Telematics and Informatics最新文献

The potential of Generative AI, such as ChatGPT, has sparked discussions among researchers and the public. This study empirically explores the capabilities and limitations of ChatGPT, specifically its portrayal of environmental justice issues. Using OpenAI’s ChatGPT API, we asked ChatGPT (GPT-4) to answer questions about environmental justice issues in 3,108 counties in the contiguous United States. Our findings suggest that ChatGPT provides a general overview of environmental justice issues. Consistent with research, ChatGPT appears to acknowledge the disproportionate distribution of environmental pollutants and toxic materials in low-income communities and those inhabited by people of color. However, our results also highlighted ChatGPT’s shortcomings in detailing specific local environmental justice issues, particularly in disadvantaged (e.g., rural and low-income) counties. For instance, ChatGPT could not provide information on local-specific environmental justice issues for 2,593 of 3,108 counties (83%). The results of the binary logistic regression model revealed that counties with lower population densities, higher percentages of white population, and lower incomes are less likely to receive local-specific responses from the ChatGPT. This could indicate a potential regional disparity in the volume and quality of training data, hinting at geographical biases. Our findings offer insights and implications for educators, researchers, and AI developers.

Game live streaming has become a flourishing phenomenon worldwide, yet research has primarily focused on understanding user consumption behavior from a socio-psychological perspective, neglecting the role of technological affordances. This study addresses this gap by proposing a theoretical framework that integrates affordance-based gratifications, user identification, and user engagement. Drawing upon the uses and gratifications theory, social media engagement theory, and social identity theory, we conducted a survey with 565 participants in mainland China. The findings reveal that interactivity-based gratifications significantly influence both preliminary and further levels of engagement, while modality-based gratifications primarily impact preliminary engaging behaviors. Furthermore, user identification acts as a mediating factor in the relationship between affordance-based gratifications and engagement. These results offer valuable empirical evidence for scholars and practitioners to consider technological factors and streamers’ roles while examining user engagement on game live streaming platforms. The study suggests enhancing interactivity functions and emphasizing the role of live streamers to foster user engagement. By understanding the importance of technological affordances, this research contributes to a more comprehensive understanding of engagement in the game live streaming industry.

Metaverse is a new emerging platform that enables users to interact with each other and engage in many activities inside a collective virtual shared space. The metaverse is rapidly evolving, and it is essential to understand the perception and attitude of its potential users. Therefore, this research aims to study the users of the metaverse and obtain their opinions on some of the key variables of the metaverse. These variables include the core metaverse concept, readiness, ease of use, intention to belong to metaverse, intention to adopt, and value realization. We designed an online questionnaire that aims to measure these variables. Then, a deep analysis of the collected data was conducted using various exploratory descriptive statistics on all participants. Subsequently, we performed further descriptive and correlation analysis on the participants from Saudi Arabia, which resulted in clustering participants into three groups. The result of this work can help in understanding the current and potential users of the metaverse, especially the users from Saudi Arabia. This study will consequently help in developing the metaverse space, enhancing its features, and providing users with the best experiences. Our research contributes to the theory of the metaverse by justifying a bold value-based framework for metaverse adoption. This study also introduces three clusters, Skeptical, Unaware, and Optimists, of potential users of metaverse platforms, providing a clear description of each group.

The prevalence of chatbots in human–computer communication has significantly increased. Emojis, as a form of emotional disclosure, have gained significant attention for their potential to boost chatbot service satisfaction. However, how and when emoji usage can increase satisfaction toward chatbots is not fully examined. This paper aims to fill this gap and contribute to the rapidly evolving field of human-chatbot communication research. Through three experiments, this paper investigates and explores the role of emojis in enhancing chatbot interactions. The results reveal that emojis heighten chatbot's perceived warmth but do not necessarily augment their competence. This warmth promoting effect leads to boosted service satisfaction and is more apparent when chatbots serve hedonic purposes and are pre-programmed rather than highly autonomous. However, the warmth upshot of emojis is not as potent for chatbots as it is for humans. While this study unravels the intricate pathway of how emojis augment service satisfaction, it also extends the dialogue of the Stereotype Content Model (SCM) and propels the new wave of the Computers Are Social Actors (CASA) paradigm. Thus, this research lays down pathways for further studies in understanding the role of emotionally simulated interactions in automated technologies.

Research on digital inequalities and their societal implications has garnered significant attention from scholars around the globe, emphasizing the urgency to understand their impact on individuals' everyday lives. However, studies investigating these inequalities within educational settings from teachers' perspectives remain scarce. This study addresses this gap by examining the experiences of eight elementary school teachers in Canada through a collective case study approach. Despite diverse urban and rural backgrounds, teachers unanimously recognized digital inequalities among their students. Data collection involved semi-structured interviews that explored available technologies, usage patterns, and frequency of implementation; the development of students’ digital literacies skills; and the digital inequalities that may or may not impact students. Analyses reveal inequalities including disparities in device availability and ratio, discrepancies in the types and purposes of online engagement and skill development, and inequalities of digital confidence and empowerment. The findings highlight the prevalence and exacerbation of digital inequalities among students, underscoring the influential role of students' digital capital in shaping their digital experiences and offline outcomes. Moreover, the study reveals that the inequalities faced by teachers can further intensify the digital divide among students. Consequently, concerted efforts are needed to address these inequalities to promote more equitable access to digital resources and opportunities for all students, as well as targeted professional development for educators. This research emphasizes the importance of considering teachers' perspectives in understanding and mitigating the impact of digital inequalities on culture and society.

The academic interest in the Open Government Data (OGD) domain has been burgeoning over the years. Conceding that the prime focus of an OGD initiative is its further re-use for value creation and innovation by stakeholders, the present study seeks to underscore the role of HEXACO personality traits on behavioral intention (BI) to adopt and use OGD in developing countries' context. We investigate the direct, indirect, and moderating effects of HEXACO personality traits provide a better understanding of how and to what extent personality traits influence future behavioral intention to use OGD. The results demonstrate that Trust and Performance Expectancy are positive predictors of BI to adopt and use OGD. Users with higher Openness to Experience tend to have higher Effort and Performance Expectancy; are characterized by exposure to Social Influence; have higher level of Trust and positive experience of Facilitating Conditions and Information Quality. Agreeable people are more likely to Voluntarily Use OGD. Conscientiousness enhances the individual's perception of OGD quality-related factors. Excessive Emotionality affects negative perception to System and Information Quality issues. Honesty–Humility and Extraversion are able to maintain the effect of OGD Information Quality and Trust on users' BI. Our findings could be useful for practitioners to level the divergence between actual and potential use of OGD by considering the user's personality traits.

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The implementation of Internet of Things (IoT) technologies faces social challenges, such as privacy and surveillance, in our so-called ‘sensor society.’ This study applies a unique theoretical framework of surveillance culture, consisting of the surveillance imaginary and surveillance practices, to comprehensively capture individual perceptions and behaviors related to privacy and surveillance in the context of smart health. We also conducted online surveys to examine the cross-national differences between South Korea and the US. Our results show that institutional trust and perceived comfort levels significantly predict individuals’ self-tracking behavior in both the US and Korean samples. Privacy concerns are significantly associated with individuals’ privacy protection behaviors for both samples. The cross-national differences between the US and South Korea are found to be statistically significant. A post-hoc analysis reveals that most differences are attributed to demographic and personal characteristics as predictors of the surveillance imaginary and surveillance practices.

Digital resilience, the ability of a system to respond to external shocks, grow and survive in a changing environment, has become a topic of increasing interest in ICT4D literature. Through the Resilience Assessment Benchmarking and Impact Toolkit (RABIT), the purpose of this article is to conceptualize digital resilience and apply this framework to analyze the Taobao village cases, where some grassroots communities in remote counties leapfrog out of poverty and digital divide, becoming e-commerce entrepreneurs in the context of global Internet mobility. This study first evaluates the performance of foundational and enabling attributes in Southern Jiangxi, Caoxian and Suichang in the face of the COVID-19 pandemic. It is found that the three cases performed well in terms of the foundational attributes, among which Suichang was better than Southern Jiangxi and Caoxian. At the same time, there was a notable difference in terms of enabling attributes. This is reflected in the three distinct grassroots action strategies employed: the “Migratory Bird” strategy, which is flexible but policy-dependent; the “Prism” strategy, which is built on local social networks and flow utilization; and the “Diamond” strategy, leverages high levels of industrial clustering to amplify advantages. Finally, this article offers insights and recommendations for policymakers and practitioners in developing countries and regions, to build a pathway towards digital resilience.

This study aims to explore the determinants of user behaviors toward an artificial intelligence (AI) tool, ChatGPT, focusing on university students and office workers. In this study, we present a comprehensive model to understand user engagement with AI tools, specifically focusing on ChatGPT. The model is grounded on four primary stages, each containing distinct variables: 1) fundamental (comprising perceived intelligence and system quality), 2) knowledge and service (covering knowledge acquisition, application, personalization, and trust), 3) gain with user tendency (encompassing utilitarian benefits, individual impact, satisfaction, and personal innovativeness), and 4) behavior (including behavioral intention, continued usage, and Word-of-Mouth (WOM)). A total of 13 variables have been examined. A survey was conducted on a sample of 645 university students and office workers, and the collected data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal significant associations between perceived intelligence and knowledge management, and personalization. System quality also significantly impacts knowledge management and personalization. Knowledge acquisition and application were found to significantly affect utilitarian benefits and individual impact, but not satisfaction. Personalization significantly influenced utilitarian benefits, individual impact, and satisfaction. Trust significantly impacts behavioral intention. Utilitarian benefits and individual impact had a positive effect on satisfaction, behavioral intention, and WOM. Personal innovativeness was significantly associated with behavioral intention. Behavioral intention significantly affected usage and WOM, while usage did not significantly associate with WOM. Among control variables, only age affects behavioral intention. This study also confirmed the indirect effects and conducted a multi-group analysis (MGA) between students and workers. MGA results show that there are significant differences in three relationships (personalization-satisfaction, utilitarian benefits-WOM, and behavioral intention-WOM) between students and workers. This research extends the understanding of AI tool usage and provides theoretical and practical insights for researchers, practitioners, and policymakers in AI and related fields.

With the advancement of the internet, the public now has easy access to news from various media outlets. However, a number of news outlets tend to report their content based on their political orientations or affiliations, which may compromise the objectivity of the news. This research used machine learning to analyze whether it is possible to tell the political orientation and news outlet apart based on news articles in South Korea. We collected a lot of news articles spanning over five years and used the text data. We chose major conservative and progressive news outlets and tried classifying them into two groups. We even looked into classifying articles by each news outlet. We used different machine learning methods like Logistic Regression, Random Forest Classifier, and eXtreme Gradient Boosting, and tried to improve the performance by combining these models. The research found that the combined model had high accuracy, up to 91.9% for binary classification of news outlet political orientations and up to 84.0% for classifying news outlets in multiple categories. This shows that you can determine the political leaning of news outlets based on their articles, highlighting the importance of considering bias in news outlets when evaluating information instead of solely relying on the article content.