Re‐Imagining the Epistemic Possibilities of GPT for Public Administration Research in Competitive Settings
Datos Bibliográficos
| ID | 11587232 |
|---|---|
| Autores | Yanto Chandra (0000-0003-1083-5813, Department of Public and International Affairs, Laboratory for Public Management and Policy (LAMP), Technology, Policy and Law Laboratory City University of Hong Kong Hong Kong SAR China, autor de correspondencia), Jianxiang Tan (0009-0008-2900-3826, Department of Public and International Affairs, Laboratory for Public Management and Policy (LAMP), Technology, Policy and Law Laboratory City University of Hong Kong Hong Kong SAR China) |
| Año | 2026 |
| Fecha de publicación | 2026-02-13 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Public Administration Review (JOURNAL) |
| Identificadores de la revista | ISSN: 0033-3352 • E-ISSN: 1540-6210 |
| Editorial | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/puar.70098 |
| OpenAlex | W7128778127 |
| Idioma | EN |
| Referencias citadas | 71 |
Innovation is desirable for the public sector. Yet understanding what and how some innovation projects survive and thrive in a competitive landscape—or public sector innovation—is often challenging. The challenges not only rest in the invisibility of the features of an innovation to human eyes but also in the lack of their accessibility for analysis. This study showcases a methodological framework using a generative pre‐trained transformer (GPT) for scale development and synthetic data generation to measure, predict, retrodict, and calibrate innovation outcomes using real‐world and synthetic data and a human‐in‐the‐loop process. This study demonstrates the epistemic gains of the framework in predicting and manipulating competitive texts to simulate the past, present, and possibly the future. The approach offers avenues for future research on a wide range of competitive phenomena using large‐scale text analysis across the social sciences
Competitive advantage · Generative grammar · Invisibility · Public sector · Social innovation · Computational and Text Analysis Methods · Innovative Approaches in Technology and Social Development · Qualitative Comparative Analysis Research
Generative artificial intelligence, human creativity, and art
AI and the transformation of social science research
Psychological Aspects of Natural Language Use
ChatGPT outperforms crowd workers for text-annotation tasks
Can Generative AI improve social science?
Public sector innovation in context
Algorithms for a new season? Mapping a decade of research on the artificial intelligence-driven digital transformation of public administration
Organizing innovation contests for public procurement of innovation – a case study of smart city hackathons in Tampere, Finland
Ideology and Policy Preferences in Synthetic Data
The Ethics of Artificial Intelligence
Two decades of public sector innovation
Design science in public administration
The Use of Text as Data Methods in Public Administration
Innovation in the Public Sector
Innovation Inducement Prizes
Utilizing AI questionnaire translations in cross-cultural and intercultural research
The future of public administration research
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The Motivations for the Adoption of Management Innovation by Local Governments and its Performance Effects
Implementing Open Innovation in the Public Sector
Out of One, Many
Text as Data
Scaling Up With Integrity
GPT models for text annotation
Topic Modeling and Text Analysis for Qualitative Policy Research
Applying design science in public policy and administration research
The Dynamics of Sources of Knowledge on the Nature of Innovation in the Public Sector
Making Narrative Count
How ensembling AI and public managers improves decision-making
Machine Learning for Public Administration Research, With Application to Organizational Reputation
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ChatGPTest
The Psychological Meaning of Words
Integrating Generative Artificial Intelligence into Social Science Research
Goodbye human annotators? Content analysis of social policy debates using ChatGPT
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |