Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks

Digital epidemiology is the process of investigating the dynamics of disease-related patterns, both social and clinical, as well as the causes of these trends in epidemiology. Digital epidemiology, utilising big data from a variety of digital sources, has emerged as a viable method for early detection and monitoring of viral outbreaks. The present review gives an overview of digital epidemiology, emphasising its importance in the timely detection of infectious disease outbreaks. Researchers may discover and track outbreaks in real time using digital data sources such as search engine queries, social media trends, and digital health records. However, data quality, concerns about privacy, and data interoperability must be addressed to maximise the effectiveness of digital epidemiology. As the global landscape of infectious diseases evolves, integrating digital epidemiology becomes critical to improving pandemic preparedness and response efforts. Integrating digital epidemiology into routine monitoring systems has the potential to improve global health outcomes and save lives in the event of viral outbreaks.

Using infographics in disseminating healthy lifestyle information on social media is likely to increase uptake and sharing

BACKGROUND

Infographics facilitate rapid information dissemination with enriched eye-catching content on social media, but it is unclear what factors affect the adoption of information presented in this way.

OBJECTIVES

We tested whether the Information Acceptance Model applies to infographics on healthy lifestyle and fitness topics.

METHODS

Two hundred and four university students were invited to participate in an online survey on their acceptance after reading some healthy lifestyle and fitness topics infographics shared on social media. The data collected were analysed using Partial Least Square path modelling.

RESULTS

The results confirmed information usefulness as a predictor of information adoption; attitude towards information and information adoption were the predictors of behavioural intention. Information credibility and attitude towards information, but not information quality and needs, were significantly related to information usefulness. Social media usage and education level were factors affecting infographics impressions.

DISCUSSION

Results support most hypotheses. It confirms information usefulness as a predictor of infographics adoption. Attitudes towards information and information adoption are predictors of behavioural intentions of following healthy lifestyle and fitness suggestions through social media infographics.

CONCLUSION

Social media facilitates interpersonal communication, information exchange and knowledge sharing, and infographics may draw people into healthy lifestyle and fitness information items relevant to them.

Social Media Authentication and Users' Assessments of Health Information: Random Assignment Survey Experiment

BACKGROUND

In an effort to signal the authenticity of user accounts, social networking sites (SNSs) such as Facebook and X, formerly known as Twitter, use visual heuristics (blue checkmarks) to signify whether accounts are verified. While these verification badges are generally well recognized (and often coveted) by SNS users, relatively little is known about how they affect users' perceptions of accuracy or their likelihood of engaging with web-based information. This is particularly true in the case of information posted by medical experts and health care professionals.

OBJECTIVE

This study aims to use an experimental survey design to assess the effect of these verification badges on SNS users' assessments of information accuracy as well as their proclivity to recirculate health information or follow verified medical experts in their social network.

METHODS

A survey experiment using random assignment was conducted on a representative sample of 534 adult SNS users in Florida, United States. A total of 2 separate experimental scenarios exposed users to vaccine-related posts from verified medical experts on X. In each case, the original post contained a platform-issued verification badge (treatment group), which was subsequently edited out of the image as an experimental control. For each scenario, respondents were randomly assigned to either the treatment or control group, and responses to 3 follow-up questions were assessed through a series of chi-square analyses and 2 logit regression models. Responses were fielded using a stratified quota sampling approach to ensure representativeness of the state's population based on age, sex, race, ethnicity, and political affiliation.

RESULTS

Users' assessments of information accuracy were not significantly impacted by the presence or absence of verification badges, and users exposed to the experimental treatment (verification badge) were not any more likely to repost the message or follow the author. While verification badges did not influence users' assessments or subsequent behaviors, reliance on social media for health-related information and political affiliation were substantial predictors of accuracy assessments in both experimental scenarios. In scenario 1, which included a post addressing COVID-19 vaccine efficacy, users who relied on social media "a great deal" for health information were 2 times more likely to assess the post as accurate (odds ratio 2.033, 95% CI 1.129-3.661; P=.01). In scenario 2, which included a post about measles vaccines, registered Republicans were nearly 6 times less likely to assess the post as accurate (odds ratio 0.171, 95% CI 0.097-0.299; P<.001).

CONCLUSIONS

For health professionals and medical experts wishing to leverage social networks to combat misinformation and spread reliable health-related content, account verification appears to offer little by way of added value. On the basis of prior research, other heuristics and communication strategies are likely to yield better results.

Electronic word-of-mouth (eWOM) on social networking sites (SNS): Roles of information credibility in shaping online purchase intention

Electronic word of mouth (eWOM) on social networking sites (SNS) has become crucial in shaping online consumers' purchase intentions, reflecting the growing reliance on rich nature of user-generated content and recommendations shared on these platforms. This study aims to investigate the intricate relationships among different factors of eWOM information, including information credibility, information usefulness, information adoption, attitude towards information, and their impacts on online purchase intention. This study draws upon the Information Adoption Model (IAM) and the Information Acceptance Model (IACM) foundation theory which reveal the steps individuals take in adopting new information, and focuses on how perceptions and attitudes towards information shape acceptance. The study used a quantitative approach, utilizing a structured questionnaire to collect data from 337 Vietnamese individuals actively engaged in eWOM activities on various SNS platforms. Statistical analysis was conducted using partial least squares structural equation modeling (PLS-SEM). The findings show that information credibility significantly influences both information usefulness and adoption. Interestingly, while information usefulness shapes user attitudes, it does not directly impact online purchase intention. Similarly, a positive attitude toward eWOM does not necessarily lead to information adoption. This suggests that consumers may hold favorable views of eWOM content without actively adopting it. Information adoption emerges as a critical determinant of online purchase intention, emphasizing the importance of promoting user engagement and trust in eWOM information. These findings contribute to a deeper understanding of online consumer behavior and provide valuable guidance for stakeholders seeking to leverage eWOM effectively in digital marketing.

Integrating deep learning techniques for personalized learning pathways in higher education

The rapid improvement of artificial intelligence (AI) in the educational domain has opened new possibilities for enhancing the learning experiences for students. This research discusses the critical need for personalized education in higher education by integrating deep learning (DL) techniques to create customized learning pathways for students. This research intends to bridge the gap between constant educational content and dynamic student needs. This research presents an AI-driven adaptive learning platform implemented across four different courses and 300 students at a university in Faisalabad-Pakistan. A controlled experiment compares student outcomes between those using the AI platform and those undergoing traditional instruction. Quantitative results demonstrate a 25 % improvement in grades, test scores, and engagement for the AI group, with a statistical significance of a p-value of 0.00045. Qualitative feedback highlights enhanced experiences attributed to personalized pathways. The DL analysis of student performance data highlights key parameters, including enhanced learning outcomes and engagement metrices over time. Surveys reveal increased satisfaction compared to one-size-fits-all content. Unlike prior AI research lacking rigorous validation, our methodology and significant results deliver a concrete framework for institutions to implement personalized, AI-driven education at scale. This data-driven approach builds on previous attempts by tying adaptations to actual student needs, yielding measurable improvements in key outcomes. Overall, this work empirically validates that AI platforms leveraging robust analytics to provide customized and adaptive learning can significantly enhance student academic performance, engagement, and satisfaction compared to traditional approaches. These findings have insightful consequences for the future of higher education. The research contributes to the growing demand for AI in education research and provides a practical framework for institutions seeking to implement more adaptive and student-centric teaching methodologies.

A hierarchical model of accelerating factors to promote urban renewal and reconstruction of unsafe and old buildings

The issue of urban renewal is complex and multifaceted. In this study, six specialists in the construction industry were invited to conduct audio interviews, which were compiled into verbatim text. The key phrases were extracted by Grounded theory, and three levels of coding were retrieved. The data were categorized into ten accelerating urban renewal factors in three constructs to establish an Analytic Hierarchy Process framework. Using institutional theory to construct outcomes based on grounded theory, transforming these into specific urban renewal relation issue elements. 113 AHP questionnaires were collected from five types of specialists, including practitioners, professionals, participants in urban renewal, academics, and government staff. The results show that relaxing the plot ratio control and incentives is ranked No. 1 by practitioners, participants in urban renewal, professionals, and academics, indicating that the factor is highly valued by specialists but neglected by government staff. Secondly, practitioners, academics, participants in urban renewal, and professionals identified incentives and rewards for urban renewal and enhancing the trust and credibility of urban renewal projects as crucial factors. However, the government staff showed a different weighting. This indicates that government staff is determined to accelerate urban renewal. Finally, the suggestion of this study is in line with the views of the specialists interviewed, who suggest that the government should hold public hearings regularly and seriously to listen to people and specialists. Only through public hearings can all parties reach a consensus. The government should consolidate the views of all parties to amend or enact a bill on urban renewal that is more in line with the changes of the times, including appropriately relaxing the control of building plot ratio and other accelerating factors, to promote urban renewal in Taiwan.

Facebook reactions in the context of politics and social issues: a systematic literature review

In February 2016, Facebook expanded the original "Like" button by introducing five additional "Reactions"-Love, Haha, Wow, Sad, and Angry-using modified versions of Unicode emojis. These reactions enable users to express more nuanced emotions towards posts. This literature review investigates scholarly research on user behavior in response to these reactions, with a focus on a broad spectrum of socioeconomic and psychological issues. We conducted a systematic search across databases including Scopus and Google Scholar, using keywords such as "Facebook" and "Reaction," combined with various key phrases and Boolean operators. Our review synthesizes sixty-four articles published from 2016 to 2023, exploring diverse topics such as political news, far-right and extremist parties, racism, and hate speech during the COVID-19 pandemic. We organized these articles by theme and publication date. Our meta-analysis reveals that lifestyle and entertainment posts predominantly receive positive reactions, while sociopolitical content tends to elicit a broader spectrum of emotions, including negative sentiments. Furthermore, emotionally charged content consistently attracts higher volumes of reactions, regardless of sentiment. This research highlights the intricate relationship between user reactions and content characteristics, providing deeper insights into the dynamics of online engagement. By understanding these interaction patterns, we gain a better grasp of emotional responses and engagement levels, which ultimately shape online discourse and user interactions.

A synergistic study of policy and literature on rumor governance

Rumor governance is an important guarantee for social stability and public safety. Based on the life cycle and crisis cycle model, this paper conducts a synergistic analysis of China's rumor governance policies and regulations and the core scientific research literature on rumor governance in WOS and CNKI. In this paper, we use the TF-IDF algorithm to count the word frequencies of 326 policy and regulation texts, the Jieba-RoBERTa-Kmeans model to cluster high-frequency keywords, and CiteSpace software and the LLR clustering algorithm are utilized to extract and cluster keywords from 391 documents in the WOS database and from 703 documents in the CNKI database. Based on the synergistic analysis of the life cycle model, it is found that the research on policies and regulations precedes the research on literature, and both are in the period of refinement.Based on the synergistic analysis using the co-occurrence comparison of subject terms in the crisis cycle model, it is found that there is a lack of research in the stages of prevention, monitoring, and governance, and this paper proposes the systematic governance mechanism and strategy for crisis resolution that conforms to the trend of life cycle evolution and is synergistic with policy and literature. This study has only selected Chinese policies and regulations, and the proposed governance strategies have not yet been verified in practice; future research can expand the scope and depth of the study and conduct empirical research and pilot projects.

How Does Smartphone Use Impact Loneliness in the Post-COVID Landscape in Japan?

Smartphone use during the active phase of the COVID-19 pandemic emerged as a crucial means of facilitating communication when strict physical distancing was recommended. Previous studies conducted during the pandemic have suggested that smartphone use contributes to reduced loneliness. However, the influence of smartphone usage on the experience of loneliness in the aftermath of the active phase of the COVID-19 pandemic, also referred to as the post-COVID era, remains unclear, particularly because many physical communication restrictions were lifted during this period. To explore the association of smartphone use with the experience of loneliness in the post-COVID era, we analyzed the latest data from 2022 and 2023, when the COVID-19 pandemic gradually concluded. Our findings revealed that, in 2023, smartphone use increased the risk of loneliness among individuals aged 50-64 years. Conversely, among the younger generations, increased smartphone use was associated with decreased loneliness. The results of our study suggest that smartphones can serve as a significant tool for alleviating loneliness among the younger generations during the post-pandemic period.

Controlled experiment finds no detectable citation bump from Twitter promotion

Multiple studies across a variety of scientific disciplines have shown that the number of times that a paper is shared on Twitter (now called X) is correlated with the number of citations that paper receives. However, these studies were not designed to answer whether tweeting about scientific papers causes an increase in citations, or whether they were simply highlighting that some papers have higher relevance, importance or quality and are therefore both tweeted about more and cited more. The authors of this study are leading science communicators on Twitter from several life science disciplines, with substantially higher follower counts than the average scientist, making us uniquely placed to address this question. We conducted a three-year-long controlled experiment, randomly selecting five articles published in the same month and journal, and randomly tweeting one while retaining the others as controls. This process was repeated for 10 articles from each of 11 journals, recording Altmetric scores, number of tweets, and citation counts before and after tweeting. Randomization tests revealed that tweeted articles were downloaded 2.6-3.9 times more often than controls immediately after tweeting, and retained significantly higher Altmetric scores (+81%) and number of tweets (+105%) three years after tweeting. However, while some tweeted papers were cited more than their respective control papers published in the same journal and month, the overall increase in citation counts after three years (+7% for Web of Science and +12% for Google Scholar) was not statistically significant (p > 0.15). Therefore while discussing science on social media has many professional and societal benefits (and has been a lot of fun), increasing the citation rate of a scientist's papers is likely not among them.

Does it pay to pay? A comparison of the benefits of open-access publishing across various sub-fields in biology

Authors are often faced with the decision of whether to maximize traditional impact metrics or minimize costs when choosing where to publish the results of their research. Many subscription-based journals now offer the option of paying an article processing charge (APC) to make their work open. Though such "hybrid" journals make research more accessible to readers, their APCs often come with high price tags and can exclude authors who lack the capacity to pay to make their research accessible. Here, we tested if paying to publish open access in a subscription-based journal benefited authors by conferring more citations relative to closed access articles. We identified 146,415 articles published in 152 hybrid journals in the field of biology from 2013-2018 to compare the number of citations between various types of open access and closed access articles. In a simple generalized linear model analysis of our full dataset, we found that publishing open access in hybrid journals that offer the option confers an average citation advantage to authors of 17.8 citations compared to closed access articles in similar journals. After taking into account the number of authors, Journal Citation Reports 2020 Quartile, year of publication, and Web of Science category, we still found that open access generated significantly more citations than closed access (p < 0.0001). However, results were complex, with exact differences in citation rates among access types impacted by these other variables. This citation advantage based on access type was even similar when comparing open and closed access articles published in the same issue of a journal (p < 0.0001). However, by examining articles where the authors paid an article processing charge, we found that cost itself was not predictive of citation rates (p = 0.14). Based on our findings of access type and other model parameters, we suggest that, in the case of the 152 journals we analyzed, paying for open access does confer a citation advantage. For authors with limited budgets, we recommend pursuing open access alternatives that do not require paying a fee as they still yielded more citations than closed access. For authors who are considering where to submit their next article, we offer additional suggestions on how to balance exposure via citations with publishing costs.

Self-perception evolution among university student TikTok users: evidence from China

The effects of short movies on social media platforms are gaining worldwide popularity and are now attracting global academic attention. Employing self-perception theory and qualitative research methodology, the study examines the influence of short video applications (TikTok) on app-user engagement and evaluates the self-perceived cognitive psychological understanding of Chinese university students. The findings show that identity, attitude change, emotional perception, and civic engagement are the most influential aspects of Chinese youths' self-perceptions. Furthermore, the positive and negative correlated components influence the distribution of short video values. Such tactical use of personality construction contributes to the present psychological research of Chinese university students.

Fake news research trends, linkages to generative artificial intelligence and sustainable development goals

In the digital age, where information is a cornerstone for decision-making, social media's not-so-regulated environment has intensified the prevalence of fake news, with significant implications for both individuals and societies. This study employs a bibliometric analysis of a large corpus of 9678 publications spanning 2013-2022 to scrutinize the evolution of fake news research, identifying leading authors, institutions, and nations. Three thematic clusters emerge: Disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection. This work introduces three novel contributions: 1) a pioneering mapping of fake news research to Sustainable Development Goals (SDGs), indicating its influence on areas like health (SDG 3), peace (SDG 16), and industry (SDG 9); 2) the utilization of Prominence percentile metrics to discern critical and economically prioritized research areas, such as misinformation and object detection in deep learning; and 3) an evaluation of generative AI's role in the propagation and realism of fake news, raising pressing ethical concerns. These contributions collectively provide a comprehensive overview of the current state and future trajectories of fake news research, offering valuable insights for academia, policymakers, and industry.

Generating synthetic population for simulating the spatiotemporal dynamics of epidemics

Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method's efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.

Research on online public opinion dissemination and emergency countermeasures of food safety in universities-take the rat head and duck neck incident in China as an example

In recent years, food safety accidents have occurred frequently in colleges and universities, and students are prone to emotional resonance with food safety. It triggered heated discussions among the whole society and gradually formed a unique online public opinion on food safety in universities. After food safety incidents broke out in universities, some universities deliberately avoided responsibility or made mistakes in handling the incidents, which will create greater risks of online public opinion. Therefore, this paper takes the "Rat Head and Duck Neck" incident at Jiangxi Institute of Technology in China as an example. The purpose is to study the dissemination of public opinion on food safety online in universities and propose emergency countermeasures. Above all, the food safety online public opinion is divided into five stages: incubation period, burst period, spreading period, recurring period and dissipation period. Then, methods such as text mining and cluster analysis were used to deeply analyze the influencing factors at each stage of the development of food safety online public opinion. And analyze the role of different subjects in the development of public opinion based on the perspective of stakeholders. Finally, this paper provides corresponding countermeasures for different stages of online public opinion on food safety in universities, which provides suggestions and references for university governance. This study found that: (1) The resonance effect of online public opinion media on food safety in universities is significant. (2) Public opinion on food safety in universities is repetitive. (3) Improper response to food safety incidents in universities can easily trigger negative secondary public opinion.

The role of education attainment on 24-hour movement behavior in emerging adults: evidence from a population-based study

Purpose

The purpose of this study was to explore the relationship between education level and health behavior including sleep, work activity, exercise activity, and sedentary behavior among emerging adults.

Methods

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) collected between 2007 and 2018. The study sample included 4,484 emerging adults aged 18-25 years and the weighted participants were 30,057,813. Weighted multivariable regression analysis was performed to investigate the association between education level and the aforementioned health behavior, adjusting for age, gender, race/ethnicity, marital status, poverty-income ratio, BMI, smoking, and alcohol drinking status.

Results

This study revealed that higher education level was associated with shorter sleep duration [Fully adjusted model, β (95% CI): -0.588 (-0.929, -0.246), p < 0.001]. Additionally, those with higher education levels were more likely to allocate time in sedentary behavior [β (95% CI): 90.162 (41.087, 139.238), p < 0.001]. Moreover, higher education level was related to less work activity [β (95% CI): -806.991 (-1,500.280, -113.703), p = 0.023] and more exercise activity time [β (95% CI): 118.196 (-21.992, 258.385), p = 0.097]. Subgroup analysis further verified this trend and detected that males with higher education level tended to participate in less work activity [β (95% CI): -1,139.972 (-2,136.707, -143.237), p = 0.026] while females with higher education level tended to engage in more exercise activity [Fully adjusted model, β (95% CI): 141.709 (45.468, 237.950), p = 0.004].

Conclusion

This study highlighted the importance of education level as a significant factor in promoting healthy behavior among emerging adults. The findings underscored the need for the Ministry of Education to prioritize educating this demographic about the significance of maintaining adequate sleep patterns and reducing sedentary habits. Encouraging them to allocate more time for work and physical activities can significantly contribute to their overall wellbeing and success, ultimately fostering a healthier next generation.

Is information evaluated subjectively? Social media has changed the way users search for medical information

Objective

In recent years, social media platforms, such as TikTok and RedBook, have emerged as important channels through which users access and share medical information. Additionally, an increasing number of healthcare professionals have created social media accounts through which to disseminate medical knowledge. This paper explores why users obtain their medical information from social media and how the signals transmitted by social platforms affect use behaviours.

Methods

We combined the elaboration likelihood model and signal theories to construct a comprehensive model for this study. We used simple random sampling to investigate users' behaviours related to social media usage. A total of 351 valid questionnaires were completed by people in Mainland China. The participants were enthusiastic about social media platforms and had searched for health-related information on social media in the past three months. We analysed the data using partial least squares structural equation modelling to investigate the influence of two pathways and two signals (objective and subjective judgement pathways and positive and negative signals) on social media use behaviours.

Results

When seeking medical information on social media, users tend to rely on subjective judgment rather than objective judgment, although both are influential. Furthermore, in the current era, in which marketing methods involving big data algorithms and artificial intelligence prevail, negative signals, such as information overload, have a more pronounced impact than positive signals.

Conclusions

This study demonstrates that the subjective judgment path has a greater impact on users than the objective judgment path. Platforms are encouraged to focus more on users' emotional needs. The paper also discusses the negative impact of information overload on users, sounding an alarm for enterprises to control their use of homogeneous information resulting from the excessive use of big data algorithms.

Mapping the knowledge structure and trends in Australian Indigenous health and wellbeing research from 2003 to 2022: a scientometric analysis

The health and wellbeing of Australian Indigenous peoples is a nationally sanctioned priority, but despite this, few studies have comprehensively analyzed the features and characteristics of the research in the field. In this regard, a comprehensive scientometric analysis and knowledge mapping to systematically summarize and discuss the current state of research, research trends, and emerging areas of research were conducted. Original articles and reviews published between 2003 and 2022 were obtained from the Web of Science Core Collection. CiteSpace and VOSviewer software were used to perform scientometric analysis and knowledge mapping. An examination of document and citation trends, authors, institutions, countries/regions, journals, and keywords was untaken, while co-citation, co-occurrence, and burst analysis provide insights and future development in this area. A total of 2,468 documents in this field were retrieved. A gradual increase in the number of documents over the past two decades is observed, with the number of documents doubling every ~7.5 years. Author Thompson SC and Charles Darwin University published the most documents, and 85.6% were affiliated with only Australian-based researchers. The Australian and New Zealand Journal of Public Health is the most prominent journal publishing in the field. The most commonly co-occurring keyword was "health," and the keyword "risk" had the longest citation burst. Five keyword clusters were identified; "cultural safety" was the largest. This study articulates the knowledge structure of the research, revealing a shift from population-level and data-driven studies to more applied research that informs Indigenous peoples health and wellbeing. Based on this review, we anticipate emergent research areas to (1) reflect a more comprehensive understanding of the multidimensional factors that shape Indigenous health and wellbeing; (2) move beyond a deficit-based perspective; (3) respect cultural protocols and protect the rights and privacy of Indigenous participants; (4) address racism and discrimination within the healthcare system; (5) foster respectful, equitable, and collaborative research practices with Indigenous peoples; (6) provide culturally appropriate and effective interventions for prevention, early intervention, and treatment; and (7) ensure equitable change in systems to enhance access, quality, and outcomes in health and wellbeing.

The impact of Public Health Emergency (PHE) on the news dissemination strength: Evidence from Chinese-Speaking Vloggers on YouTube

News dissemination plays a vital role in supporting people to incorporate beneficial actions during public health emergencies, thereby significantly reducing the adverse influences of events. Based on big data from YouTube, this research study takes the declaration of COVID-19 National Public Health Emergency (PHE) as the event impact and employs a DiD model to investigate the effect of PHE on the news dissemination strength of relevant videos. The study findings indicate that the views, comments, and likes on relevant videos significantly increased during the COVID-19 public health emergency. Moreover, the public's response to PHE has been rapid, with the highest growth in comments and views on videos observed within the first week of the public health emergency, followed by a gradual decline and returning to normal levels within four weeks. In addition, during the COVID-19 public health emergency, in the context of different types of media, lifestyle bloggers, local media, and institutional media demonstrated higher growth in the news dissemination strength of relevant videos as compared to news & political bloggers, foreign media, and personal media, respectively. Further, the audience attracted by related news tends to display a certain level of stickiness, therefore this audience may subscribe to these channels during public health emergencies, which confirms the incentive mechanisms of social media platforms to foster relevant news dissemination during public health emergencies. The proposed findings provide essential insights into effective news dissemination in potential future public health events.

Effective interactive engagement strategies for MOOC forum discussion: A self-efficacy perspective

This mixed methods sequential explanatory study identified and explained the features of engagement strategies for MOOC forum discussion that help low-achieving students make significant progress. Triangulated data were collected from MOOC learners' (n = 335) scores in two reading assessments, their posts to the embedded online discussion forum, and their self-reflection learning journals. Based on learning progress between pre- and post-assessment tasks, MOOC learners are divided into three groups: 1) little, 2) moderate, and 3) significant progress. According to a statistical analysis of the quantified posts, surprisingly, the low-scoring students from the pre-test who demonstrated significant progress later engaged in significantly fewer peer-peer and peer-teacher interactions in the online discussion forum. Guided by self-efficacy literature, the reflective journals of these learners suggested that 1) learner-content interactions may help them advance learning and obtain new information and linguistic knowledge from the peer-made learning materials in the discussion forum; 2) they did not share and exchange ideas and answers with their peers. Instead, they prefer learning from others' discussions and wish to get quick feedback and suggestions on their contributions to the discussion forum; and 3) peer-peer and peer-teacher interactions were proposed as two solutions to regulate their online learning experience as they lack self-discipline and time-management skills. Implications include teachers' continuous support to encourage low-achieving students to learn peer-generated content and quick feedback on their contributions to the discussion forum.

The influence of consumer perception on purchase intention: Evidence from cross-border E-commerce platforms

In the context of the continuous development of Internet technology and international logistics, the impact of cross-border e-commerce is expanding. Cross-border e-commerce transactions are characterized by a wide variety of products, low prices, and short procurement times. As a result, consumers are increasingly intention to shop on cross-border e-commerce platforms. The number of consumers placing orders is also increasing. Simultaneously, consumer perception, trust and attitude play crucial roles in influencing consumers' shopping behavior on cross-border e-commerce platforms. This study employs structural equation and intermediary effect analysis to explore the mechanism through which various factors influence consumers' purchase intention. The authors examine the relationship between five components: consumer perception, trust, attitude, and purchase intention. The findings reveal the following: (1) The improvement of consumers' perceived value and subjective display intention positively influences their purchase intention. Conversely, a decrease in these factors hampers consumers' intention to buy. (2) When the level of shopping risk increases, consumers' purchase intention tends to decrease. Conversely, when the risk of shopping is reduced, consumers' intention to buy shows an increase. (3) Consumer trust and attitude play a significant mediating role in the relationship between independent variables and dependent variables. This study lays an important theoretical foundation for future research in this field. It expands the application scenarios of related research methods. Additionally, the conclusions of this study provide valuable references for managers of cross-border e-commerce enterprises in making informed management decisions.

Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study

BACKGROUND

Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site.

OBJECTIVE

This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs.

METHODS

We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain.

RESULTS

We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis.

CONCLUSIONS

Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.

Exploring college students' continuance learning intention in data analysis technology courses: the moderating role of self-efficacy

Introduction

In today's digital economy, data resources have gained strategic recognition. Enterprises view data analytic capabilities as a core organizational competitiveness. This study explored factors influencing college students' continuance learning intention in data analysis technology courses to inform the role of self-efficacy on the relationship between interactivity and continuance learning intention.

Methods

The research model underpinning the study was based on the Stimulus-Organism-Response model and flow theory. The model was validated using SmartPLS. A total of 314 valid questionnaires were collected via the standard online survey approach.

Results

Among internal factors, study results showed both cognitive interest and self-efficacy had significant positive effects on continuance learning intention. Also, cognitive interest had a significant positive effect on self-efficacy. Among external stimuli, content quality, software quality, and interactivity had significant positive effects on self-efficacy. Software quality did not have a significant effect on cognitive interest. Importantly, self-efficacy registered a significant moderating role on the relationship between interactivity and continuance learning intention.

Knowledge graph empowerment from knowledge learning to graduation requirements achievement

A deep understanding of the relationship between the knowledge acquired and the graduation requirements is essential for students to precisely meet the graduation requirements and to become human resources with specific knowledge, skills and professionalism. In this paper, we define the ontology layer of the knowledge graph by deeply analyzing the relationship between graduation requirement, course and knowledge. Based on the implementation of the concept of Outcome Based Education, we use Knowledge extraction, fusion, reasoning techniques to construct a hierarchical knowledge graph with the main line of "knowledge-course-graduation requirements. In the process of knowledge extraction, in order to alleviate the huge labor overhead brought by traditional extraction methods, this paper adopts a transfer learning method to extract triadic knowledge using the multi-task framework EERJE, Finally, knowledge reasoning was also performed with the help of LLM to further expand the knowledge scope. The comprehensiveness, correctness and relatedness of the data were evaluated through the experiment, and the F1 value of the ternary group extraction was 87.76%, the accuracy rate of entity classification was 85.42%, the data coverage was more comprehensive, and the results showed that the data quality was better, and the knowledge graph constructed in this way can fully optimize the organization and management of teaching resources, help students intuitively and comprehensively grasp the correlation and difference between graduation requirements and various knowledge points, and let the Students can carry out personalized independent learning through the navigation mode of knowledge graph, strengthen their weak links, and complete the relevant graduation requirements, which effectively improves the degree of students' graduation requirements achievement. This new paradigm of knowledge graph enabled teaching is of reference significance for engineering education majors to improve the degree of graduation requirements achievement.

Knowledge heterogeneity and corporate innovation performance: The mediating influence of task conflict and relationship conflict

Innovation has emerged as a crucial factor in the sustenance and growth of enterprises. Nonetheless, small and medium-sized enterprises (SMEs) confront numerous challenges in their pursuit of innovation, owing to constraints in capital, expertise, and knowledge resources. Drawing on the resource-based theory and the input-process-output (IPO) model, this study devises a mechanism model to assess the impact of knowledge heterogeneity and innovation performance on small and medium-sized manufacturing enterprises in Guizhou Province, China. The objective is to offer recommendations for the advancement and innovation of enterprises with relative knowledge resource deficiencies. A total of 324 valid questionnaires were gathered, and the acquired data were analyzed employing SPSS 23.0 and Amos 26.0. The findings reveal that knowledge heterogeneity exerts a significantly positive influence on innovation performance. Task conflict and relationship conflict serve as partial mediators in the effects of knowledge heterogeneity on innovation performance. By capitalizing on the heterogeneity of internal and external knowledge, enterprises can effectively enhance their innovation outcomes. Furthermore, the study demonstrates that knowledge sharing possesses a moderating effect on the impact of knowledge heterogeneity on task conflict, relationship conflict, and innovation performance. In a conducive sharing environment, the ultimate effect of knowledge heterogeneity on innovation is subject to alteration.

Lay representations of artificial intelligence and autonomous military machines

This study is about how lay persons perceive and represent artificial intelligence in general as well as its use in weaponised autonomous ground vehicles in the military context. We analysed the discourse of six focus groups in Estonia, using an automatic text analysis tool and complemented the results by a qualitative thematic content analysis. The findings show that representations of artificial intelligence-driven machines are anchored in the image of man. A cluster analysis revealed five dominant themes: artificial intelligence as programmed machines, artificial intelligence and the problem of control, artificial intelligence and its relation to human life, artificial intelligence used in wars and ethical problems in developing autonomous weaponised machines. The findings are discussed with regard to people's tendency to anthropomorphise robots despite their lack of emotions, which can be seen as a last resort when confronting an autonomous machine where the usual interpersonal understanding of intentions does not apply.

Unlocking luxury purchase intentions in China: A study of consumer attitude, perceived value, and the moderating effect of perceived enjoyment

This research aims to provide a picture of the elements influencing customers' intentions to purchase luxury products online using social exchange theory (SET). Second, we determined whether perceived enjoyment moderates the relationship between perceived value, consumer attitude, and intention to purchase luxury products. Data were collected from 389 participants through an online survey. Structural equation modelling (SEM) was used to analyze the data. The results showed that eWOM, perceived value, and consumer attitudes significantly affected luxury purchase intention. Moreover, perceived enjoyment moderates the relationship between perceived value, consumer attitude, and purchase intention. This study is designed to help practitioners and researchers understand why young Chinese consumers are interested in purchasing luxury goods online. Luxury brand companies can improve their marketing strategies by developing strategies aligned with the mindset of digital native Generation Z.

Negative psychological and physiological effects of social networking site use: The example of Facebook

Social networking sites (SNS), with Facebook as a prominent example, have become an integral part of our daily lives and more than four billion people worldwide use SNS. However, the (over-)use of SNS also poses both psychological and physiological risks. In the present article, we review the scientific literature on the risk of Facebook (over-)use. Addressing this topic is critical because evidence indicates the development of problematic Facebook use ("Facebook addiction") due to excessive and uncontrolled use behavior with various psychological and physiological effects. We conducted a review to examine the scope, range, and nature of prior empirical research on the negative psychological and physiological effects of Facebook use. Our literature search process revealed a total of 232 papers showing that Facebook use is associated with eight major psychological effects (perceived anxiety, perceived depression, perceived loneliness, perceived eating disorders, perceived self-esteem, perceived life satisfaction, perceived insomnia, and perceived stress) and three physiological effects (physiological stress, human brain alteration, and affective experience state). The review also describes how Facebook use is associated with these effects and provides additional details on the reviewed literature, including research design, sample, age, and measures. Please note that the term "Facebook use" represents an umbrella term in the present work, and in the respective sections it will be made clear what kind of Facebook use is associated with a myriad of investigated psychological variables. Overall, findings indicate that certain kinds of Facebook use may come along with significant risks, both psychologically and physiologically. Based on our review, we also identify potential avenues for future research.

Determining the role of music attitude and its precursors in stimulating the psychological wellbeing of immigrants during COVID quarantine - a moderated mediation approach

Based on social cognitive theory (SCT), the purpose of this study is to examine the role of music attitude and its essential precursors in stimulating the psychological wellbeing of immigrants in isolation (quarantine) during the COVID pandemic. This study employed quantitative methodology; an online survey was administered to collect sufficient data from 300 immigrants who traveled to China during the pandemic. Data were collected from five centralized quarantine centers situated in different cities in China. Additionally, the valid data set was analyzed using structural equation modeling (SEM) via AMOS 24 and SPSS 24. The results indicate that potential predictors such as cognitive - music experience (MEX), environmental - social media peer influence (SPI), and cultural factors such as native music (NM) have a direct, significant, and positive effect on music attitude (MA), which further influences immigrants' psychological wellbeing (PW) during their quarantine period. Moreover, in the presence of the mediator (MA), the mediating relationships between MEX and PW, and NM and PW, are positive, significant, and regarded as partial mediation. However, the moderated mediation effects of music type (MT) on MEX-MA-PW and NM-MA-PW were found to be statistically not significant and unsupported. This study contributes to the literature on the effectiveness of individuals' music attitude and its associated outcomes, focusing on mental health care in lonely situations such as quarantine during the COVID pandemic. More importantly, this study has raised awareness about music, music attitude, and their beneficial outcomes, such as mental calmness and peacefulness for the general public, particularly during social distancing, isolation, and quarantine in the COVID pandemic situation.

Bibliographic mapping for heat tolerance in pigs and poultry

Heat tolerance, especially under climate change scenarios, plays an increasingly import factor in pig and chicken production. We therefore evaluated bibliographic mapping of citation, co-occurrence of keywords, co-citation and bibliographic coupling for heat tolerance, and these species. Data was obtained from Scopus (Elsevier) and analysed in Vosviewer. We found a total of 2023 documents from 102 countries, of which 10 countries account for 50% of the publications (USA, China, Brazil, Iran, India, UK, Turkey, Germany, Egypt, and Australia). While heat tolerance is important worldwide, Global South countries, especially China, have become more prominent in publishing on this topic in recent years. Researchers from South America appear relatively isolated using the metrics of this study, with no clear explanation why. We speculate funding for research and publication may be a governing factor. The literature reviewed suggests an emphasis on mitigation strategies that include nutrition and genetics. An emphasis in poultry, especially Gallus gallus was observed and suggests more attention is needed on other species (for example, ducks and turkey). Biases in the analysis could arise due to lack of citations from recent papers, those not indexed in Scopus or in other languages. The paper advances understanding tendencies in this field of research and may point to future actions for policy makers addressing animal production and climate change research.

Exploring the impact of sentiment on multi-dimensional information dissemination using COVID-19 data in China

The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.

The moderating influence of perceived government information transparency on COVID-19 pandemic information adoption on social media systems

Introduction

Social media systems are instrumental in the dissemination of timely COVID-19 pandemic information to the general population and contribute to the fight against the pandemic and waves of disinformation during the COVID-19 pandemic. This study uses the information adoption model (IAM) as the theoretical framework to examine the moderating influence of perceived government information transparency on the adoption of COVID-19 pandemic information on social media systems from the Ghanaian perspective. Government information transparency regarding the pandemic is crucial since any lack of transparency can negatively affect the global response to the pandemic by destroying trust (in government and public health authorities/institutions), intensifying fears, and causing destructive behaviors.

Methods

It applies a convenient sampling technique to collect the responses from 516 participants by using self-administrated questionnaires. The data analysis was computed and analyzed with SPSS-22. The following statistical tests were conducted to test the hypotheses: descriptive statistics, scale reliability test, Pearson bivariate correlation, multiple linear regressions, hierarchical regression, and slope analysis.

Results

The results indicate that information quality, information credibility, and information usefulness are significant drivers of COVID-19 pandemic information adoption on social media systems. Furthermore, the perceived government information transparency positively moderates the influence of information quality, information credibility, and information usefulness on the adoption of COVID-19 pandemic information on social media systems.

Conclusion

The theoretical and managerial implications of these findings suggest the utilization of social media systems as an effective tool to support the continued fight against the current COVID-19 pandemic and its future role in national and global public health emergencies.

Is the Area Under Curve Appropriate for Evaluating the Fit of Psychometric Models?

In the literature of modern psychometric modeling, mostly related to item response theory (IRT), the fit of model is evaluated through known indices, such as χ2, M2, and root mean square error of approximation (RMSEA) for absolute assessments as well as Akaike information criterion (AIC), consistent AIC (CAIC), and Bayesian information criterion (BIC) for relative comparisons. Recent developments show a merging trend of psychometric and machine learnings, yet there remains a gap in the model fit evaluation, specifically the use of the area under curve (AUC). This study focuses on the behaviors of AUC in fitting IRT models. Rounds of simulations were conducted to investigate AUC's appropriateness (e.g., power and Type I error rate) under various conditions. The results show that AUC possessed certain advantages under certain conditions such as high-dimensional structure with two-parameter logistic (2PL) and some three-parameter logistic (3PL) models, while disadvantages were also obvious when the true model is unidimensional. It cautions researchers about the dangers of using AUC solely in evaluating psychometric models.

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department

Background

The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA).

Materials and methods

A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation.

Results

The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration.

Conclusion

The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors.

How to cite this article

Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416-425.

Modeling information diffusion in social media: data-driven observations

Accurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform's algorithms direct who sees which message, which affects in opaque ways how information spreads. This article describes our research efforts in the SocialSim program of the Defense Advanced Research Projects Agency. As formulated by DARPA, the intent of the SocialSim research program was "to develop innovative technologies for high-fidelity computational simulation of online social behavior ... [focused] specifically on information spread and evolution." In this article we document lessons we learned over the 4+ years of the recently concluded project. Our hope is that an accounting of our experience may prove useful to other researchers should they attempt a related project.

A hot topic diffusion approach based on the independent cascade model and trending search lists in online social networks

In online social networks, users can quickly get hot topic information from trending search lists where publishers and participants may not have neighbor relationships. This paper aims to predict the diffusion trend of a hot topic in networks. For this purpose, this paper first proposes user diffusion willingness, doubt degree, topic contribution, topic popularity and the number of new users. Then, it proposes a hot topic diffusion approach based on the independent cascade (IC) model and trending search lists, named the ICTSL model. The experimental results on three hot topics show that the predictive results of the proposed ICTSL model are consistent with the actual topic data to a great extent. Compared with the IC, independent cascade with propagation background (ICPB), competitive complementary independent cascade diffusion (CCIC) and second-order IC models, the Mean Square Error of the proposed ICTSL model is decreased by approximately 0.78%-3.71% on three real topics.

University teachers' profiles based on digital innovativeness and instructional adaptation to COVID-19: Association with learning patterns and teacher demographics

The purpose of this study was to understand the factors behind university teachers' ability to implement instructional changes during the COVID-19 pandemic. An online questionnaire comprised of open-ended and Likert-scale questions was administered to teachers at a Finnish university in April 2020. The sample consisted of 378 university teachers who were categorised into four groups based on their digital innovativeness and the extent to which they implemented changes to adapt their teaching practices to COVID-19 restrictions: Avoider Survival Adapters, Avoider Ambitious Adapters, Embracer Survival Adapters, and Embracer Ambitious Adapters. We examined the association between the teacher groups and their learning patterns and background characteristics. The findings showed that Embracer Ambitious Adapters have significantly more meaning-oriented and application-oriented learning patterns than Embracer Survival Adapters, though Avoider Survival Adapters have more problematic learning patterns. Furthermore, the results indicated that pedagogical training and having more teaching experience helped innovative teachers embrace more changes in their teaching practices during the COVID-19 pandemic. In terms of discipline, the results showed that teachers working in hard disciplines (e.g., physics) were more likely to belong to the Embracer Survival Adapters group, while teachers working in soft disciplines (e.g., history) were more likely to belong to the Embracer Ambitious Adapters group. Possible interpretations of these findings and perspectives for further research are discussed.

Reviewing the critical challenges that influence the adoption of the e-learning system in higher educational institutions in the era of the COVID-19 pandemic

Purpose

This paper aims to review the critical challenges and factors influencing the successful adoption of electronic learning (e-learning) systems in higher educational institutions before and during the current propagation of the coronavirus disease 2019 (COVID-19) pandemic.

Design/methodology/approach

This study undertook a literature review concerning the in-depth revision of previous studies published in 2020 and 2021. A total of 100 out of 170 selected research papers were adopted to identify and recognise the factors restricting the application of e-learning systems.

Findings

The findings determine and illuminate the most challenging factors that impact the successful application of online learning, particularly during the wide propagation of the COVID-19 pandemic. The review of the literature provides evidence that technological, organisational and behavioural issues constitute significant drivers that frontier the facilitation of the e-learning process in higher educational institutions.

Practical implications

The current paper suggests a guide for managers and scholars in educational institutions and acts as a roadmap for practitioners and academics in the educational field and policymakers as this research spotlights the significant factors challenging the e-learning process before and during the pandemic crisis.

Originality/value

The provided in-depth literature review in this research will support the researchers and system designers with a comprehensive review and recent studies conducted before and during the COVID-19 pandemic considering the factors limiting the e-learning process. This paper formulates a valuable contribution to the body of knowledge that will assist the stakeholders in the higher educational institutions' context.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2022-0085.

Understanding National Nonprofit Data Environments

We develop the concept of the nonprofit data environment as all data collected and reported in a country resulting from law implemented into practice. We map data environments across 20 countries and propose explanations for differences between the information nongovernmental organizations report (collected) and what is made publicly available (reported). Domestic factors including regime type, civil society autonomy, and regulatory quality increase the amount of information collected and released publicly. Exposure to international political forces, including aid flows and globalization, increases the gap, which runs counter to expectations of greater openness with global engagement. Our findings point to the need for a better understanding of patterns in non-profit organizations (NPOs) data environments; while all governments collect information, countries with similar legal codes have widely varying data environments. This matters for NPOs as their ability to learn and improve depends on access to quality data and coincides with a feared global political backlash.

Combating Fake News and Digital Deception at the Workplace: An Integrative Review and Open Systems Theory-led Framework for Future Research

Research on fake news and related acts of deception in the domain of human resource management is growing but still in its infancy. This escalating crisis necessitates immediate attention, as fake news evolves into an all-pervasive phenomenon that surpasses domain boundaries and affects organizations at scale. This study analyzes the growing corpus of research on fake news and concomitant acts of deceit in the domain of human resource management through an integrative review of 64 scholarly papers published in peer-reviewed journals over the last 30 years. We identify key themes and draw attention to gaps that merit scrutiny. We then propose an open systems theory-led conceptual framework that elucidates the relationships between fake news, related acts of deceit and its effects on various facets of human resource management practice and serves as a guide to advance contributions in the field. Directions for future research and implications for practice are discussed.

Mapping the Landscape of Indian Genomics Research: A Scientometric Analysis

This Scientometric study aimed to provide state-of-the-art information on research growth and trends, areas of potential growth and development in genomics in India, and identify the key players (organizations or institutions, and funding agencies). It was found that the number of publications and citations related to genomics research has been steadily increasing over the years, indicating a growing interest and investment in the field as the Indian Council of Agricultural Research was the leading contributor to the field. Among the 159 contributing countries from 2012 to 2021, India contributed 4.46 percent of publications. The Department of Biotechnology (Ministry of Science and Technology, India) provided the most funds for genomics research. In the last decade, research was primarily focused on "Genetic Diversity", "Polymorphism", "Comparative Genomics", "Phylogeny", " Random amplification of polymorphic DNA (RAPD)", "Single-nucleotide polymorphism (SNP)", "Polymerase chain reaction (PCR)", "Gene Expression", etc. The study's findings may shed light on the strengths and weaknesses of the country's research infrastructure, as well as the effectiveness of government policies and funding mechanisms.

Customer values and patronage intention in social media networks: mediating role of perceived usefulness

Purpose

This paper aims to validates a framework using the Uses and Gratifications Theory (UGT) to study the effect of values, i.e. Functional Value (FV), Social Value (SV), Emotional Value (EV) and Monetary Value (MV), on the Patronage Intention (PI) of Social Network Users (SNU’s) with mediating role of Perceived Usefulness (PU).

Design/methodology/approach

A survey method was used to collect responses from 302 SNUs, and the variance-based structural equation method was used to understand the relationships among the constructs.

Findings

The results found a significant positive effect of FV and EV on Perceived Usefulness (PU) and MV and PU on Patronage intention (PI) of SNUs. Further, PU partially mediated the relationship of EV with PI.

Originality/value

This study used the UGT to understand the effect of values on the PI of SNUs. This research study contributes to the existing social networks/social media literature.

A study on mediation effect of relationship quality outcome between customer relationship management and mutual fund decision

Purpose

Usage of updated technology is continuously empowering customer relationship management (CRM) to be convenient and user friendly, where customers are kept engaged with knowledge and information. This enables them on decision-making and managing their portfolio, especially in mutual fund investments. To improve toward a positive decision, certain quality related variables needed to be considered. Thus, this study aims to estimate the mediation effect of relationship quality and outcome (RQO) between CRM and investment decision-making in mutual funds (MFD).

Design/methodology/approach

The descriptive study adopted the constructs from existing empirical literatures to conceptualize the model with three higher order constructs with 12 dimensions. Survey method is used, and with a structured questionnaire, a total of 323 mutual fund investors were approached using nonprobability criterion sampling technique, of which 262 relevant responses were considered for estimating the structural model. Smart PLS was used to establish the relationship of the constructs.

Findings

The result emphasizes a significant direct and indirect relationship indicating that investors are more inclined to MFD through technology-enabled CRM and RQO plays a vital role in explaining the direct relationship between CRM and MFD. The results of the study are in-line with the existing literature.

Practical implications

The study highlights that financial institutions must focus not only on technological diffusion but also needs to ensure quality service by providing knowledge and information during every access of transactions by customers, making them independent and confident during investments.

Originality/value

This study indicates how capacity efficiency, which is a part of service productivity, can be managed without affecting the outcome efficiency by incorporating technology in the place of human interaction during relationship acquiring and retaining process.

Using machine learning to predict factors affecting academic performance: the case of college students on academic probation

This study aims to employ the supervised machine learning algorithms to examine factors that negatively impacted academic performance among college students on probation (underperforming students). We used the Knowledge Discovery in Databases (KDD) methodology on a sample of N = 6514 college students spanning 11 years (from 2009 to 2019) provided by a major public university in Oman. We used the Information Gain (InfoGain) algorithm to select the most effective features and ensemble methods to compare the accuracy with more robust algorithms, including Logit Boost, Vote, and Bagging. The algorithms were evaluated based on the performance evaluation metrics such as accuracy, precision, recall, F-measure, and ROC curve, and then validated using 10-folds cross-validation. The study revealed that the main identified factors affecting student academic achievement include study duration in the university and previous performance in secondary school. Based on the experimental results, these features were consistently ranked as the top factors that negatively impacted academic performance. The study also indicated that gender, estimated graduation year, cohort, and academic specialization significantly contributed to whether a student was under probation. Domain experts and other students were involved in verifying some of the results. The theoretical and practical implications of this study are discussed.

Social media aids for business learning: A quantitative evaluation with the 5E instructional model

Social media use is widely accepted in higher education, connecting students' learning with daily life. Considering the potential of social media to revolutionize the whole spectrum of teaching and learning, this study examines social media usage of business school students of different majors and their perception of social media as learning aids through a quantitative online survey guided by the 5E instructional model, with 423 valid responses from students majoring in accounting, finance, and economics. Results indicated that respondents perceived social media as influential in helping them access study-related information. It facilitated comprehensive learning, information access, information sharing, and student communication with instructors. Significant differences in perceptions of using social media as aids for business learning were found among students of different demographics, including gender, education level, and residence, but not quite for different majors. While various studies have examined the use of social media in education, scant studies focus on business school students under the lens of the 5E instructional model, especially Asians.

Factors driving consumer attitude to online shopping hate

Purpose

The purpose of this study is to empirically investigate the determinants and consequences of psychological reactance in the online shopping context. Leveraging the psychological reactance and self-congruity theories, functional and symbolic discrepancies enhance the psychological reactance toward online shopping. In addition, trustworthiness moderates the impact of online customers attitude ambivalence on their psychological reactance.

Design/methodology/approach

The authors conduct an empirical study on online customer cognitive factors. In this research paper, the postpositivism research view is used. The Smart PLS-SEM is used to analyze the data.

Findings

The current study findings reveal that self-concept and operational incongruence (i.e. symbolic and functional) are the main factors that lead to psychological reactance and resulting in online shopping hate. Poor website quality and other matters are so significant they create functional incongruence. Moreover, low trustworthiness strengthens psychological reactance in the online shopping hate context.

Originality/value

This study extends the psychological reactance and self-congruence theories to online shopping. Previously, literature has extensively studied the social commerce intention.

Analysis of the characteristics and evolution of knowledge label networks in the Q&A community: taking the Zhihu platform as an example

Purpose

In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

Design/methodology/approach

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

Findings

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

Originality/value

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

Analysing the relationship between altmetric attention score (AAS) and citation: a correlational study

Purpose

The purpose of this study is to assess the value of altmetrics or other indicators, showcasing the impact of academic output, which is seen too often correlated with the citation count.

Design/methodology/approach

This study considered three reputed journals of Library and Information Science (LIS) published by Elsevier. A total of 1,164 articles were found in these journals from 2016 to 2020 and the relationships between altmetric attention scores (AAS) and citations were examined. The analysis was extended to compare the grouped data set based on percentile ranks of AAS like top 50%, top 25%, top 10% and top 1%.

Findings

Using Spearman correlation analysis, the findings reveal a positive correlation between AAS and citations with different significant levels for all articles, and articles with AAS, as well as for normalized AAS in the top 50%, top 25%, top 10% and top 1% data set. For the three journals International Journal of Information Management (IJIM), Journal of Informetrics (JIF) and Library and Information Science Research (LISR), a significant positive correlation is observed across all data sets. But an unexpected result was observed: in the case of the top 50% of articles for the IJIM and JIF showed no significant correlation but the LISR journal showed a positive correlation for the whole data set. This journal though has fewer articles in comparison to the other two.

Research limitations/implications

A source item that is highly cited may not be having high social media attention as reflected in the findings. This demarcates AAS with citations implying various factors on which these measurements are dependent. The study distinguishes these metrics lucidly. There is not a single guideline or uniformity in assessing the correlation found. But the problem is that the interpretation of the correlation strength affects the conclusion of the study. Moreover, this study will be a role model as a draft for librarians to select relevant journals for their libraries and will facilitate authors in the choice of the publication outlets for their papers, particularly concerning the journals that have both visibility and research impact.

Originality/value

The study reported devising a comprehensive tool to validate AAS as a measure of scholarly impact to include appropriate social media sources and verify its relationship with other metrics. To the best of the authors’ knowledge, this paper is the first attempt to discover the correlation between AAS and citations for the highly impactful LIS journal published by Elsevier. The empirical evidence lies in the citation and altmetric data extracted from the dimension database.