Predicting Acceptance of Novel Technology from Social Network Data - An Agent-based Simulation-Approach

Abstract

With digitization of production technology changes in technology infrastructure will become more frequent and more important for the competitiveness of organizations. Here, a crucial factor lies in the acceptance of such novel technology by users. Technology acceptance models aim to predict the adoption of new technology in an organization. They are however static in nature and fail to capture to dynamic process of adoption. To overcome this limitation, we utilize quantitative data from a small organization to understand both acceptance patterns and social structure of the organization. Both are used in an agent-based simulation to predict acceptance integrating social effects of diffusion over time. Our simulation achieves very similar results as the quantitative real-world data.