Network Analysis Tools Research Articles

Health and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study.

The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development. This study aims to analyze free-text responses from children and young people participating in the Children and Young People With Long COVID study to get a clearer understanding of how young people were feeling during the pandemic. A total of 8224 free-text responses from children and young people were analyzed using InfraNodus, an artificial intelligence-powered text network analysis tool, to determine the most prevalent topics. A random subsample of 411 (5%) of the 8224 responses underwent a manual sentiment analysis; this was reweighted to represent the general population of children and young people in England. Experiences fell into 6 main overlapping topical clusters: school, examination stress, mental health, emotional impact of the pandemic, social and family support, and physical health (including COVID-19 symptoms). Sentiment analysis showed that statements were largely negative (314/411, 76.4%), with a small proportion being positive (57/411, 13.9%). Those reporting negative sentiment were mostly female (227/314, 72.3%), while those reporting positive sentiment were mostly older (170/314, 54.1%). There were significant observed associations between sentiment and COVID-19 status as well as sex (P=.001 and P<.001, respectively) such that the majority of the responses, regardless of COVID-19 status or sex, were negative; for example, 84.1% (227/270) of the responses from female individuals and 61.7% (87/141) of those from male individuals were negative. There were no observed associations between sentiment and all other examined demographics. The results were broadly similar when reweighted to the general population of children and young people in England: 78.52% (negative), 13.23% (positive), and 8.24% (neutral). We used InfraNodus to analyze free-text responses from a large sample of children and young people. The majority of responses (314/411, 76.4%) were negative, and many of the children and young people reported experiencing distress across a range of domains related to school, social situations, and mental health. Our findings add to the literature, highlighting the importance of specific considerations for children and young people when responding to national emergencies.

Mechanistic insights into pachymic acid’s action on triple-negative breast Cancer through TOP2A targeting

Triple-negative breast cancer (TNBC) is characterized by the absence of estrogen and progesterone receptors, and lack of human epidermal growth factor receptor 2 (HER2) expression. Traditional Chinese medicine (TCM) has demonstrated promising efficacy in treating TNBC. This study explored the mechanisms of pachymic acid (PA) on TNBC by merging network pharmacology with experimental validation. We acquired Microarray data of TNBC from the Gene Expression Omnibus (GEO). The related targets of PA were predicted and screened using the following 6 databases: Swiss Target Prediction, HERB (Herbal Medicine Database), ETCM (Encyclopedia of Traditional Chinese Medicine), BATMAN (Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine), HIT (Herb Ingredients’ Targets Database), and PharmMapper. The STRING interaction network analysis tool was used to create Protein-Protein Interaction (PPI) networks. Enrichment analysis included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We conducted a pan-cancer analysis, tumor immune microenvironment analysis, and molecular docking. We performed cell experimental, included cytotoxicity assay, apoptosis analysis, proliferation assay, and migration and invasion assays. PA has potential for treating TNBC with the target of TOP2A, and platinum drug resistance possibly serving as the KEGG pathway through which PA exerts its therapeutic effects. PA is involved in processes such as nuclear division, chromosome segregation, mitotic nuclear division, condensed chromosome formation, and protein C-terminus binding. PA probably exert its therapeutic effects through the tumor immune microenvironment, involving elements such as Dendritic cells activated, Eosinophils, Macrophages M0, Macrophages M1, and T cells CD4 memory activated. The therapeutic effects of PA may vary across different subtypes of TNBC such as TNBC-BL1, TNBC-Metaplastic, and TNBC-BL2. This study provides compelling evidence that PA holds significant promise as a therapeutic agent for TNBC, primarily through its action on TOP2A and its influence on the TNBC.

Network analysis of cross-income-level collaboration on multiple myeloma in sub-Saharan Africa

Cross-income-level collaboration (CILC) is crucial for developing global health approaches that benefit low- and middle-income countries (LMICs). Multiple myeloma (MM) is a representative example of a complex, understudied disease in sub-Saharan Africa (SSA). Based on publications, we developed a network analysis tool to assess scientific collaborations. Here, we present findings from a systematic analysis of publications retrieved from PubMed between January 2002 and June 2022. We evaluated individual institutional contributions and collaboration patterns using undirected weighted networks. Our findings reveal that intra-income-level collaborations dominate MM research in SSA, with high-income countries (HICs) primarily engaging with a few local institutions, mainly in South Africa and Nigeria. Increasing CILC is essential to advance research in this area. Our analysis tool provides insights into the collaboration strength, highlights gaps in the field and identifies leading institutions, ultimately aiming to support the development of more effective international collaboration and research strategies.

Investigating cognitive impairments and hippocampal proteome alterations in aged male rats with TAA-Induced minimal hepatic encephalopathy.

The aging population faces a gradual decline in physical and mental capacities, with an increased risk of liver cirrhosis and chronic liver diseases leading to hepatic encephalopathy (HE). The intertwining of physiological manifestations of aging with the pathophysiology of HE significantly impairs cognitive ability, reduces quality of life, and increases mortality. Hence, effective therapeutic intervention is imperative. The present study investigated the impact of minimal HE (MHE) on cognitive impairment in an aged rat population by analyzing hippocampal proteome dynamics. For this purpose, an old MHE rat model was induced via thioacetamide. The label-free LC‒MS/MS method was employed to explore hippocampal proteomic changes and associated dysregulated biological pathways. A total of 1533 proteins were identified, and among these, 30 proteins were significantly differentially expressed (18 upregulated, and 12 downregulated). Three upregulated proteins, namely, fetuin-A, p23, and intersectin-1 were selected and validated for their increased expression via western blotting and immunofluorescence analysis, which confirmed the mass spectrometry results. These proteins have not been reported previously in MHE cases. We also identified the possible dysregulated biological pathways associated with the differentially expressed proteins via Metascape, a network analysis tool. We found that the differentially expressed proteins may be involved in the generation of precursor metabolites and energy, the neurotransmitter release cycle, positive regulation of dendritic spine development, chaperone-mediated protein folding and protein stabilization. This study highlights the potential mechanisms underlying neurological dysfunction in the aged population with MHE and identifies novel therapeutic targets for improved disease management.

The FunCoup Cytoscape App: multi-species network analysis and visualization.

Functional association networks, such as FunCoup, are crucial for analyzing complex gene interactions. To facilitate the analysis and visualization of such genome-wide networks, there is a need for seamless integration with powerful network analysis tools like Cytoscape. The FunCoup Cytoscape App integrates the FunCoup web service API with Cytoscape, allowing users to visualize and analyze gene interaction networks for 640 species. Users can input gene identifiers and customize search parameters, employing various network expansion algorithms like group or independent gene search, MaxLink, and TOPAS. The app maintains consistent visualizations with the FunCoup website, providing detailed node and link information, including tissue and pathway gene annotations. The integration with Cytoscape plugins, such as ClusterMaker2, enhances the analytical capabilities of FunCoup, as exemplified by the identification of the Myasthenia gravis disease module along with potential new therapeutic targets. The FunCoup Cytoscape App is developed using the Java OSGi framework, with UI components implemented in Java Swing and build support from Maven. The App is available as a JAR file at https://bitbucket.org/sonnhammergroup/funcoup_cytoscape/ repository, and can be downloaded from the Cytoscape App store https://apps.cytoscape.org/apps/funcoup. Supplementary data are available at Bioinformatics online.

Statistical Network Analysis of Macroeconomic Variables in Ghana

In the rapidly evolving economic landscape of Ghana, understanding the intricate interdependencies between macroeconomic variables is pivotal for informed policymaking and strategic economic planning. The study employed network analysis to enhance our comprehension of Ghana&amp;apos;s macroeconomic dynamics. Data was sourced from the world development indicators. Initially, a statistical network was constructed to represent the interconnections between Ghana&amp;apos;s principal macroeconomic variables using partial correlation matrix, offering a visual and analytical perspective of their relationships. Subsequently, centrality measures and other network analysis tools were utilized to identify and quantify the influence of key economic indicators within this network. Results showed that Exports, Inflation, Exchange rate, Gross Domestic Saving, Manufacturing and Gross National Expenditure played a significant role in the network. However, Agriculture and Imports were identified as most influential variables with high centrality scores across all centrality measures. Finally, Exponential Random Graph Model was employed to provide a comparative baseline, shedding light on the uniqueness or randomness of the observed interrelationships. The significant parameters in the model include the presence of edges between nodes and the presence of generalized geodesic triads (gwesp), which capture the tendency for nodes to form connections based on common neighbors. The findings also revealed that there is a probability of 16.19% for a relationship to exist between two macroeconomic variables if they are both connected to the same third variable.

SNoMaN: a visual analytic tool for spatial social network mapping and analysis

ABSTRACT Spatial social networks (SSNs) are node-link structures that evidence interpersonal or inter-organizational relationships, where nodes and edges have a defined geographic location. To model SSNs, users need both geographic and social network metrics. However, there are few GUI-based analytic tools that enable simultaneous spatial and social network exploration. In this paper, following the research framework of Exploratory Spatial Data Analysis (ESDA) and design principles of social network analysis tools, we derived three design goals of exploratory spatial social network analysis (SSNA). Guided by these design goals, we provide a visual analytic tool, SNoMaN, which links network and geographical layouts and helps users conduct SSNA by interactively computing and visualizing SSN metrics, describing spatial distributions, exploring associations, and detecting anomalies. We introduce new types of visual diagrams, including Cluster–Cluster Plots, Centralization Plots, on-the-fly mapping of geometrically bounded network modules, and Route Factor Diagrams. We illustrate these new approaches using use case studies of a 1960s network of Mafia members, a global flight network, and a food donation-sharing network in southwestern Virginia. We find that SNoMaN can be used to generate data insights that fuse a system’s spatial and social dimensions that are hard to obtain otherwise.

Network analysis: An overview for mental health research.

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time-varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross-sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution

Food safety remains a crucial concern in both public health and societal stability. In the age of information technology, social media has emerged as a pivotal channel for shaping public opinion and disseminating information, exerting a substantial influence on how the public perceives incidents related to food safety. This study specifically focuses on the “Rat-Headed Duck Neck” incident as a case study, conducting a comprehensive analysis of extensive social media data to investigate how online public discourse molds perceptions of such events. To accomplish this research, data were initially gathered using a custom web crawler technology. These data encompassed various aspects, including user interactions, emotional expressions, and the evolution of topics. Subsequently, the study employed an innovative approach by combining BERT-TextCNN and BERTopic models for a thorough analysis of sentiment and thematic aspects of the textual data. This analysis provided insights into the intricate emotions and primary concerns of the public regarding incidents related to food safety. Furthermore, the research harnessed Gephi, a network analysis tool, to scrutinize the dissemination of information within the network and to monitor dynamic shifts in public opinion. The findings from this study not only shed light on the role of online public sentiment in the propagation of food safety events but also provide fresh perspectives for policymakers and business leaders when responding to similar crises, taking into account the subtleties of online public sentiment. These innovative methodologies and findings significantly enhance our comprehension of public responses to food safety incidents within the realm of social media.

Анализ транспортной доступности населенных пунктов Республики Мордовия с использованием геоинформационных технологий

The study is devoted to the analysis of transport accessibility of the settlements in the Republic of Mordovia, which is proposed by the authors as one of the indicators for assessing the level of the transport system development. Methods of spatial analysis and graph theory are used to create a transport network model. The selection of graph elements includes settlements in Mordovia with a population of 2 000 people or more, as well as all sections of highways that provide the shortest paths between them. The measure of distance is used as impedance. The article discusses the usability of geographic and potential accessibility, as well as the detour index to detect problematic elements of the transport network, and compares the indicators using the ranking and classification methods. Using web mapping services and network analysis tools in GIS, the selected indicators were calculated and a series of maps were created to display them, using natural breaks classification method and graduated symbols as a method of cartographic representation. During the study, it was found that some of the shortest paths between the considered settlements of the Republic of Mordovia pass through the Nizhny Novgorod Oblast, which indicates that the regional transport network goes beyond the borders of the federal subject. The problem of artificial barriers within one country at the level of municipal districts and federal subjects has been identified. The possibility of creating a project to improve transport accessibility of the region is being considered, and arguments in favor of the construction of missing direct connections between settlements of the Republic of Mordovia are given. The target indicators and limiting factors for improving the transport accessibility of settlements in the region are identified. The possibilities of further research using the obtained results for a comprehensive analysis of the regional transport system are considered.

HORNET: Tools to find genes with causal evidence and their regulatory networks using eQTLs.

Nearly two decades of genome-wide association studies (GWAS) have identify thousands of disease-associated genetic variants, but very few genes with evidence of causality. Recent methodological advances demonstrate that Mendelian Randomization (MR) using expression quantitative loci (eQTLs) as instrumental variables can detect potential causal genes. However, existing MR approaches are not well suited to handle the complexity of eQTL GWAS data structure and so they are subject to bias, inflation, and incorrect inference. We present a whole-genome regulatory network analysis tool (HORNET), which is a comprehensive set of statistical and computational tools to perform genome-wide searches for causal genes using summary level GWAS data that is robust to biases from multiple sources. Applying HORNET to schizophrenia, we identified differential magnitudes of gene expression causality. Applying HORNET to schizophrenia, we identified differential magnitudes of gene expression causality across different brain tissues. Freely available at https://github.com/noahlorinczcomi/HORNETor Mac, Windows, and Linux users. njl96@case.edu .

Resilience patterns in the analysis of social-ecological networks of small-scale fishing in a subtropical estuarine system

Network analysis concepts and tools are being increasingly applied to Social-ecological systems (SES) in human-environment and sustainability sciences. In this study, we used the Gao metric (βeff) to measure the resilience of networks in small-scale fisheries (SSF) in the Paranaguá Estuarine Complex (PEC). The purpose was to evaluate the variations and trends in network resilience across eight hydrological sectors of the PEC during different seasons. Our analysis revealed that the βeff metric varied significantly across the eight SES sectors, suggesting that resilience is not uniformly distributed. Notably, the sectors located internally the estuary exhibited lower resilience values than the others. The average trophic level of the fishery catches was the higher hierarchical position variable that most affected the network topological metrics, followed by variables magnitude of conflicts, water and sediment quality, and rainfall. Our hydrodynamic sectorization, reflecting ecological and social interactions, enabled us to identify distinct interaction networks within the PEC. This shows the complexity of managing these SES to ensure the sustainability and resilience of the system. While other variables and spatial and temporal scales could lead to different estimates and relationships, our findings shed light on the importance of resilience quantification and the interaction networks within SES to manage them effectively.

Weak dangling block reordering and multi-step block compression for efficiently computing and updating PageRank solutions

The PageRank model is a powerful tool for network analysis, utilized across various disciplines such as web information retrieval, bioinformatics, community detection, and graph neural network. Computing this model requires solving a large-dimensional linear system or eigenvector problem due to the ever-increasing scale of networks. Conventional preconditioners and iterative methods for general linear systems or eigenvector problems often exhibit unsatisfactory performance for such problems, particularly as the damping factor parameter approaches 1, necessitating the development of specialized methods that exploit the specific properties of the PageRank coefficient matrix. Additionally, in practical applications, the optimal settings of the hyperparameters are generally unknown in advance, and networks often evolve over time. Consequently, recomputation of the problem is necessary following minor modifications. In this scenario, highly efficient preconditioners that significantly accelerate the iterative solution at a low memory cost are desirable. In this paper, we present two techniques that leverage the sparsity structures and numerical properties of the PageRank system, as well as a preconditioner based on the computed matrix structure. Experiments demonstrate the positive performance of the proposed methods on realistic PageRank computations.

Mapping epistemic pluralism: A network analysis of discursive practices in communities promoting refused knowledge about healthcare and wellbeing

This article presents an analysis of discourses performed in communities that share and disseminate knowledge refused by institutional science. The study focuses on an online community concerned with alkaline water, food, and lifestyle, aiming at understanding how promoters of refused knowledge in this community enrol other forms of knowledge, including science. Theoretically, this work is framed in Science & Technology Studies, and in the tradition of actor-network theory, situating itself in a recent turn that takes epistemic instability and pluralism into due consideration, thus overcoming opaque views of the opposition between science and non-science. Empirically, this fine-grained analytic purpose is addressed by a mixed-method strategy in which discursive practices are observed through a web-ethnography conducted between January 2020 and December 2021 on the relevant online spaces and then analysed qualitatively and quantitively by means of formal techniques. Relying on the tools of social network analysis, the discursive space of the community under study is formalised as a two-mode network of knowledge claims and heterogeneous actors enrolled in discourse to sustain those claims. Then, community detection is performed to map the different assemblages of claims and actors and the relevant repertoires characterising those assemblages. Finally, multiple correspondence analysis applied to two-mode networks is used to highlight the dimensions of concern and meaning expressed in the knowledge organisation of this community.

POI VizNet: New QGIS Tool to construct Visibility Networks in Cities

Abstract. Network science and graph theory offer attractive models to describe urban phenomena and examine cases of urban planning and design. However, none of the existing network analysis tools consider simultaneously human visual perception and locations of urban activities. In this paper we introduce POI VizNet, a new QGIS plug-in that constructs various 2-dimentianal undirected graphs of unobstructed lines of sight. The plug-in integrates an increasing amount of available GIS-based data of Point Of Interest (POI) and visibility into one readily accessible analytical framework. Graphs are created between two types of decision points during urban travel – street intersections and POIs, origins, and destinations of travel, by connecting these potential observer’s decision locations in an open space between buildings. In addition, POI VizNet provides advanced options to build graphs using a predefined viewing distance and perceptual perspective. Visibility graphs are constructed and visualised as new layers in QGIS and delivered as network files suitable for further exploration, analysis, and visualisation in various network software packages.

Discursive Practices during an Electoral Cycle: Public Opinion and Political Disillusionment on Twitter

Electoral cycles are highly charged, politically intense moments that influence public discourse. Political elites, citizens, and the traditional news media seek to generate opinions and interactions on social networks. This research is motivated by the following questions: What are the communicative and deliberative practices used in these spaces? Is it possible to identify the characteristics that –through deliberative conversations– potentially foster or undermine democratic debate, particularly when using populist and polarizing discourse? Using a mixed methods approach, we apply a social network analysis tool to track conversations and identify the volume of political discourse (N=346,000). Using selective and staged filtering, we identified posts from nine Peruvian Twitter accounts during the September 2022 electoral campaign in Lima: three candidates, three media outlets, and three accounts with high levels of engagement. Our data comprised the comments from these nine accounts and was extracted using an Application Programming Interface (API). Subsequently, we carried out ethnographic content analysis on publications with more than 30 comments. In this phase, we analyzed Twitter comments using a codebook to identify deliberative practices and user responses. Our findings underscore the significant role of principal media outlets in shaping political conversation on Twitter. We also discovered that attempts to interact and deliberate were often overshadowed by heated comments attempting to impose opinions on others. Most importantly, our research reveals a widespread disillusionment with politics, political institutions, and even the political preferences of fellow citizens in Peru, highlighting a key challenge for democratic discourse on social media.

Generative AI-based knowledge graphs for the illustration and development of mHealth self-management content.

Digital therapeutics (DTx) in the form of mobile health (mHealth) self-management programs have demonstrated effectiveness in reducing disease activity across various diseases, including fibromyalgia and arthritis. However, the content of online self-management programs varies widely, making them difficult to compare. This study aims to employ generative artificial intelligence (AI)-based knowledge graphs and network analysis to categorize and structure mHealth content at the example of a fibromyalgia self-management program. A multimodal mHealth online self-management program targeting fibromyalgia and post-viral fibromyalgia-like syndromes was developed. In addition to general content, the program was customized to address specific features and digital personas identified through hierarchical agglomerative clustering applied to a cohort of 202 patients with chronic musculoskeletal pain syndromes undergoing multimodal assessment. Text files consisting of 22,150 words divided into 24 modules were used as the input data. Two generative AI web applications, ChatGPT-4 (OpenAI) and Infranodus (Nodus Labs), were used to create knowledge graphs and perform text network analysis, including 3D visualization. A sentiment analysis of 129 patient feedback entries was performed. The ChatGPT-generated knowledge graph model provided a simple visual overview with five primary edges: "Mental health challenges", "Stress and its impact", "Immune system function", "Long COVID and fibromyalgia" and "Pain management and therapeutic approaches". The 3D visualization provided a more complex knowledge graph, with the term "pain" appearing as the central edge, closely connecting with "sleep", "body", and "stress". Topical cluster analysis identified categories such as "chronic pain management", "sleep hygiene", "immune system function", "cognitive therapy", "healthy eating", "emotional development", "fibromyalgia causes", and "deep relaxation". Gap analysis highlighted missing links, such as between "negative behavior" and "systemic inflammation". Retro-engineering of the self-management program showed significant conceptual similarities between the knowledge graph and the original text analysis. Sentiment analysis of free text patient comments revealed that most relevant topics were addressed by the online program, with the exception of social contacts. Generative AI tools for text network analysis can effectively structure and illustrate DTx content. Knowledge graphs are valuable for increasing the transparency of self-management programs, developing new conceptual frameworks, and incorporating feedback loops.

A complex network analysis of groundwater wells in and around the Doñana Natural Space, Spain

This paper proposes the use of Complex Network Analysis tools to study the spatial distribution and temporal evolution of groundwater abstraction wells within a region of interest. To that end a weighted undirected network connecting wells within a certain distance was built. The edge weights take into account the distance between the wells as well as their corresponding abstraction volumes. In addition to the conventional complex network metrics, two specific abstraction-based centrality indexes are proposed, based on the total impact of a groundwater abstraction well on its surrounding wells and on the total impact of surrounding wells on a specific location. The proposed approach has been applied to the area of the Doñana Natural Space, one of the most important wetlands in Europe. The topology of the network reveals a large number of small, disconnected components and a few large components. This is reflected also in the degree distribution, which follows a Power Law distribution. The network has a large average clustering coefficient, a small average path length and a low level of centralisation. Global and average local network efficiency are also high. The network is assortative in terms of degree-degree and strength-strength correlations. Eigenvector centrality of the nodes reveals the location of influential wells.

Systems Modeling of the Water-Energy-Food-Ecosystems Nexus: Insights from a Region Facing Structural Water Scarcity in Southern Spain

The complex relationship between water, energy, food, and ecological systems, known as the WEFE nexus, has emerged as a major topic in the debate about sustainable economic development and resource management. This subject is of special interest in Mediterranean coastal areas as rapid economic expansion driven by population growth, higher influx of tourists, and intensification of agriculture is leading to structural water scarcity conditions. However, addressing the diverse range of issues associated with the nexus is a difficult task due to the existence of intricate interconnections, interdependencies, and nonlinearities within and across its various components. Accordingly, this case study applies a combination of participatory systems modeling and network analysis tools to yield insights into the complexity of this nexus in Axarquia, a region with features that make it an example of water-stressed jurisdictions in the Mediterranean. Overall, our results provide a strong foundation to understand the dynamics that govern this nexus in regions where the availability of freshwater resources is a significant concern. Furthermore, they lay the groundwork for the development of models and scenarios to simulate the impact of various policies and interventions on the overall system.

From CFOs to crypto: exploratory study unraveling factors in corporate adoption

Cryptocurrency adoption has gained significant attention across various fields owing to its disruptive potential and associated challenges. However, companies' adoption of cryptocurrencies remains relatively low. This study aims to comprehensively examine the factors influencing cryptocurrency adoption, their interrelationships, and their relative importance. To achieve this objective, we employ a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach coupled with network analysis tools. By adopting a practical approach rather than a purely theoretical one, our unique contribution lies in the valuable insights derived from experienced Chief Financial Officers (CFOs) of various companies with experience in both traditional finance and cryptocurrencies. Furthermore, the unique blend of analytical rigor and industry expertise supports the study's relevance, offering nuanced insights that are not only academically robust but also immediately applicable in the corporate landscape. Our findings highlight the paramount importance of safety in transactions and trust in the chosen platform for companies considering cryptocurrency adoption. Additionally, criteria such as faster transactions without geographical limitations, lower transaction fees, seamless integration with existing systems, and potential cost savings are identified as crucial drivers. Both the DEMATEL approach and network analysis reveal strong interconnections among the criteria, emphasizing their interdependence and, notably, their reliance on transactional safety. Furthermore, our causes and effects analysis indicates that CFOs perceive company-led cryptocurrency adoption to positively impact the broader cryptocurrency market.