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Public AI Canvas for AI-Enabled Public Value : A Design Science Approach

Title data

Fatima, Samar ; Desouza, Kevin C. ; Buck, Christoph ; Fielt, Erwin:
Public AI Canvas for AI-Enabled Public Value : A Design Science Approach.
In: Government Information Quarterly. Vol. 39 (2022) Issue 4 . - No. 101722.
ISSN 0740-624x
DOI: https://doi.org/10.1016/j.giq.2022.101722

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Künstliche IntelligenzNo information

Abstract in another language

Public agencies have a strong interest in artificial intelligence (AI) systems. However, many public agencies lack tools and frameworks to articulate a viable business model and evaluate public value as they consider investing in AI systems. The business model canvas used extensively in the private sector offers us a foundation for designing a public AI canvas (PAIC). Employing a design science approach, this study reports on the design and evaluation of PAIC. The PAIC comprises three distinctive layers: (1) the public value-oriented AI-enablement layer; (2) the public value logic layer; and (3) the public value-oriented social guidance layer. PAIC offers guidance on innovating the business models of public agencies to create and capture AI-enabled value. For practitioners, PAIC presents a validated tool to guide AI deployment in public agencies.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: AI; public agency; business model; canvas
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration VII - Information Systems Management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration VII - Information Systems Management > Chair Business Administration VII - Information Systems Management - Univ.-Prof. Dr. Torsten Eymann
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 04 Jul 2022 08:27
Last Modified: 22 Nov 2022 10:30
URI: https://eref.uni-bayreuth.de/id/eprint/70361