AI Insight
This theoretical article introduces the Motivation-Affordance-Capacity-Outcome (MACO) framework to explain why short-form video use produces different mental health outcomes among university students despite similar usage duration. The framework proposes that entry motivations, platform affordances, self-regulatory capacity, and algorithmic feedback loops interact to create either protective or harmful engagement patterns. It distinguishes itself from existing theories by focusing on session-specific contexts, motivational drift during use, and how algorithms can reinforce adaptive or maladaptive behaviors over time.
Why it matters
The framework shifts intervention strategies away from simple screen-time limits toward targeted approaches based on individual motivations and usage contexts. It provides testable predictions that could inform platform design changes and culturally responsive mental health support for university students, particularly in East Asian contexts.
Understand the Science
Short-form video platforms have become a central psychological environment in university life, yet their mental health significance cannot be explained by total screen time alone. This hypothesis-and-theory article develops the Motivation-Affordance-Capacity-Outcome framework (MACO) to explain why similar short-form video duration may produce protective, neutral, problematic, or clinically meaningful outcomes among university students. MACO is revised here as a shorter, more testable, and platform-specific framework. Its distinctive contribution rests on three mechanisms: session-in-context analysis, motivational drift, and algorithmic feedback loops. The framework argues that entry motives are translated by short-form-video affordances into engagement modes; that self-regulatory capacity and baseline vulnerability shape whether use remains flexible; and that algorithmic feedback can stabilize either adaptive or maladaptive patterns over repeated sessions. The article clarifies how MACO differs from I-PACE, the active-passive model, compensatory Internet use theory, and differential susceptibility approaches by generating comparative predictions about short-form video versus long-form video, text forums, traditional television, and general social media. It also specifies falsifiable propositions, disconfirmation criteria, and operational indicators for constructs such as motivational drift, socially saturated loneliness, perceived algorithmic agency, and motivational alignment. Particular attention is given to Chinese and East Asian university contexts as theoretically important boundary conditions rather than assumed universal settings. The framework supports interventions that move beyond generic screen-time reduction toward motive-specific diagnosis, digital mindfulness, sleep-protective friction, credibility support, platform design changes, and culturally responsive mental health education.