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AI Innovations in Service and Tourism
Marketing

AI Innovations in Service and Tourism Marketing

Leveraging Data Science and Machine Learning

Predictive Intelligence for Transforming Healthcare Insurance

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection

Hart Studies in Comparative Public Law

Theory, Practice and Problems

Automation in Governance

The primary objective of this work is to develop a behavioral basis for an AI algorithm to assist managers of finance, production, and marketing divisions of firms in imperfect markets. Production, sales revenue, external finance, and networth (market value of the fixed assets) have the pivotal role as objectives. However, all of them cannot be maximized simultaneously due to the interdependence among them. The managers tend to pursue the most valuable objective using the most productive strategy at each point of time. This was utilized as a basis for the AI algorithm. Non-linearities in the switches in objectives and strategies are a short run phenomenon while swaps will be preferred if firms have a long run advantage. Such observed phenomena were also incorporated in the specification of the AI algorithms. The important results are as follows. (i) In most industries the shortage of demand is not a constraint. Instead, the ability of firms to increase their strategic supply is dominant. (ii) Most firms pursue strategic supply based on the capital stock available. This indicates a preference for external finance. (iii) In the short run firms utilize working capital finance and selling costs to ensure that the sales revenue achieved is commensurate with the strategic supply. (iv) Thus, the postulate that expected demand determines the strategic supply as well as the profit maximization postulate for the short run do not appear as the priorities.

The primary objective of this work is to develop a behavioral basis for an AI algorithm to assist managers of finance, production, and marketing divisions of firms in imperfect markets. Production, sales revenue, external finance, and networth (market value of the fixed assets) have the pivotal role as objectives. However, all of them cannot be maximized simultaneously due to the interdependence among them. The managers tend to pursue the most valuable objective using the most productive strategy at each point of time. This was utilized as a basis for the AI algorithm. Non-linearities in the switches in objectives and strategies are a short run phenomenon while swaps will be preferred if firms have a long run advantage. Such observed phenomena were also incorporated in the specification of the AI algorithms.&nbsp;The important results are as follows. (i) In most industries the shortage of demand is not a constraint. Instead, the ability of firms to increase their strategic supply is dominant. (ii) Most firms pursue strategic supply based on the capital stock available. This indicates a preference for external finance. (iii) In the short run firms utilize working capital finance and selling costs to ensure that the sales revenue achieved is commensurate with the strategic supply. (iv) Thus, the postulate that expected demand determines the strategic supply as well as the profit maximization postulate for the short run do not appear as the priorities.

Towards an AI Algorithm for Multiple Objectives in Imperfect Markets

Routledge Studies in Marketing

Contemporary Issues in Inter-Brand Communication

Brand-to-Brand Engagement

A brilliant exploration of the phenomenon of prediction markets that asks: what happens when democracy becomes another commodity? Prediction markets are sweeping the world, fundamentally changing the nature of gambling - and the very ways in which we attempt to divine the future.Posed as binary 'Yes/No' questions about future events, prediction markets allow any difference of opinion - whether on pop culture or politics, wars or assassinations - to be financialised. A regulatory minefield with moral implications that divide nations, governments and bettors, they have proved as effective at accurately predicting the timing of missile strikes as they have at calling the outcomes of elections.But are these markets really a new phenomenon? And what does it mean when those tasked with safeguarding democracy hold financial stakes in its volatility?From Renaissance Rome, where merchants wagered on papal conclaves, through nineteenth-century London's wartime coffee house gossip, to the Pentagon's ill-fated terrorism futures exchange and blockchain-based betting on Polymarket today, The Big Bet tells the gripping five-hundred-year story of a permissive regime that no one designed and no one voted for.Drawing on deep expertise, Michael Auerbach examines how these markets are changing our world, and what they mean for the future where democracy becomes just another commodity to be priced and traded. Because prediction markets are not an anomaly - they are the logical endpoint of a society that has applied market capitalism to every aspect of life. And in a world where everything is a wager and nothing is off limits, knowing where the chips will fall matters more than ever.

Prediction Markets and the Future of Democracy

The Big Bet

Taylor and Francis Proceedings in Computer Science and Engineering

Reimagining Tomorrow: AI and Sustainable Imperative presents a curated collection of high-quality research contributions that explore the transformative role of Artificial Intelligence in addressing pressing sustainability challenges across a variety of sectors.This book highlights how advanced AI techniques-ranging from machine learning and deep learning to intelligent optimization-are being leveraged to enhance efficiency, reduce environmental impact, and support data-driven decision-making. The volume emphasises the growing importance of integrating technological innovation with sustainability goals to create resilient and future-ready systems. The primary focus of this book is on human-centred Artificial Intelligence, with particular attention given to its ethical, societal, and governance dimensions. It examines critical issues such as transparency, fairness, accountability, and privacy, while also showcasing AI-driven solutions for sustainable development in key domains, including healthcare, education, smart infrastructure, and Industry 5.0. The contributions further address interdisciplinary approaches, combining AI with emerging paradigms such as edge computing, IoT, and green technologies to enable scalable and responsible innovation.The book provides valuable insights for researchers, academicians, industry practitioners, policymakers, and students by offering a comprehensive perspective on both theoretical advancements and practical implementations of AI for sustainability and serves as a reference for understanding current trends, identifying research gaps, and exploring future directions in the design of intelligent, ethical, and sustainable systems.

AI and Sustainable Imperative

Reimagining Tomorrow

Build an organization that adapts, evolves and performs in constant uncertainty. Workplace Agility by Natal Dank is a strategic guide for senior HR leaders, business executives and transformation leads who need to move beyond reactive change and design organizations that thrive in disruption. As AI accelerates change and workforce expectations shift, this book shows how to replace outdated resilience models with a more dynamic, future-ready approach.Introducing the Workplace Elasticity (WE) model, it explains how to align people strategy, business strategy and Industry 5.0 thinking to create organizations that can flex, stretch and re-form without losing performance. Combining strategic insight with real-world examples from companies including TomTom, Novartis and Liberty Latin America, it translates complexity into practical action.You will learn how to: - Align workforce planning with AI-enabled business strategy - Design operating models that adapt to constant change - Enable effective human-machine collaboration at scale - Build cultures that support purpose, connection and performance - Apply tools and frameworks for agile decision-makingWorkplace Agility equips you with the tools to lead with confidence in uncertainty, building adaptive organizations that sustain performance and competitive advantage.Themes include: workplace agility, organizational design, HR strategy, AI in business, workforce transformation, Industry 5.0

An HR Approach to Organizational Design and People Strategy

Workplace Agility

A step-by-step guide to AI-native analytics systems for BI practitioners Building Business Intelligence Apps with AI provides a step-by-step guide for data analysts, data engineers, and BI developers who need to integrate AI into their analytics workflows. Written by Ryan Dolley, a data industry veteran with fifteen years of experience across BI development, consulting, and data vendor roles, this book shows how to partner with AI to deliver better data models, dashboards, and business queries while building AI-native experiences for end users. The book addresses data foundations, outsourcing repetitive tasks to AI, and scaling autonomous analytics systems that maintain accuracy and data governance. Readers learn practical steps for increasing individual and team effectiveness using AI while avoiding common pitfalls such as hallucination, data inaccuracy, and user abandonment. It delivers operating models that data departments need, covering everything from data blending and dashboard creation to personalization and responsiveness. Readers will also find: <ul> <li>Practical techniques for partnering with LLMs to accelerate data modeling, querying, and dashboard development across BI workflows</li> <li>Strategies to outsource repetitive analytics tasks to AI while maintaining rigorous data governance and accuracy standards</li> <li>Operating models for individuals and data departments to scale AI-native analytics systems that end users adopt</li> <li>Methods for building autonomous, personalized analytics experiences that address user expectations for speed and responsiveness</li> <li>Guidance on avoiding AI pitfalls including hallucination, data inaccuracy, and user abandonment in production analytics environments</li> </ul> Data analysts, data engineers, BI developers, and their managers will find in this book the specific skills and operating models needed to integrate AI into daily analytics work. It equips practitioners to build accurate, autonomous analytics systems while strengthening data governance and accelerating delivery across their teams.

Building Business Intelligence Apps with AI

How Leaders Build Customer Experience that Scales Without Fragmenting the Enterprise

Coherence at Velocity

Fusing High Tech with Human Touch for Organizational Success

The Pulse of Progress

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

AI Innovations in Service and Tourism Marketing

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