Telematics and Informatics Reports最新文献

This study examined the relationship between video game playing and loneliness and compared competitive players and recreational players in gaming, motivations, gratifications and future intentions during the COVID-19 pandemic period. Overall, the study found players felt moderately lonely during the pandemic, and first-person shooter (FPS) games, fighting games and sports games made a difference in overcoming the lonely feelings. The study also found competitive players spent more time on all genres of games. In particular, multiplayer online battle arena (MOBA) and fighting games were more popular among competitive players as they were more skill demanding. The study found competitive players and recreational players had different motivations. Competitive players were more motivated by competition, achievement, and Schwabism, whereas the recreational players were more motivated by escape and passing time. Lastly, recreational players felt more gratified than competitive players and expressed a stronger intent to return to gaming in the future than competitive players.

In recent times, few studies on program comprehension have incorporated the use of psycho-physiological devices and self-report tools to measure participants’ emotions and cognitive workload. The use of self-report scales is beneficial in settings where there is no access to biosensors or a need for a general understanding of programmers’ emotions and workload during learning or in an assessment of a large group. Hence, we designed a study that would use self-report tools — the Self-Assessment Manikin (SAM) test and NASA TLX (Task Load Index) to obtain the emotions and workload of the participants on two types of programming tasks (logical and conceptual). Our aim was to identify the relationship between emotions and expertise, and workload and expertise on conceptual and logical questions in timed and untimed conditions. Our findings indicate that participants showed Low Valence-High Arousal (LVHA) in timed conditions and High Valence-Low Arousal (HVLA) in untimed conditions. Findings further indicate that high performers showed High Valence-Low Arousal (HVLA) on conceptual questions and High Valence-High Arousal (HVHA) on logical questions, whereas low performers showed High Valence-Low Arousal (HVLA) on conceptual questions and Low Valence-High Arousal (LVHA) on logical questions. We found that participants had a higher workload in timed conditions than untimed conditions and had a higher workload on logical questions than conceptual questions. Furthermore, we employed machine learning algorithms to predict expertise using emotions and workload and the best classifier produced an accuracy of 71.4%, with 72% weighted precision, 71.5% weighted recall, and 71.11% weighted F-score.

This paper reviews the Internet of Things' use and its impact on operations management through an analysis of the extensive literature. The purpose of this research is to analyze a collection of scientific articles (from a bibliographic and content perspective) that are indexed with the keywords "Internet of things" and "Operations management" in the Scopus and WOS databases. This study compiles and analyses 1623 articles using different approaches such as bibliometric, life cycle analysis, and text analysis to classify the Internet of Things applications in operations management. This research shows that 5 clusters of digitization, digital operations, monitoring system, tracking, and Smartification are controversial areas in Internet of things applications in operations management. Among the findings of this study, we can mention identifying the most constructive, influential, and contributing researchers, universities, and countries. This study increases scientific assistance in investigating the effects of the Internet of things on operational management.

Opportunistic Network (Oppnets) is one of the influencing network of wireless ad-hoc Network in today's era of research. Massive publications have already existed in Oppnets, which considers diverse techniques of Opportunistic Routing (OR) to provide an adequate selection of relay nodes. This article contributes to the bibliometric evaluation of the routing protocols and study of network simulation tools. Bibliometric analysis is the statistical analysis of published articles by researchers at particular time and place, with the relation among these articles. The data were pre-processed and analyzed with the VOS viewer and Bibliometrics, the professional mapping tool. 16 years of publication (from 2006 to 2022) were included in this evaluation process. Firstly, persuasive citation evaluation of countries and articles were addressed. Secondly, contribution assessment of documents according to countries and researchers. Thirdly, most effective sources, keywords, and trending topics with co-occurrence of bibliographic coupling were encountered. At last, 23 wireless-tools were analysed and also compared. These tools were also discussed with their unique features, leverages, and detriments. The primary objective is to give researchers enormous knowledge and help them elaborate their work in OR, and select appropriate tools to simulate their work. From this study; Readers, Researchers, and Authors can find easily the most reliable “researcher”, “document”, and “source of journal”. The author can find what work has been already published and illuminates their research work with more outcome.

In response to the coronavirus disease 2019 (COVID-19) pandemic, many individuals turned to synchronous online video communication technologies as a substitute for real-world face-to-face interactions. Evidence indicates that some users of such technologies show symptoms of exhaustion and fatigue during and after videoconferences (VCs) – this phenomenon is referred to as Videoconference fatigue (VC fatigue). Research characterizing the possible vulnerability factors for VC fatigue is still scarce and considered to be in its early stage. Contributing to closing this gap in the existing literature is the motivation for the present study. Survey data was collected from 311 German-speaking participants to explore the relationships of VC fatigue with several psychological factors including autistic traits, social phobia, Fear of COVID-19, tendencies towards Internet Use Disorders (IUD tendencies), and Fear of Missing Out (FoMO, trait and state variables). Results showed that VC fatigue was significantly positively correlated with all of these psychological factors except state-FoMO, and cross-sectional mediation analyses provided further evidence for the positive association between autistic traits and VC fatigue. Specifically, the relationship between autistic traits and VC fatigue was mediated by Fear of COVID-19 and IUD tendencies rather than social phobia, with the latter being a preregistered hypothesis. This study adds to the literature by revealing several possible vulnerability factors associated with VC fatigue. In essence, the present work sheds light on the complex association between autistic traits and VC fatigue. We discuss the implications of our study as well as its limitations and potential avenues for future research.

The rise of information technology has transformed the business landscape, with organizations increasingly relying on information systems to collect and store vast amounts of data. To stay competitive, businesses must harness this data to make informed decisions that optimize their actions in response to the market. Business intelligence (BI) is an approach that enables organizations to leverage data-driven insights for better decision-making, but implementing BI comes with its own set of challenges. Accordingly, understanding the key factors that contribute to successful implementation is crucial.

This study examines the factors affecting the implementation of BI projects by analyzing the interactions between these factors using system dynamics modeling. The research draws on interviews with five BI experts and a review of the background literature to identify effective implementation strategies. Specifically, the study compares traditional and self-service implementation approaches and simulates their respective impacts on organizational acceptance of BI. The results show that the two approaches were equally effective in generating organizational acceptance until the twenty-fifth month of implementation, after which the self-service strategy generated significantly higher levels of acceptance than the traditional strategy. In fact, after 60 months, the self-service approach was associated with a 30% increase in organizational acceptance over the traditional approach. The paper also provides recommendations for increasing the acceptance of BI in both implementation strategies. Overall, this study underscores the importance of identifying and addressing key factors that impact BI implementation success, offering practical guidance to organizations seeking to leverage the power of BI in today's competitive business environment.

The impact of hypersensitive narcissism and anxiety sensitivity on online compulsive buying were examined. These transdiagnostic personality factors have not previously received attention in virtual, as opposed to real-world, contexts, and the study explored their interaction with the use of visual and textual social media platforms for online buying. The present study sampled 440 participants (264 female; 174 male), aged 18 – 37 years. Participants completed an online survey comprising: Online Edwards Compulsive Buying Scale Revised, Hypersensitive Narcissism Scale, and the Anxiety Sensitivity Index-3. Visual social networking usage was a risk factor for online compulsive buying, and there were significant positive relationships between both hypersensitive narcissism, and anxiety sensitivity, with online compulsive buying. Anxiety sensitivity mediated the relationship between hypersensitive narcissism and compulsive online buying when the dominate form of social media use was textual, but not when it was visual.

There are many annotation methods for the English language based on adapting an argumentation model according to the study domain. However, as far as research has been done, there are no annotation methods for detecting argumentative content in Spanish, not only due to the complexity of identifying the evidence but also because of the lack of data available for this task. The research aims to present and evaluate an annotation method consisting of an adapted argumentation model, an annotation guide, and an annotation process based on Twitter data analysis. The Inter Annotator Agreement (IAA) study achieves 0.63 Fleiss Kappa for Argument/Non-Argument tagging, 0.35 Fleiss Kappa for Argument Component tagging, and 0.53 Fleiss Kappa for Non-Argument Component tagging, while the best Cohen's kappa (k) index achieved, was 0.73, 0.52 and 0.75 respectively. The results' assessment highlights the need to include linguistic segmentation rules for the second annotation task. It is crucial to use discourse markers for the claim and evidence detection. For the first annotation task, it determined that if the prevalence index and the bias index are very low, the prevalence index predominates over the bias index because k increases (0.52<=k<=0.72); likewise, for the third annotation task, when the observed agreement index is almost perfect (0.92) the value of k increases (k=0.75) despite a high prevalence index and a low bias index. The annotated corpus with a Fleiss Kappa >= 0.60, agreement and disagreement tables, and confusion matrices code are available on Mendeley Data Repository.

The rise of videoconferencing amidst the COVID-19 pandemic has brought about a new phenomenon, videoconference fatigue (VF), which refers to the emotional and physical exhaustion felt after videoconference meetings. Features of videoconference platforms, such as the self-view function and small screen size, increases self-awareness and cognitive load, resulting in increased negative affect and VF. However, AR face filters can soften facial expressions to reduce self-awareness and increase positive affect. Drawing from the theory of objective self-awareness, this study thus assesses the influence of AR face filters and self-view on users’ affect and perceived VF, through a 2 × 2 dyadic between-subjects experiment (N = 154). Our findings do not support the theory of objective self-awareness. Using AR face filters led to higher VF, but neither AR face filters nor self-view was significantly associated with affect. An alternative theory such as the expectancy violations theory may explain such results. Theoretical and practical implications are discussed.

Using a random sample of active social media users (N = 1,156), this study examined the effectiveness of social media fact-checking against online misinformation sharing. Data indicates that these fact-checks are minimally effective in stopping the spread of misinformation on social media. Being aware of the fact-checks, being fact-checked, or even having content deleted from one's account were not deterrents to sharing misinformation. The fear of isolation was the strongest deterrent, suggesting that account freezes, suspensions, or bans were the most effective ways to curtail the spread of misinformation. The study contributes to research on fact-checking, to research on online surveillance, and to research on online expression and the spiral of silence theory.