Technical decision-making in startups and its impact on growth and technical debt

Abstract: The rapid pace of digitalization has resulted in increased management of software development, and today a majority of startups are reliant on software. How to manage software development projects is a well-researched area and agile methods are widely adopted by companies in all industries and sizes. However, prior to working with agile methods or any other software development methodology, the founders and management of a startup have to make several technical decisions that could potentially affect the whole software development process and the company's success. Furthermore, studies show that only three programming languages are known by more than 50% of developers, suggesting that the potential effects of technical decisions stretch outside the software development process.  By performing a multiple-case study on startups with a mixed-methodology approach, the researcher has analyzed the literature, interviewed several founders and Chief Technology Officers, and quantitatively analyzed hundreds of thousand lines of code, to find how to organize to make better technical decisions in order to enhance growth and generate less technical debt. The results show that the effects of technical decisions stretch outside the software development process, having an apparent effect on a startup's ability to attract and retain talent. Furthermore, the results show that access to talent is an important but not deciding factor in technical decision-making. Additionally, it is found that in the initial stage of a startup, ease of development and speed are important factors in technical decisions as the main objective is to find product-market fit. When product-market fit has been found and the startup matures, the focus shifts and quality and durability are becoming prominent factors. It is found that scooping features only to implement the absolute core functionality is an effective approach to develop quickly and generate less technical debt while maintaining customer satisfaction. Lastly, it is found that programming language affects the number of issues generated per line of code and the time spent on building features. However, as found in the literature, there is no evidence of this being related to the type of programming language.  The findings have both practical and academic implications. In academics, this thesis lays the foundation for further studies and provides new insights into the field of startups in general, and technical decision-making in particular. For practitioners, this thesis provides a basis for discussion and execution of technical decisions in the early stages of a startup.