Forthcoming and Online First Articles

International Journal of Networking and Virtual Organisations

International Journal of Networking and Virtual Organisations (IJNVO)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Networking and Virtual Organisations (6 papers in press)

Regular Issues

  • Leveraging linguistic signaling to prompt feedback in open innovation communities   Order a copy of this article
    by Suya Hu, Di Xu, Yan Li 
    Abstract: The rise of open innovation communities (OICs) has enabled organizations to gain ideas from the outside. Although current studies mainly focus on ideas generation behaviors among participants, little attention has been paid to the subsequent interactive feedback, which is equally important for the success of running OICs. Drawing on signaling theory, we empirically examine how to leverage signals expressed in idea descriptions to influence feedback from two key parties: the moderator and peers. Two linguistic features, i.e., affective signaling (linguistic style matching, negative emotion, and impoliteness) and informative signaling (post length and quality) are proposed. Analyzing data collected from the Huawei community, we find that feedback from the moderator is indeed influenced by both affective and informative signaling. Furthermore, only negative emotion is positively associated with feedback from peers, while the effects of other signals show different trends. This study offers practical insights into how to maintain the viability of OICs.
    Keywords: feedback; signaling theory; ideas; open innovation communities.

  • Understanding residents continued usage intention of community sharing platforms in smart communities in a post-COVID era: Evidence from China   Order a copy of this article
    by Minghua He 
    Abstract: A community sharing platform has demonstrated its significant role in prevention and control of COVID-19 pandemic in communities throughout China. Despite its increasing popularity, the widespread adoption and continued usage intention of smart community sharing platforms by residents is still far underexplored. Thus, this study applied the Technology Acceptance Model (TAM) as the research framework to empirically investigate the factors influencing residents continued intention to use community sharing platforms in the post-COVID era. Empirical results show that perceived ease of use, perceived usefulness and sharing attitude significantly impact residents continued intention to use smart community sharing platforms and that residents education level significantly moderates the relationships between the three kinds of antecedents and continued usage intention. This study contributes to the literature in the field of smart communities and provides practical implications for governments and platform operators to achieve sustainable development of community sharing platforms.
    Keywords: community sharing platform; smart communities; continued usage intention; Technology Acceptance Model.

  • Optimization Assisted CNN Framework for Bearing Fault Diagnosis   Order a copy of this article
    by Azim Naz M, Sarath R 
    Abstract: Nowadays, intellectual fault diagnosis mechanism with DL schemes was extensively deployed in production firms to develop the effectiveness of fault diagnosis. The rolling bearings connect the support and rotor and are considered as a critical element in rotating equipments. Nevertheless, the working state of bearing varies based on composite operation demand that may drastically corrupt the performances of the intellectual fault diagnosis technique. Thereby, this scheme develops novel fault diagnosis schemes that included 2 most important stages like Feature extraction and Classification. Initially, the features namely, Empirical Wavelet Transform, Empirical Mode Decomposition and Wavelet Transform are extracted. Subsequent to this, the derived features are classified via Optimized Convolutional Neural Network is employed. Further, to get better accuracy using adopted model, the weights of CNN is tuned via Self Adaptive Moth-flame optimization. Eventually, the primacy the offered scheme is proven regarding varied measures. Eventually, the proposed technique has obtained a superior value of 0.922, and it is 1.62%, 1.92%, 50.76%, and 57.34%, superior to existing MFO, FF, SVM and RF models for dataset 1.
    Keywords: Fault diagnosis; EWT features; EMD features; CNN; SA-MFA algorithm.

  • Research on Community-based Group Communication Behaviors in Convergence Media   Order a copy of this article
    by Haixiang He 
    Abstract: The emergence of the Internet has led to the convergence of traditional media, greatly improving the speed of information dissemination but also enabling the rapid spread of undesirable information. To avoid the rapid spread of undesirable information that affects social stability, it is necessary to analyze the community-based group communication behavior in convergence media. This paper briefly introduces convergence media and the community-based group communication behavior in convergence media and uses structural equation modeling to analyze the group communication behavior. The results showed that the stimulation degree of stimulating events, individual characteristics, and group structure was positively correlated with the implementation intention; contextual factors were positively correlated with stimulating events, individual characteristics and group structure.
    Keywords: community-based; convergence media; group communication behavior; structural equation model.

  • The Impact of Big Data Capability: a Perspective of Supply Chain and Innovation Capabilities   Order a copy of this article
    by Guangqian Peng, Yan Zhang, Ping Gao, Yiming Yuan, Junfeng Yin 
    Abstract: The study on big data capability was initiated just recently. The impact of big data capability on companies, such as on companies other capabilities, is still unclear. Grounded on dynamic capabilities theory, this study proposed a model and tested the impact of big data capability on supply chain dynamic capability and dynamic innovation capability. The research objects are high-tech listed companies in China. By PLS path modelling, the major findings are: (1) big data capability showed positive impact on supply chain dynamic capability; (2) However, unexpectedly, big data capability was found showing negative impact on dynamic innovation capability. In our opinion, the potential reason is that big data application is just initiated, so may lead to certain disorder in the companies. Based on this finding, we call for attention to big data paradox. (3) Further, supply chain dynamic capability showed positive impact on dynamic innovation capability. (4) Possibly owing to the mediating and moderating effects of supply chain dynamic capability, the total effect of big data capability on dynamic innovation capability is positive and significant. Managerial applications and limitations are generalized at the end.
    Keywords: big data capability; supply chain dynamic capability; dynamic innovation capability; big data paradox.

Special Issue on: Deep Neural Networks and Evolutionary Computation for Biomedical Applications

  • A Deep Learning Model Framework for Diabetic Retinopathy Detection   Order a copy of this article
    by Padmapriya M, Pasupathy S, Sumathi R, Punitha V 
    Abstract: Diabetic retinopathy (DR) is the typical diabetic eye issue and a main reason of blindness around the world. As per the International Diabetes Federation (IDF), the rates of diabetes would rise to 552 million by 2034. Breakthroughs in computer science techniques inclusive of artificial intelligence (AI) and deep studying (DL) have multiplied opportunities for early detection of DR. This indicates that the risk of eyesight loss could be minimized in due course. A deep learning model (ResNet) for medical DR detection was examined in this article. The data set of Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 was used to train and test the DL model. To demonstrate the vitality of the chosen ResNet model, performance measures and testing accuracy like recall, precision, and F1 score were determined. The modified ResNet model reduced the training time and computational complexity and attained a testing accuracy of around 84%.
    Keywords: Diabetic Retinopathy; Convolutional Neural Network; Machine Learning; Deep Learning; ResNet model.