Technology Acceptance and Innovation Diffusion: Are Users More Inclined Toward AIGC-Assisted Design?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial Intelligence Generated Content (AIGC) has shown significant potential in design, driven by advancements in artificial intelligence technology. However, understanding designers’ willingness to embrace this technology and the factors influencing their decision-making requires further research. In this study, we develop a theoretical model of user behavioral intention in AIGC-assisted design, drawing upon the Diffusion of Innovations theory and the Unified Theory of Acceptance and Use of Technology. Through empirical analysis using the PLS-SEM structural equation model, we investigate the mechanisms behind various influencing factors on behavioral intention. Our findings highlight the relative advantage as the most significant positive factor, emphasizing the importance of the innovation’s benefits in the Diffusion of Innovations theory. Designers prioritize the innovation and assistance provided by AIGC technology in the design process and ideas, recognizing the advantages of the innovation itself over mere performance improvement. This study provides valuable insights into the psychological and behavioral mechanisms guiding designers’ decision-making regarding the application of AIGC technology in design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it