Analysis of rural broadband adoption dynamics: A theory-driven agent-based model

Area of Science:

• Behavioral economics
• Computational social science
• Telecommunications policy

Background:

• Broadband internet demand exceeds availability, exacerbated by the COVID-19 pandemic.
• Federal and state funding aims to expand broadband infrastructure in unserved/underserved areas.
• Predicting consumer adoption and formulating effective strategies are crucial for maximizing broadband deployment.

Purpose of the Study:

• To develop and validate an agent-based model for simulating residential broadband adoption.
• To provide policymakers and Internet Service Providers (ISPs) with tools for predicting take rates.
• To inform strategies for increasing high-speed internet adoption and reducing the digital divide.

Main Methods:

• Utilized an agent-based model grounded in the Theory of Planned Behavior.
• Simulated consumer decision-making based on market competition, service attributes, and consumer characteristics.
• Conducted a use case in Missouri to demonstrate the model's predictive capabilities.

Main Results:

• Broadband adoption (take rates) increased with a higher density of existing internet users.
• Lower broadband prices were associated with increased adoption rates.
• Simulation results effectively informed predictions of broadband adoption in the studied region.

Conclusions:

• Agent-based modeling offers a valuable tool for understanding and predicting broadband adoption.
• Simulation insights can guide investments in broadband infrastructure and digital literacy programs.
• The model can support the design of targeted market subsidies to bridge the digital divide.