Capturing value by using data-driven business models in European railways

Abstract

In today’s data-driven economy, many organisations are progressively adopting data-driven business models (DDBMs) to attain a competitive advantage and extract value. DDBMs leverage the potential of data to discover new business opportunities, improve operations and elevate customer experiences. Nevertheless, there is a “deployment gap” which refers to the paradox of value and opportunities associated with DDBMs and the lack of tangible success stories thereof, which hinders practitioners to deploy DDBMs. This exploratory based doctoral research aims to provide a contextual understanding of “why” and “how” rail companies are using Big Data to plan new and move from existing business models to DDBMs. Through a systematic analysis and comparison of empirically derived insights of five leading European train companies with a strong focus on the German rail system and its main operator Deutsche Bahn (DB) it is hoped that valuable insights to the strategic and organisational theory will be revealed. To gain rich insights about complex realities within multinational companies this thesis used the humanistic paradigm. Subjectivism as an ontological perspective and interpretivism as an epistemological approach were selected. A comprehensive literature review was undertaken and empirical evidence gathered. To help ensure valid and reliable findings the study relied on a mixed methods approach, for which semi-structured interviews and a structured online survey were conducted within a pilot and main study. Key findings reveal that several drivers of strategic change in the mobility industry such as increased opportunities to gain and requirements to handle Big Data and DDBMs as well as market pressures from a growing number of competitors is “why” established quasi-monopolist train operators like DB increasingly deploy DDBMs. “How” DDBMs are deployed has involved the establishment of many data and DDBM related resources, activities and processes which have led to a growing portfolio of DDBM value offers. Nevertheless, data and business-related barriers/inhibitors are faced which need to be overcome by train operators in this planning/transitioning process. This thesis contributes to the existing research by addressing existing research gaps, such as “DDDBM types”, “DDBM dimensions”, “DDBM deployment drivers”, and “DDBM deployment challenges” and aids practitioners in the train and mobility industry wishing to address the opportunities and challenges presented by DDBMs deployment.