makers and business leaders, as well as representatives from civil society organizations, understand the importance of accelerating digital law adoption, enhancing public awareness, and strengthening cross-sector collaboration. They also recognized existing challenges, such as the uneven adoption of DSR among SMEs, underdeveloped digital infrastructure, and a still-maturing digital culture. This research contributes to the gap in the global literature by providing empirical evidence on DSR and sustainable economic development in Iraq and the MENA region, where comprehensive research is lacking. Overall, the study indicates that Iraq can navigate a responsible shift to digital systems while also achieving sustainable economic development, provided effective policy initiatives embed DSR, strengthen regulatory frameworks, and advance digital inclusion and trust. These are essential for Iraq to bridge the gap with its regional peers and leverage digital innovation to achieve inclusive and sustainable development.

As robots are increasingly perceived not only as functional tools but also as socially interactive agents endowed with perceived minds, inconsistent findings on the role of human-like mind in robot acceptance underscore the need to clarify the underlying psychological mechanisms. Drawing on the three-dimensional mind perception framework and the theory of reasoned action, this study investigated how perceptions of a robot′s body, cognitive, and socioemotional capacities—termed body, mind, and heart—influence robot acceptance among middle-aged and older adults in China. Using a two-stage analytical approach that combines set-explorative structural equation modeling (set-ESEM) and necessary condition analysis (NCA), this study investigated both sufficiency and necessity of mind perception and attitudes in shaping trust and behavioral intentions. Data were collected from a cross-sectional questionnaire survey of 407 Chinese adults aged 50 and above (M = 56.16, SD = 4.56). The set-ESEM results indicated that body dimension heightened negative attitudes toward robot interaction (β = 0.316, p = 0.024), whereas the mind (β = –0.452 , p < 0.001) and heart (β = −0.567, p < 0.001) dimensions mitigate negative attitudes toward interaction and emotional discomfort, respectively. NCA revealed that none of these mind dimensions individually served as necessary conditions for behavioral intentions. By contrast, trust in robots emerged as both a necessary (β = 0.320, p = 0.001) and sufficient condition (d = 0.18; p < 0.001). A minimal level of trust (21.4%) is required to reach a moderate intention to use (60%). The study advances theoretical understanding of mind perception in robot acceptance and provides practical guidance for designing robots that balance bodily sensation, cognitive, and socioemotional capacities. By prioritizing trust while integrating targeted mind perception dimensions, developers and policymakers can create inclusive and acceptable robots for older populations.

Although OMC is a convenient healthcare technology, it poses challenges related to user satisfaction and data confidentiality. Previous studies have overlooked the importance of OMC in densely populated developing countries, where a healthcare application could potentially serve millions of patients. Moreover, patients in developing countries view OMC as more convenient and affordable in terms of saving time and transportation costs from face-to-face medical consultations. On the other hand, there are associated risks such as losing data confidentiality and satisfaction comparing in-person visits. Therefore, the proposed theoretical framework extends the existing UTAUT model by emphasizing user-perceived satisfaction and the perceived risk of adopting OMC. A cross-sectional survey was conducted using a structured questionnaire and random sampling of 978 Pakistani respondents, and the data were analyzed using partial least square structural modelling. The results indicate that perceived satisfaction (β = 0.219) was the strongest predictor of users’ behavioral intention, followed by performance expectancy (β = 0.204), effort expectancy (β = 0.155), trust (β = 0.147), social influence (β = 0.124), and self-efficacy (β = 0.082), accounting for (R² = 0.553) of the variance in OMC adoption. However, perceived risk (β = 0.012) appeared to be an insignificant factor for behavioral intention in the acceptance of OMC. The findings underscore that as users perceive OMC systems to be more trustworthy, their behavioral intentions to engage with these digital healthcare platforms experience a notable and positive upswing. Saving travel expenses and time is the major benefit for the patients in the developing countries who are struggling in managing their socioeconomic conditions. It is recommended that the government should regulate and promote the use of OMC applications by leveraging patients’ trust towards this technology. In addition, the developing countries with significant rural populations and infrastructure gaps can benefit from strategies that enhance trust in digital platforms and emphasize user satisfaction to drive OMC adoption.

Cancel culture is a notable, but not well theorised social phenomenon, widely understood as a way of punishing those in the public eye who are perceived to do or say the wrong thing. This study aimed to elucidate the cancelling process and the role of morality- and emotion-related traits in this process. Adult social media users (n = 298) undertook an online survey containing cancel culture–related vignettes and scales. As hypothesised, moral outrage was found to positively mediate the relationship between transgression perception and cancel culture engagement. Moral sensitivity also had a positive correlation with transgression perception. Moral sensitivity was not related to cancel culture engagement, however, whilst emotion regulation difficulty and moral identity did not moderate the relationship between moral outrage and cancel culture engagement. These findings suggest that those sensitive to moral problems, who feel strong negative emotions, are more likely to engage in cancel culture. Furthermore, findings indicate that Crockett’s (2017) online moral outrage theory has some explanatory power, and that moral outrage and sensitivity may be important to consider when regulating cancel culture. Features of social media and artificial intelligence can be potential obstacles in this process, however, and this warrants consideration in instances where preventing cancellation is desirable. Future research is needed to corroborate these findings and evaluate other prospective cancel culture theories.

Over the last four decades, as populations around the world have expanded their use of social networks, cyberbullying incidents have likewise risen. Although social networks, including Twitter (now known as X), provide numerous benefits, such as quick communication with people both locally and globally, they also have negative consequences, the most common of which is cyberbullying. Studies show that users who have experienced cyberbullying have more negative feelings about themselves than those who have not. Thus, having technology that can effectively detect cyberbullying instances on social networks, such as Twitter, flag them and find ways to prevent them in the future is of utmost importance. This paper evaluates the available literature on utilising sentiment analysis to detect cases of cyberbullying. The research then explores sentiment analysis by constructing a machine learning model and training and testing the model using a dataset from Twitter. The algorithms used are naive Bayes, recurrent neural network (RNN) and support vector machine (SVM). These are all built on Python with the aid of existing Python libraries. The models are then evaluated to establish their performance, including the recall score, which measures false negatives. A performance comparison is carried out across the three models to find the most suitable algorithm for the task. The SVM, RNN and naive Bayes achieved accuracy scores of 91.37%, 90.59% and 83.62%, respectively. The results reveal that the SVM algorithm consistently outperformed the other two in detecting cyberbullying tweets. SVM has the potential to alter the way social media platforms and online communities moderate content, offering a strong balance of performance, speed and interpretability, making it well-suited for real-time cyberbullying detection on large-scale platforms. This allows for faster intervention to safeguard users, particularly vulnerable persons, from harassment and abuse, resulting in safer digital environments and improved overall user well-being.

Anxiety is a prevalent issue among university students, with recent studies indicating that one in three students experiences it or another emotional disorder. To address this, the use of standardized scales has been proposed to assess anxiety in this population. However, large-scale assessment remains challenging due to the lack of digital tools that facilitate widespread application. Traditional paper-based scales are time-consuming to administer and difficult to analyze efficiently. This article introduces AMAS-Mobile, a digital version of the AMAS-C scale designed for mobile devices, and presents its evaluation of validity and reliability through a nonexperimental exploratory study with Mexican university students between the ages of 18 and 50. This evaluation implies that a statistical analysis was conducted, which included calculating McDonald’s omega coefficient (ω) to assess reliability, as well as performing an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to evaluate validity. The AMAS-Mobile is reliable since ω = 0.87, indicating satisfactory internal consistency for both the overall instrument and the individual subscales. EFA revealed a four-factor structure, explaining 37.48% of the total variance. In addition, CFA indicated that the model fit accuracy index was analyzed (χ² = 2055.554, p < 0.001), indicating differences between the observed and expected matrices. A model fit analysis was also performed (RMSEA = 0.056; CFI = 0.794), which indicated that the model presented an adequate fit but was outside the expected range. This finding suggests a new arrangement of items.

Online discrimination is an alarming phenomenon that draws growing attention across academic disciplines. However, this interest has led to fragmented knowledge, with research often confined within disciplinary boundaries. This study introduces an innovative hybrid approach that combines a scoping review with textual analysis to bridge this gap by (1) mapping the existing literature, (2) identifying key concepts across disciplines, and (3) offering an open, interactive tool for scholars, policymakers, and professionals. Following PRISMA guidelines, we selected 374 scientific publications from 2011 to 2024 across diverse fields (i.e., arts and humanities, history, information and communication technology, law, medicine, psychology, and social sciences). Then, key concepts were identified through textual analysis of the titles and abstracts of the selected contributions, revealing five thematic classes: “consequences on mental health,” “online discrimination detection,” “critical political discourse,” “laws and regulations,”, and “perceptions and reactions.” For each class, we conducted a similarity analysis to further explore its structure and associations. Based on our findings, we propose a transdisciplinary framework to better understand online discrimination and provide a publicly accessible interactive tool and database for further exploration. This tool enables practitioners to perform targeted analyses and support evidence-based decision-making.

The study examines the associations between the adoption of AI, worker training, customer communication by the adoption of AI, and regulatory awareness and customer satisfaction in the Jordan context. Through the application of SmartPLS software on 308 participants who have knowledge of the variables, the study findings provide the centrality of the adoption of AI, worker training, and customer communication to customer satisfaction. Organizational culture also has the key role to play as the moderator between the adoption of AI and customer satisfaction. The study findings provide insightful policy recommendations to practitioners and policy implementers in the context of the first Arab Kingdom to embed the adoption of AI, prioritize worker education, and maintain the positive organizational culture to obtain customer satisfaction and realize the long-term business goals.