Social media platforms and social enterprise: Bibliometric analysis and systematic review

Social media usage and cyberbullying: the moderating role of tie strength

Introduction

Cyberbullying is a pervasive issue in the digital age, closely linked to social media usage. However, existing research has largely overlooked the role of tie strength on social media platforms in shaping cyberbullying dynamics. This study, grounded in tie strength theory and medium theory, investigates the association between social media usage and cyberbullying, focusing on how tie strength moderates this relationship.

Methods

A sample of 813 healthy adults (Mage = 20.06 ± 2.30 years, 498 females) completed an online survey, including the Chinese version of the Social Network Site Intensity Scale and the Cyberbullying Inventory for College Students (CICS).

Results

(1) Tie strength varies from platform to platform. The order of tie strength between users and the four platforms is as follows: WeChat > Bilibili > Weibo > Douyin. (2) Strong-tie social media platforms exhibited higher levels of users'social media engagement compared to those with weak ties. (3) Weak ties significantly moderated the relationship between social media usage and cyberbullying, whereas strong ties did not. Increased social media usage was associated with a higher likelihood of both engaging in and being a victim of cyberbullying on weak-tie platforms, while strong-tie platforms showed a lower likelihood of both engaging in or being a victim of cyberbullying with increased usage.

Conclusion

These findings highlight the interplay between tie strength theory and medium theory in explaining cyberbullying dynamics and underscore the need for platform-specific interventions to address this pervasive issue.

Key drivers and priorities of consumer decisions for refurbished electronics: A mix-method approach

Product refurbishment enhances waste minimization and environmental sustainability. However, the sale of these products relies on consumer attitudes, influenced by various factors. This study adopts a novel mixed-methods approach, utilizing structural and network analysis based on over 60,000 comments and tweets from X.com (formerly Twitter), complemented by insights from expert interviews, to analyze factors affecting consumers viewpoints when making decisions about purchasing refurbished electronics. The research identifies and prioritizes 17 key influencing factors, highlighting the interplay between large-scale consumer sentiment and expert opinions. A customer-centric purchase decision model is presented, showing that consumer priorities when purchasing refurbished products typically revolve around retailer reputation, brand, pricing, warranty, and product quality. These findings offer valuable guidance for producers on how to target higher-priority consumer concerns, ultimately helping to expand their market and boost purchasing intention.

Scientometric trends and knowledge maps of global polychlorinated naphthalenes research over the past four decades

Polychlorinated naphthalenes (PCNs) were included in the banned list of the Stockholm Convention due to their potential to provoke a wide range of adverse effects on living organisms and the environment. Many reviews have been written to clarify the state of knowledge and identify the research needs of this pollutant class. However, studies have yet to analyse the scientometric complexities of PCN literature. In this study, we used bibliometric R and vosviewer programs as a scientometric tool to fill this gap by focusing on articles indexed on Web of Science and Scopus databases and those published between 1973 and 2022. A total of 707 articles were published within this period with a publication/author, author/publication, and co-authors/publication ratios of 0.45, 2.19, and 4.86, respectively. Developed countries dominated most scientometric indices (number of publications, citations, and collaboration networks) in the survey period. Lotka's inverse square rule of author productivity showed that Lotka's laws do not fit PCN literature. An annual percentage growth rate of 7.46% and a Kolmogorov-Smirnoff goodness-of-fit of 0.88 suggests that more output on PCNs is likely in years to come. More research is needed from scholars from developing countries to measure the supremacy of the developed nations and to effectively comply with the Stockholm Convention agreement.

Emerging Themes and Research Frontiers in Suicide Prediction: A Scientometric Analysis

Suicide remains a critical global health issue despite advancements in mental health treatment. The purpose of this analysis is to emphasize the development, patterns, and noteworthy outcomes of suicide prediction research. It also helps to uncover gaps and areas of under-researched topics within suicide prediction. A scientometric analysis was conducted using Biblioshiny and VOSviewer. To thoroughly assess the academic literature on suicide prediction, various scientometric methodologies such as trend analysis and citation analysis were employed. We utilized the temporal features of the Web of Science to analyze publication trends over time. Author affiliation data were used to investigate the geographic distribution of research. Cluster analysis was performed by grouping related keywords into clusters to identify overarching themes within the literature. A total of 1,703 articles from 828 different sources, spanning from 1942 to 2023, were collected for the analysis. Machine learning techniques might have a big influence on suicide-related event prediction, which would enhance attempts at suicide prevention and intervention. The conceptual understanding of suicide prediction is enhanced by scientometric analysis, which further uncovers the research gap and literature in this area. Suicide prediction research underscores that suicidal behavior is not caused by a single factor but is the result of a complex interplay of multiple factors. These factors may include biological, psychological, social, and environmental factors. Understanding and integrating these factors into predictive models is a theoretical advancement in the field. Unlike previous bibliometric studies in the field of suicide prediction that have typically focused on specific subtopics or data sources, our analysis offers a comprehensive mapping of the entire landscape. We encompass a wide range of suicide prediction literature, including research from medical, psychological, and social science domains, thus providing a holistic overview.

The Intellectual Landscape of Nanovaccines: A Bibliometric Perspective on Scientific Progress and Future Directions

This bibliometric study provides a comprehensive analysis of the burgeoning field of nanovaccine research, leveraging data sourced from Scopus and employing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flowchart for the meticulous screening, inclusion, and exclusion of relevant studies. Utilizing sophisticated bibliometric tools, such as Biblioshiny and CiteSpace, we dissected the expansive literature to unearth critical insights into the annual scientific output, identifying key contributors and pivotal publications that have shaped the domain. The analysis delineates the most influential authors, sources, and globally cited documents, offering a macroscopic view of the field's intellectual structure and growth trajectory. Trend topics and thematic mapping underscored the evolution of research foci, from fundamental immunological mechanisms to cutting-edge nanomaterial applications. Factorial analysis and keyword co-occurrence networks revealed the intricate associations and thematic concentrations within the literature. The study's robust methodology also pinpointed the keywords exhibiting the strongest citation bursts, signifying emergent areas of intense academic interest. Networks of cited authors illuminated collaborative patterns among scholars, while timeline network visualizations of country collaborations depicted the global interplay in nanovaccine development. Crucially, this study identified notable research gaps and practical implications, suggesting directions for future investigation and highlighting the translational potential of nanovaccines in public health and personalized medicine. This bibliometric investigation not only maps the current landscape but also charts a course for the trajectory of nanovaccine research, emphasizing its role as a cornerstone of innovative immunotherapeutic strategies.

Business intelligence and the leverage of information in healthcare organizations from a managerial perspective: a systematic literature review and research agenda

PURPOSE

Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations' (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

DESIGN/METHODOLOGY/APPROACH

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

FINDINGS

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research's stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

ORIGINALITY/VALUE

To the authors' knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach

Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete. That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.

Research trends in social media addiction and problematic social media use: A bibliometric analysis

Despite their increasing ubiquity in people's lives and incredible advantages in instantly interacting with others, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays in mental health. Much research has discovered how habitual social media use may lead to addiction and negatively affect adolescents' school performance, social behavior, and interpersonal relationships. The present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013-2022. Bibliometric analysis was conducted on 501 articles that were extracted from the Scopus database using the keywords social media addiction and problematic social media use. The data were then uploaded to VOSviewer software to analyze citations, co-citations, and keyword co-occurrences. Volume, growth trajectory, geographic distribution of the literature, influential authors, intellectual structure of the literature, and the most prolific publishing sources were analyzed. The bibliometric analysis presented in this paper shows that the US, the UK, and Turkey accounted for 47% of the publications in this field. Most of the studies used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, the findings in this study show that most analysis were cross-sectional. Studies were performed on undergraduate students between the ages of 19-25 on the use of two social media platforms: Facebook and Instagram. Limitations as well as research directions for future studies are also discussed.