The increasing use of blockchain technology in various industries has generated a significant amount of interest among researchers and practitioners. This study aims to determine a predictive classification model of technological service innovation adoption for blockchain implementation across cultures. Specifically, the study examines 762 firms between Saudi Arabia and the United States to gauge firm and country penetration adoption drivers and rates. The key questions of the research are: 1) What dimensions have the highest impact on service innovation adoption rates between Saudi Arabia and the United States? and, 2) Are penetration rates of blockchain adoption significantly different between Saudi Arabia and the United States? Model formulation and analysis will be supported using non-linear neural networks in an effort to predictively classify firm adoption rates and those variables explaining variations in adoption patterns. Results of this study will help organizations understand the factors that impact the adoption of blockchain technology and make informed decisions about its implementation and can also inform policymakers about the potential benefits and cultural challenges of using blockchain technology as a significant sustainability tool.