Public AI canvas for AI-enabled public value: A design science approach

被引:18
|
作者
Fatima, Samar [1 ]
Desouza, Kevin C. [1 ,2 ]
Buck, Christoph [2 ]
Fielt, Erwin [3 ]
机构
[1] Queensland Univ Technol, Sch Management, QUT Business Sch, GPO Box 2434, Brisbane, Qld 4001, Australia
[2] Queensland Univ Technol, Ctr Future Enterprise, QUT Business Sch, Brisbane, Qld, Australia
[3] Queensland Univ Technol, Sch Informat Syst, Brisbane, Qld, Australia
关键词
Artificial intelligence; Business model canvas; Public agencies; Public value; Design science; BUSINESS MODEL INNOVATION; ARTIFICIAL-INTELLIGENCE; INFORMATION-SYSTEMS; E-GOVERNMENT; CITIZEN SATISFACTION; SECTOR; TRANSPARENCY; PERSPECTIVE; CHALLENGES; ALGORITHMS;
D O I
10.1016/j.giq.2022.101722
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
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.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] AI-enabled recruiting in the war for talent
    Black, J. Stewart
    van Esch, Patrick
    BUSINESS HORIZONS, 2021, 64 (04) : 513 - 524
  • [32] AI-Enabled Trust in Distributed Networks
    Li, Zhiqi
    Fang, Weidong
    Zhu, Chunsheng
    Gao, Zhiwei
    Zhang, Wuxiong
    IEEE ACCESS, 2023, 11 : 88116 - 88134
  • [33] AI-enabled IT capability and organizational performance
    Wang, Fang
    SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE, 2022, 39 (03) : 609 - 617
  • [34] AI-enabled transformations in telecommunications industry
    Khan, Muhammad Khurram
    TELECOMMUNICATION SYSTEMS, 2023, 82 (01) : 1 - 2
  • [35] AI-enabled manufacturing process discovery
    Quispe, D.
    Kozjek, D.
    Mozaffar, M.
    Xue, T.
    Cao, J.
    PNAS NEXUS, 2025, 4 (02):
  • [36] AI-Enabled Monitoring, Diagnosis & Prognosis
    Ruqiang Yan
    Xuefeng Chen
    Weihua Li
    Robert X.Gao
    Chinese Journal of Mechanical Engineering, 2021, 34 (03) : 14 - 15
  • [37] AI-Enabled Blended Collaborative Education
    Wang, Xiaoxia
    E-BUSINESS: NEW CHALLENGES AND OPPORTUNITIES FOR DIGITAL-ENABLED INTELLIGENT FUTURE, PT I, WHICEB 2024, 2024, 515 : 313 - 324
  • [38] AI-enabled transformations in telecommunications industry
    Muhammad Khurram Khan
    Telecommunication Systems, 2023, 82 : 1 - 2
  • [39] Investigating Teachers' Use of an AI-Enabled System and Their Perceptions of AI Integration in Science Classrooms: A Case Study
    Shi, Lehong
    Ding, Ai-Chu
    Choi, Ikseon
    EDUCATION SCIENCES, 2024, 14 (11):
  • [40] The dynamics of AI capability and its influence on public value creation of AI within public administration
    van Noordt, Colin
    Tangi, Luca
    GOVERNMENT INFORMATION QUARTERLY, 2023, 40 (04)