Telematics and Informatics最新文献

Aiming to contribute to the sparse literature on social mapping methodologies, this article elaborates on the principles, procedures and research design of a social mapping project. It first demonstrates the design and implementation of a mapping project of active Facebook groups in Sweden that focus on the environment, by elaborating on the project’s mapping principles and procedures. The developed mapping methodology integrates a variety of data gathering and analysis methods (survey research, online search, big data analysis, content analysis), applying the principle of triangulation, to ensure the completeness of the mapping. It also combines small-scale and big data analysis, addressing the advantages and limitations of these methods, in the framework of the specific research. In the second part, the article gives the overview of the social map of the 152 identified environment-focussed Facebook groups, connected to Sweden, during 2019–2020, presenting their basic features, networks and environmental focal points. In their majority these groups adopt ecocentric positions towards the environment and openly critique anthropocentrism, often being connected to civil society and activist networks, concerning environmental issues and action. These findings point to the potential of coordinated environmental action, being communicated and mediated through online spaces.

Few studies have examined user motivations to use generative artificial intelligence (AI). This research aims to address this gap by examining how user motivations for ChatGPT usage affect perceived creepiness, trust, and the intention to continue using AI chatbot technology. The findings of an online survey (N = 421) reveal a negative relationship between personalization and creepiness, while task efficiency and social interaction are positively associated with creepiness. Increased levels of creepiness, in turn, result in decreased continuance intention. Furthermore, task efficiency and personalization have a positive impact on trust, leading to increased continuance intention. The results contribute to the field of human–computer interaction by investigating the motivations for utilizing generative AI chatbots and advancing our comprehension of AI creepiness, trust, and continuance intention. The practical ramifications of this research can inform the design of user interfaces and the development of features for generative AI chatbots.

With the evolution of media technologies, social media have developed as a means to purposive and incidental use and subsequent benefits and detriments. This study investigates how individuals’ use of information via social media helps them achieve psychological and behavioral advances in the context of the COVID-19 pandemic. This study’s novelty lies in its positioning of social media information seeking (SMIS) as a precursor to the staged behavioral processes of the Theory of Planned Behavior. This novel extension entails the testing of a conceptual model of the influence of SMIS on three psychological factors (i.e., attitudes, subjective norms, perceived behavioral control), which, in turn, predict behavioral intention. It implements cross-sectional data from a survey of adults in New York City in 2020 (N = 1,021). The structural equation models were well-fitting and generally validated by the data, including the posited two steps from SMIS to psychological factors to behavioral intention. Structural equation modeling underscores the role of SMIS as a core driver of wearing a facemask, with positive effects on attitudes and perceived behavioral control and subsequent mediated positive effects on behavioral intention. In terms of sheltering in place, there was evidence of positive effects of SMIS on perceived behavioral control and, in turn, from the three psychological factors to behavioral intention, but the mediation role of the psychological factors was weaker in this model. Theoretical and practical implications are discussed.

This paper examines the relationship between employment outcomes and broadband Internet access, education and digital skills using the pre- and post-COVID-19 survey waves of the Eurostat Community Statistics on Information Society from 2017, 2019, and 2021 in 27 European countries. Joint estimates of individuals’ employment status and skills employ external controls based on Eurostat and COVID-19 European Regional Tracker NUTS1-level regional statistics and Oxford COVID-19 Government Response Tracker information on governments’ containment and economic support measures. Broadband access, digital skills and educational attainment combine to raise employment outcomes, but COVID-19 has changed these relationships in distinct ways. It has increased employment benefits from formal education and roughly tripled the labor market advantages from having household members with tertiary education. The pandemic has increased the employment value of having at least some digital skills, while the relative benefits of more advanced digital skills shrank.

In China, Internet scams against migrant workers have grown increasingly prevalent. This study uses routine activity theory and uses and gratifications theory to investigate how migration stress affects susceptibility to risky internet use and scam-related outcomes among Chinese migrant workers. The present study utilized a survey of 543 migrant workers. Findings revealed that migration stress was a significant predictor of engaging in risky online leisure activities, risky online shopping, privacy disclosure, and a lack of protective measures. The failure to adopt protective behaviors and engagement in risky online leisure activities, online loaning, and privacy disclosure were also found to increase migrant workers’ exposure to scam information and victimization. Study results sheds light on the impact of migration stress on the online behavior of migrant workers in China and provides insights into protecting them from Internet scams. Our findings may inform the development of interventions aimed at preventing online scams against migrant workers and promoting safer Internet use.

While texts are the primary carriers of information for government decision making, few studies have examined the role of textual complexity in government-citizen communication. Using a variety of natural language processing methods, this paper measured textual complexity from the perspectives of word complexity, logical complexity, and abnormal negative emotion based on the textual data of the 1.15 million online messages left by the citizens to government leaders on China’s online public service platform, and explored its impact on government responses. Based on the Double-hurdle model, this paper found that the government response can be composed of two decision-making processes: response-intention, which indicates whether to respond, and response-level, which represents the extent of response. For response-intention decision-making, the simpler the words and logic of the message, the more likely it is to receive a reply from the government. For response-level decision-making, messages with more complex words and logic received a higher level of government response. Abnormal negative emotion in the message significantly reduced the government’s response intention and response level, and the negative effect of abnormal negative emotion on response intention was much greater than on the response level. This study not only helps to understand how the government makes decisions based on textual information, but also has important value for responsive government construction and equalization of government services.

To effectively utilize artificial intelligence (AI)-based technologies such as ChatGPT and realize their novel ethical issues, individuals must have a variety of knowledge and skills about AI. Such knowledge and skills have led to the emergence of AI literacy. Despite the importance of AI literacy in everyday life, little is known about its determinants. To better understand the determinants of AI literacy, we attempted to build a research model relying on previous research and different theoretical frameworks. The model incorporated digital divide, cognitive absorption, and computational thinking. As a major finding from the current study, computational thinking was found to be a significant determinant of AI literacy, which facilitate using, recognizing, and evaluating AI-based technologies. Moreover, we found out that individuals with physical access to information and communication technologies (ICTs) are more expected to use and recognize AI. Also, motivation and skills in using ICTs enable individuals to better evaluate the outcomes of AI-based technologies. The findings also showed that convenient access to ICTs contributes to a deep involvement with AI-based technologies in the use. Further, individuals with higher motivation and skills to use AI technologies are likely to have a pleasant experience after using these technologies.

Emoji use, despite being pervasive in digital communication, is often dependent on individual characteristics (e.g., gender, age, personality), relational dimensions (e.g., intimacy with specific others), motives for using them (e.g., because emoji allow to better express emotions), and the context in which they are used (e.g., emoji use is considered more appropriate with closer interlocutors, such as friends). However, research has yet to examine if and how these variables associate with emoji use frequency when considered together. In a correlational study (N = 444), we explored the relative contribution of individual characteristics, perceived relatedness to others, and motives to explain the frequency of emoji use with different interlocutors. Hierarchical linear regressions showed that being younger, scoring higher on conscientiousness, and resorting to emoji for personal contact were correlates of emoji use frequency with closer interlocutors (e.g., family and friends). In contrast, being older, scoring lower on agreeableness, and perceiving more relational intimacy were correlates of emoji use frequency with more distant interlocutors (e.g., supervisors and doctors). Overall, results highlight the need to account for multiple variables at different levels to examine emoji use patterns in digital communication.

Smartphone use by adolescents is increasingly attracting the interest of social scientists, parents, and educational stakeholders (teachers, educators, educational policy makers), generally in a negative context. There are, however, many myths surrounding the issue, resulting from the reproduction of stereotypes about the psychosocial functioning of adolescents in cyberspace, as well as inadequately constructed research tools that measure how new media are used. This text is an attempt to show the phenomenon of problematic Internet use through the prism of screen time measured using real data from smartphones. The research was conducted in first half of 2022 among adolescents aged 13–19 in Bosnia and Herzegovina (N = 1185, mean age = 15.47 with standard deviation = 1.84). The research was implemented using a triangulation of tools such as the Smartphone Addiction Scale (SAS Scale), Software Installed on the Smartphone (SIS Scale), The Bergen Social Media Addiction Scale (BSMAS), and the Screen Time Scale (STS). From the data collected, it was noted that: 1) Screen time correlates weakly with problematic smartphone use and social networking; 2)The average period of smartphone use in the study group is 3 h49 minutes per day, of which 37 min are related to web browsing, while 2 h22 minutes are related to social networking; 3) A quarter of adolescents use a smartphone more than 5.5 h per day, of which more than 2 h15 minutes is screen time related to social media; 4) Approximately half of the respondents use smartphones slightly more than 2 h per day; 5) Girls use smartphones for longer than boys; 6) Place of residence (city or countryside) does not affect screen time; 7) Screen time is related to a medium degree to the type of software installed on the smartphone; 8) There is a need to refine the measurement methodology of screen time and to abandon self-declaration in this area.

Online trolling toward politicians is noticeable now more than ever before. The increase in online political discourse in recent years on one hand and the increased efforts to curb the spread of trolling and disinformation on the other hand may affect trolling in opposing direction. We examine how four trolling tactics toward politicians evolved between the 2016 and 2020 US presidential election cycles. We also examine the variations in trolling toward Democrat and Republican politicians in light of the asymmetric nature of American politics. Based on thematic content analysis of 9,461 comments on tweets posted by candidates from both parties, we found differences in the extent and style of trolling tactics between the two election cycles, with a significant increase in insulting. We also found trolling asymmetry in the extent and style of trolling comments toward Republicans and Democrats. Informed by theories of online disinhibition and ideological asymmetry, we discuss the trolling asymmetry, and we explain the change in the degree of trolling tactics over time in light of the shift from the Russian trolls to domestic trolling.