Enabling Collaborative Governance through Systems Modeling Methods | Public Policy Design and Implementation

Descripción:

Enabling Collaborative Governance through Systems Modeling Methods | Public Policy Design and Implementation | Carmine Bianchi (u. a.) | Buch | xxvi | Englisch | 2020 | Springer International Publishing | EAN 9783030429690 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de ref. del artículo 118002685

Denunciar este artículo

Sinopsis:

Acerca del autor:

Carmine Bianchi is Professor of Business & Public Management at the University of Palermo, Italy. He is the director of the Doctoral level program in “Model Based Public Planning, Policy Design, & Management,” and of the Master level program in Public Management. His research focuses on the design of policies and the outline of programs linking strategy and implementation. He has published in numerous academic and professional journals. He also serves on the Scientific Committee of various academic publications.

Luis Felipe Luna-Reyes is an Associate Professor in the University at Albany's Department of Public Administration, as well as the Ph.D. Program Director in Information Science. His research focuses on electronic government and on modeling collaboration processes in the development of information technologies across functional and organizational boundaries. His research interests are related to areas such as inter-organizational collaboration, information sharing, success of government-wide Web sites, and information policy to promote economic exchange in the NAFTA region.

Eliot Rich is an Associate Professor in the Department of Information Technology Management, School of Business, University at Albany. He researches simulation, software systems, knowledge management, information and infrastructure security. He studies organizational and individual behavior through the lens of system dynamics, a perspective that uses causal structures, feedback, and information delays to understand outcomes.