Journal Information
Computers in Human Behavior
Impact Factor:
Call For Papers
Computers in Human Behavior is a scholarly journal dedicated to examining the use of computers from a psychological perspective. Original theoretical works, research reports, literature reviews, software reviews, book reviews and announcements are published. The journal addresses both the use of computers in psychology, psychiatry and related disciplines as well as the psychological impact of computer use on individuals, groups and society. The former category includes articles exploring the use of computers for professional practice, training, research and theory development. The latter category includes articles dealing with the psychological effects of computers on phenomena such as human development, learning, cognition, personality, and social interactions. The journal addresses human interactions with computers, not computers per se. The computer is discussed only as a medium through which human behaviors are shaped and expressed. The primary message of most articles involves information about human behavior. Therefore, professionals with an interest in the psychological aspects of computer use, but with limited knowledge of computers, will find this journal of interest.
Last updated by Dou Sun in 2024-07-12
Special Issues
Special Issue on Recent Trends and Future Advances in Personalised/Adaptive Ubiquitous Learning
Submission Date: 2024-07-20

The changes in education and teaching techniques have resulted in incorporating new technology into both teaching and learning models. With the emergence of new illnesses such as the Covid-19, lockdowns were used that limited direct contact between individuals. The imposition of constraints on human movement resulted in the development of new technologies that efficiently remove the challenges associated with daily tasks. One such technology is the ubiquitous device, which adds computer capacity to all devices via embedded technology. Due to advancements in wireless computing, technologies such as IoT, wearable devices, robotic devices, and pervasive gadgets continue to grow in popularity, which contributes to the development of ubiquitous systems. The learning models applied in conjunction with the ubiquitous adaptive devices analyse data acquired from students and correlate it to the learning techniques. Guest editors: Dr. Faheem Khan, Gachon University, Seongnam, South Korea, Dr. Umme Laila, Sir Syed University of Engineering & Technology, Pakistan, Dr. Muhammad Adnan Khan, Riphah International University, Pakistan, Dr. Inam Ullah, Chungbuk National University, Special issue information: The benefit of pervasive or ubiquitous computing is that communication may occur everywhere and at any time, regardless of the communication channel. Additionally, customised ubiquitous devices have applications in e-learning, corporate settings, geographical applications, electronic highway tolls, smartphones, and wearables. Furthermore, with ubiquitous learning, several contemporary technologies, such as machine learning models, are included in the analysis of acquired data. Additional advancements include fuzzy approaches, augmented/virtual reality technologies for device interaction, a four-dimensional modelling environment, and IoT in learning settings. Further, advancements in data analytics, such as reinforcement learning, fuzzy learning models, neural networks, and deep learning techniques, may allow the development of efficient data models. With distributed processing methods such as blockchain models, edge/fog computing techniques may efficiently process and store data. As a result of the adaptive technologies employed in education, pupils may continuously perceive learning via ubiquitous gadgets. Furthermore, mobile devices, personal assistants, RFID, and 6G communications allow efficient data transfer between devices through wireless media. This special issue examines the use of personalised/adaptive learning in a contactless learning environment and new developments linked with ubiquitous gadgets. Several coupled learning models may be exploited via successful wireless deployment and current advancements in artificial intelligence technology. Thus, researchers and academics might provide their perspectives on different technological implementations that could be combined with ubiquitous learning models like the digital twins, MIMO, vehicular networks and personal ubiquitous computing models related to learning. LIST OF TOPICS AREAS INCLUDE, BUT ARE NOT LIMITED TO: Pervasive computing environment and the implementation of the technology in learning models Enhanced deployment of context-aware systems in the adaptive ubiquitous learning environments Enhanced wireless 5G/6G communications with effective ubiquitous learning models Personalisation and adaptive learning models for effective learning deployment Federated learning models with the effective deployment of blockchain in an adaptive learning environment Reinforced learning methods for data prediction and analysis in ubiquitous learning Frameworks and architectures for the deployment of ubiquitous learning Design and development of ubiquitous embedded systems related to learning Deployment of ubiquitous learning models with effective data analysis with fuzzy neural methods Manuscript submission information: Authors are asked to submit original, unpublished research papers with no more than 10,000 words and structured abstracts with 200–250 words. All submissions will be peer-reviewed by at least two reviewers. With the large number of proposed submissions, we guarantee to comply to JCAL’s high quality standards, allowing rejections where quality criteria are not met. Authors are asked to set up their manuscripts in line with JCAL’s Author Guidelines. Manuscripts will be submitted electronically via JCAL’s online submission system. Authors need to select “Recent Trends and Future Advances in Personalized/ Adaptive Ubiquitous Learning” in the special issue question during submission. Please submit your manuscript by 20th July 2024.
Last updated by Dou Sun in 2024-07-12
Special Issue on Mental Health, War Trauma and Migration - Computers in Human Behavior
Submission Date: 2024-09-30

The impact of a military conflict on mental health of affected community is deep and long lasting. Guest editors: Professor Matthieu Guitton, Laval University, Québec, Quebec, Canada Special issue information: We are looking for new research as well as reviews, guidelines and analysis that will support primary care providers, psychologists, social workers, teachers, carers, and any community members working with people affected by war, exposed to traumatic events induced by was and/or forced to migrate. Manuscript submission information: All interested researchers are invited to submit your manuscript at The Journal’s submission system is open for receiving submissions to our Special Issue. To ensure that all manuscripts are correctly identified for inclusion into the special issue, it is important to select “VSI:War Trauma and Migration” when you reach the “Article Type” step in the submission process. Full manuscripts will undergo double-blind review as per the usual procedures for this journal. Deadline for manuscript submissions: Sep 30th, 2024 Inquiries related to the special issue, including questions about appropriate topics, may be sent electronically to the Guest Editor Professor Matthieu Guitton at Learn more about the benefits of publishing in a special issue: Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: Keywords: migrant, migration, refugee, war, military conflict, mental health, trauma, PTSD and anxiety
Last updated by Dou Sun in 2024-07-12
Special Issue on Generative Artificial Intelligence in Social Interaction and Internet of Behavior
Submission Date: 2024-10-31

The rapid breakthroughs in information technologies have driven substantial developments in Artificial Intelligence (AI) applications, particularly the widespread use of deep learning techniques in domains such as speech, image and text recognition. Generative AI is an important subset of AI and one of the most rapidly expanding areas in recent years. Generative AI utilizes machine learning and deep learning techniques to generate original data. At the heart of generative AI lies the generative model, which is responsible for modeling the possible distribution of data and generating fresh data that closely resembles the original data’s distribution. Guest editors: Dr. Mu-Yen Chen National Cheng Kung University, Taiwan E-mail: Dr. Miltiadis D. Lytras Department of Management Information Systems American College of Greece, Athens, Greece Email: Dr. Patrick C. K. Hung Faculty of Business and IT Ontario Tech University, Canada Email: Special issue information: With the maturation of AI of Things, many countries have promoted the smart city concept to improve citizens’ quality of life, encouraging many technology developments on the Internet of Behavior (IoB) that utilize the Internet of Things (IoT) to analyze behavioral patterns. IoB is at its initial stage, which requires combinations of diverse techniques, such as IoT, big data, and AI. With the development of large-scale sensors and data collection, it is predictable that more and more IoB applications and frameworks will be proposed. IoB needs scholars to be involved in in-depth research and present more effective frameworks, enabling IoB to achieve real-time behavioral analysis. Given IoB's significance and diverse applications, it is a highly worthwhile and promising research topic. Social interaction is defined as an exchange between two or more individuals and is a building block of society. By interacting with one another, people design rules, institutions and systems within which they seek to live. Considering the social interaction residing in the IoB and big data content, opportunities are emerging to enable promising smart applications for easing individual or group peers' needs, creating company business models, and facilitating smart life development. However, the nature of Generative AI also poses fundamental challenges to the techniques and applications relying on the social big data from multiple perspectives, such as algorithm effectiveness, computation speed, energy efficiency, user privacy, user trust, server security, data heterogeneity and system scalability. Generative AI has a broad range of applications, including but not limited to creating images, processing natural language, and generating music. This special issue aims to bring together social interaction research and development achievements in exploring techniques, applications, and challenges that apply Generative AI for IoB, such as smart homes/buildings, smart cities, smart environments, intelligent healthcare, and the Internet of Vehicles (IoV). Both theoretical and experimental studies related to designing, analyzing, and implementing Generative AI for IoB and social interaction are encouraged. We hope to explore IoB applications and research in more areas of study and see how IoB models can take a vast amount of available data and help us uncover undiscovered phenomena, retrieve useful knowledge, and draw conclusions and reasoning. Topics of interest include, but are not limited to: Topics Methodologies and Techniques Adaptive machine learning and soft computing algorithms for Generative AI Evolutionary and soft computing-based tuning and optimization of Generative AI Robust data augmentation methods for Generative AI Metaheuristics aspects and soft computing algorithms in deep learning for improved convergence of Generative AI Faster reinforcement learning and transfer learning methods for Generative AI Human Behavior Human behavior and user interfaces for human-centered Generative AI Human participation and social sensing for human-centered Generative AI The applications of personality and social psychology for Generative AI Artificial intelligence and mental processes in human-centered Generative AI Trust, security, and privacy issues for human-centered Generative AI Applications AI and Deep Learning applications with Generative AI for the IoB and Social Interaction Context-aware services and Social Interaction for IoB with Generative AI Speech processing applications with Generative AI for the IoB Image processing and Video applications with Generative AI for the IoB Sustainability and Environmental, Social, and Governance (ESG) with Generative AI Impact Manuscript submission information: Authors are invited to submit research contributions representing original, previously unpublished work by 31st Oct 2024. Submitted papers will be carefully evaluated based on originality, significance, technical soundness, and clarity of exposition. Authors should prepare their manuscript according to the Guide for Authors at To ensure the manuscript is correctly identified for inclusion in the special issue, authors must select "VSI: GAI in Social Interaction and IoB" when they reach the “Article Type” step in the submission process.
Last updated by Dou Sun in 2024-07-12
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