Telematics and Informatics Reports最新文献

Internet technology has changed the way we interact, and online communication has become increasingly common. There is a need to know more about how individuals present and behave online. The study examined relationships between perfectionism (self-oriented, socially prescribed, other-oriented), online self-presentation types (idealised self, multiple selves and consistent self), cyberbullying experiences (victimisation and perpetration) and psychological outcomes (rumination and depressive symptoms). An online questionnaire measuring these variables was completed by 139 general population adults (91.2 % from the UK, 73 % female; age range 17–70 years; Mean = 33.32 SD = 13.76). Regression analysis showed a role of self-oriented and socially prescribed perfectionism in idealised self and multiple selves online presentation. Further, self-presentation was related to individuals’ likelihood to engage in cyberbullying as a perpetrator. Finally, self-presentation and cyberbullying perpetration were predictive of rumination and depressive symptoms. The findings provide insight into adults’ online behaviour and related outcomes thus contributing to our understanding of the psychology within online interactions.

Social media use has become a critical form of social interaction connecting people of all ages, and across geographic regions, political, and philosophical persuasions. The results of these interactions are massive amounts of data which, through exploratory methods like sentiment analysis, can provide insight into overall public emotion and response to public and key sociopolitical events. The present study explores the sentiment surrounding the Black Lives Matter Facebook page by analyzing the comments under the video of the killing of George Floyd, posted on May 25, 2020. This examination of sentiment uses assumptions of selective exposure theory. We employed a three-pronged sentiment analysis approach and topic modeling and found an overall negative polarity in public sentiment among 7,137 Facebook comments on the video of George Floyd's death. The findings reveal a divisive response to the video which captured the death of George Floyd, which more generally suggests negative emotion in response to the Black Lives Matter movement. A sentiment test also returned a noticeably high happiness score, which might imply the widely unavoidable effects of trolls in internet discourse in the current racial context.

The development of digital technology has changed the audience's reading behavior and reading habits, and the difference between digital reading and paper reading has become a research hot spot. However, the academic circle has not reached an agreement on the reading effect of digital reading and paper reading. This study included 37 experimental studies for meta-analysis and analyzed the influence of moderating variables such as audience characteristics, text characteristics, and reading conditions. This study deepens the understanding of the reading effect and provides a reference for improving it. The study shows no significant difference in overall reading comprehension between digital reading and paper reading, and there are differences under moderating variables. The study has revealed that digital reading and paper reading have advantages and disadvantages and complement each other. The publishing industry should develop corresponding knowledge service products based on the characteristics of reading effects to promote the healthy development of the reading industry.

While intelligence of voice-based personal assistants (VPAs) is evolving, limited research has investigated how various intelligence attributes impact consumer trust and adoption of VPAs. Drawn from social agency theory and the technology acceptance model, the current study developed and empirically tested a holistic research framework of the effects of intelligence attributes on consumers’ behavioral responses to VPAs through trust. We identified three aspects of intelligence attributes as functional (ease of use, usefulness), hedonic (enjoyment, innovativeness), and anthropomorphic attributes (humanness, autonomy) and two types of trust in VPAs as competence and integrity. Results showed dynamic relationships among VPAs’ intelligence attributes, consumer trust, and behavioral intentions. Specifically, both functional and hedonic attributes promote competence, whereas anthropomorphic attributes increase integrity. Further, both competence and integrity lead to positive word-of-mouth (WOM), while only competence positively influences consumers’ usage intention. In addition, a user's level of technology anxiety (an individual difference variable) was found to serve as a moderator in the trust model. This study offers both theoretical contributions and practical implications regarding consumer trust development in the context of VPAs.

Many studies have examined why people believe in conspiracy theories, yet less is known about why people choose to consume conspiratorial content. This study addresses this gap by examining people's willingness to pay for online conspiracy theory content. Grounded in uses and gratifications theory and the willingness to pay concept, it employs a survey experiment fielded in Japan (n = 1,448) that asks respondents whether they would subscribe to a hypothetical online video channel with a randomly assigned conspiracy theory and a monthly subscription fee (0 to 1,000 JPY/∼9 USD). Results show that while many respondents held varying degrees of belief in the conspiracy theories in question, few were willing to pay for the channel and cost was the main determining factor. The subscription rate would drop from 19 % if the channel was free to 3–5 % if it had a non-zero subscription fee (average 324 JPY/∼3 USD). This study also finds that conspiracy mentality and media diet (the combination of using social media for news and avoiding mainstream media news) are consistent predictors of people's willingness to subscribe to and pay for online conspiracy theory content.

Augmented Reality (AR) is increasingly recognized as a transformative tool for creating restorative environments within museums. It has the potential to provide psychological benefits for visitors, including attention restoration, stress reduction, and anxiety alleviation. This study explores how AR can foster these benefits within museum spaces. By adopting AR technology, museums can go beyond their traditional roles of knowledge dissemination. The immersive, adaptive, and interactive features of AR can enhance the museum experience, transforming it into an innovative therapeutic space. By combining real exhibits with virtual elements, AR can restore visitors’ psychological energy within museum settings. This integration of digital innovation into restorative contexts surpasses the traditional functions of visual service. Through empirical investigation of multiple dimensions of restorative environments, AR museum experiences offer comprehensive attention restoration. In this study, a survey was conducted with 279 participants to assess the impact of AR museum experiences on visitors’ psychology. The results revealed that such experiences contribute to heightened attention restoration levels, stress reduction, and anxiety relief. With the latest advancements in generative artificial intelligence, AR technology is empowered to integrate within museums. This integration will merge individuals with customized technology, expanding human perceptual experiences and highlighting AR's significant influence within the museum environment.

The opportunities for prosperity and sustainable development offered by Electronic Payment Systems (EPS) are of great interest to both authorities and various stakeholders, especially in developing countries. Needless to say, UTAUT2 explains only 40 % of the actual use's variance. This model lacks certain salient constructs that have proven to be relevant. Based on 41 reviewed articles, this current paper aims to provide an innovative model built on robust theoretical foundations, including two new concepts Contextual Acquaintance that encompasses experience, familiarity, and situational normality, and Overall Reliability which gathers consistency of the technology itself and stakeholders’ reliability. This led us to uncover new endogenous mechanisms without burdening the model with multiple variables. Furthermore, this model incorporates for the first time horizontal and vertical dimensions of collectivism and individualism in the context of EPS adoption, providing a more profound understanding of behaviors in multicultural societies. Finally, this paper provides a research framework for scholars to reduce the redundancies observed in this context.

This paper examines the chaos caused by introducing advanced language models, specifically ChatGPT, to art. Our focus is on the potential impact of ChatGPT on art creation and collaboration. We explore how it has been utilized to generate art and assist in creative writing and how it facilitates collaboration between artists. This exploration includes an investigation into the use of AI in creating art, music, and literature, emphasizing ChatGPT’s role in generating poetry and prose and its ability to provide valuable suggestions for sentence structure and word choice in creative writing. We conduct case studies and interviews with diverse artists and AI experts to understand the benefits and challenges of using ChatGPT in the creative process. Our findings reveal that artists find ChatGPT helpful in generating new ideas, overcoming creative blocks, and improving the quality of their work. It enables remote collaboration between artists by providing a real-time communication and idea-sharing platform. However, ethical concerns relating to authorship ownership and authenticity have emerged. Artists fear using ChatGPT may lead to losing their artistic identity and ownership of their work. While our data suggests that ChatGPT holds the potential to transform the art world, careful consideration must be given to the ethical implications of AI in art. We recommend future research to focus on developing guidelines for the responsible use of AI in art, safeguarding artists’ rights, and preserving artistic authenticity.

OFDM (Orthogonal Frequency Division Multiplexing) is a widely used modulation technique in many standards such as Wi-Fi (Wireless Fidelity), LTE (Long Term Evolution), and 4 G. It allows for high transmission data rates and excellent spectral efficiency. However, one of the main limitations of OFDM is its high PAPR (Peak-to-Average Power Ratio). In this paper, we will first describe some methods to reduce the PAPR associated with OFDM signal. We will discuss some methods such as the clipping, the Selective mapping SLM, the Partial Transmit Sequence (PTS), and the Tone Reservation (TR). Let's list their advantages and disadvantages before focusing on the TR method which we will detail. This work aims to improve the performance of the TR method in terms of speed of convergence and reduction of the PAPR, using the conjugate different algorithms while respecting the frequency specifications of the IEEE 802.11a standard. The proposed algorithm which is based on the gradient method, Conjugate gradient methods, and Quasi-Newton method shows good performance regarding PAPR reduction and speed of convergence. The choice of an appropriate technique depends on several factors: required PAPR reduction, acceptable complexity, and performances (spectral efficiency, latency, etc.). A trade-off must be found in practice between these different criteria. It has been shown that the Quasi-Newton method algorithm with its two variants converges more quickly.

This paper proposes the category of real-time artificial intelligence (AI) systems as applications of computerized control systems in dynamic, time-constrained contexts normally managed by human intelligence. Noting the accountability challenges which these systems introduce, the paper posits the need for robust documentation and records capacities within these systems. The paper surveys four real-time AI systems with significant records needs: autonomous vehicles, online content targeting systems, mixed-reality tools for surgical contexts, and digital twin systems in airport facilities management. The paper identifies paradata, or the data leading up to an output in a system's operation, as a key data category necessitating preservation for full transparency in the records generated by these systems. Paradata is defined as “information about the procedure(s) and tools used to create and process information resources, along with information about the persons carrying out those procedures.” Paradata uncovers opaque technological processes underlying the production of other datasets and at a granular level must be identified and preserved to delineate the boundaries between human and system agency in semi-autonomous systems. With a basis in control theory, the paper finally offers a framework for assessing the functions of real-time AI systems' operations and their documentation and records needs.