Understanding technology adoption is a critical part of managing information technology (IT) enablement. In this paper, Muduganti, Sogani, and Hexmoor assert that an existing approach, user acceptance of information technology (UAIT), limits the field’s ability to understand technology adoption. They propose agent-based modeling (ABM) as an alternative. To complete their model, the authors implement the theory of reasoned action (TRA), a predictor of human behavior that articulates an individual’s intention, made up of attitude and subjective norm. Scenarios can then be run to see how changes in some parameters affect virtual technology adoption.

The paper models three scenarios that support past research: broad appeal leads to rapid adoption, narrow appeal leads to innovator adoption, and average appeal leads to broad adoption over time. In each scenario, the behavior maps to the expected population characteristics, demonstrating that modeling technology adoption is a possible alternative to the traditional survey approach. Additional development to model and validate scenarios that are more complex is required to ensure adoption can be accurately modeled.

Agent modeling provides an exciting approach to understanding technology adoption without the overhead often associated with surveying. This paper provides a good first step toward that goal. However, as the authors point out, additional technology adoption models and dynamic social networks need to be implemented. Although not addressed here, another topic that should be considered is the set of characteristics of the technology being adopted. Often, technology can be evaluated based on the value and change it introduces to the environment [1]. For example, disruptive technology might have broad appeal in concept, but be too early to accommodate. Understanding the technology and its impact on the individual’s intention, as well as the dynamic social network, would be an interesting model to build out and validate.