Structural Equation Modeling Approaches To E Service Adoption

With the modernization of services offered through the internet, many traditional face-to-face services have adopted new e-service phenomena. Especially prevalent among the younger generations, this change in service has promoted many industries to rethink how to best reach their consumers using modern technology. Structural Equation Modeling Approaches to E-Service Adoption is a pivotal reference source that aims to share the latest empirical research findings within technology acceptance, information systems, information technology, human-computer interaction, and management information systems. While highlighting topics such as e-commerce, internet banking, and technology acceptance, this publication explores the understanding of today’s e-services in a dynamic and complex environment, as well as the methods within the field of information systems and information technologies. This book is ideally designed for academics, students, managers, and scholars interested in the up-and-coming research surrounding the field of information technology.

Volume 23B includes two chapters covering problems and implementations of solutions in e-services adoption processes in developing nations. These are exciting and useful chapters for executives and researchers seeking knowledge and theory of how to influence e-service adoptions in developing nations!

Processing information and analyzing data efficiently and effectively is crucial for any company that wishes to stay competitive in its respective market. Nonlinear data presents new challenges to organizations, however, due to its complexity and unpredictability. The only technology that can properly handle this form of data is artificial neural networks. These modeling systems present a high level of benefits in analyzing complex data in a proficient manner, yet considerable research on the specific applications of these intelligent components is significantly deficient. Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature of artificial neural networks and their specific implementations within data analysis. While highlighting topics including propagation functions, optimization techniques, and learning methodologies, this book is ideally designed for researchers, statisticians, academicians, developers, scientists, practitioners, students, and educators seeking current research on the use of artificial neural networks in diagnosing and solving nonparametric problems.

In today’s modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method. Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.

During the last decades, a considerable amount of research has been directed towards explaining the concept of Digital Human Resource Management (DHRM). Yet, a holistic assessment of DHRM antecedents and consequences with respect to possible contextual contingencies is still missing. To this end, this thesis introduces a research framework illuminating the multifaceted phenomenon of DHRM from various perspectives. An exploratory four-step meta-analytic structural equation modelling (E-MASEM) approach tailored to address the domain-specific challenges of DHRM is introduced and applied. Results identify 32 constructs associated with the DHRM usage phenomenon which are categorized into DHRM antecedents and DHRM consequences. Findings reveal that user perceptions, expectations, attitudes, and intentions are essential in predicting DHRM usage while HRM service quality and user satisfaction are found crucial in explaining other DHRM consequences. Further, practitioners are informed about the relative importance of factors for both facilitating DHRM adoption and measuring DHRM success. Lastly, this thesis also contributes to the MASEM methodology by outlining a new approach to summarize statistical inferences from multiple moderator tests.

FinTech, an abbreviated term for financial technology, is a digital revolution changing the way banking and financial services are being used both by individuals and businesses. As these changes continue to take place, the financial industry is focused on technological innovation and feeding into this digital revolution to better serve consumers who are looking for easier ways to invest, transfer money, use banking services, and more. FinTech is increasing accessibility to financial services, automating these services, expanding financial options, and enabling online payments and banking. While the benefits are being continually seen and this technology is becoming more widely accepted, there are still challenges facing the technology that include security concerns. To understand FinTech and its role in society, both the benefits and challenges must be reviewed and discussed for a holistic view on the digital innovations changing the face of the financial industry. The Research Anthology on Concepts, Applications, and Challenges of FinTech covers the latest technologies in FinTech with a comprehensive view of the impact on the industry, where these technologies are implemented, how they are improving financial services, and the security applications and challenges being faced. The chapters cover the options FinTech has unlocked, such as mobile banking and virtual transactions, while also focusing on the workings of the technology itself and security applications, such as blockchain and cryptocurrency. This book is a valuable reference tool for accountants, bankers, financial planners, financial analysts, business managers, economists, computer scientists, academicians, researchers, financial professionals, and students.

Human resource departments have been a crucial part of business practices for decades and particularly in modern times as professionals deal with multigenerational workers, diversity initiatives, and global health and economic crises. There is a necessity for human resource departments to change as well to adapt to new societal perspectives, technology, and business practices. It is important for human resource managers to keep up to date with all emerging human resource practices in order to support successful and productive organizations. The Research Anthology on Human Resource Practices for the Modern Workforce presents a dynamic and diverse collection of global practices for human resource departments. This anthology discusses the emerging practices as well as modern technologies and initiatives that affect the way human resources must be conducted. Covering topics such as machine learning, organizational culture, and social entrepreneurship, this book is an excellent resource for human resource employees, managers, CEOs, employees, business students and professors, researchers, and academicians.

This book recognizes the uniqueness of the moment; the number of new users of e-services will double during 2015-2018 (moving from 2 billion users mostly from developed nations to an additional 2 billion mostly from developing nations). This radical embrace of new e-service technologies will improve the quality of lives for residents globally.