Recent Advances In Technology Acceptance Models And Theories

This book tackles the latest research trends in technology acceptance models and theories. It presents high-quality empirical and review studies focusing on the main theoretical models and their applications across various technologies and contexts. It also provides insights into the theoretical and practical aspects of different technological innovations that assist decision-makers in formulating the required policies and procedures for adopting a specific technology.

This book tackles the recent research trends on the role of AI in advancing automotive manufacturing, augmented reality, sustainable development in smart cities, telemedicine, and robotics. It sheds light on the recent AI innovations in classical machine learning, deep learning, Internet of Things (IoT), Blockchain, knowledge representation, knowledge management, big data, and natural language processing (NLP). The edited book covers empirical and reviews studies that primarily concentrate on the aforementioned issues, which would assist scholars in pursuing future research in the domain and identifying the possible future developments of AI applications.

Technology acceptance can be defined as a user’s willingness to employ technology for the tasks it is designed to support. Over the years, acceptance researchers have become more interested in understanding the factors influencing the adoption of technologies in various settings. From the literature, much research has been done to understand technology acceptance in the business contexts. This is understandable, given the close relationship between the appropriate uses of technology and profit margin. In most of the acceptance studies, researchers have sought to identify and understand the forces that shape users’ acceptance so as to influence the design and implementation process in ways to avoid or minimize resistance or rejection when users interact with technology. Traditionally, it has been observed that developers and procurers of technological resources could rely on authority to ensure that technology was used, which is true in many industrial and organizational contexts. However, with the increasing demands for educational applications of information technology and changing working practices, there is s need to re-examine user acceptance issues as they emerge within and outside of the contexts in which technology was implemented. This is true in the education milieu where teachers exercise the autonomy to decide on what and how technology will be used for teaching and learning purposes. Although they are guided by national and local policies to use technology in the classrooms, teachers spent much of their planning time to consider how technology could be harnessed for effective lesson delivery and assessment to be conducted. These circumstances have provided the impetus for researchers to study technology acceptance in educational settings. Although these studies have typically involved students and teachers as participants, their findings have far-reaching implications for school leaders, policy makers, and other stakeholders. The book is a critical and specialized source that describes recent research on technology acceptance in education represented by educators and researchers from around the world such as Australia, Belgium, China, Hong Kong, Malaysia, Singapore, United Kingdom, and United States of America.

This book explores the latest research trends in intelligent systems and smart applications. It presents high-quality empirical and review studies focusing on various topics, including information systems and software engineering, knowledge management, technology in education, emerging technologies, and social networks. It provides insights into the theoretical and practical aspects of intelligent systems and smart applications.

This open access handbook offers a one-stop-shop for both new and established researchers, educators, policy makers and administrators in the field of open, distance and digital education (ODDE) to gain a comprehensive overview of the history, theory and practice at all levels of ODDE, and at the same time stimulates in-depth discussions on various themes and issues of ODDE for today and future. Researchers, scholars and students in the field of ODDE can use this handbook as a major reference to conduct their own research and learning agendas. To cover the field comprehensively, the handbook is structured following the 3M framework developed by one of the chief editors Zawacki-Richter. The 3M framework categorizes the major research areas and issues in ODDE on three levels. Accordingly, the handbook is divided into six sections in total, two section at each of the three levels: 1) Macro Level – ODDE Systems and Theories, 2) Meso Level - Institutional Perspectives, Management and Organization, and 3) Micro Level – Learning and Teaching in ODDE. This is an open access book.

This book sheds light on the emerging research trends in intelligent systems and their applications. It mainly focuses on four different themes, including Artificial Intelligence and Soft Computing, Information Security and Networking, Medical Informatics, and Advances in Information Systems. Each chapter contributes to the aforementioned themes by discussing the recent design, developments, and modifications of intelligent systems and their applications.

This book constitutes the refereed proceedings of the 8th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2022, held in Cairo, Egypt, during May 5-7, 2022. The 8th edition of AMLTA will be organized by the Scientific Research Group in Egypt (SRGE), Egypt, collaborating with Port Said University, Egypt, and VSB-Technical University of Ostrava, Czech Republic. AMLTA series aims to become the premier international conference for an in-depth discussion on the most up-to-date and innovative ideas, research projects, and practices in the field of machine learning technologies and their applications. The book covers current research on advanced machine learning technology, including deep learning technology, sentiment analysis, cyber-physical system, IoT, and smart cities informatics and AI against COVID-19, data mining, power and control systems, business intelligence, social media, digital transformation, and smart systems.