This volume constitutes the proceedings of the 20th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2021, held in Galway, Ireland, in September 2021.* The total of 57 full and 8 short papers presented in these volumes were carefully reviewed and selected from 141 submissions. The papers are organized in the following topical sections: AI for Digital Transformation and Public Good; AI & Analytics Decision Making; AI Philosophy, Ethics & Governance; Privacy & Transparency in a Digitized Society; Digital Enabled Sustainable Organizations and Societies; Digital Technologies and Organizational Capabilities; Digitized Supply Chains; Customer Behavior and E-business; Blockchain; Information Systems Development; Social Media & Analytics; and Teaching & Learning. *The conference was held virtually due to the COVID-19 pandemic.

Featuring state-of-the-art research from leading academics in technology and organization studies, this timely Research Handbook provides a comprehensive overview of how AI becomes embedded in decision making in organizations, from the initial considerations when implementing AI to the use of such solutions in strategic decision making.

Intelligent business analytics is an emerging technology that has become a mainstream market adopted broadly across industries, organizations, and geographic regions. Intelligent business analytics is a current focus for research and development across academia and industries and must be examined and considered thoroughly so businesses can apply the technology appropriately. The Handbook of Research on Foundations and Applications of Intelligent Business Analytics examines the technologies and applications of intelligent business analytics and discusses the foundations of intelligent analytics such as intelligent mining, intelligent statistical modeling, and machine learning. Covering topics such as augmented analytics and artificial intelligence systems, this major reference work is ideal for scholars, engineers, professors, practitioners, researchers, industry professionals, academicians, and students.

The surge in technological advancements, coupled with the exponential growth of data, has left marketers grappling with the need for a paradigm shift. The once-established methods of consumer engagement are now overshadowed by the complexities of the digital age, demanding a profound understanding of artificial intelligence (AI) and data analytics. The gap between academic knowledge and practical applications in the field of marketing has widened, leaving industry professionals, educators, and students seeking a comprehensive resource to navigate the intricacies of this transformative era. AI-Driven Marketing Research and Data Analytics is a groundbreaking book that serves as a beacon for marketers, educators, and industry leaders alike. With a keen focus on the symbiotic relationship between AI, data analytics, and marketing research, this book bridges the gap between theory and practice. It not only explores the historical evolution of marketing but also provides an innovative examination of how AI and data analytics are reshaping the landscape. Through real-time case studies, ethical considerations, and in-depth insights, the book offers a holistic solution to the challenges faced by marketing professionals in the digital age.

This book constitutes the refereed proceedings of the 21st IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2022, which took place Newcastle-upon-Tyne, UK, in September 2022. The 37 papers presented in this volume were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Artificial intelligence; Data and Analytics; Careers and ICT; Digital Innovation and Transformation; Electronic Services; Health and Wellbeing; Pandemic; Privacy, Trust and Security.

This book addresses the development and adoption of artificial intelligence (AI) in and by companies and the consequent need for private sector AI regulation. Highlighting the challenges to responsible business conduct and considering stakeholder interests, it identifies ethical concerns and discusses AI standards and AI norms. Based on this needs-based analysis, the author chooses relational economics as a suitable approach to develop a theoretical AI governance model. In doing so, AI is conceptualized within relational economics in the form of an autopoietic system. Building on this theoretical contribution, the book specifies the governance adaptivity of the relational AI governance approach for an unregulated AI market and for the case of the pending E.U. AI regulation, and complements it with inductively conducted categories that summarize the main research streams in AI ethics.

Digital era reporting undergoes a seismic shift as automation takes center stage. The transition from manual reporting to real-time automated systems enhances precision and efficiency and reduces errors, empowering decision-makers. However, this era of digital reporting brings forth a new set of challenges, from data security and privacy concerns to the imperative need for robust cybersecurity measures. Impact of Digitalization on Reporting, Tax Avoidance, Accounting, and Green Finance delves into this transformative wave, comprehensively exploring its consequences on these critical domains. The book meticulously dissects both the positive and negative repercussions, encapsulating the challenges and opportunities that arise in this era of digital metamorphosis. Navigating tax avoidance in a digitalized world unveils a complex dynamic. Digitalization, on the one hand, empowers tax authorities to access and analyze vast datasets, making it challenging for businesses to engage in tax evasion. On the other hand, sophisticated digital tools provide fertile ground for corporations to devise intricate tax avoidance schemes. Striking a balance becomes paramount, necessitating regulatory measures and international cooperation while addressing ethical dimensions in leveraging technology for tax strategies. The book offers a nuanced understanding of the intricate relationship between digitalization and these critical domains, from business professionals and financial analysts to regulatory bodies and sustainability advocates.

In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised. The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation. This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field.