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The Korean Journal of Public Administration
[ Article ]
Korean Journal of Public Administration - Vol. 63, No. 4, pp.33-65
ISSN: 1229-6694 (Print)
Print publication date 31 Dec 2025
Received 28 Oct 2025 Revised 14 Dec 2025 Accepted 17 Dec 2025
DOI: https://doi.org/10.24145/KJPA.63.4.2

공무원의 인공지능(AI) 인식과 활용이 성과에 미치는 영향

박순애** ; 이혜연***
**제1저자, 서울대학교 행정대학원 교수, 한국행정연구소 겸무연구원 psoonae@snu.ac.kr
***교신저자, 서울대학교 행정대학원 정보지식정책연구소(공공성과관리연구센터) 연구원 hylee01@snu.ac.kr
How Civil Servants’ Perceptions and Use of Artificial Intelligence (AI) Affect Performance
Soon-Ae Park** ; Hyeyoun Lee***

초록

본 연구는 2021~2024년 ｢공공부문 성과에 대한 공무원 인식조사｣ 자료를 활용하여 공무원의 인공지능(AI)에 대한 인식과 활용 수준, 그리고 개인성과 간의 관계를 분석하였다. 분석 결과, 공무원들은 AI 예산 투입의 우선 분야로 재난･재해 예방을 가장 중요하게 인식하고 있었으며, 전반적으로 기술에 대한 불신은 완화되는 추세를 보였다. 그러나 예산 부족과 전문성 결여는 여전히 AI 활용을 제약하는 주요 요인으로 나타났다. 집단별 분석 결과, 중앙정부 소속 공무원, 상위 직급, 고학력자일수록 AI 활용 수준이 높은 것으로 나타났으며, 여성 공무원의 경우 AI에 대한 불신과 활용상의 어려움을 상대적으로 더 크게 인식하고 있는 것으로 확인되었다. 또한 기술직에 비해 행정직 공무원이 AI에 대한 우려 수준이 높았으며, AI의 효과성과 활용에 대해서는 경력채용으로 입직한 공무원들의 인식이 상대적으로 우호적인 것으로 나타났다. 회귀분석 결과, AI의 효과성에 대한 인식과 교육 등 지원 수혜 경험은 AI 활용 수준을 유의미하게 제고하는 요인으로 작용하였다. 특히 AI 활용 개수는 모든 개인성과 변수에 정(+)의 영향을 미친 반면, 업무대체에 대한 우려는 개인성과에 부(-)의 영향을 미치는 것으로 나타났다. 본 연구의 분석 결과는 공공부문에서 AI에 대한 인식과 활용 수준이 개인성과와 밀접하게 연관되어 있음을 보여주며, 향후 공무원의 AI 활용 역량 제고를 위한 제도적･환경적 조건을 종합적으로 검토할 필요성을 시사한다.

Abstract

This study analyzes the relationship between civil servants’ perceptions of artificial intelligence (AI), their level of AI utilization, and individual performance using data from the Civil Servants’ Perception Survey on Public Sector Performance collected between 2021 and 2024. The results show that civil servants identify disaster and emergency prevention as the top priority area for AI budget allocation, while overall technological distrust toward AI has gradually declined. However, insufficient budget and lack of professional expertise remain significant constraints on AI utilization.

Group-level analyses reveal that civil servants working in the central government, those in higher-ranking positions, and those with higher levels of education exhibit higher levels of AI utilization. In contrast, female civil servants tend to exhibit higher levels of distrust toward AI and greater difficulties in its utilization. In addition, administrative officials show higher levels of concern about AI compared to technical officials, whereas civil servants recruited through experienced-based hiring exhibit more favorable perceptions regarding the effectiveness and utilization of AI.

The regression analysis indicates that perceived effectiveness of AI and experiences of receiving organizational support, such as education and training, significantly increase the level of AI utilization. Notably, the number of AI applications used has a positive effect on all individual performance measures, whereas concerns about job replacement have a negative effect on individual performance. Overall, the findings demonstrate that civil servants’ perceptions and utilization of AI are closely associated with individual performance in the public sector, highlighting the importance of systematically considering institutional and environmental conditions to enhance civil servants’ AI utilization capabilities.

Keywords:

artificial intelligence (AI), performance, technology acceptance, civil servants, organizational management

키워드:

인공지능(AI), 성과, 기술수용, 공무원, 조직관리

Acknowledgments

본 연구는 서울대학교 행정대학원 공공성과관리연구센터의 지원으로 수행되었다.

References

  • 남상지. (2025). 공무원의 AI 위험인식이 AI 정부 성과 인식에 미치는 영향: AI 협업역량의 조절효과를 중심으로. ｢한국거버넌스학회보｣, 32(2), 141-169.
  • 대한민국정부. (2025). ｢이재명정부 123대 국정과제｣. 대한민국정부.
  • 디지털플랫폼정부위원회. (2025). ｢디지털플랫폼정부위원회 백서｣. 디지털플랫폼정부위원회.
  • 박승규･황창호. (2023). 인공지능 (AI) 의 효과성 인식에 영향을 미치는 요인 분석: 공공부문 공무원의 인식을 중심으로. ｢한국거버넌스학회보｣, 30(2), 55-81.
  • 정혜진. (2025). 공무원의 생성형 AI 이용 의도에 관한 요인 분석: 계획된 행동 이론을 중심으로. ｢공공정책연구｣, 42(1), 133-164.
  • 주상돈･김태영. (2025). 공무원들의 생성형 AI 챗봇 업무수용성에 관한 연구. ｢한국지역정보화학회지｣, 28(3), 45-79.
  • 최혜민･안용진. (2025). 공무원의 인공지능(AI) 기술 수용 인식과 태도에 대한 분석 : 기술 도입 초기 단계를 중심으로, ｢한국행정논집｣, 37(3), 473-503.
  • Ahmad, A., Mimoun, M. S. B., & El-Gohary, H. (2025). Artificial Intelligence and Organisational Performance: A Systematic Review of Service Employee Perspective. Journal of Information & Knowledge Management, 2550075. [https://doi.org/10.1142/S0219649225500753]
  • Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in human behavior, 114, 106548. [https://doi.org/10.1016/j.chb.2020.106548]
  • Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for artificial intelligence and digital technologies. International Journal of Information Management, 62, 102433. [https://doi.org/10.1016/j.ijinfomgt.2021.102433]
  • Baabdullah, A. M., Alalwan, A. A., Slade, E. L., Raman, R., & Khatatneh, K. F. (2021). SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices. Industrial Marketing Management, 98, 255-270. [https://doi.org/10.1016/j.indmarman.2021.09.003]
  • Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of organizational behavior, 45(2), 159-182. [https://doi.org/10.1002/job.2735]
  • Beltran, M. A., Ruiz Mondragon, M. I., & Han, S. H. (2024, June). Comparative analysis of generative AI risks in the public sector. In Proceedings of the 25th Annual International Conference on Digital Government Research (pp. 610-617). [https://doi.org/10.1145/3657054.3657125]
  • Benhmama, A., & Sabiri, B. (2025, May). Can AI improve individual employee performance?. In 2025 International Conference on Innovation in Artificial Intelligence and Internet of Things (AIIT) (pp. 1-6). IEEE. [https://doi.org/10.1109/AIIT63112.2025.11082834]
  • Bright, J., Enock, F., Esnaashari, S., Francis, J., Hashem, Y., & Morgan, D. (2025). Generative AI is already widespread in the public sector: Evidence from a survey of UK public sector professionals. Digital Government: Research and Practice, 6(1), 1-13. [https://doi.org/10.1145/3700140]
  • Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. [https://doi.org/10.1016/j.technovation.2021.102312]
  • Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340. [https://doi.org/10.2307/249008]
  • Dell'Acqua, F., McFowland III, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., ... & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper, (24-013). [https://doi.org/10.2139/ssrn.4573321]
  • Ding, L. (2021). Employees’ challenge-hindrance appraisals toward STARA awareness and competitive productivity: a micro-level case. International Journal of Contemporary Hospitality Management, 33(9), 2950-2969. [https://doi.org/10.1108/IJCHM-09-2020-1038]
  • Ellikkal, A., & Rajamohan, S. (2024). AI-enabled personalized learning: empowering management students for improving engagement and academic performance. Vilakshan-XIMB Journal of Management, 22(1), 28-44. [https://doi.org/10.1108/XJM-02-2024-0023]
  • Engin, Z., Crowcroft, J., Hand, D., & Treleaven, P. (2025). The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government. arXiv preprint arXiv: 2503.08725, .
  • Eom, S. J., & Lee, J. (2022). Digital government transformation in turbulent times: Responses, challenges, and future direction. Government Information Quarterly, 39(2), 101690. [https://doi.org/10.1016/j.giq.2022.101690]
  • European Commission. (2024). Public Sector Tech Watch: Adoption of AI, Blockchain and Other Emerging Technologies within the European Public Sector. Publications Office of the European Union.
  • Fischer, C., Siegel, J., Proeller, I., & Drathschmidt, N. (2023). Resilience through digitalisation: How individual and organisational resources affect public employees working from home during the COVID-19 pandemic. Public Management Review, 25(4), 808-835. [https://doi.org/10.1080/14719037.2022.2037014]
  • Giest, S. N., & Klievink, B. (2024). More than a digital system: How AI is changing the role of bureaucrats in different organizational contexts. Public Management Review, 26(2), 379-398. [https://doi.org/10.1080/14719037.2022.2095001]
  • Giraldi, L., Rossi, L., & Rudawska, E. (2024). Evaluating public sector employee perceptions towards artificial intelligence and generative artificial intelligence integration. Journal of Information Science, 01655515241293775. [https://doi.org/10.1177/01655515241293775]
  • Hržica, R., Debelak, K., & Pevcin, P. (2025). A Dual-Level Model of AI Readiness in the Public Sector: Merging Organizational and Individual Factors Using TOE and UTAUT. Systems, 13(8), 705. [https://doi.org/10.3390/systems13080705]
  • Kehayov, M., Holder, L., & Koch, V. (2022). Application of artificial intelligence technology in the manufacturing process and purchasing and supply management. Procedia Computer Science, 200, 1209-1217. [https://doi.org/10.1016/j.procs.2022.01.321]
  • Khanfar, A. A., Kiani Mavi, R., Iranmanesh, M., & Gengatharen, D. (2024). Determinants of artificial intelligence adoption: research themes and future directions. Information Technology and Management, 1-21. [https://doi.org/10.1007/s10799-024-00435-0]
  • Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy, 44(6), 101976. [https://doi.org/10.1016/j.telpol.2020.101976]
  • Lee, Y. F., Hwang, G. J., & Chen, P. Y. (2022). Impacts of an AI-based chabot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational technology research and development, 70(5), 1843-1865. [https://doi.org/10.1007/s11423-022-10142-8]
  • Leonard, P., & Tyers, R. (2023). Engineering the revolution? Imagining the role of new digital technologies in infrastructure work futures. New Technology, Work and Employment, 38(2), 291-310. [https://doi.org/10.1111/ntwe.12226]
  • Madan, R., & Ashok, M. (2022). A public values perspective on the application of Artificial Intelligence in government practices: A Synthesis of case studies. In Handbook of research on artificial intelligence in government practices and processes(pp. 162-189). IGI Global Scientific Publishing. [https://doi.org/10.4018/978-1-7998-9609-8.ch010]
  • Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: a systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774. [https://doi.org/10.1016/j.giq.2022.101774]
  • Majrashi, K. (2024). Determinants of public sector managers' intentions to adopt ai in the workplace. International Journal of Public Administration in the Digital Age (IJPADA), 11(1), 1-26. [https://doi.org/10.4018/IJPADA.342849]
  • Mannuru, N. R., Shahriar, S., Teel, Z. A., Wang, T., Lund, B. D., Tijani, S., ... & Vaidya, P. (2025). Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development. Information development, 41(3), 1036-1054. [https://doi.org/10.1177/02666669231200628]
  • Medaglia, R., Gil-Garcia, J. R., & Pardo, T. A. (2023). Artificial intelligence in government: Taking stock and moving forward. Social Science Computer Review, 41(1), 123-140. [https://doi.org/10.1177/08944393211034087]
  • Mergel, I., Edelmann, N., & Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government information quarterly, 36(4), 101385. [https://doi.org/10.1016/j.giq.2019.06.002]
  • Mergel, I., Dickinson, H., Stenvall, J., & Gasco, M. (2024). Implementing AI in the public sector. Public Management Review, 1-14. [https://doi.org/10.1080/14719037.2023.2231950]
  • Neumann, O., Guirguis, K., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114-141. [https://doi.org/10.1080/14719037.2022.2048685]
  • Nikiforova, A., Lnenicka, M., Melin, U., Valle-Cruz, D., Gill, A., Casiano Flores, C., Sirait, E., Luterek, M., Dreyling, R. M., and Tesarova, B. (2025). Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges. In Proceedings of the 59th Hawaii International Conference on System Sciences
  • Nouws, S., Janssen, M., & Dobbe, R. (2022, August). Dismantling digital cages: Examining design practices for public algorithmic systems. In International Conference on Electronic Government (pp. 307-322). Cham: Springer International Publishing. [https://doi.org/10.1007/978-3-031-15086-9_20]
  • Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic journal of information systems evaluation, 14(1), 110-121.
  • O’Shaughnessy, M. R., Schiff, D. S., Varshney, L. R., Rozell, C. J., & Davenport, M. A. (2023). What governs attitudes toward artificial intelligence adoption and governance?. Science and Public Policy, 50(2), 161-176. [https://doi.org/10.1093/scipol/scac056]
  • Oxford insights. (2024). Government AI Readiness Index 2024. Oxford insights.
  • Russell, S. J., & Norvig, P (1995). Artificial intelligence : a modern approach / Stuart Russell and Peter Norvig ; contributing writers, John F. Canny, Jitendra M. Malik, Douglas D. Edwards. Prentice Hall.
  • Scutella, M., Plewa, C., & Reaiche, C. (2024). Virtual agents in the public service: examining citizens’ value-in-use. Public Management Review, 26(1), 73-88. [https://doi.org/10.1080/14719037.2022.2044504]
  • Sedkaoui S., & Benaichouba R. (2024). Generative AI as a transformative force for innovation: a review of opportunities, applications and challenges. European Journal of Innovation Management. [https://doi.org/10.1108/EJIM-02-2024-0129]
  • Selten, F., & Klievink, B. (2024). Organizing public sector AI adoption: Navigating between separation and integration. Government Information Quarterly, 41(1), 101885. [https://doi.org/10.1016/j.giq.2023.101885]
  • Su, F., Liu, W., Xiong, K., & Zeng, Q. (2024). How and when artificial intelligence usage facilitates task performance. Social Behavior and Personality, 52(10), 1-11. [https://doi.org/10.2224/sbp.13634]
  • Tangi, L., Janssen, M., Benedetti, M., & Noci, G. (2021). Digital government transformation: A structural equation modelling analysis of driving and impeding factors. International Journal of Information Management, 60, 102356. [https://doi.org/10.1016/j.ijinfomgt.2021.102356]
  • Tangi, L., Müller, A. P. R., & Janssen, M. (2025). AI-augmented government transformation: Organisational transformation and the sociotechnical implications of artificial intelligence in public administrations. Government Information Quarterly, 42(3), 102055. [https://doi.org/10.1016/j.giq.2025.102055]
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view1. MIS quarterly, 27(3), 425-478. [https://doi.org/10.2307/30036540]
  • Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178. [https://doi.org/10.2307/41410412]
  • Vogl, T. M., Seidelin, C., Ganesh, B., & Bright, J. (2020). Smart technology and the emergence of algorithmic bureaucracy: Artificial intelligence in UK local authorities. Public Administration Review, 80(6), 946-961. [https://doi.org/10.1111/puar.13286]
  • Willems, J., Schmid, M. J., Vanderelst, D., Vogel, D., & Ebinger, F. (2023). AI-driven public services and the privacy paradox: do citizens really care about their privacy?. Public Management Review, 25(11), 2116-2134. [https://doi.org/10.1080/14719037.2022.2063934]
  • Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector-applications and challenges. International Journal of Public Administration, 42(7), 596-615. [https://doi.org/10.1080/01900692.2018.1498103]