Earticle

초록

영어

Purpose The purpose of this study is to examine how perceived privacy risk, perceived usefulness, and perceived ease of use affect system trust in the data memory context of generative artificial intelligence (AI), and how system trust influences usage intention. Design/methodology/approach Based on the Technology Acceptance Model (TAM), this study proposes a research model integrating perceived privacy risk, system trust, and privacy protection technology self-efficacy. Survey data were collected from Chinese users with prior experience using generative AI services. Of the 340 responses obtained, 304 valid responses were analyzed using covariance-based structural equation modeling (CB-SEM). Findings Perceived usefulness and perceived ease of use had significant positive effects on system trust, whereas perceived privacy risk had a significant negative effect. System trust also had a significant positive effect on usage intention. However, the moderating effect of privacy protection technology self-efficacy was not supported. The findings highlight that user trust in generative AI is influenced not only by functional benefits and ease of use, but also by privacy concerns related to data retention and processing.

목차

Ⅰ. 서론
1.1 연구 배경 및 문제 제기
1.2 연구 목적
Ⅱ. 이론적 배경
2.1 생성형 AI의 데이터 기억과 프라이버시 쟁점
2.2 프라이버시 위험 인식 관련 이론 및 실증 연구
2.3 기술수용모형
2.4 시스템 신뢰 개념 및 AI 맥락의 확장
2.5 프라이버시 보호 기술 효능감의 개념
III. 연구 설계
3.1 연구모형
3.2 연구가설
3.3 설문지 구성
IV. 연구방법 및 분석결과
4.1 자료 수집 및 분석
4.2 측정모형의 평가
4.3 구조모형의 평가 및 가설검정
V. 결론
참고문헌

키워드

저자

• 서조미 [ 부산대학교 경영학과 석사과정 ] 제1저자
• 김종기 [ 부산대학교 경영학과 교수 ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658