Emerging Trends and Impacts of the rise of AI, Data Analytics and Blockchain

Special issue call for papers from Journal of Enterprise Information Management (Q1)


BACKGROUND

There are emerging trends and impacts due to the rise of AI, Data Analytics and Blockchain as follows. Data Analytics can present scientific and business outputs in the form of graphs and visualisation based on the extraction and analysis of complex datasets (Keim et al., 2008; Chang, 2018 a). Behind the development of Data analytics, smart algorithms can process large data quickly and efficiently, and can extract the most meaningful data to report to the decision-makers (Chang et al., 2019). The use of Blockchain can enhance security, privacy and optimisation of the entire services since Blockchain offers secure application development with smart contract and its distributed Fintech technology (Alketbi et al., 2018; Kaur et al., 2018; Zeng, 2018). Integration of AI, Data Analytics and Blockchain can be essential for new development of pioneering research (Mamoshina et al., 2018; Zeng, 2018). For example, in driverless cars, their integration makes impacts so that we can predict drivers’ intents (Birek et al., 2018). In another example, the integration can enable multi-disciplinary research in healthcare, finance, weather studies and natural disasters (Chang, 2018 b).

In other words, the integration of AI, Data Analytics and Blockchain can produce greater impacts and availability of different services, such as 5G, IoT, Cloud, Fintech, Cybersecurity and Software Engineering (Skilton and Hovsepian, 2017; Gai et al., 2018; Mohamed and Ali, 2018; Chang et al., 2019 b; Saha, 2019). The integrated services have emerged to tackle large-scale standalone IT facilities for improving computational efficiency and reducing cost. This also changes the way Software Engineering has been developed, since service-oriented architecture can offer a framework for service components which is a natural attribute of distributed ledger services to make more impacts offered by AI, Data Analytics, Blockchain and its integrated uses. The objectives of this SI include:

• Bring together researchers and research practitioners in emerging trends and impacts of AI, Data Analytics and Blockchain.
• Develop pioneering methods, techniques, theories and services for AI, Data Analytics and Blockchain and its integration.
• Demonstrate the effectiveness of the adoption cases, recommendation and real-world solutions for AI, Data Analytics and Blockchain and its integration.
• Strengthen the innovation and development for AI, Data Analytics and Blockchain integration, as well as development of its related areas, such Software Engineering, IoT-Fog-Edge-Cloud Computing and Cybersecurity.


CALL FOR PAPERS’ SUBMISSIONS
In this special issue (SI), we are interested to discover and promote the latest trends, adoption cases, techniques, innovation, case studies and real-world solutions individually for AI, Data Analytics and Blockchain, as well as its integrated uses, and new development of the related areas such Software Engineering, IoT-Fog-Edge-Cloud Computing and Cybersecurity. Apart from quantitative research methods, we welcome papers with innovative mixed and qualitative methods. We welcome high-quality and unpublished papers for this SI submission.
Call for papers on various issues include, but not limited to:

• AI and smart algorithms
• Data Analytics and new services
• Advanced methods and techniques in Data Analytics with IoT and/or AI
• Blockchain technologies and adoption
• Blockchain in supply chain management, health applications, financial applications, cybersecurity and software engineering.
• The integration of AI, Data Analytics and/or Blockchain
• Impacts, innovation and real-world solutions in AI, Data Analytics and/or Blockchain
• Theories, services, architectures and engineering for AI, Data Analytics and/or Blockchain
• New development of its related areas such Software Engineering, IoT-Fog-Edge-Cloud Computing and Cybersecurity due to AI-Data Analytics-Blockchain integration


SUBMISSION AND REVIEW PROCESS
Manuscripts should not have been previously published or be under review in other journals. Outstanding papers presented at the International Conference on Industrial IoT, Big Data and Supply Chain 2020 (IIoTBDSC 2020), are welcomed for submission. The guest editors also welcome submissions of high-quality papers from the research community. All authors are expected to explicitly state in their cover letter the contributions to the impacts of AI, Data Analytics, Blockchain and its integrated uses. Submissions to the Journal of Enterprise Information Management are made using ScholarOne Manuscripts, the online submission and peer review system. The manuscript must comply with the author guidelines available on the journal's page.

Authors must use the official JEIM submission portal and select ‘Impacts of AI, Data Analytics, Blockchain’ special issue for their submission. We will accept online submissions until December 15, 2020.
The following important dates will guide the development of this SI:
• Submission deadline: December 15, 2020.
• Fully reviewed manuscript ready for production: April 15 2021.
• Target Publication Date: Summer or Autumn 2021.

All papers will need to pass pre-screening stages. For papers out of scope or caught with high percentage of plagiarism or not matching to the expected quality, they will be returned to authors with reasons for desk reject. The selected papers will be screened by at least two guest editors (and desk rejected if not deemed suitable) before being sent to at least two referees. Papers will undergo a maximum of two rounds of revision to meet the scope and high standards of JEIM without any guarantee of final publication. We anticipate that the special issue will be published by the second half of 2020. For any queries regarding submission, please email the special issue guest editors.


Special Issue Guest Editors
Prof. Victor Chang (Managing Guest Editor),
Professor of Data Science and Information Systems, Teesside University, Middlesbrough, UK
ic.victor.chang@gmail.com
https://research.tees.ac.uk/en/persons/victor-chang and https://scholar.google.com/citations?hl=en&user=IqIYZ14AAAAJ

Dr. Raul Franco Valverde
Senior Lecturer in Supply Chain and Business Technology Management, Concordia University, Québec, Canada
raul.valverde@concordia.ca
https://www.concordia.ca/jmsb/faculty/raul-valverde.html and https://scholar.google.com/citations?user=Tb4APMgAAAAJ

Dr. Stéphane Gagnon
Associate Professor, Université du Québec en Outaouais, Québec, Canada
Stephane.Gagnon@uqo.ca
https://gagnontech.org/index.php/en/downloads/category/1-stephane-gagnon-public and https://scholar.google.com/citations?user=46daFzUAAAAJ

Dr. Muthu Ramachandran
Principal Lecturer, Leeds Beckett University, Leeds, UK
M.Ramachandran@leedsbeckett.ac.uk
https://www.leedsbeckett.ac.uk/staff/dr-muthu-ramachandran/ and https://scholar.google.com/citations?user=RLmKWYYAAAAJ



References

Alketbi, A., Nasir, Q., & Talib, M. A. (2018, February). “Blockchain for government services—Use cases, security benefits and challenges”. In IEEE 2018 15th Learning and Technology Conference (L&T), pp. 112-119.
Birek, L., Grzywaczewski, A., Iqbal, R., Doctor, F., & Chang, V. (2018). A novel Big Data analytics and intelligent technique to predict driver's intent. Computers in Industry, 99, 226-240.
Chang, V. (2018 a). “A proposed social network analysis platform for big data analytics”. Technological Forecasting and Social Change, 130, 57-68.
Chang, V. (2018 b). An overview, examples, and impacts offered by Emerging Services and Analytics in Cloud Computing virtual reality. Neural Computing and Applications, 29(5), 1243-1256.
Chang, V., Li, T., & Zeng, Z. (2019 a). “Towards an improved Adaboost algorithmic method for computational financial analysis”. Journal of Parallel and Distributed Computing, 134, 219-232.
Chang, V., Abdel-Basset, M., & Ramachandran, M. (2019 b). Towards a reuse strategic decision pattern framework–from theories to practices. Information Systems Frontiers, 21(1), 27-44.
Gai, K., Qiu, M., & Sun, X. (2018). “A survey on FinTech”. Journal of Network and Computer Applications, 103, 262-273.
Kaur, H., Alam, M. A., Jameel, R., Mourya, A. K., & Chang, V. (2018). “A proposed solution and future direction for blockchain-based heterogeneous medicare data in cloud environment”. Journal of medical systems, 42(8), 156.
Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In Information visualization (pp. 154-175). Springer, Berlin, Heidelberg.
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., ... & Ogu, I. O. (2018). “Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare”. Oncotarget, 9(5), 5665.
Mohamed, H., & Ali, H. (2018). Blockchain, Fintech, and Islamic finance: Building the future in the new Islamic digital economy. Walter de Gruyter GmbH & Co KG.
Saha, K. (2019). “Analytics and Big Data: Emerging trends and their impact on our lives”. Journal of Public Affairs, 19(4), e1944.
Skilton, M. and Hovsepian, F. (2017). The 4th industrial revolution: Responding to the impact of artificial intelligence on business. Springer.
Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018). “Blockchain challenges and opportunities”: A survey. International Journal of Web and Grid Services, 14(4), 352-375.