Explore the driving factors of designers’ AIGC usage behavior based on SOR framework

Despite the widespread recognition of artificial intelligence’s advantages, it cannot replace human independent thinking and creativity, especially in fields such as artistic design that require creativity. Previous studies often examined its development trends from the perspective of technical advantages or application processes. This study explores the attitudes and acceptance of creative industry practitioners towards Artificial Intelligence Generated Content (AIGC) from the perspective of user behavior modification. Utilizing the Stimulus-Organism-Response Model (SOR) as the theoretical background, this research integrates the Technology Acceptance Model, Theory of Planned Behavior, and Self-Efficacy to form the research framework. By employing a mixed-method approach combining quantitative and qualitative analyses, data from 226 designers were explored, and structural equation modeling was used to verify the correlations between endogenous factors. The results indicate that users’ facilitating conditions significantly influence self-efficacy, which in turn determines their intention to adopt AIGC. Additionally, semi-structured interviews revealed that factors hindering the widespread application of AIGC mainly encompass legal security, ethical risks, and fairness. This study extends the application scope of the Stimulus-Organism-Response Model (SOR), enriches the Technology Acceptance Model, and provides a new research framework for the application of AIGC in the creative industry, detailing the responsibilities, processes, and content of designers in the Artificial Intelligence Generated Design (AIGD) process.

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Publication Details

Journal
Frontiers in Computer Science
Published
2024-09-09
DOI
https://doi.org/10.3389/fcomp.2024.1417016
Citations
21
Primary Topic
Color perception and design
Type
article
Field-Weighted Citation Impact
13.52
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article

Explore the driving factors of designers’ AIGC usage behavior based on SOR framework

Chun-Ching Chen, Shao-Feng Wang
21 citations
Frontiers in Computer Science
Color perception and design
13.52
article

Explore the driving factors of designers’ AIGC usage behavior based on SOR framework

Chun-Ching Chen, Shao-Feng Wang
article en
21 citations

Abstract

Despite the widespread recognition of artificial intelligence’s advantages, it cannot replace human independent thinking and creativity, especially in fields such as artistic design that require creativity. Previous studies often examined its development trends from the perspective of technical advantages or application processes. This study explores the attitudes and acceptance of creative industry practitioners towards Artificial Intelligence Generated Content (AIGC) from the perspective of user behavior modification. Utilizing the Stimulus-Organism-Response Model (SOR) as the theoretical background, this research integrates the Technology Acceptance Model, Theory of Planned Behavior, and Self-Efficacy to form the research framework. By employing a mixed-method approach combining quantitative and qualitative analyses, data from 226 designers were explored, and structural equation modeling was used to verify the correlations between endogenous factors. The results indicate that users’ facilitating conditions significantly influence self-efficacy, which in turn determines their intention to adopt AIGC. Additionally, semi-structured interviews revealed that factors hindering the widespread application of AIGC mainly encompass legal security, ethical risks, and fairness. This study extends the application scope of the Stimulus-Organism-Response Model (SOR), enriches the Technology Acceptance Model, and provides a new research framework for the application of AIGC in the creative industry, detailing the responsibilities, processes, and content of designers in the Artificial Intelligence Generated Design (AIGD) process.

Frontiers in Computer ScienceVol. 6
National Taipei University of Technology (TW), Fuzhou University (CN)
Openalex Percentile: Top 1%
Color perception and design
13.52
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