International Journal of Networking and Virtual Organisations (4 papers in press)

Regular Issues

• Analysis of online public opinion for public crisis events: a case study of the COVID-19 pandemic in Shanghai  
  by Dandan Jin, Haixiang He 
  Abstract: This paper conducted a case study of online public opinion during the Shanghai Coronavirus Disease 2019 (COVID-19) pandemic. The latent Dirichlet allocation (LDA) model was used to identify the topics and their related terms. The sentiment dictionary was used to analyse the sentiment tendencies of public opinion and calculate the group polarisation values under different topics. The regression analysis method was used to test the influence of official intervention strategies on group polarisation values. The results showed that the topics of information released by the public were divided into lower management, pricing, living security, and epidemic management, and the topics of information released by the government were divided into public statements, action countermeasures, and accountability and rumour refutation. The overall degree of group polarisation showed a trend of rising first and then falling over time. Official intervention strategies were found to be effective in suppressing online public opinion polarisation.
  Keywords: online public opinion; public crisis; LDA model; official intervention; COVID-19 pandemic; group polarisation; regression analysis.
  DOI: 10.1504/IJNVO.2026.10078238
   
  
• Harnessing Artificial Intelligence for Organizational Performance: A Bibliometric and Morphology-Based Cluster Analysis Using VOS Viewer  
  by Suraiya Rajput, Baby Iffat 
  Abstract: AI boosts organisational performance (OP), which is an organisations ability to meet its strategic goals. OP includes operational efficiency, innovation, consumer satisfaction, market share, and profitability. This study uses a bibliometric method to examine scholarly studies current state and progress on artificial intelligence and organisational performance on 318 publications using VOSviewer. The studys primary goal is to look at the conceptual framework of research on artificial intelligence and organisational performance to identify authors, journals, existing and developing topics, and suggested areas for future research. This study helps to fill an important gap by applying morphology technique, which has not yet been used in artificial intelligence & organisational performance research to map its intellectual and thematic structure. Morphology analysis provides a deeper understanding of the topic and a detailed description of a subject by breaking it down into smaller components, also previous studies have been done with cluster analysis, but no study has shown the analysis with separate clusters.
  Keywords: Artificial Intelligence; Organizational Performance; Separate Clusters; Trending Themes; Bibliometric; Cluster and Morphology Analysis.
  DOI: 10.1504/IJNVO.2026.10079213
   
  
• Optimised Ensemble for Enabling IOT-Based Remote Health Monitoring System  
  by Dr. Divya Nath K, Lekshmi Babu, Prabhu Kettavarampalayam Ramanathan 
  Abstract: The incorporation of Internet of Things (IoT) technology with intelligent forecasting systems has grown faster due to the increasing demand for continuous healthcare monitoring (HM). A new Protozoa CatBoost Prediction Framework (PCPF) for analysing biological measures and classifying medical conditions is presented in this work. The process begins with IoT-based body sensors that collect physiological data. At the pre processing level, noisy and redundant features are removed, and the data is normalised to enhance reliability. Afterwards, feature analysis is performed using the Protozoa optimisation algorithm, which identifies the most important features and assigns optimal weights. The CatBoost forecasting framework is then provided with these features to estimate biological health scores. According to predetermined threshold values, health status is classified into three conditions: Good, Average, and Poor. According to experimental results, the PCPF model significantly improves classification efficiency and prediction accuracy, reaching 99.7%.
  Keywords: Health Monitoring System; Biological Parameters; Feature analysis; Health Condition; Prediction.
  DOI: 10.1504/IJNVO.2026.10079901
   
  
• Innovative Development Strategy of Enterprise Digital Marketing Based on Internet of Things under Sustainable Development  
  by Huaifang Chen, Lijun Liu 
  Abstract: The rapid development of the Internet of Things (IoT) and digital technology has created a new digital marketing (DM) model, significantly impacting the marketing field. DM is crucial for enhancing enterprise competitiveness and development. Traditional marketing strategies can no longer meet modern needs, making diversification essential. However, a lack of technological innovation and effective strategies hinders DM progress. This paper examines DM's characteristics, impacts, and challenges under IoT, emphasizing the need for innovation. DM innovation shows increasing membership and standardized fuzzy weights, with averages of 1.71 and 0.80, respectively. Membership weight increased by 1.20, and normalized fuzzy weight by 0.74 throughout the process. DM innovation under IoT delivers better marketing effects and economic benefits, improving marketing effectiveness by 11.33% and economic benefits by 8.36% compared to traditional models. Thus, IoT and sustainable development drive DM advancement.
  Keywords: Enterprise Mathematical Marketing; Sustainable Development; Internet of Things; Innovative Development Strategy.
  DOI: 10.1504/IJNVO.2026.10079902