Knowledge Management: Systems And Processes

This book is focused on AI-empowered knowledge management to improve processes, implementation of technology for providing easy access to knowledge and the impact of knowledge management to promote the platform for generation of new knowledge through continuous learning.

This book combines the Technology Acceptance Model (TAM) and empirical analysis to investigate the adoption of integrated enterprise business information systems (ISs). TAM has been used extensively in the study of ERP acceptance as well as factors in the success or failure of ERP. To date, the adoption of information systems has been studied using various approaches, including Theory of Reasoned Action (TRA) and Theory of Planned Behaviour (TPB), both of which have roots in cognitive psychology (Nicolas et al., 2008). Even though these models have been used in research, the TAM has succeeded in capturing the attention of the information systems community. The book also analyses Knowledge Management Systems (KMSs), illustrating their function as information technologies for storing, improving and distributing intra- and intercompany knowledge. Knowledge management systems are presented as likely the most debated and polemicized topic in the field of knowledge management. Even though these systems are not a crucial aspect of knowledge management, it is evident that all major players in the ERP industry have cloud- and installation-based KMS solutions that they integrate with their ERP modules.

Data management, knowledge discovery, and knowledge processing are core and hot topics in computer science. They are widely accepted as enabling technologies for modern enterprises, enhancing their performance and their decision making processes. Since the 1990s the Internet has been the outstanding driving force for application development in all domains. An increase in the demand for resource sharing (e. g. , computing resources, s- vices, metadata, data sources) across different sites connected through networks has led to an evolvement of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications prov- ing high scalability.