دانلود کتاب هوش مصنوعی مولد برای تجارت و اقتصاد: نظریه و کاربردها

The accelerating diffusion of artificial intelligence has become one of the defining forces shaping contemporary business environments and economic systems. In recent years, this transformation has entered a new phase with the emergence of Generative Artificial Intelligence—a class of AI systems capable not only of analyzing and predicting outcomes but also of creating original content, models, strategies, and solutions. Generative AI has begun to influence how firms innovate, how markets function, how value is created and distributed, and how economic decision-making is conceptualized. As a result, traditional theoretical frameworks and managerial practices in business and economics are increasingly being re-examined in light of these technological capabilities. Historically, the evolution of business and economic thought has been closely intertwined with technological progress. From the Industrial Revolution and mechanization to the rise of information technology and digital platforms, each wave of innovation has reshaped productivity, organizational structures, labor relations, and market dynamics. Earlier generations of artificial intelligence primarily supported automation, optimization, and data-driven decision-making, enhancing efficiency within established systems. Generative AI, however, represents a qualitative departure from these earlier tools. By learning from vast datasets and generating novel outputs—such as textual analysis, software code, marketing content, _inancial forecasts, strategic scenarios, and policy simulations—generative AI challenges conventional boundaries between human cognition and machine intelligence. This shift raises fundamental questions about creativity, knowledge production, economic agency, and competitive advantage. The edited volume Generative AI for Business and Economics: Theory and Applications is situated within this rapidly evolving landscape. The book seeks to provide a comprehensive and critical examination of generative AI as both a technological innovation and an economic force. Rather than approaching generative AI solely from a technical or computational perspective, this volume positions it within broader business, economic, and institutional contexts. It recognizes generative AI as a socio-technical system whose implications extend far beyond efficiency gains, influencing organizational learning, innovation ecosystems, labor markets, governance structures, and long-term economic development. A key motivation behind this book is the need to bridge the growing gap between theory and practice. While industry adoption of generative AI tools has accelerated at an unprecedented pace, academic inquiry into their economic and managerial implications is still emerging. Existing theories of the firm, market competition, productivity, and innovation were largely developed in contexts where human cognition was assumed to be the primary source of creativity and strategic reasoning. Generative AI disrupts these assumptions by introducing machine-generated intelligence into core business and economic processes. This volume therefore invites scholars to revisit established theories and to develop new conceptual frameworks that better explain value creation, decision-making, and competitive dynamics in an AIenabled economy. On the theoretical front, the chapters in this book engage with fundamental questions in business and economics. Contributors examine how generative AI affects productivity growth, firm boundaries, transaction costs, and knowledge-based competition. The book explores the implications of generative AI for innovation economics, particularly its role in accelerating research and development, reducing experimentation costs, and enabling new forms of collaborative and open innovation. It also considers macroeconomic perspectives, including the potential impact of generative AI on economic growth, income distribution, labor demand, and structural transformation. By situating generative AI within these theoretical debates, the volume contributes to a deeper and more nuanced understanding of its long-term economic significance. Equally important, this edited book emphasizes practical applications across diverse business domains. The application-oriented chapters illustrate how generative AI is being deployed in marketing, finance, operations, human resource management, entrepreneurship, supply chain management, and strategic planning. From AI-driven personalization and content generation to automated financial analysis, risk assessment, and decision support, the book demonstrates how generative AI is reshaping organizational processes and managerial roles. These applications highlight both the opportunities and the complexities involved in integrating generative AI into existing business models and organizational cultures. The volume also addresses the growing importance of data, analytics, and digital infrastructure in enabling generative AI adoption. Generative models rely heavily on data quality, computational resources, and algorithmic design, making issues of data governance, privacy, and security central to their effective use. Contributors examine how firms and institutions can develop the necessary capabilities to leverage generative AI responsibly, including investments in digital skills, organizational learning, and cross-functional collaboration. In doing so, the book underscores that successful AI adoption is as much an organizational and strategic challenge as it is a technological one. Beyond efficiency and innovation, the book gives significant attention to ethical, legal, and policy considerations. The deployment of generative AI raises pressing concerns related to algorithmic bias, transparency, explainability, intellectual property rights, and accountability. In business and economic contexts, these concerns are closely linked to issues of trust, legitimacy, and social acceptance. Several chapters critically analyze the regulatory and governance frameworks needed to ensure that generative AI contributes to inclusive and sustainable economic outcomes. Rather than viewing regulation as a constraint on innovation, the book emphasizes the role of adaptive governance in aligning technological progress with societal values and economic stability.