Quality- or/and category-based output control strategies for user-generated content platforms

Digital platforms provide arenas in which autonomous participants produce and consume content, and the design of their governance constitutes a critical source of firm performance. This study focuses on output control strategies in user-generated content platforms and introduces a novel theoretical framework that goes beyond the traditional quality-based approach by incorporating category-based mechanisms. Specifically, we classify output control strategies along two domains—quality and category—and introduce a fourfold typology consisting of open, quality-based, category-based, and dual control strategies. Furthermore, we examine two boundary conditions—user composition and revenue models—and conduct an agent-based simulation to compare the relative effectiveness of each strategy. This study makes two theoretical contributions. First, by incorporating participants’ within-category preferences, we establish the theoretical validity of category-based control and refine the conventional open/closed governance dichotomy. Second, we demonstrate that dual control strategies can simultaneously pursue both participant numbers and utility, offering a new perspective of compatibility that challenges the traditional view of governance strategies as a trade-off between the two. Through these contributions, this research extends the theoretical scope of platform governance studies.