Open E-Logistics Standards (OELS) is important to facilitate the integration of the supply chain. In OELS, the transmission and the manipulation of data are governed by open data and process standards that define their format, structure, and the semantics of data flow between trading partners. Despite the significant investments made by governments and leading firms, there remain concerns about OELS’ slow development progress and low adoption rates. The potential failure of OELS represents a significant stumbling block for governments and supply chain practitioners who have envisioned a globalized supply chain network electronically enabled by OELS. Researchers are also concerned about the inadequate models that are used to explain and understand the adoption of OELS. Although analysing adopter configurations in what is known as configuration analysis has been examined in disciplines related to science and economics, its application in the study of OELS remains sparse. This research aims to integrate multiple theoretical views, and apply configuration analysis with an improved methodological approach in order to examine OELS diffusion decisions and processes. The project will result in a new algorithm integrating structural equation modelling and neural network, and a decision support system which helps firms improve their OELS adoption decision.