International Journal of Networking and Virtual Organisations (5 papers in press)

Regular Issues

• Topic text detection by clustering algorithm for social network media  
  by Sha Sha 
  Abstract: The advent of the Internet era has promoted the development of social network media, making the number of people active in these social network platforms greatly increase, and the resulting large amount of data and information makes the fast location retrieval of topics of interest a problem. This paper detected topic texts of social network media by the modified particle swarm optimisation-based K-means (MPSO-means) clustering algorithm to achieve topic text clustering effect and alleviate the problem of inconvenience caused by information overload. The results of the study showed that the clustering results of short texts showed a trend of outperforming long texts; and the MPSO-means algorithm was closer to 1 than the other two algorithms in the values of silhouette coefficient, clustering purity, and homogeneity, with better clustering effect, and also consumed the shortest time in detection, only 1,196 s.
  Keywords: text clustering; social network media; topic text; modified particle swarm optimisation-based K-means.
  DOI: 10.1504/IJNVO.2024.10060582
   
  
• Anomalous Data Detection in Cognitive IoT Sensor Network  
  by Vidyapati Jha, Priyanka Tripathi 
  Abstract: Recent research in the internet of things (IoT) focuses on the insertion of cognition into its system architecture and design, which introduces the new discipline known as cognitive IoT (CIoT). The cognitive internet of things sensor network defines a new paradigm for bridging the gap between the virtual and the real world. Sensors integrated into the CIoT network serve as the primary data collectors. These sensors are used in hazardous or unmanaged a situation, which makes sensor readings prone to errors and abnormalities. Since sensor data are essential to the system's operation, the quality of various data-centric CIoT services will ultimately depend on the accuracy of sensor readings. However, detecting anomalies in sensor data is a complex process because CIoT sensor networks are frequently resource-constrained devices with limited computation, networking, and storage power. To fulfil the objectives, an effective and affordable cognitively-inspired detecting method is required. Therefore, this research proposed a novel technique to identify the anomaly in sensor node data. The experimental evaluation is conducted on the environmental data of 21.25 years, and detection accuracy reveals the efficacy of the proposed method over competing approaches.
  Keywords: anomaly; probability; sensor network; cognitive IoT; CIoT.
  DOI: 10.1504/IJNVO.2024.10061110
   
  
• Hybridized Pre-trained deep network with Aspen Lupus Bidirectional long short term memory classifier for Image-based Event classification  
  by Shrikant P. Sanas, Tanuja Sarode 
  Abstract: The proposed Aspen-Lupus optimisation-based BiLSTM classifier (ALO opt BiLSTM) is employed in this research to develop an event classification model that accurately identifies the events. The pre-trained hybridised model, which is proposed for feature extraction, is developed via a conventional hybridisation of the VGG-16 and ResNet-101 models. The deep BiLSTM classifier gathers the collected features and utilises them to effectively increase prediction accuracy. The development of the proposed ALO algorithm resulted from the typical hybridisation of the Aspen and Lupus optimisation. Based on the achievements, at training percentage 90, the accuracy of 95.65%, sensitivity of 94.27%, specificity of 96.63% in database-1 respectively is attained and for database-2, achievements of 94.22% in accuracy, 92.86% insensitivity and 95.18% in specificity is acquired.
  Keywords: event classification model; Aspen-Lupus optimisation; BiLSTM classifier; border collie; grey wolf; hybrid pre-trained model.
  DOI: 10.1504/IJNVO.2024.10061488
   
  
• Influence of Sense of Virtual Brand Community on Value Co-Creation  
  by Bingzhou Li, Wei Yu 
  Abstract: The research objective is to formulate and verify a theoretical model about the influence of sense of virtual brand community on value co-creation of enterprises with self-efficacy and psychological contract as moderating variables. Four hypotheses are presented based on theoretical deduction. This article uses the empirical research design and survey methodology. The data analysis approaches include reliability test, validity analysis, descriptive statistics, variance analysis, correlation analysis, regression analysis, path analysis of structural equation model and hierarchical regression analysis. By collecting data with 275 valid questionnaires in many virtual brand communities, this research empirically confirms that sense of virtual brand community has a positive impact on value co-creation. Moreover, customer self-efficacy and psychological contract with an enterprise respectively positively moderate the relationship between sense of virtual brand community and value co-creation. However, the model does not give the specific mediating influence mechanism of sense of virtual brand community on value co-creation. An enterprise should enhance the cultivation of sense of virtual brand community and improve a customer's self-efficacy and psychological contract. Theoretically, this article enriches the human sense and communication analysis in the marketing context and explores new antecedents and moderating factors of customer value co-creation for the whole enterprise.
  Keywords: sense of virtual brand community; value co-creation; self-efficacy; psychological contract; communication platform.
  DOI: 10.1504/IJNVO.2024.10061492
   
  
• Retaining remote workers: Factors that affect virtual and hybrid workers' job retention  
  by Vasu Thirasak, Nopadol Rompho 
  Abstract: This study examines factors from Herzberg's motivation-hygiene theory, Deci's self-determination theory, and life-course fit theory to understand their effects on virtual and hybrid workers' job retention. Data were collected from 623 respondents in Thailand, and structural equation modelling and data analysis techniques were used to test the relationships between pay, promotion, supervision, fringe benefits, life-course fit, intrinsic motivation, and extrinsic motivation and job retention for virtual and hybrid workers, as well as the moderating effects of job level and virtual intensity. The results indicate that motivator-hygiene factors pay, promotion, supervision, and fringe benefits do not significantly contribute to the job retention of virtual and hybrid workers. However, the relationships between life-course fit, intrinsic motivation, and extrinsic motivation and job retention were significant. This is one of the very few studies that applies these theories in the context of virtual and hybrid work, which expands the theories boundaries of knowledge.
  Keywords: remote work; virtual work; hybrid work; job retention; motivators; hygiene factors; intrinsic motivation; extrinsic motivation.
  DOI: 10.1504/IJNVO.2024.10062140