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Korean Academic Society of Business Administration
[ Article ]
korean management review - Vol. 54, No. 6, pp.1511-1540
ISSN: 1226-1874 (Print)
Print publication date 31 Dec 2025
Received 27 Jun 2025 Revised 16 Jul 2025 Accepted 31 Jul 2025
DOI: https://doi.org/10.17287/kmr.2025.54.6.1511

생성형 AI 특성이 과업 성과와 지속 사용 행동에 미치는 영향: SOR과 TTF 이론을 중심으로

박현선 ; 김상현 ; 이민영
(주저자) 경북대학교 경영학부
(교신저자) 경북대학교 경영학부
(공저자) 경북대학교 경영학부
The Effects of Generative AI Characteristics on Task Performance and Continued Usage: Based on SOR Framework and TTF Theories
Hyunsun Park ; Sanghyun Kim ; Minyoung Lee
(First Author) Contract Professor, BK21, School of Business Administration, Kyungpook National University sunny09@knu.ac.kr
(Corresponding Author) Professor, School of Business Administration, Kyungpook National University ksh@knu.ac.kr
(Co-Author) Ph.D. School of Business Administration, Kyungpook National University bibianna0910@naver.com


Copyright 2025 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.

초록

본 연구는 과업 수행에 생성형 AI 활용이 증가함에 따라 기존 AI와 차별화되는 특성이 사용자 만족과 과업-기술 적합성에 미치는 영향을 살펴보고자 하였다. 또한, 생성형 AI 사용자의 만족과 과업-기술 적합성이 과업성과와 지속 사용에 미치는 영향을 살펴보았다. 이를 위해 생성형 AI의 대표적 서비스인 ChatGPT 사용자를 대상으로 설문조사를 실시하였으며, 총 315부의 설문을 취합하여 AMOS 29.0을 이용해 분석하였다. 연구 결과, ChatGPT의 특성 중 의인화를 제외한 개인화, 지능성, 희소성, 정확성은 사용자 만족과 과업-기술 적합성에 유의한 영향을 미치는 것으로 확인되었다. 또한, 과업-기술 적합성은 사용자 만족, 과업성과, 지속 사용 의도에 모두 유의미한 영향을 미치는 것으로 확인되었으며, 사용자 만족은 과업성과와 지속 사용 의도에 유의미한 영향을 미치는 것으로 확인되었다. 이러한 본 연구의 결과는 생성형 AI 관련 연구의 이론적 범위를 넓히고 과업 수행에 생성형 AI를 도입하려는 조직에 유의미한 시사점을 제공할 수 있을 것이다.

Abstract

With the increasing use of Generative AI in actual task performance, this study aims to investigate how the characteristics that differentiate Generative AI from traditional AI influence user satisfaction and task-technology fit, based on the Stimulus-Organism-Response framework. Additionally, the study examines the effects of user satisfaction and task-technology fit on task performance and continued usage intention. To analyze the proposed research model, a survey was conducted with users of ChatGPT, a representative Generative AI service. A total of 315 valid responses were collected, and structural equation modeling was performed using AMOS 29.0. The results show that among the characteristics of ChatGPT (Personalization, Intelligence, Rareness, Accuracy) significantly influence both user satisfaction and task-technology fit, while Anthropomorphism has a significant effect only on user satisfaction. Furthermore, task-technology fit significantly affects user satisfaction, task performance, and continued usage intention, and user satisfaction also has a significant impact on task performance and continued usage intention. These findings contribute to expanding the theoretical scope of research on Generative AI and offer practical implications for organizations seeking to adopt generative AI to enhance task performance.

Keywords:

Generative AI, ChatGPT, S-O-R framework, Task-Technology Fit, Satisfaction, Task Performance, Continued Usage Intention

키워드:

생성형 AI, S-O-R 프레임워크, 과업-기술 적합성, 사용자만족, 과업성과, 지속사용의도

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∙ 저자 박현선은 현재 경북대학교 경영학부 BK교육연구단 계약교수로 재직중이며, 경북대학교 경영학부에서 경영학 박사학위를 취득하였다. 주요 관심분야는 디지털플랫폼, 정보보안, 클라우드, AI 등이 있다.

∙ 저자 김상현은 미국 University of Mississippi에서 경영정보전공으로 박사학위를 취득하였으며, 현재 경북대학교 경영학부 교수로 재직 중이다. 주요 연구 분야는 정보보안, Human-Computer Interaction, AI 윤리, 클라우드 컴퓨팅 등이 있으며, 다수의 연구 실적이 International Journal of Information Management, Information and Management, Journal of Computer Information Systems.

∙ 저자 이민영은 현재 경북대학교 경영학부에서 박사과정을 수료하였고 동 대학원에서 석사학위를 취득했다. 연구 관심 분야는 인공 지능 서비스, 온라인 플랫폼, 지능 정보 시스템 및 Human-computer Interaction 등 이다.