Human Behavior and Emerging Technologies最新文献

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.

The acceptance and adoption of emerging technologies are crucial for their effective integration. This study examines the factors influencing educators’ acceptance of Generative AI (Gen AI) tools in higher education, guided by the UTAUT model. It also develops a structural model to explore the relationships between UTAUT constructs and behavioral intention (BI) to use Gen AI. Using a quantitative approach, the study collected data through a self-administered online survey based on prior research findings. The survey gathered responses from 307 educators across various Arab countries who are early adopters of Gen AI in teaching. PLS-SEM was used to analyze the data. Findings indicate that UTAUT constructs significantly and positively influence educators’ intention to use Gen AI. Additionally, the results highlight the complex role of gender and work experience, revealing diverse perspectives among educators from different countries. This study contributes to the literature by deepening the understanding of technology adoption factors. It also offers theoretical and practical implications for researchers and policymakers in designing strategies to integrate Gen AI into higher education in developing countries.

Climate change is a global phenomenon that affects every living being on our planet. Raising awareness among people about climate change and helping them realize the possible consequences of their actions is key to mitigating climate change problems. Our research was aimed at achieving this by building a persuasive intervention that combines visualization of climate change data and an interactive narrative that demonstrates how our actions can impact the climate. We conducted a user study with 100 participants and found evidence showing that our system was effective in significantly promoting behavioral intention to mitigate climate change. We found defensive responses as a key factor that is negatively influencing the effect of our intervention on the participants. Compelling visuals and multiple interaction options, simulating climate actions and their consequences, and reducing the effort to learn about the phenomenon were significant positive techniques used in the intervention. Additionally, the social elements of our intervention played a major role in promoting participants’ willingness to perform proenvironmental behavior. Our work contributes to the field of persuasive technology, data visualization, interactive narratives, and climate research by introducing a new persuasive way of communicating climate change information to the public using a combination of data visualizations and interactive narratives.

The adoption of blockchain technology continues to grow, a direct result of its potential to provide new solutions to old problems in several industries, including the electoral sector. Blockchain technology is proposed to have the potential to address and overcome the traditional pen and paper scheme’s challenges and limitations, as well as trust concerns around more modern e-voting systems. Ultimately, with the aim to revert the recent downward trend in voter turnover, despite the interest and potential, there remains a significant research gap in understanding citizen response to this technology. This research is aimed at investigating whether citizens would be willing to embrace blockchain technology, as well as at exploring the factors that influence its adoption. A model designed to combine the extended unified theory of acceptance and use of technology methodology with an experimental approach is applied. The results of the study (N = 416) show that the intention to use blockchain-based e-voting systems can be predicted by five of seven constructs, that is, citizens are more likely to adopt e-voting systems when they perceive them to be effective, socially endorsed, enjoyable, trustworthy, and low in perceived risk. However, we do not find a direct influence of blockchain technology, over cloud-based e-voting, on voting intentions indicating that the benefits of this approach may not be well understood by consumers or may not drive the desired increase in voting intention. By understanding citizens’ willingness and concerns to adopt new voting technologies and the factors influencing this disposition, policymakers are better equipped to develop strategies on the development and implementation of electronic voting systems and can make informed choices about the use of blockchain technology.

Online game playing of youth in China, especially their problematic online gaming (POG), has become one of the social issues that affects large numbers of people and their families. However, studies about the impact of player’s motivation on problematic playing are sparse and lack systematic approaches. Our current study is aimed at investigating the relationship between gaming motivations and POG. This paper presents the results of a large-scale survey conducted in China with 1557 participants, of whom 1358 (87.2%) were male. A multiple regression analysis with 10 game motivations as predictors has been performed to explore which factors have effects on game addiction. It is shown that the best predictors of game addiction are the escapism motivation, followed by the competition motivation and then the advancement motivation. The mediation effect of demographic variables on the relationships between player’s motivations and game addiction is further examined using the casual steps, and a significant mediating effect of age on game addiction is revealed. The POG differences across gender and age were also examined. The findings enable a better understanding of the underlying mechanics of POG and to minimize the risks and maximise the positive impact of games on society.

R. H. Mustofa, S. A. Prestianawati, D. E. Sari, H. Riyanti, and A. Setiawan, “Celebrity Endorsements and Promotions: Enhancing Young Muslim Online Shoppers’ Satisfaction” Human Behavior and Emerging Technologies 2024, no. 1 (2024): 1-16, https://doi.org/10.1155/2024/3895680

In the article titled “Celebrity Endorsements and Promotions: Enhancing Young Muslim Online Shoppers’ Satisfaction,” there was an error in the Funding section, where the grant number was wrongly mentioned as 275/A.3-III/LRI/IX/202. The corrected section appears below:

The authors fully funded the publication costs for this article. The data collection expenses were agreed to be covered using the individual resources of each author involved in this research. Additionally, this research received funding support from Universitas Muhammadiyah Surakarta under grant number 303.7/A3-III/LRI/X/2023.

We apologize for this error.

Integrating collaborative robots (cobots) in work environments is advancing rapidly, with growing attention to designing systems that can effectively collaborate with humans. A key aspect of this effort is enhancing cobots’ adaptability, that is, their ability to adjust behavior in real time based on workers’ needs and characteristics, particularly their psychological states. Despite increasing research, a synthesis of the most considered psychological states and the corresponding adaptation mechanisms is still lacking. This review examines recent experimental evidence on cobots which modify their behavior in response to workers’ psychological states and evaluates how these adaptations influence human–robot collaboration outcomes. Following preregistration on PROSPERO, this study adhered to PRISMA-P guidelines to select 23 studies focusing on cobots’ adaptation mechanisms and their impact on task performance and worker well-being. The findings reveal that most adaptations target cognitive states, particularly workload, attention, and situational awareness, reflecting a strong research emphasis on optimizing decision-making and efficiency. Emotional adaptation has been explored to a lesser extent, while real-time adjustments based on motion intention are gaining traction in movement coordination tasks. Cobots primarily rely on physiological and behavioral signals—such as heart rate variability, electrodermal activity, and gaze fixation—to infer workers’ psychological states. Various adaptation strategies, including task reallocation and speed modulation, demonstrate measurable improvements in collaboration fluency, cognitive load management, and operational performance. This review highlights the critical role of psychology in robotics research, promoting multidisciplinary collaboration to develop adaptive cobots that enhance both productivity and worker well-being.

Evidence about the associations between mental health and problematic social media use (PSMU) over time is mixed. While some studies have found mental health predicted PSMU over time, others found nonsignificant relationships. Therefore, the present study was aimed at investigating the impact of mental health (depression, anxiety, and wellbeing) on PSMU among young adults over time and investigating the potential mediating role of motives for social media use. The eMediate study participants (n = 431, 49.7% female, age = 22.6 ± 1.8 years) who completed four waves of online questionnaires assessing social media use and mental health at 3-month intervals were included. Multilevel mediation analysis was used to examine the association between mental health and PSMU, and the possible mediating effect of motives for social media use. Depressive and anxiety symptoms and wellbeing significantly predicted PSMU over time, and social media use was motivated to cope with bad feelings, conform with others, be entertained, social interaction, escape from daily problems and stress, support seeking, and increase positive and decrease negative emotions. The escapism motive mediated the associations between symptoms of depression and anxiety and PSMU over time. The enhancing motive mediated the associations between depressive symptoms and wellbeing and PSMU over time. These findings provide insights into the motivational processes that may be driving the associations between mental health and PSMU, which could be targeted for intervention.