How Algorithmically Generated Content Reduces Evaluation Costs for Experience Goods
Abstract
Algorithmically generated content (AGC) is increasingly used by digital platforms to enhance user decision-making by reducing evaluation costs. This study examines the impact of AGC on the adoption of online experience goods, using Kaggle’s algorithm-generated exploratory data analysis (EDA) reports as a case study. We analyze how AGC facilitates dataset adoption by reducing the time and cognitive effort required to evaluate datasets, particularly in the early-stage cold start phase when user-generated content (UGC) is scarce. Our findings reveal that AGC significantly increases early adoption, with a more pronounced effect on datasets that lack alternative evaluation mechanisms, such as detailed product descriptions or provider reputation. However, AGC’s impact diminishes over time as UGC accumulates and evaluation costs decline.
