Modelling the Intelligente Enterprise with Symbolic Business Model Embeddings

Abstract

The purpose of this research is therefore to provide a fine-grained, domain-specific examination of design choices and investigates how configurations of design choices are related to firm performance. By conducting an inductive multiple case study design, the uses of scalable machine learning techniques such as clustering and classification methods for data analysis to overcome methodological limitations of existing studies and providing an in-depth analysis of 188 IoT business models along with 108-dimensional characteristics. I initiated my research by taxonomy development to identify the relevant components of IoT related business models. This initial step comprised the analysis of 188 IoT ventures and extant theories from management research. In a second step, I applied a clustering analysis for identifying certain types of configurations. I then used another clustering over those types to identify successful business model archetypes. Finally, I used an ensemble of classification trees to identify patterns that distinguish successful from unsuccessful business models and the relevance of certain components in doing so.