Causal Knowledge Analytics

This timely book introduces causal knowledge analytics as a pioneering methodology to enhance scholarly productivity through the systematic digitization and analysis of causal knowledge. It presents a five-level framework for organizing and analysing codified causal knowledge, offering a structured approach for scholars to navigate growing literature landscapes.By converting knowledge into computable formats, Causal Knowledge Analytics enables scholars to harness digital capabilities for literature processing. The authors apply this new methodology to a set of publications in the information systems field, integrating advanced techniques such as graph theory, network analysis, natural language processing and machine learning methods. They also explore machine learning and computational techniques which help automate and expedite literature processing, enabling scholars to reach the knowledge frontier more efficiently.Causal Knowledge Analytics is a fundamental resource for business and social science scholars. Students in business and management will also benefit from the book’s theoretical and practical insights.

Yuanyuan (April) Song, Assistant Professor of Management Information Systems, Management Department, Marquette University, Richard T. Watson, Research Director, Digital Frontier Partners, Regents Professor and J. Rex Fuqua Distinguished Chair for Internet Strategy Emeritus, University of Georgia and Xia Zhao, Associate Professor of Management Information Systems, Department of Management Information Systems, University of Georgia, USA