Big data in digital product innovation: Identifying the antecedents and consequences of using big data in digital product innovation – a multiple case study

Innovation is critical in driving economic growth. It fosters new products and services to move the economy forward. On the other hand, innovation requires new technologies to thrive. One such technology that has revolutionised various industries is big data. By harnessing big data, businesses can unlock valuable insights to enhance decision-making processes and ultimately encourage innovation across sectors. While big data holds the potential to drive innovation and economic growth, managing big data-enabled digital product innovation projects is challenging. Organisations need the resources, knowledge, and skills to manage these projects. In addition, companies should also be aware of the privacy and security risks associated with these big data innovation projects. Considering all the many factors influencing big data innovation projects, it is necessary to understand how they impact these projects in order to manage them well. This doctoral project answers the main research question: How do companies develop big data analytics capabilities in digital product innovation? This doctoral research identifies the different factors that influence big data innovation projects and reveals the mechanisms behind how these factors influence each other and affect digital product innovation. This research contains a multiple-case study with four cases to explore the answers to the research questions. The case studies analysed four new digital service development projects from four big companies operating in different industries (i.e. transportation, internet, navigation, and cybersecurity) in the past five years. In these cases, big data from various sources are used as input for machine learning and statistical models to enable digital product innovation. For collecting research data for this study, the semi-structured interview protocol was used, focusing on the process of using big data in innovation projects, the challenges and factors that affect this process, and its consequences. This study’s findings highlight the mechanism of how antecedents influence big data analytics capabilities (BDAC) and subsequently impact innovation performance. The study applies the resource-based view (RBV) and dynamic capabilities view as theoretical frameworks to clarify the mechanism behind enhancing innovation performance. It investigates the antecedents of BDAC constructs, explores the interactions among these constructs, and evaluates their effects on innovation performance. This research enriches the existing literature by revealing how BDAC constructs promote their development, unveiling the dynamics within BDAC, and emphasising the vital role of data variety. Furthermore, it extends the literature by uncovering novel connections between dynamic capabilities and BDAC and between environmental uncertainty and BDAC, broadening the scope of environmental uncertainty and examining the effects of BDAC constructs on innovation performance. For practitioners, this research suggests that innovation performance can be ultimately improved by enhancing data variety and establishing supportive internal and external environments for BDAC.