Generative AI and Algorithmic Curation: The Rise of AI Slop and Its Impact on Information Ecosystems
Ji Yeon Park, Yong Tae Shin · The Journal of Internet Electronic Commerce Resarch · 2026
The proliferation of generative AI has led to a rapid increase in low-quality, automatically generated content known as “AI slop,” threatening the credibility of platform-based information ecosystems. To elucidate this phenomenon from a user acceptance perspective, this study extended the Technology Acceptance Model (TAM) to empirically verify the causal relationships among Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Perceived Risk (PR), Attitude (ATT), Trust (TR), and Behavioral Intention (BI). A survey was conducted among 39 users with experience in generative AI content, and the results were analyzed using structural equation modeling (SEM) with SPSS 27.0 and AMOS 27.0. Of the nine hypotheses, eight were supported. PU and PEOU had a significant positive (+) influence on attitude formation, while trust and attitude formed a mutually reinforcing relationship and each had a significant influence on behavioral intention. Conversely, contrary to expectations, perceived risk had a positive (+) influence on attitude (β=0.636) but did not significantly influence trust, confirming a high-risk high-acceptance pattern. Moderating effects based on AI usage frequency, understanding, platform type, and gender were not significant, suggesting that the generative AI acceptance framework operates universally across groups.