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Book synopsis

This book investigates organizational learning from information processing perspectives. Continuous change and complexity in regulatory, social and economic environments increasingly demand organizations and their employees to acquire the necessary job-specific knowledge in right time and right format. Much of regulatory documentation may now available in digital form, but due to their complexity and diversity it is a challenging task to identify the relevant elements to a particular context. In such scenarios, business processes tend to be important knowledge sources, containing rich, but in many case embedded, hidden knowledge. This book discusses the possible connection between business process models and corporate knowledge assets; knowledge extraction approaches from organizational processes; corporate knowledge base development and maintenance; semantic business process management and its relation with organizational learning approaches. The chapters will go through a knowledge management solution, which aims to extract, organize and preserve knowledge embedded in business processes to enrich organizational knowledge base in a systematic and controlled way, support employees to acquire their job role specific knowledge and help organizational learning, and to govern human capital investment. All these topics are analyzed through real world cases from the domains of insurance, food safety, innovation, and funding.

Book details

Book category Books in English Economics, finance, business & management Business & management Management & management techniques

• Full title: Corporate Knowledge Discovery and Organizational Learning
• Subtitle: The Role, Importance, and Application of Semantic Business Process Management
• Author: András Gábor, Andrea Ko
• Language: English
• Binding: Hardback
• Number of pages: 173
• EAN: 9783319289151
• ISBN: 3319289152
• ID: 02876375
• Publisher: Springer International Publishing AG
• Weight: 4085 g
• Dimensions: 235 × 155 × 16 mm
• Date of publishing: 20. April 2016

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