Making sense of digital innovation

The rapid development of digital technologies has been reshaping the way of designing products, services, and business models by stimulating remarkable innovations to industries. Specifically, the paradigm of production in digital products has shifted from merely increasing hardware parameters to providing innovative digital capabilities and services. In line with this, conventional business models (e.g., finance, transportation, and accommodation industries) are being challenged by digital innovation. However, it is still unclear how the digital innovation (i.e. digital product innovation and digital business model innovation) interact and make sense to focal stakeholders (i.e., consumers, regulators, industrial competitors and partners). Therefore, it is necessary to collect a large volume of data on multiple organizations and various industries engaging in both production and business model innovation. To make it work, advanced information technologies (i.e. Text Mining, Machine Learning, Natural Language Processing, and Deep Learning) enable us to gather and analyze large textual dataset. This also allows us to utilize quantitative methods (e.g., econometrics modeling and data mining) to understand qualitative (and unstructured) data. Combining rich data and quantitative approaches is expected to help us to better comprehend the dynamics in related industries. As a result, my dissertation works is expected to provide both theoretical and methodological contributions to management and digital innovation literatures.Doctor of Philosoph