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Yu-Che Chen
University of Nebraska at Omaha, College of Public Affairs and Community Service, 109 CPACS, 6320 Maverick Plaza, Omaha, NE 68182, United States

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Short Biography

Yu-Che Chen, Ph.D., is a Professor in the School of Public Administration and holds the campus-wide Isaacson Professorship at the University of Nebraska at Omaha, where he serves as the Director of the Digital Governance and Analytics Lab. Dr. Chen received his Master of Public Affairs and Ph.D. in Public Policy from Indiana University - Bloomington. His current research interests are public policy and governance of emerging technologies (artificial intelligence and drones), cyberinfrastructure governance, and collaborative digital governance. In addition to three books, he has published forty-three peer-reviewed journal articles, book chapters, and management reports in the area of digital governance.

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Journal article
Published: 23 March 2021 in Government Information Quarterly
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To lay the foundation for the special issue that this research article introduces, we present 1) a systematic review of existing literature on the implications of the use of Artificial Intelligence (AI) in public governance and 2) develop a research agenda. First, an assessment based on 26 articles on this topic reveals much exploratory, conceptual, qualitative, and practice-driven research in studies reflecting the increasing complexities of using AI in government – and the resulting implications, opportunities, and risks thereof for public governance. Second, based on both the literature review and the analysis of articles included in this special issue, we propose a research agenda comprising eight process-related recommendations and seven content-related recommendations. Process-wise, future research on the implications of the use of AI for public governance should move towards more public sector-focused, empirical, multidisciplinary, and explanatory research while focusing more on specific forms of AI rather than AI in general. Content-wise, our research agenda calls for the development of solid, multidisciplinary, theoretical foundations for the use of AI for public governance, as well as investigations of effective implementation, engagement, and communication plans for government strategies on AI use in the public sector. Finally, the research agenda calls for research into managing the risks of AI use in the public sector, governance modes possible for AI use in the public sector, performance and impact measurement of AI use in government, and impact evaluation of scaling-up AI usage in the public sector.

ACS Style

Anneke Zuiderwijk; Yu-Che Chen; Fadi Salem. Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly 2021, 38, 101577 .

AMA Style

Anneke Zuiderwijk, Yu-Che Chen, Fadi Salem. Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly. 2021; 38 (3):101577.

Chicago/Turabian Style

Anneke Zuiderwijk; Yu-Che Chen; Fadi Salem. 2021. "Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda." Government Information Quarterly 38, no. 3: 101577.

Journal article
Published: 07 January 2021 in Smart Cities
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Realizing the benefits of drones while minimizing public concerns requires development and implementation of drone use policies that are grounded in an understanding of drone users and their behavior. This study aims to contribute to data-driven smart cities by filling our gap in knowledge about city drone users and their compliance behavior. The literature review has identified the main factors affecting drone policy compliance. This study collects data via a national survey of adults on drone behavior and focuses on city drone users. The results show that city drone users are younger with more dispersed educational backgrounds and income distribution than those in the general population. Moreover, civic duty, trust in government, and knowledge about regulatory requirements are motivators for drone users to comply with drone regulation.

ACS Style

Yu-Che Chen; Chenyu Huang. Smart Data-Driven Policy on Unmanned Aircraft Systems (UAS): Analysis of Drone Users in U.S. Cities. Smart Cities 2021, 4, 78 -92.

AMA Style

Yu-Che Chen, Chenyu Huang. Smart Data-Driven Policy on Unmanned Aircraft Systems (UAS): Analysis of Drone Users in U.S. Cities. Smart Cities. 2021; 4 (1):78-92.

Chicago/Turabian Style

Yu-Che Chen; Chenyu Huang. 2021. "Smart Data-Driven Policy on Unmanned Aircraft Systems (UAS): Analysis of Drone Users in U.S. Cities." Smart Cities 4, no. 1: 78-92.

Articles
Published: 11 March 2019 in Public Performance & Management Review
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ACS Style

Yu-Che Chen; Teng-Wen Chang. Explaining Government’s Online Transparency on Collaborative Policy Platforms: Risk Management and Configurational Conditions. Public Performance & Management Review 2019, 43, 560 -586.

AMA Style

Yu-Che Chen, Teng-Wen Chang. Explaining Government’s Online Transparency on Collaborative Policy Platforms: Risk Management and Configurational Conditions. Public Performance & Management Review. 2019; 43 (3):560-586.

Chicago/Turabian Style

Yu-Che Chen; Teng-Wen Chang. 2019. "Explaining Government’s Online Transparency on Collaborative Policy Platforms: Risk Management and Configurational Conditions." Public Performance & Management Review 43, no. 3: 560-586.

Journal article
Published: 09 March 2019 in Government Information Quarterly
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This study examines the performance of a cross-boundary e-government system. It draws from studies in the fields of e-government, collaborative public management, and information system success with a focus on inter-organizational information systems to develop a conceptual framework. This framework includes efficiency, effectiveness, and accountability as key performance measures; identifies technical, managerial, and inter-organizational factors for success; and develops hypotheses accordingly. Empirical investigation utilizes user-level data from an inter-organizational e-government system that provides integrated commerce and industry service. The results underscore the importance of management support, shared goals, and inter-agency trust in improving all three measures of performance. In addition, citizen-centric and innovative organizational culture enhances efficiency and accountability while administrative interdependence impacts effectiveness and accountability. The managerial and theoretical implications of these findings and future research opportunities are also explored.

ACS Style

Yu-Che Chen; Lung-Teng Hu; Kuan-Chiu Tseng; Wen-Jong Juang; Chih-Kai Chang. Cross-boundary e-government systems: Determinants of performance. Government Information Quarterly 2019, 36, 449 -459.

AMA Style

Yu-Che Chen, Lung-Teng Hu, Kuan-Chiu Tseng, Wen-Jong Juang, Chih-Kai Chang. Cross-boundary e-government systems: Determinants of performance. Government Information Quarterly. 2019; 36 (3):449-459.

Chicago/Turabian Style

Yu-Che Chen; Lung-Teng Hu; Kuan-Chiu Tseng; Wen-Jong Juang; Chih-Kai Chang. 2019. "Cross-boundary e-government systems: Determinants of performance." Government Information Quarterly 36, no. 3: 449-459.

Articles
Published: 31 March 2017 in Public Management Review
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This study aims to advance the theory and practice of managing collaborative data networks for information and decision-support services that exist in over 400 US metropolitan areas. Integrating insights from collaborative governance, network management, and cross-boundary information sharing, this study develops a framework to outline the interplay between context, management, collaborative dynamics, technology, and performance. This study further utilizes the framework to conduct an exploratory in-depth case study of a metropolitan transportation data network to examine such interplay. The findings suggest ways to improve the performance of collaborative data networks and their implications are discussed.

ACS Style

Yu-Che Chen; Jooho Lee. Collaborative data networks for public service: governance, management, and performance. Public Management Review 2017, 20, 672 -690.

AMA Style

Yu-Che Chen, Jooho Lee. Collaborative data networks for public service: governance, management, and performance. Public Management Review. 2017; 20 (5):672-690.

Chicago/Turabian Style

Yu-Che Chen; Jooho Lee. 2017. "Collaborative data networks for public service: governance, management, and performance." Public Management Review 20, no. 5: 672-690.