Blockchain smart contracts, programs with the potential to automate transactions and beyond, have gained tremendous popularity over the past years. Central to the original of smart contracts is that every computable clause of a contract or agreement is encoded into arbitrary computer logic with the aim of coding this logic into computer programs, and let the program decide and execute what happens during the contract's life span. The term smart legal contract has been coined to describe smart contracts that aim to capture legally binding agreements between parties. This dissertation presents a method to facilitate the creation of smart legal contracts that constitute a legally binding contract and that can (partially) self-enforce their terms and conditions within that contract, regardless of the blockchain platform. Understanding how blockchain technology works is pivotal to grapple the ramifications of this choice for smart contracts. Chapter 2 presents an overview of the literature on blockchain to delineate architectural perspectives on the technology, and to define its properties. Finally, the chapter points out the current challenges for the technology and gaps in literature. In Chapter 3, a background on smart contracts will be provided using a motivational example. Chapter 4 expounds the research methodology, the research paradigm adopted for the research, and the philosophy underpinning the method called Model Driven Architecture. Following, in Chapter 5 a domain model for smart legal contracts is presented. The chapter demonstrates how the contents of a legal contract could be captured in a model using a motivational example. Chapter 6 is devoted to describing the models that can be employed to write smart contracts. The chapter presents a platform specific model for the Ethereum and Hyperledger Fabric blockchain platforms. A platform agnostic model for blockchain technology is thereafter presented that captures the commonalities between these platforms. Chapter 7 discusses how the main research question is addressed. Derived from the insights of the discussion some opportunities for future research are discussed. Finally, Chapter 8 concludes the dissertation
