Developing a methodology to predict the adoption rate of Connected Autonomous Trucks in transportation organizations using peer effects

This paper presents a methodology for predicting the adoption rate of Connected Autonomous Trucks (CATs) in transportation organizations using peer effects. There are a number of different factors that must be considered when developing innovation adoption models for organizations. This paper briefly describes each of the relevant variables and combines them into a discrete choice model for predicting the adoption rate of CATs by a hypothetical sample of transportation organizations. The model incorporates new peer effect modeling techniques to simulate the competition and informal communication network. Preliminary results suggest that organizations which are larger are less likely to change their decisions due to the decisions of other, competing organizations, whereas smaller organizations are more easily influenced by the decisions of larger organizations. The methodology developed in this paper produces reasonable results using a hypothetical dataset, and the methodology has been designed to be transferrable to any number of organizational innovations.