Earticle

초록

영어

The success of generative AI heavily relies on the quality of the information it generates in response to user prompts. However, there has been limited research focused on investigating the multifaceted dimensions of information quality and how they interact in the context of generative AI outputs. In an initial effort to fill this gap, our study embarked on identifying the key dimensions of information quality specific to generative AI (IQ-GAI) by integrating prior information/data quality studies with insights from a focus group of business analytics experts, and investigated the nomological networks through the cognitive mapping method. This exploration led to the identification of eleven IQ-GAI dimensions and their nomological networks. Our study is expected to offer valuable insights to assess and enhance the IQ-GAI outputs and to develop theoretical frameworks to assess the impact of IQ-GAI on the perceptions and behaviors of generative AI users.

목차

Introduction
Literature Review
Information Quality
Dimensions of Information Quality
Relationships between Information Quality Constructsand User Perceptions
Research Methods and Results
Study 1: Identify Dimensions of Information Quality
Study 2: Identifying the Nomological Networks amongIQ-GAI Constructs
Discussion and Implications
Acknowledgments
References

키워드

저자

• Younghwa Gabe Lee [ Miami University Department of Information Systems and Analytics ]
• Allison Jones-Farmer [ Miami University Department of Information Systems and Analytics ]
• Fadel Megahed [ Miami University Department of Information Systems and Analytics ]
• Ruiyun Xu [ Miami University Department of Information Systems and Analytics ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658