D. D. Sharma – författare

Research on the internationalisation process of firms shows that the development of experiential knowledge is a major factor in explaining firms' internationalisation. However, our knowledge of how this takes place is limited. The detailed mechanisms of learning, and the effects of the duration of the firm's international operations, have not been studied in depth. Using examples from Denmark, Finland, South Korea, New Zealand and Sweden, the contributors to this book examine these factors and test the basic assumptions of the internationalisation process of firms.In doing so, they explore how firms accumulate knowledge on foreign markets and analyse whether the number of countries in which firms operate influences the quantity and quality of knowledge accumulated. The effect is to expand our understanding of the use of knowledge and the international transfer of knowledge in the internationalisation process.Learning in the Internationalisation Process of Firms will be of great interest to scholars, researchers and practitioners of international business and management.

Food security is a critical global challenge, aiming to provide sufficient and healthy food for all. The United Nations has set Sustainable Development Goals (SDGs) to achieve global prosperity while ensuring environmental protection. Machine learning (ML) techniques play a crucial role in understanding and predicting food security. Key applications include cropland mapping, crop type identification, yield prediction, and field delineation. Challenges include handling complex data and ensuring rigorous evaluation. Looking ahead, advanced techniques such as AI and interdisciplinary collaborations will drive progress toward a hunger-free and sustainable future. This book concentrates on the fundamentals and uses of environment science perspective on Food security using cutting-edge methods of spatial information and artificial intelligence. Experts and researchers in the fields of agriculture, environmental science and engineering, disaster management, remote sensing, and geographic information systems have contributed to this volume.

Food security is a critical global challenge, aiming to provide sufficient and healthy food for all. The United Nations has set Sustainable Development Goals (SDGs) to achieve global prosperity while ensuring environmental protection. Machine learning (ML) techniques play a crucial role in understanding and predicting food security. Key applications include cropland mapping, crop type identification, yield prediction, and field delineation. Challenges include handling complex data and ensuring rigorous evaluation. Looking ahead, advanced techniques such as AI and interdisciplinary collaborations will drive progress toward a hunger-free and sustainable future. This book concentrates on the fundamentals and uses of environment science perspective on Food security using cutting-edge methods of spatial information and artificial intelligence. Experts and researchers in the fields of agriculture, environmental science and engineering, disaster management, remote sensing, and geographic information systems have contributed to this volume.