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S. J. Sanjana

From Human Bias to Machine Intelligence. An Empirical Study on Investor Behaviour and AI-Based Investment Decision Making

Routledge Studies in Accounting

Implications for Practice, Education, and Research

Generative AI and Agentic Systems in Accounting

Digital Transformation and Innovation

This book explores how Generative Artificial Intelligence is reshaping creativity, innovation, and human–AI collaboration across art, design, business, and education. Taking an interdisciplinary and practice-oriented approach, the book offers conceptual frameworks and practical insights to support the active shaping of creative processes in the context of digital transformation. The book brings together international contributions from artistic practice, critical and philosophical inquiry, design and innovation research, entrepreneurial ecosystems, and future-oriented education. Structured around three thematic sections, creative practices and critical perspectives; human–AI collaboration in design and innovation; and education and future skills, the volume develops a coherent narrative on how AI transforms not only creative outputs, but also processes, roles, and ways of thinking.A distinctive feature of the book is its integration of reflective and artistic perspectives with empirical studies and real-world applications. It traces historical developments of AI in creative domains, challenges dominant narratives of generative AI, and introduces novel approaches to co-creation and human–machine partnerships. At the same time, it addresses applied contexts such as storytelling in entrepreneurial ecosystems, AI-supported design, and practical experiments in collaborative creativity. Overall, the book provides guidance for developing human-centred, responsible, and sustainable approaches to creativity in an age of co-creative intelligence.

Reframing Human–Machine Collaboration

Creativity and Artificial Intelligence

The Essential Guide to Launching Your Career in Artificial Intelligence

Break into AI

Algorithmic Management: Artificial Intelligence and the Transformation of Organizations is the first comprehensive textbook designed to prepare management students to engage critically and responsibly with artificial intelligence in organizations. Bringing together classical management theory, contemporary AI applications, and systematic power analysis, the book equips future managers to both harness AI's potential and confront its risks.Structured in three parts, the book begins with foundational frameworks-socio-technical systems theory, technology and society perspectives, and core management theories reinterpreted for the AI era. It then examines applications across all major management functions-Marketing, HR, Finance, Operations, Supply Chain, and Strategy-using over 30 real-world case studies drawn from different industries, parts of the world, and types of organizations from multinational corporations to nonprofits and social ventures. The final section addresses organizational design, leadership, governance, and the future of algorithmic organizations. Throughout, pedagogical features-learning objectives, vignettes, discussion questions, toolkits, and ethical dilemmas-make the book classroom-ready and adaptable across MBA, undergraduate, and executive education settings. The text advances an approach of pragmatic criticality: AI is neither celebrated uncritically nor rejected outright but treated as a socio-technical phenomenon requiring managerial judgment, ethical reflection, and contextual awareness.By bridging management theory, functional practice, and critical perspectives, this book fills a critical market gap and is positioned to become the definitive teaching resource on AI and management for business education worldwide.

Artificial Intelligence and the Transformation of Organizations

Algorithmic Management

<div><div>Das erste Lexikon im Fintech-Bereich stellt mit über 800 Stichworten einschlägiges Know-how zur "Financial Technology" bereit und hilft damit bei der Klärung aktueller Begrifflichkeiten. Dem interdisziplinären Charakter der Fintech-Entwicklung zufolge sind die Begriffe an der Schnittstelle von (Wirtschafts-)Informatik, Finanzwissenschaft und Innovationsmanagement verortet. Die Fintech-Terminologie richtet sich dabei mit ihren konzise beschriebenen und mit anschaulichen Grafiken ergänzten Inhalten gleichermaßen an Studierende sowie Interessierte aus Wissenschaft und Praxis.</div><div> </div><div>Die Autoren</div><div>Prof. Dr. Rainer Alt ist Professor für Wirtschaftsinformatik an der Universität Leipzig und Autor zahlreicher Publikationen im Fintech-Bereich.&nbsp;</div><div>Dr. Stefan Huch ist Senior Director bei Capgemini Invent und vertritt die Professur Digitalisierung von Unternehmensprozessen an der Hochschule Merseburg.</div><div> </div><div>Würdigungen</div><div>"Aus meiner Sicht als Führungskraft einer global agierenden Bank ist das Fintech-Lexikon ein sehr gelungenes Nachschlagewerk, das alle relevanten Begrifflichkeiten in einer sehr verständlichen Art strukturiert darstellt und erläutert- eine gute Stütze, um in der Praxis die relevanten Begrifflichkeiten der Fintech-Szene im Überblick zu haben." Achim Lebeau, COO, DB Privat- und Firmenkunden AG</div><div>"Selbst gründen bedeutet in vielen Bereichen ins kalte Wasser geworfen zu werden. Man muss in kürzester Zeit zum Allround-Experten werden: im Sales, im Marketing, im Finance oder im Fundraising. Hier eine strukturierte Übersicht aller Begrifflichkeiten der Startup-Welt an der Hand zu haben, ist eine enorme Hilfe." Jenny Saft, Geschäftsführung und Co-Founderin, Oviavo GmbH</div><div>"Dieses Fintech-Lexikon bietet einen hervorragenden Orientierungsrahmen, indem es alle relevanten Begrifflichkeiten im Kontext der digitalisierten Finanzwelt aufgreift. Diese sind konsequent strukturiert und inverständlichen Worten definiert. Ein wertvoller Beitrag für die Praxis." Thomas Zerndt, CEO, Business Engineering Institute St. Gallen AG</div>
</div>

Dieses Buch hilft dem Leser im Umfeld der Digitalisierung bei der Begriffsklärung und stellt einschlägiges Know-how zur Verfügung.&nbsp;Was bedeutet 42? Was ist eine Subchain? Was ist der Unterschied zwischen Robotics und Artificial Intelligence? Der Schwerpunkt liegt dabei auf den Entwicklungen, die seit 2010 mit der Digitalisierung unter dem Namen "Financial Technology" eine zunehmende Verbreitung erlebt haben. Die Begrifflichkeiten umfassen grundlegende Begriffe, die Eingang in die allgemeine Sprache gefunden haben, wie etwa Artificial Intelligence oder Blockchain, aber auch fachspezifische Termini, die für das Verständnis der einzelnen Technologien notwendig sind (zum Beispiel 42, Nonce). Die Fintech-Terminologie bietet dem Leser einen konzisen und gleichzeitig anschaulich dargestellten alphabetisch geordneten Überblick mit den darin enthaltenen praxisorientierten Antworten.

Begriffe für die digitalisierte Finanzwelt

Fintech-Lexikon

Artificial Intelligence and the Rule of Law

This book offers a timely examination of AI regulation in the era of Industry 5.0, where innovation must be balanced with human well-being, sustainability, and responsible governance. It explores how artificial intelligence is transforming healthcare, environmental stewardship, corporate governance, digital commerce, and emerging technologies while creating new regulatory, ethical, and societal challenges. As AI increasingly influences critical decisions across healthcare systems, innovation ecosystems, educational institutions, and business environments, policymakers and practitioners face growing questions regarding accountability, transparency, safety, trust, privacy, and human rights. Addressing these concerns, this edited volume brings together leading scholars and experts to examine the evolving legal, ethical, and governance frameworks needed to support responsible AI adoption.The chapters cover a diverse range of contemporary topics, including AI-enabled drug discovery, healthcare decision-making, medical liability, patient rights, sustainability governance, climate and environmental regulation, AI in education, intellectual property and innovation, corporate governance, e-commerce regulation, cybersecurity, regulatory sandboxes, and explainable AI in high-risk sectors. Together, these contributions provide interdisciplinary perspectives on how AI can be developed and governed in ways that foster innovation while protecting societal interests.A central theme of the book is the Industry 5.0 vision of human-centric technological progress. The contributors highlight how effective regulation can promote trustworthy AI systems that support sustainable development, strengthen innovation, encourage responsible technological deployment, and create long-term value for society. By bridging law, policy, governance, healthcare, sustainability and technology studies, this volume offers valuable insights into the opportunities and challenges of AI-driven transformation. It provides a forward-looking resource for understanding how regulatory frameworks can enable innovation while ensuring that technological advancement remains ethical, inclusive, and sustainable.This book is intended for researchers, academics, policymakers, regulators, healthcare professionals, technology practitioners, industry leaders, graduate students, and readers interested in artificial intelligence, Industry 5.0, sustainability, healthcare innovation and emerging technologies.

Healthcare, Sustainability, Innovation and Emerging Technologies

AI Regulation in Industry 5.0

This is the updated and expanded edition of the only radical, multidisciplinary, international introduction to intellectual property in its socio-economic contexts. Bringing together voices from law, sociology, economics, political science, and cultural studies from across the Global North and Global South, experts and activists Mat Callahan and Jim Rogers offer a detailed history and analysis of IP before asking critical questions designed to underscore its importance in the broader struggle between capital and the commons. What are the broader implications of IP, whether for agribusiness, pharmaceuticals, education, or entertainment? Is IP law a fundamental pillar of our culture, or has it been used to protect and assert the ownership of, and thus limit, the spread of ideas and creativity? With a chapter structure moving from historical and theoretical foundations to industry-specific conflicts and policy analysis, this book covers the breadth and depth of IP areas, including patents, copyrights, trademarks, and trade secrets, as well as emerging issues such as digital rights and biotechnology. Key features include: - voluminous, up-to-date case studies, both Western and non-Western; - an extensive, jargon-busting list of abbreviations of specialized or technical terms; - a new chapter on copyright and socialism; - revisions and updates accounting for the seismic impacts of artificial intelligence. This is a foundational must-read for all researchers, students, and activists interested in critical analyses of IP laws and policies--not just their legal frameworks, but also their broader social, economic, and political implications.

A Radical Guide to the Past, Present, and Future of the Ownership of Ideas

Rethinking Intellectual Property

Turn enterprise AI into a source of strategic insight, not just productivity gains.The AI Gap by leading AI expert and keynote speaker Richard Foster-Fletcher shows business and technology leaders how to unlock greater value from enterprise AI by changing the structures, processes and culture surrounding it. Organizations invest in AI expecting new intelligence and transformative insights. Yet AI is often introduced through procurement, security, legal and compliance systems designed for standard software, restricting its potential before it can deliver on that promise. This book explains why this gap persists and what organizations must change to close it. You'll learn how to: - Identify organizational barriers limiting the value of AI - Move beyond productivity-focused AI implementation - Create conditions where AI can generate deeper insights - Enable employees to explore, experiment and challenge established thinking &gt;The AI Gap equips leaders to rethink AI implementation, create space for experimentation and turn artificial intelligence into a powerful driver of organizational insight and long-term business value. Themes include: enterprise AI, AI implementation, AI strategy, business transformation, organizational culture, AI innovation

How Business Decisions Expand What AI Can Do

The AI Gap

International Series in Operations Research & Management Science

This book presents a rigorous, practitioner-ready framework for determining when interventional causal claims can be validly drawn from observational data. By integrating potential outcomes (PO), structural causal models (SCMs), directed acyclic graphs (DAGs), quasi-experimental and longitudinal designs, information-theoretic approaches, and modern machine-learning methods with decision-analytic principles, it identifies necessary conditions for each step of the causal inference process, from defining estimands and identifying causal effects to study design, measurement, estimation, interpretation, robustness, and external validity. The framework is operationalized through practical checklists, tables, diagnostic and falsification tests, and AI-assisted tools, making it accessible across fields such as epidemiology, environmental and occupational health, and regulatory risk analysis. Designed to meet the needs of professionals making high-stakes decisions based on nonexperimental data, the book addresses the lack of clear, testable criteria for credible causal inference. Real-world examples throughout the book illustrate diagnostics, falsification tests, and robustness checks, linking evidence directly to decision-making. Bridging multiple causal frameworks and modern machine-learning methods, it equips readers to assess when data support cause-and-effect conclusions, supporting more responsible, evidence-based policy and risk management.The book offers health risk analysts, epidemiologists, and policy professionals a practical guide to valid causal inference from observational data. It is also ideal for graduate students and workshop participants in data science, decision analysis, risk analysis, and AI/ML applications.

Valid Causal Inference from Observational Data

Research in Creative Writing

Writing Human in the Age of AI

Reciprocity begins with a simple but unsettling question: why have we come to believe that human beings are naturally self-interested, competitive and acquisitive? This galvanising, perspective-shifting book dismantles the myth of Homo economicus to show us the way towards a world whose realities align with our true selves.Drawing on a variety of disciplines, including anthropology, archaeology, evolutionary biology, psychology, economics and social epidemiology, Richard Wilkinson demonstrates that the characteristics which we assume to be our human nature are in fact effects of the market societies we live in. He traces how various systems of exchange have created profoundly different forms of social life throughout the course of history, shaping our behaviours, motivations, and the kinds of people we become. Though it may often feel like it, the avarice and oppressions that defines the twenty-first century are not an unchangeable reality we simply have to live with.The book explains why inequality, status competition and consumerism hold so much power over us and reveals the depths to which these forces are entwined with the ever-rising crises of mental illness, political polarisation and environmental destruction. Rather than asking how we can make individuals behave better, it makes a compelling case for restructuring our societies to make life happier, meaningful and more fulfilling for one and all.Rigorously researched, bracingly practical and full of passion and hope in equal measure, Reciprocity offers a bold reinterpretation of the world we live in. It equips us with the necessary tools to look beyond and remake it in our very own, amiably human image.

Reclaiming Our Humanity from the Market

Reciprocity

The Fight for the Future of Work

We Are Not Machines

From Human Bias to Machine Intelligence. An Empirical Study on Investor Behaviour and AI-Based Investment Decision Making

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