Calls for Papers (special): Journal of Global Information Management (JGIM)


Special Issue On: e-Engagement in Emerging Economies

Submission Due Date
6/30/2021

Guest Editors
Manish Gupta, IBS Hyderabad, a Constituent of IFHE, Deemed to be University, India
Abhishek Behl, Indian Institute of Technology Bombay, India

Introduction
Digital communities and excessive exploration of the internet has extended contemporary engagement literature. Engagement no longer remains a concept of marketing or human resources, but has transformed into what is called "human-computer interaction". Engagement theories have mostly tested their respective constructs and antecedents to engagement in a context-specific situation or concept centric situation. The theoretical framework are mostly tested in developed nations and mostly with established brands and processes. Following the guidelines of Sobh and Perry (2006), it is important to understand the idea of replicating this for developing nations which either offer opportunities in terms of its size of the population or in terms of the demand pool. A large part of the population of these economies lies non-inclusive on digital platforms and others who are hooked to such digital platforms lack proper channelization. While geographic divide makes people access restricted resources, digital citizenship has helped firms and companies to break the geographic barrier and expand their business. This has also helped internet providers and digital aggregators to attract and engage customers and potential customers through various digital channels. Of late, companies have also started to harness the power of utilizing tools like big data analytics, machine learning, augmented and virtual reality, game mechanics, artificial intelligence etc to understand the behaviour of customers better and design techniques and ways to engage them better.

Objective
This issue aims at exploring the antecedents and consequences of electronic engagement in the emerging economies. The articles of this issue are expected to provide insights into the psychological and technological ways through which organizations can engage their stakeholders such as employees, customers, students, among others using the internet.

Recommended Topics
  • Personality characteristics and electronic engagement relationships among an organization's stakeholders
  • Role of Information quality in customizing customer/consumer engagement
  • E-engagement and experience relating to employees and brands
  • Online engagement and performance (employee/student/brand/organization)
  • Effect of contextual factors on online engagement
  • Outcomes of excessive online engagement
  • Role of advanced technologies like artificial intelligence, big data analytics, augmented and virtual reality for engagement
  • Engagement for the bottom of the pyramid
  • Ethical electronic engagement and concerns related to information breach


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on e-Engagement in Emerging Economies on or before June 30, 2021. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Manish Gupta; Abhishek Behl
Guest Editors
Journal of Global Information Management (JGIM)
E-mail: manish.gupta.research@gmail.com
Email: abhishekbehl27@gmail.com

Special Issue On: Artificial Intelligence in Global Production Systems and Supply Chain Management

Submission Due Date
7/27/2021

Guest Editors
Prof. Samuel Fosso Wamba, Toulouse Business School, France
Prof. Maciel M. Queiroz, Universidade Paulista, Brazil

Introduction
The digital disruption imposed by information and communication technologies (ICT) (Grant & Yeo, 2019; Wu & Raghupathi, 2015) by recent emerging cutting-edge technologies has become a buzzword and a challenge to scholars and practitioners. An emerging and highly disruptive of these technologies is Artificial intelligence (AI) (Davenport, 2018). In summary, AI refers to machines performing activities that commonly require some human intelligence to perform it (Minsky, 1968). AI has been changing the decision-making process drastically, by employing sophisticated approaches such as machine learning, natural language processing, augmented and virtual reality, voice recognition, cognitive analytics, robotics, smart machines, and vision, among others. AI has been applied in a vast of fields, e.g., medicine (Becker, 2019; Wong, Zhou, & Zhang, 2019), market knowledge in B2B (Paschen, Kietzmann, & Kietzmann, 2019), and production management systems (Burggräf, Wagner, & Koke, 2018; Davies, Thomas, & Shaw, 1994). Nowadays, AI is transforming the global society and organizations’ behavior due to its pervasiveness capacity. From an organization's perspective, AI can help the development and improvement of products and processes to support various business benefits, and consequently, generate competitive advantage (Davenport, 2018). For instance, with AI, organizations can improve the functions related to sales management by the automation of all repetitive tasks (Syam & Sharma, 2018), monitoring infectious disease (Wong et al., 2019), combat card fraud (Ryman-tubb, Krause, & Garn, 2018), among others. In this context, AI can be used in any economic field, notably to support planning, monitoring, and the decision-making process. In addition, AI has been changing profoundly the role of the workers by human-machine interaction to develop operational activities (Faraj, Pachidi, & Sayegh, 2018). Despite the recent advances of AI techniques, there is a great debate between scholars and practitioners, especially about how AI can be implemented and managed, and how it creates value for the stakeholders (Davenport, 2018). In the same spirit, there are uncertainties related to AI issues, for example, ethical and legal issues (Duan, Edwards, & Dwivedi, 2019) related to data exploration and responsibility, the threats associated with AI usage (Clarke, 2019), and the relationship and the role of human-machine interaction (Jarrahi, 2018). At the global operations management and supply chain lens, there are several opportunities and challenges facing AI adoption and management. Besides, new skills are demanded to a new generation of global production systems (Freddi, 2018). However, little is known about how AI can support and bring benefits to the global operations and supply chain management (OSCM) and related-fields.

Objective
The purpose of this Special Issue is to publish the latest and finest advances of AI techniques applied in Global Production Systems and OSCM, as also advance the information systems field. Moreover, we intend to stimulate research and debate between scholars, managers, and practitioners interested in gaining a more in-depth understanding of the role, benefits, and complexities of AI in global production, manufacturing systems, SCM, and information systems. Following the tradition and rigor of high-quality papers published by JGIM in innovative technologies, we expect original contributions from scholars and practitioners that unlocks and shed more light on the comprehension of AI technologies in the global OSCM context, by employing quantitative and qualitative methods.

Recommended Topics
• How can production and supply chain managers improve firm performance, capture benefits, and gain a competitive advantage with AI techniques?
• AI techniques to support the scheduling and demand planning in global logistics and OSCM
• How can AI be managed at different levels of planning, and support operational decisions in global OSCM?
• Ethical issues and societal implications related to AI applied to develop global products
• Global logistics and OSCM capabilities required to implement AI
• AI and the reconfiguration of global production systems business models
• Barriers related to AI implementation in intra-organizational and inter-organizational levels
• What is the role of the interaction between human-machine in global OSCM systems?
• Frameworks to support managers in the decision-making process using AI in planning and control of the global production/OSCM
• Which are the critical factors of AI in global manufacturing, production systems, and supply chain management?
• The impacts of AI in knowledge and innovation in global manufacturing, production systems, and supply chain management
• Governance models to support AI diffusion globally between stakeholders
• How can AI be used to transform traditional global manufacturing and production systems in smart manufacturing?
• The role and consequences of trust, commitment, and power enabled by AI use in global supply chain management
• AI applied to improve quality management
• Workers skills required to work with AI in operational, tactical, and strategic levels?
• How to measure the benefits of AI in global manufacturing, production systems, and supply chain management?
• The threats associated with AI usage

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Artificial Intelligence in Global Production Systems and Supply Chain Management on or before July 27th, 2021. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
All inquires should be should be directed to the attention of:
Prof. Samuel FOSSO WAMBA, Ph.D., HDR
Guest Editor
Journal of Information Global Management
E-mail: s.fosso-wamba@tbs-education.fr

Special Issue On: Information Management of Machine Learning and Data Science Analysis for COVID-19

Submission Due Date
10/30/2021

Guest Editors
Prof. Victor Chang, Teesside University, UK
Prof. Reinhold Behringer, Knorr-Bremse GmbH, Germany

Introduction
COVID-19 has become the greatest challenge the human beings have encountered since World War 2 (WW2). COVID-19 itself is highly infectious and speed in which it can mutate is rapid and in different varieties, with reported six strands of active coronaviruses widely spread worldwide. It has infected several millions of the population worldwide. In early March 2020, the total infected cases were still not reaching 100,000 (WHO, 2020). The challenge itself is not only causing rapidly increased numbers of infected cases, but also death and the way we live, such as social distancing. This has caused a lack of medical resources and healthcare crisis to fight against the infection before the development of vaccines and drugs. Other economic and social problems are common, such as job loss, insecurity, lack of movements, increases in crimes, increases in fighting limited resources and has been seen (Ecke, 2020). In addition to this, the computing services for the identification and development of drugs are also challenging. In such cases, the quality and the quantity of the collected data plays a major role which uses cloud computing architectures (Chang, 2014; Hosseinian-Far et al, 2018). The technology of Internet of Things combined with Artificial Intelligence techniques may provide good solutions to this health oriented problem (Vaishya et al, 2017).

Useful recommendations for those urgent needs are required globally to understand how to tackle this challenge. Scientists have a crucial role, not only in research and development, but also provide positive impacts to society. In terms of Machine Learning and Data Science research, scientists can offer insights, new discoveries and pioneering recommendations, which may offer positive impacts and findings to the causes, cure and analysis of treatment. We need better analysis on the past, current and future, including review, analysis and information management. In this special issue, we seek unpublished and high quality work based on review, analysis and information management of Machine Learning and Data Science findings. Best paper winners and top authors from IoTBDS 2021, COMPLEXIS 2021, FEMIB 2021 and IIoTBDSC 2021 will also be invited.

Objective
It has already been widely recognized that blending Machine Learning and Data Science can help to analyze high-quality work, which can be applied to statistical analysis, predictive modeling and decision-making. We seek high-quality review, statistical and data analysis and also high-level recommendation papers. In general, information management of Machine Learning and Data Science for COVID-19. In this special issue, we seek unpublished and high quality work based on information management of Machine Learning and Data Science research and findings.

Recommended Topics
• Information Management on blending Machine Learning and Data Science for COVID-19 research
• Information Management using Machine Learning and Data Science for patient analysis of COVID-19
• Information Management using Machine Learning and Data Science for healthcare and general health of COVID-19
• Information Management using Machine Learning and Data Science for economy and financial analysis of COVID-19
• Information Management using Machine Learning and Data Science for data mining and analytics of COVID-19
• Information Management using Machine Learning and Data Science for predictive modeling of COVID-19
• Information Management using Machine Learning and Data Science for large-scale statistical analysis of COVID-19
• Information Management using Machine Learning and Data Science for strategies, frameworks, and recommendations in COVID-19
• Innovative Recommendation System for treatment of COVID-19 patients based on psychological factors and analysis

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Information Management of Machine Learning and Data Science Analysis for COVID-19 on or before 30 October 2021. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Prof. Victor Chang
Guest Editor
Journal of Global Information Management (JGIM)
Email: victorchang.research@gmail.com

Special Issue On: Big Data Analytics in Managing Natural Resource Assets for Economic Development and Economic Security

Submission Due Date
12/31/2021

Guest Editors
Prof. Ron Fisher, Cardiff School of Management, Cardiff Metropolitan University, UK.
Prof. Xiaohua Han, School of Business, Guangdong University of Foreign Studies, China.
Prof. Ernesto D.R. Santibañez Gonzalez, Industrial Engineering Department, Universidad de Talca, Chile.
Prof. Malin Song, School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, China.

Introduction
Natural resources are not only the base of living and developing, but also the indispensable nature support factors for sustainable development and economic security (Islam and Managi, 2019; Schanes, Jäger and Drummond, 2019). However, a surprising feature of resource-rich country is slow economic growth and unreasonable resource exploitation is considered as one of the main reasons for the resource curse (Behbudi, Mamipour, and Karami, 2010; James, 2015). After the industrial revolution, demand for natural resources, e.g., land, water, and energy, is sharply increasing to meet the needs of rising global population and economic development. Resource utilization may cause a far-reaching influence and a dual effect (both advantages and disadvantages). On one hand, the natural resources provide the motive force for social and economic development. On the other hand, high resource consumption and serious environmental destruction brought by extensive economic growth mode are placing massive strains on economic security and sustainable development (Song et al., 2019). In this context, how to coordinate the relationship among natural resource assets, economic growth, and economic security has been an important issue, which needs researching. Recently, the rapid development and application of new technologies such as big data have offered new opportunities for the study of the natural resource and the economy (Song, Fisher and Kwoh, 2019). Appropriate implementation of these advanced approaches may help to find better ways to make improvements in sustainable management of natural resources.

Objective
It has already been widely recognized that natural resource assets have played an important role in promoting economic development and ensuring economic security. However, the complex coordination mechanism remains unclear and requires further research. The purpose of this special issue is to publish the latest and high quality work related to the management of natural resource assets for economic growth and economic security. Moreover, we are especially interested in the finest research based on big data analytics and information management.

Recommended Topics
-Big data and natural resource management
-Big data modeling and mining involving green growth
-Data-driven natural resource asset pricing
-Resource utilization efficiency in the big data context
-Natural resource management with big data for economic growth
-Natural resource management with big data for economic security
-Economic security evaluation based on big data assessment tools
-Big data and information management for water-energy-food security nexus
-Policies related to natural resource management and their impacts on economic security and sustainable development

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Big Data Analytics in Managing Natural Resource Assets for Economic Development and Economic Security on or before December 31, 2021. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Malin Song
Guest Editor
Journal of Global Information Management (JGIM)
E-mail: songml@aufe.edu.cn

Special Issue On: Global Information Management with Artificial Intelligence and Machine Learning in Information System and Networking

Submission Due Date
1/31/2022

Guest Editors
Prof. Sang-Bing Tsai, School of Business, Wuyi University, China
sangbing@hotmail.com
Prof. Lianyong Qi, Qufu Normal University, China
lianyongqi@qfnu.edu.cn

Introduction
Breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML), including deep neural networks and the availability of powerful computing platforms, have recently received much attention as a key enabler for future 5G and beyond wireless information networks. AI/ML has become one of the key technologies to realize intelligent information networks, intelligent services, and intelligent internet-of-things (IoT). AI/ML could provide many new opportunities in the way we manage and optimize information communications and networks for global information management and the way we manage different global user services and user content.
However, the evolution towards learning-based networks and communications is still in its early days, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions such as where and how AI/ML can really complement the well-established, well-tested information communication systems still remain a challenge in global information management. Besides, the adaptation of AI/ML-based methods is likely needed to realize their full potential in the information system context and networks for global information management. Moreover, the research of security problems, hardware aspects, and network edge in wireless communications and networking is also necessary to establish quality-of-service guarantees common in communication system design for global information management.

Objective
This special issue aims to share and discuss recent advances and future trends global information management with Artificial Intelligence and Machine Learning in information system and networking, and to bring academic researchers and industry developers together.

Recommended Topics
 Advanced AI/ML algorithms for Global Information System
 AI/ML-based Global Information System design
 AI/ML-based Big Data System design for global information management
 AI/ML-based sensor networks and IoT applications for global information management
 AI/ML-based network resource allocation and optimization for global information management
 AI/ML-based secure communications and networking for global information management
 AI/ML-based computing on the network edge for global information management
 Service performance optimization in wireless networks for global information management
 Security, privacy, and trust in wireless networks for global information management
 The design of AI-enabled hardware aspects of wireless networks for global information management
 Distributed and decentralized signal processing via AI/ML algorithms for global information management

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Global Information Management with Artificial Intelligence and Machine Learning in Information System and Networking on or before January 31, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write//. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Sang-Bing Tsai
Guest Editor

Journal of Global Information Management
sangbing@hotmail.com