Calls for Papers (special): Journal of Organizational and End User Computing (JOEUC)


Special Issue On: INNOVATIVE APPROACH AND BIG DATA ANALYTICS FOR ORGANIZATIONAL LEARNING AND PRACTICE

Submission Due Date
7/15/2021

Guest Editors
Dr. VICENTE GARCÍA DÍAZ
University of Oviedo, Spain.
Official Email id: garciavicente@ieee.org

Dr. JERRY CHUN-WEI LIN
Western Norway University of Applied Sciences,
Bergen, Norway.
Official Email id: jerrylin@ieee.org

Dr. JUAN ANTONIO MORENTE MOLINERA
PDI, University of Granada.
Official Email id: jamoren@decsai.ugr.es

Introduction
When organizations dedicate their time and resources to develop a learning culture and implement organizational learning, it is considered competitive. Due to the fast-changing market conditions, the need for organizational learning practices has increased in all the corporate sectors. Developing an environment where employees can share their knowledge with the co-employees and the management allows each person's contribution substantially. So, management heads utilize one of the advanced technologies, the big data, to make the organization leaning process efficient. Big data is the frontrunner for adding business value to the companies for revamping the existing modules of the organizational learning process. An organization's initial concept is employee learning and development, where big data brings a complete spectrum of analytics that resonates with a particular workgroup. A recent trend has been used by companies in the hyper-personalization of data with big data to improve employees' performance with an efficient learning process. This innovative approach can be done by gathering all the customer data to tailor products and services as per requirements building a better learning progress. An E-commerce branding with online and offline platforms would be a best suitable example.

Objective
When organizations dedicate their time and resources to develop a learning culture and implement organizational learning, it is considered competitive. Due to the fast-changing market conditions, the need for organizational learning practices has increased in all the corporate sectors. Developing an environment where employees can share their knowledge with the co-employees and the management allows each person's contribution substantially. So, management heads utilize one of the advanced technologies, the big data, to make the organization leaning process efficient. Big data is the frontrunner for adding business value to the companies for revamping the existing modules of the organizational learning process. An organization's initial concept is employee learning and development, where big data brings a complete spectrum of analytics that resonates with a particular workgroup. A recent trend has been used by companies in the hyper-personalization of data with big data to improve employees' performance with an efficient learning process. This innovative approach can be done by gathering all the customer data to tailor products and services as per requirements building a better learning progress. An E-commerce branding with online and offline platforms would be a best suitable example.

Recommended Topics
• Big data analytics for enhancing organizational performance
• Role of analytical software’s for decision making and employee’s performance growth
• Big data evolution and its impact on business in 2020
• Big data’s impact on the employee engagement and development
• Big data analytics for product development and enhancement
• Statistical model selections for an organization using big data analytics
• Big data and its impacts on business revenue and growth
• Implementing big data software virtually during COVID-19 in organizations
• Big data analytics to reduce employee turnover and increase productivity
• New approaches of big data to enhance the employee knowledge management
• Failure analysis using big data analytics in an organizational environment
• Big data and insight collection from employees for better organizational learning practices

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Innovative Approach and Big Data Analytics for Organizational Learning and Practice on or before July 15th, 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:
Dr. VICENTE GARCÍA DÍAZ
Guest Editor
Journal of Organizational and End User Computing (JOEUC)
Email id:garciavicente@ieee.org

Special Issue On: Big Data for Social Sciences

Submission Due Date
9/30/2021

Guest Editors
Chia-Huei Wu
Minghsin University of Science and Technology, Taiwan

Andrew W. H. Ip
University of Saskatchewan, Canada

Xuyun Zhang
Macquarie University, Australia

Chi-Hua Chen
Fuzhou University, China

Wei Liu
The University of Sydney, Australia

Introduction
With the advancement of social media such as Twitter, Facebook, etc., billions of registered users are getting connected via social interactions. Through pervasive social interactions, massive social data are accumulated, making it available for social analysis to improve the quality of life of human beings. Example social analysis applications could be found in the field of social sciences: environmental protection, public healthcare, educational programs, to name a few.

The huge amount of social data produced by interactions of people requires the use of efficient processing and analysis techniques. In general, typical challenges for big social data processing and analysis are including data storage, data quality, computational power of computers, algorithm customization for special applications, security and privacy concerns, etc.

Objective
In this special issue, we look for significant findings to discuss the potential of big data in different areas of social sciences. Specifically, we solicit novel contributions to address the challenges in social sciences applications with big data processing and analysis techniques, e.g., data model algorithms, security strategies, etc.

Recommended Topics
• Innovative applications of data analytics to social sciences
• Machine learning algorithms for big data applications with social sciences
• Advanced techniques for handling unstructured, unlabeled and/or missing social data
• Data quality control of big data for social sciences
• Big data research driven policy making for social sciences
• Standardization for big data infrastructure and framework
• Human centric big data research
• Big data implications to society
• Threat models and attack strategies for social sciences
• Trust, reliability and dependability in social sciences

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Big Data for Social Sciences on or before September 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:
Chia-Huei Wu
Guest Editor
Journal of Organizational and End User Computing (JOEUC)
E-mail: chiahuei530@gmail.com

Special Issue On: Service Automation with Data-driven Decision Analytics

Submission Due Date
9/30/2021

Guest Editors
Dr. Daniel Y.W. MO, The Hang Seng University of Hong Kong, Hong Kong
Dr. C.H. WU, The Hang Seng University of Hong Kong, Hong Kong
Dr. Y.M. TANG, The Hong Kong Polytechnic University, Hong Kong
Prof. W.H. IP, University of Saskatchewan, Canada


Introduction
Service automation builds on the innovative applications of big data analytics and automated technologies to enhance service design and delivery. The emergence of big data analytics and technologies has primarily changed the landscape of the digital era for business operation.
The outbreak of COVID-19 has also made a massive impact on most of the businesses – from restaurants to luxury goods brands. Due to coronavirus quarantine and self-isolation, many retailers shorten their opening hours of their physical stores and moving their service online so as to manage manpower disruptions. At the same time, many companies prepare their workplaces for the new normal with the aid of technologies.
With big data analytics, hidden patterns and characteristics of consumer behaviours are discovered for the enhancement of service design and process efficiency. As service is intangible and varies during the delivery process, systematic identification of process commonality provides new opportunities for process automation. This also unlocks various applications of a digital workforce.
The digital workforce is an emerging topic which investigates process automation via robotic machines, not only in traditional production areas but also in service areas. Based on the process commonality identified by decision analytics, robotic machines complement human involvements to increase the speed of service delivery, remove redundancy manpower, reduce process errors, and react ad-hoc requests with intelligent recommendations. Process automation and decision analytics are intertwined for service automation. The theme of this special issue is focused on big data analytics in the aspect of decision science to facilitate service automation. In general, big data analytics and technologies, data-driven modelling in service automation, and decision support system using various techniques and algorithms can also be proposed.

Objective
This special issue mainly focuses on the Service Automation with Data-driven Decision Analytics, addressing businesses operation issues that are knowledge-intensive and labour intensive. We are calling for papers for this special issue in both algorithm development and applications of service automation from academia and industry, as well as the means to tackle the manpower disruptions in the outbreak of COVID-19. By assembling a coherent set of original research papers, researchers have more understanding of the topic of service automation empirically and theoretically.

Recommended Topics
• Big data analytics and technologies in service design, delivery, and automation
• Automated Information Systems (AIS) including data structures, data management, data mining
• Knowledge discovery systems in service automation
• Automation, algorithms, and Artificial Intelligence (A.I.) development
• Data-driven modelling and analysis in service automation
• Decision support systems regarding the use of artificial intelligence techniques and machine learning algorithm
• End-user computing (EUC) for any system and/or platform that help non-programmers or business analysts create digital workforce applications
• Intelligent process automation system design and development
• Service automation challenges for ICT Service Integrators
• Case studies, theories, and technological innovation in service automation

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Service Automation with Data-driven Decision Analytics on or before 30-Sep-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:
Dr Daniel Y.W. MO
Guest Editor
Journal of Organizational and End User Computing (JOEUC)
danielmo@hsu.edu.hk

Special Issue On: Secure Smart Solutions for Organizational and End User Computing

Submission Due Date
12/31/2021

Guest Editors
Dr. Sang-Bing Tsai, School of Business, Wuyi University
sangbing@hotmail.com
Dr. Xiaochun Cheng, Middlesex University, UK
xiaochun.cheng@gmail.com
Dr. Yu-Hsi Yuan, Chinese Culture University, Taiwan
yuanyh@gm.ypu.edu.tw

Introduction
Journal of Organizational and End User Computing” attracts the state of art in organizational computing and end user computing. Due to epidemic spreading of COVID-19, many companies have to stop old fashioned face to face team working and face to face service to clients. While each employee is working remotely from home, or the client is served through a networked computer or mobile device, secure smart solutions for organizational data or for end user data processing are important for data driven business decision making, which can avoid epidemic spreading of the contagious virus, save some travel and traffic, hence reduce usage of petrol and air pollution; which is important for sustainable development of environment and economy in the long run.

Objective
The main goal of the proposed special issue is to attract the most recent innovative secure smart solutions for organizational computing and end user computing in Covid-19 epidemic environment, to support remote working team members and clients being connected through available cabled or wireless networks. The focus on security is to meet the need to keep the organizational secrets and to protect the individual privacy for such emerging decentralized working and service modality. We want to best support diverse needs, personalized requirements, different organizational and users constraints with available limited communication and computing facilities, hence a range of smart solutions can be applied to optimize the infrastructural communication and computing services, to most effectively support distributed co-workers and clients.

Recommended Topics
• Secure Smart Organizational or End User Computing Solutions for Medical Healthcare
• Secure Smart Organizational or End User Computing Solutions for Crowd Sourcing
• Secure Smart Organizational or End User Computing Solutions for Mobile Shopping
• Secure Smart Organizational or End User Computing Solutions for Delivery Transportation
• Secure Smart Organizational or End User Computing Solutions for E-government
• Secure Smart Organizational or End User Computing Solutions for Finance Service
• Secure Smart Organizational or End User Computing Solutions for Fighting Crimes / Frauds
• Secure Smart Organizational or End User Computing Solutions for Emergency Response
• Secure Smart Organizational or End User Computing Solutions for Education
• Secure Smart Computing Solutions for Geoscience and Earth Science
• Secure Smart Computing Solutions for Environment Protection
• Secure Smart Computing Solutions for Weather Forecast

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Secure Smart Solutions for Organizational and End User Computing 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:
Dr. Xiaochun Cheng
Guest Editor
Journal of Organizational and End User Computing
xiaochun.cheng@gmail.com

Special Issue On: Network Traffic Monitoring and Analysis For Quality of Experience at End Users

Submission Due Date
1/31/2022

Guest Editors
Prof. Tai-Hoon Kim, Beijing Jiaotong University, China (Lead Guest Editor)
Prof. Sabah Mohammed, Lakehead University, Canada
Prof. Khurram Khan, King Saud University, Saudi Arabia
Prof. Muhammad Usman Shahid Khan, COMSATS University Islamabad, Pakistan
Prof. Dalin Zhang, Beijing Jiaotong University, China

Introduction
Currently, many content providers over the internet are offering different type of data services to the end users. Understanding Quality of Experience (QoE) perceived by the end users for all different types of services is important for network operators to provide efficient management of network services. However, the operators do not have access to the applications at the end user devices to observe the real metrics to understand the quality perceived by the users. The network operators have to rely on passive measurements of the network traffic to estimate the QoE metrics. Similarly, content providers are encrypting their network traffic till the end users. It is making extremely difficult to passively measure the QoE on the network traffic. Therefore, it is a requirement at the network providers end to monitor and understand the network traffic (both encrypted and non-encrypted) to provide efficient QoE to the end users.

Objective
The objective of this special issue is to focus on network traffic analysis for improved end user Quality of Experience. This special issue will bring focus on recent advances and innovations in network traffic analysis. This special issue will include a collection of articles elaborating state-of-the-art research traffic classification, user applications identification, video stream identification, and providing efficient QoE metrics.

Recommended Topics
• End user QoE improvement using traffic analysis
• Estimation of HTTP-based Video QoE metrics from Encrypted network traffic
• Network Traffic classification
• Benign and malware network identification
• User application identification using network analysis
• Data stream identification using network analysis
• Side channel attacks on HTTPS traffic
• Packet analysis for network forensic
• Videos identification in encrypted network traffic
• Risks and Ethics involved in encrypted network traffic analysis

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Network traffic Monitoring and analysis for Quality of Experience at End Users 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. Tai-hoon Kim
Guest Editor
Journal of Organizational and End User Computing
taihoonkim@bjtu.edu.cn