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Exploring Drivers of AIGC-Designer Collaborative Innovation

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  • Shao-Feng Wang
  • Chun-Ching Chen

Abstract

The trend in technological development is to utilize AI-based methods to optimize workflows and swiftly achieve desired objectives. Despite extensive exploration into the principles, acceptance, and application modes of new technologies, the research on the compatibility of artificial intelligence with work tasks remains limited. This study integrates the Stimulus-Organism-Response (SOR) model as a framework, combining Task Technology Fit (TTF), and Technology Acceptance Model (TAM), employing a mixed research approach that combines qualitative and quantitative methods to analyze data from 226 designers. Structural equation modeling is utilized to validate research hypotheses. The findings reveal that perceived usefulness and perceived security significantly influence designers’ behavioral intention to use Artificial Intelligence Generated Content (AIGC). Moreover, Technology Characteristics significantly impact technological compatibility and perceived ease of use, while technological compatibility significantly affects perceived usefulness. This study extends the application scope of the SOR theory, enriches the research on technological compatibility of AIGC in the creative design industry, explores the application process of human-machine collaborative innovation, provides valuable theoretical validation for the study of designers’ behavior in using AIGC, and discusses the key factors in transforming AIGC into Artificial Intelligence Generated Design (AIGD).

Suggested Citation

  • Shao-Feng Wang & Chun-Ching Chen, 2025. "Exploring Drivers of AIGC-Designer Collaborative Innovation," SAGE Open, , vol. 15(3), pages 21582440251, July.
    • Handle: RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251344044
    DOI: 10.1177/21582440251344044
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