Abstract
dc:description.abstractThe development of Digital Twins (DT) remains high in the digital transformation agendas of organisations. However, a paucity of field research exploring how DT are implemented in practice means their organisational value is not yet well understood. DT are a digital representation of a physical asset that is dynamically updated with data from its physical twin throughout its lifecycle, and their usage introduces new organisational dynamics that current literature cannot fully explain. This Thesis contributes to the understanding of DT value by exploring how the usage of DT affects a key source of competitive advantage: Organizational Learning (OL). More specifically, the following research questions are addressed: - How do Digital Twins affect Organizational Learning processes? - What factors influence and mediate the usage of Digital Twins and their effect on Organizational Learning processes? The effect of DT on OL is investigated through an extensive, multiple case study of DT implementations in the heavy assets industry. The cases include one in-depth case study with an aircraft engine Original Equipment Manufacturer (OEM) and two supporting case studies from wind renewable OEMs. Three interrelated DT variables that drive the effect of DT on OL are theorised: Individualisation, Synchronicity, and Organisational Orchestration. Their effects on OL, as well as the organisational outcomes arising from the enactment of each of the variables, are also theorised. This Thesis also advances our understanding of how the development and implementation of highly complex IT in a B2B context affects OL. In particular, it identifies limitations to the widely accepted notion that IT has a positive effect on Knowledge Acquisition and Information Distribution processes; extends current theory on Exploration and Exploitation processes; validates and extends previous research on the Timing of Learning; contributes towards the validation of theory on the organisational effect of Advanced Information Technology.
Degree
thesis:*- Name dc:type.qualificationname
- Doctor of Philosophy (PhD)
- Level dc:type.qualificationlevel
- Doctoral
- Grantor dc:publisher.institution
- University of Cambridge
- Year dc:date.issued
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Gomez Medina, Francisco
- Advisor dc:contributor.advisor
-
- Martinez Hernandez, Veronica
Subjects
dc:subject × 8Rights
dc:rightsIdentifiers
dc:identifier.*- DOI dc:identifier.doi
- https://doi.org/10.17863/CAM.108946
- OAI identifier oai:identifier
- oai:www.repository.cam.ac.uk:1810/368930