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 Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Networking and Virtual Organisations (11 papers in press)

Regular Issues

  • Analysing the challenges in stakeholder relationship management in the healthcare process: A social network perspective   Order a copy of this article
    by Farooq Ali, Harri Haapasalo, Kari-Pekka Tampio, Henriikka Haapasalo 
    Abstract: We investigate stakeholder relationship management and identify challenges that impact relationships at the healthcare process level using the inductive research approach. We adopt an inductive approach and grounded theory method since there is a need for detailed descriptions on stakeholder network, especially on relationship management. The emergent grounded theoretical model explains the challenges that impact stakeholder relationship management, i.e. gaps in the healthcare network, challenges in articulating a healthcare vision, triggers of challenges, contextual challenges, healthcare landscape, challenges in trust-building, and collaboration. Additionally, our findings show how the network structure and stakeholders position in the network, based on their interactions patterns, influence stakeholder relationship management. The grounded theory that emerged from our study confirms several themes and their interrelationships, which constitute our main contribution.
    Keywords: Stakeholder relationship management; Healthcare process; Social network; Stakeholder network; Network structure; Healthcare network; Healthcare management; Grounded theory; Stakeholder identification and mapping.

  • Should I Accommodate You? Cross-Cultural Code-Switching Behaviours of Global Virtual Team Members during Swift Trust Formation   Order a copy of this article
    Abstract: This conceptual paper explores the process of cross-cultural code-switching (C3S) between high-context (HC) and low context (LC) global virtual team members during the knowledge-sharing and social network exchanges. We will introduce a cross-cultural code-switching framework in a virtual setting and develop propositions to explain how GVT members attempt to switch their communicative behaviour based on two theoretical lenses: Giles Communication Accommodation Theory (1973) and Hall (1976) high context and low context theory. This paper offers several propositions to illuminate the process of code-switching behaviours among GVT members during the socialisation process and explores how these behaviours help develop swift trust. It considers whether developing swift trust is possible and, if so, how? We will provide future research directions in our concluding remarks.
    Keywords: Cross-cultural code-switching behaviour; knowledge sharing; high-context and low-context cultures; communication accommodation theory; global virtual teams; communication styles; communicative behaviour; Malaysia.

  • A Usability Evaluation of the Google Home with Non-Native English Speakers Using the System Usability Scale   Order a copy of this article
    by Aung Pyae 
    Abstract: Advancements in multidisciplinary research have made Voice User Interfaces (VUIs) become a reality in both research and commercialization. Although the existing research has indicated that VUIs are useful in peoples daily lives (e.g., personal assistant), there is limited study on non-native English-speaking users experiences in using commercial VUIs. Hence, in this study, the Google Home Smart Speaker was evaluated with 34 undergraduate students, who use English as a second language, to investigate its usability and user experiences. The SUS, PSSUQ, Heuristics questionnaires, and interview questions were used to evaluate the Google Homes usability. The findings report that the Google Home is user-friendly, usable, and useful for non-native English-speaking users. Considering unsolved usability issues found in this study, some usability guidelines are recommended for the design and implementation of future VUIs. The findings also suggest that the SUS is suitable for the assessment of non-native English speakers use of VUIs.
    Keywords: Smart Speaker; Usability; Human-Computer Interaction; Voice User Interface; System Usability Scale; PSSUQ; Heuristics.

  • Trade-off Between Two Advertising Strategies: Coverage or Penetration   Order a copy of this article
    by Wang Tian, Zhang Ding 
    Abstract: Advertising has always been an important way for firms to carry out product publicity. With the advent of the information age and the convenience of the using of Internet, the dissemination of advertisements is becoming extensive. There are two different basic advertising strategies, namely, enlarging the market coverage and improving the market penetration. Enlarging the market coverage is a commonly used advertising strategy for firm managers. With this strategy, they focus on the market quantity size. Improving the market penetration is another way to increase demand. Firm managers focus on the current market but improve product or service quality to acquire and maintain a larger penetration level. Efforts in the first (coverage) strategy can be seen as handing out leaflets, advertising board and huge-crowd acquisition. Efforts in the second (penetration) strategy can be seen as improving product quality, service environment and positive word-of-mouth. Which one is more efficient, coverage or penetration? How do firm trade-off between the two advertising strategies? Most of the existing literature only focus on one single advertising strategy, which cannot solve the above problems well. By establishing two-stage models, this paper explores the optimal advertising levels for the two strategies, respectively. After that, this paper compares the optimal profits under the two strategies in various market settings and finds the more efficient advertising strategy. Management insights are generated for decision-making of firm managers.
    Keywords: advertising; market coverage; market penetration.

  • Idea response and adoption in open innovation communities: the signaling role of linguistic style   Order a copy of this article
    by Suya Hu, Di Xu, Alan Wang 
    Abstract: Open innovation communities have become a new trend for organizations to gain external ideas and foster user innovation. However, mass user generated content is making idea selection a tricky and time-consuming work. From the perspective of linguistic styles, this article explores the effects of writing style cues in the content of ideas on idea response and adoption. Our research model is validated through logistic regression on a secondary dataset of 1,579 ideas collected from the Fantasy Westward Journey Online ? forum. The results demonstrate that a members use of self-interest oriented, cognitive oriented and future oriented writing styles has a positive effect on idea response; negative emotionality and cognitive oriented writing styles signal more possibility of idea adoption. We highlight both theoretical implications and managerial applications in innovation management domains.
    Keywords: open innovation community; user innovation; linguistic style; idea response; idea adoption.

  • Imagining Benefits and Challenges for Future Hybrid Workplace to Enable Reentry for Women on Career Break   Order a copy of this article
    by Sunaina Arora, Neeraj Kumari 
    Abstract: Covid-19 has shifted everyone to remote work. The paper reviews literature available for various challenges and benefits of remote work, requisites for a successful hybrid workplace, role of technology, access to global talent pool and impact of remote work on diversity hiring. Women on career break are females who halt their careers to bring up children with an intention to return back to work in future. Women on career break can benefit from the future model of hybrid work as, they can perform care taking duties and work simultaneously. Exploratory research with sample size 92 comprising of people working remotely as sampling unit. Study verifies that hybrid workplace would enable better talent management because it provides flexibility, ease, increases productivity and enhances communication, gives access to global talent pool with access to technology. Data verifies that hybrid workplace would enable to open doors for women on career break to reenter workforce.
    Keywords: Remote work; Hybrid Workplace; Women on career break; Work from Home.

Special Issue on: ISCV 2020 Methods and Applications of Computer Science and Information Technology

  • A Word Alignment Study to Improve the Reliability of the Statistical and Neural Translation System   Order a copy of this article
    by Safae Berrichi, Azzeddine Mazroui 
    Abstract: Word alignment is an essential task for numerous natural language processing applications, including machine translation. The performance of the statistical machine translation systems is directly impacted by the performance of their alignment modules. However, such alignment models perform worse and induce low machine translation performance when translating morphological rich or low resource languages, such as Arabic. The first objective of this paper is to examine the impact of incorporating some morphosyntactic features, like stem, lemma, root, and part of speech tag, on the statistical alignment models and on the associated translation systems for the (Arabic, English) language pair, and to identify which of these features is most suitable. We also evaluate, for each morphological representation, the impact of the training corpus enrichment on the alignment and the translation qualities. Although the standard neural machine translation system does not directly include a concept of word alignment, the attention mechanism plays an implicit alignment role in these systems. In the second part of this work, we propose a method of adjusting the attention mechanism by the statistical alignments, and we analyze the effect of this adjustment on neural machine translation systems. We also study the impact of different morphological representations on the performance of these supervised systems. The various performed tests show a substantial improvement in the alignment and the translation performances of the proposed approaches.
    Keywords: Morphosyntactic Representation; Statistical Word Alignment; Attention Mechanism; Statistical Translation; Neural Translation; Arabic language.

  • Deep learning based distributed denial-of-service detection   Order a copy of this article
    by Hanene Mennour, Sihem Mostefai 
    Abstract: The nuisance of distributed denial-of-service (DDoS) attacks has extended unremittingly nowadays. Thus, guaranteeing system availability in this open-ended pandemic is a crucial task. In this work, we propose three different deep learning strategies as network anomaly-based intrusion detection system (N-IDS) for a DDoS multiclassification task. We built a deep Convolutional Neural Network (CNN), a Stacked Long short-term memory (S-LSTM) neural network which is a distinct artificial Recurrent Neural Network (RNN), the third model is a hybridization between CNN and LSTM. Then, we evaluated them on three up to date flow-based datasets: CICIDS2017, CICDDoS2019 and BoT-IoT benchmarks. The outcomes demonstrate that hybrid CNN-LSTM outperforms the existing state-of-the-art schemes in almost all the validation metrics.
    Keywords: Deep Learning; Network Intrusion Detection System; Anomaly- Based; Distributed Denial-Of-Service; Multiclassification; Flow-Based; CNN; LSTM.

  • A Cluster Workload Forecasting Strategy Using A Higher Order Statistics Based ARMA Model For IaaS Cloud Services.   Order a copy of this article
    by Zohra AMEKRAZ, Moulay Youssef Hadi 
    Abstract: With the cloud computing services becoming more popular among Internet users, cloud providers are facing a challenge in allocating resources to users according to demand instantly. The delay caused by the Virtual Machines (VMs) start up time makes the reactive techniques, which allocate new resources only when a given load threshold is attained, not effective for the allocation process. An interesting alternative to the reactive technique is the proactive technique. This latter consists of predicting the future demand known as workload and allocating or releasing resources in advance to prevent any overload to occur and also to reduce any related costs. In this paper, we introduce an adaptive workload prediction method based on the use of Higher Order Statistics (HOS) and Autoregressive Moving Average (ARMA) model. The proposed method uses the HOS to make a Gaussianity checking test of the cloud workload and then decides the suitable identification method of the ARMA model to be used to forecast the workload. Furthermore, the proposed method updates the parameters of the ARMA model constantly whenever new workload data are available. We evaluate our proposal with two real workload traces extracted from cluster workloads. The results show that the proposed method has an average of 34% higher accuracy than the baseline ARMA model and presents a low overhead for forecasting incoming workload (<2 s).
    Keywords: IaaS Cloud Services; Workload Prediction; Cluster Workload; Autoregressive Moving Average; Higher Order Statistics.

  • Cloud Spot Price Prediction Approach Using Adaptive Neural Fuzzy Inference System With Chaos Theory   Order a copy of this article
    by Zohra AMEKRAZ, Moulay Youssef Hadi 
    Abstract: The dynamic pricing of cloud computing is a major challenge for cloud users all over the world. This challenge was first addressed by Amazon under the name of Amazon Spot Instance Market. Cloud users can bid for a spot instance using this market and obtain the requested spot if their bids exceed a dynamically changing spot price. Amazon publicizes the spot price but does not reveal how it is determined. In this paper, we perform chaotic time series analysis over the spot price trace.We also develop a chaos based Adaptive Neural Fuzzy Inference System (ANFIS) model based on phase-space vectors obtained during the phase of chaotic analysis. Next, we study the effect of chaos existence on the prediction accuracy of the spot price by comparing the proposed chaos-ANFIS model with the baseline ANFIS model (non-chaotic approach). Evaluation results show that the proposed chaos-ANFIS model yields better predictions of spot price compared to the baseline ANFIS model in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
    Keywords: dynamic pricing; cloud computing; spot instance; spot price; chaotic time series analysis; ANFIS.

  • Stock Market Manipulation Detection using Feature Modelling with Hybrid Recurrent Neural Networks   Order a copy of this article
    by Sashank Sridhar, Siddartha Mootha 
    Abstract: A stock market is a potent platform which handles a large of number of transactions within a second. Keeping track of every single transaction is a daunting task for regulatory bodies. The objective of a regulatory body is to ensure a fair trading environment and to verify that the price of a stock is not being manipulated. This paper proposes a hybrid stacked artificial neural network and recurrent neural network to model the static and dynamic features of stock data. Based on the manipulated stocks, affidavits provided by the Securities and Exchange Board of India (SEBI), a daily trading dataset is created by scraping the Bombay Stock Exchange (BSE) website. The system is capable of identifying three types of manipulation scenarios. The proposed hybrid system is compared to various supervised algorithms, and various ensemble models and the system outperforms all with an accuracy of 96.06%.
    Keywords: Manipulation Detection; Hybrid Neural Networks; Ensemble Learning; Recurrent Neural Networks; Fraud Detection; Long Short Term Memory; Bidirectional Long Short Term Memory; Stacked Generalization; Artificial Neural Networks; Feature Engineering;.