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.

Presents recent and ongoing research activities about the recent advances, techniques, and applications that involve the employment of theoretical models Provides a collection of high-quality research works that address broad challenges in both theoretical and application aspects of various technologies Covers empirical and review studies that mainly focus on the determinants affecting the continued use of technologies across different domains, applications, samples, and countries

Inhalt
Is it still valid or outdated? A bibliometric analysis of the Technology Acceptance Model and its applications from 2010-2020.- Models of Acceptance and Use of Technology Research Trends: Literature Review and Exploratory Bibliometric Study.- An integrated conceptual model for understanding the adoption of learning management systems in higher education during the COVID-19 outbreak.- Predicting Adoption of Visual Programming Languages: An Extension of the Technology Acceptance Model.- What Impacts E-Commerce Acceptance of Generation Z? A Modified Technology Acceptance Model.- A systematic review of mobile payment studies from the lens of the UTAUT model.- Conceptualizing a Framework for Understanding the Impact of Dynamic Accounting Information Systems on the Business Processes Capabilities.- A review of learning analytics studies.- Examining the factors affecting the adoption of IoT platform services based on Flipped Learning Model in Higher Education.- Evaluating the impact of knowledge management factors on m-learning adoption: A deep learning-based hybrid SEM-ANN approach.