Human Behavior and Emerging Technologies最新文献

Limited prior research provides some evidence of the cognitive and learning benefits of employing multiple pedagogical agents, each assigned to distinct knowledge bases, in a multimedia learning environment. However, follow-up studies and extensions of these findings remain scarce. To address this gap, we draw on multimedia learning and cognitive models to investigate the effects of using multiple AI voices as specialist virtual tutors for distinct programming algorithm subtopics on cognitive load and learning outcomes. A between-subjects experimental design was employed with first-year business undergraduates who had minimal programming knowledge. Participants engaged with a multimedia learning video, narrated either by a single AI voice or by three distinct AI voices, each assigned to a different subtopic. Cognitive load was measured via a survey, while learning outcomes were assessed using immediate and 2-week delayed posttests covering retention, near-transfer, and far-transfer tasks. Results indicated that participants in the multiple AI voice condition reported significantly lower intrinsic and extraneous cognitive load compared to those in the single AI voice condition. Furthermore, the multiple AI voice group outperformed the single AI voice group in both immediate and delayed retention, as well as in immediate far-transfer tasks and delayed near-transfer. This study empirically extends prior research on the cognitive effects of using multiple AI voices as virtual tutors in multimedia learning environments. It offers preliminary evidence that using unique voices to distinguish subtopics can benefit cognitive load and learning outcomes, with theoretical and instructional design implications for leveraging AI text-to-speech engines to simulate multiple virtual tutors for distinct instructional topics.

Parasocial interactions (PSIs) and relationships (PSRs) are prevalent in media use. They are influenced by media characters, viewers, the viewing situation, and combinations thereof. While characteristics of media characters and viewers have been studied extensively, little is known about the impact of situational factors tied to viewing sessions in viewers’ everyday media use. Situational factors potentially vary in each viewing situation. Especially for PSIs, a reception phenomenon bound to a specific viewing situation, these factors should be highly relevant. This preregistered study analyzed situational viewing motives, content-related and unrelated multitasking, and different forms of viewing session extensiveness (duration, number of episodes watched, and watching intensity) as potential situational drivers for PSIs and PSRs. The study applies an innovative multimethod design combining usage tracking of 95 participants and experience sampling surveys (N = 693) triggered before and after each viewing session. Through this new approach to analyzing PSIs/PSRs within everyday viewing sessions, influences on PSIs and PSRs were covered close to viewers’ everyday media use, resulting in high external validity. The results show that PSIs depend on viewers’ motives for social interaction and escapism, engagement in nonmedia multitasking, and self-assessed viewing intensity. None of the analyzed situational factors influenced viewers’ PSRs.

The rise of cryptocurrencies, powered by blockchain technology, shifts trust from centralized institutions to technology itself. However, the drivers of trust in cryptocurrency adoption (CA) remain unclear, with existing models like commitment-trust theory, trust in technology, and digital trust insufficiently addressing decentralized systems. To bridge this gap, this study integrates the task-technology fit (TTF) framework and five-factor theory (FFT) into a comprehensive cryptocurrency trust model. TTF explains how blockchain features—security, transparency, traceability, price value, and transaction speed—impact technology characteristics (TCs), while FFT captures user characteristics (UCs), including psychological and behavioral dimensions, essential for trust development. Analyzing survey data from 200 participants using structural equation modeling (SEM), the findings highlight the mediating role of crypto trust (CT) between TC, UC, and external environmental factors (EX) in driving CA. CT mitigates concerns about fraud, security breaches, and reliability, transforming technological and individual readiness into adoption, particularly in unregulated markets like Vietnam. This study updates trust frameworks by integrating TTF and FFT, emphasizing the need for trust-building strategies, technological transparency, and regulatory clarity. In particular, the findings underscore that clear, supportive, and consistent regulatory policies are essential for legitimizing cryptocurrency use, reducing uncertainty, and indirectly fostering user trust. These insights provide concrete policy directions for governments seeking to enhance adoption in decentralized financial systems while ensuring public protection and market stability.

While the challenge of distinguishing AI-generated from real images is widely acknowledged, the specific cognitive biases that systematically shape human judgment in this domain remain poorly understood. It is particularly unclear how a general awareness of AI capabilities fosters novel biases, like a pervasive skepticism (“impostor bias”), and how this interacts with established phenomena like “automation bias”. This study addresses this gap by providing the first quantitative analysis of how these two biases operate across five distinct experimental variants designed to test the context-dependency of human perception. Through a mixed-methods study with 746 participants, we demonstrate that human authentication accuracy hovered around chance levels (ranging from 47.0% to 55.5%). However, our analysis provides robust evidence for the systematic operation of cognitive biases. We validate the presence of “impostor bias” through a consistent pattern of higher doubt for AI-generated images and confirm “automation bias” through significant opinion changes following algorithmic suggestions. Our findings reveal that these biases are not uniform across populations: gender was a consistent predictor of automation bias, with males in all five variants showing a significantly stronger and more consistent tendency (Cohen’s d = 0.254–0.683) to be influenced by algorithmic suggestions. In contrast, age and academic background had minimal and highly localized effects. Furthermore, we identified a significant interaction between experimental stimuli and performance over time, isolating a pronounced fatigue effect to a single questionnaire variant where accuracy progressively declined (by approximately 1.7% per trial). By integrating human feedback with Grad-CAM visualizations, we confirm a divergence between human holistic evaluation and the localized focus of machine learning models. These findings carry direct implications for policy, as discussed within the context of the European AI Act, and inform the design of human–AI systems and media literacy programs aimed at mitigating these critical cognitive vulnerabilities.

The adoption of green ICT (GI) is considered the best alternative to address the challenges that arise due to the massive use of ICT devices in the fourth industrial revolution. Despite its advantages regarding addressing sustainable environmental challenges, its adoption in developing economies is still very low. Furthermore, the available study on green technology adoption has less considered the impact of firm size on the adoption. Therefore, this study adopts the TOE model to examine the adoption of GI in Tanzania. Additionally, the study examines the moderating effect of firm size on adopting the GI. Data from 211 purposively sampled organizations were analyzed using partial least squares structural equation modeling. Findings revealed that relative advantage, compatibility, government support, employee knowledge, top management support, and competitor pressure significantly influence the adoption of GI. Additionally, firm size moderates the relationship between compatibility and GI as well as competitor pressure and GI. The study has further provided recommendations that could help policymakers and scholars with the adoption of GI technology.

Nonwork-related internet usage is catastrophic for organizations since cyberloafing violates work ethics. The time and effort directed toward cyberloafing were meant to be invested in work-related obligations. Cyberloafing magnifies in the presence of punitive supervision, as it is a potential threat to employees’ psychological well-being. This study investigated the sequential mediation of stress and cyberloafing between punitive supervision and turnover intention. A cross-sectional design was utilized to obtain empirical data from 2008 working individuals from diverse sectors. A nonprobability purposive sampling technique was used to select the respondents. Hayes’ PROCESS Macro Model 6 was used to test the sequential mediation model. For supervised machine learning, the Python programming language and Google Colaboratory were employed as critical tools for conducting experiments to validate the research findings. This study highlighted cyberloafing as counterproductive work behavior catalyzed by punitive supervision. The diverse negative constructs with argumentation from the COR theory enriched the theoretical frameworks for understanding the psychological orientations of employees at work. The study findings facilitate fostering a supportive organizational culture for reducing turnover and enhancing well-being. This study highlights the role of workplace stability and efficiency for sustainable economic growth because a socially sustainable organization can make employees feel valued and reduce turnover. Both integrated methodologies demonstrate the hypothesized sequential mediation model. The theoretical and practical implications and directions for further studies are also discussed.

The antecedents and consequences of phubbing—ignoring others in favor of mobile phone use—have attracted a growing interest among researchers. Despite the increased attention, the field still lacks a cohesive framework to guide future research and practical interventions. To address this gap, this bibliometric review was aimed at mapping the knowledge structure of phubbing research. A total of 444 phubbing-related publications were retrieved from the Scopus database for analyses. The performance analysis highlighted key research constituents, including leading authors, journals, and institutions. Science mapping revealed three cocitation clusters and four coword clusters, shedding light on the theoretical foundations and themes in the literature. The findings underscore the need for further psychometric refinement, exploration of media use in familial contexts, and the conceptualization of phubbing as a process. This review provides insights into phubbing research and offers research directions for future studies.

Augmented reality (AR) reshapes the educational landscape by seamlessly blending digital content with physical environments to create highly immersive, interactive, and engaging learning experiences. This paper presents a comprehensive systematic literature review (SLR) of 35 peer-reviewed studies to evaluate the current state of AR integration in education critically. The review analyzes key variables such as skills acquisition, pedagogical frameworks, technological features, and domain-specific applications to understand the broader impact of AR on teaching and learning. The findings reveal that AR significantly enhances student engagement (37.14%), learning experiences (34.29%), and motivation (22.86%), making it a powerful tool for fostering active participation and long-term knowledge retention. Widely adopted AR technologies include AR toolkits, 3D AR models, mobile AR applications, and marker-based/markerless systems, which support hands-on learning across diverse disciplines. The study also highlights the pedagogical versatility of AR, showing strong alignment with models such as constructivist learning, inquiry-based learning, flipped classrooms, and experiential learning, enabling educators to tailor instructional strategies to diverse student needs. In terms of disciplinary reach, AR is most prevalent in general education (27.77%) and engineering (22.22%), followed by applications in science, chemistry, medical education, and STEM. However, the review also identifies underexplored areas, particularly the limited focus on academic achievement, visualization improvement, and content realism, especially in fields like medicine and science where accurate simulations are critical. To address these gaps, the paper explores the potential of AI-powered chatbots as a complement to AR environments. These intelligent systems offer real-time, personalized feedback, enabling adaptive learning pathways that respond to individual performance and cognitive development. The integration of AI enhances AR by making learning more inclusive, student-centered, and efficient, particularly beneficial for learners with diverse needs and learning paces. Despite the transformative potential of AR, challenges such as accessibility, cost, usability, and teacher readiness remain significant barriers to large-scale adoption. The National Education Policy (NEP) 2020 and NCERT support future educational frameworks to integrate AR and AI into curriculum design for underserved and multilingual contexts. This paper supports the development of inclusive AR systems that scale up and follow pedagogical principles to enhance experiential learning and digital equity, and cognitive development in various educational settings.

The notion of phygital, characterized by the convergence of physical and digital experiences, has garnered significant interest in both scientific and industrial domains in recent years. This research employed bibliometric techniques to assess the landscape of phygital research within the Scopus database. A total of 322 studies were identified from 2007 to 2025 through an unrestricted search for the term “phygital.” Initially, the analysis focused on the temporal trends of publications and citations, subject areas, leading nations, prominent journals, key authors, and funding sources associated with phygital research as recorded in the Scopus database. Subsequently, collaboration networks, key concept analysis, and co-occurrence analysis of the studies were conducted utilizing VOSviewer software. The results indicate a marked increase in both publications and citations in this field, particularly since 2020. An exploration of the conceptual clusters within this domain, facilitated by VOSviewer, reveals five principal axes, each categorized broadly: the cluster of new technologies, the cluster of smart tools and technologies, the cluster of phygital economy, the cluster of human–computer interaction, and the cluster of culture and crises. Furthermore, recent studies have shown a heightened focus on concepts such as the metaverse, customer experience, and virtual reality among researchers. This study clarifies existing research gaps and highlights future phygital directions, including its integration with the metaverse, enhancing customer experience, and applying virtual reality, augmented reality, and artificial intelligence for increased productivity within phygital platforms. Ultimately, this study serves as a valuable resource for researchers, academic centers, and industrial decision-makers.

In the Brazilian context, excessive media use among children is a significant issue. To assist investigations in this field, the present research is aimed at translating and validating the problematic media use measure (PMUM) in Brazil. The PMUM assesses excessive or problematic media use by children, based on their parents’ perception. The adapted Brazilian Portuguese version of the PMUM was administered online to a total of 401 parents (two studies, 200 and 201 participants, respectively) of children between 5 and 12 years old, from all over Brazil. The results indicated that PMUM presented a single-factor structure similar to the original version, with satisfactory internal consistency and model-fit indices. Furthermore, higher screen media use hours and limited parental control of screen media associated with higher PMUM scores. These results support the use of the PMUM in Brazil and highlight the importance of parenting factors regarding problematic media use in children.