This study investigates the design of business models underlying the use of Augmented Reality (AR) technology for remote service provision within mechanical and plant engineering companies. These so-called AR-based remote service business models (ARRS-BMs) describe how value is created, delivered, and captured by utilising AR technology for remote services. By adopting AR technology for remote services, mechanical and plant engineering companies increasingly aim to perform maintenance and repairs on their products remotely. This transforms their traditional service business model, which primarily involves sending skilled technicians to customer sites to provide such services. While research has long highlighted the benefits that AR technology brings to industrial remote service provision, its use in industry has only recently gained momentum. In particular, the Covid-19 pandemic has accelerated the adoption of AR technology for remote services, leaving ARRS-BMs in an early stage of development. Existing studies have largely focused on the technical aspects of AR in remote services, with hardly any attention paid to the business model perspective. As a result, ARRS-BMs remain an underexplored area, characterised by limited conceptual insights and a lack of empirical evidence on how these business models can be designed. To address this gap, this study employs a predominantly qualitative, mixed-model research approach, including a systematic literature review, focus group discussions with 19 service managers from 12 companies, and semi-structured interviews with 41 service managers from an additional 36 companies. A central contribution of this study is the development of a novel, empirically grounded taxonomy comprising 18 dimensions and 73 characteristics, offering a systematic framework for describing, classifying, and analysing ARRS-BMs. Another central contribution is the development of three archetypes of ARRS-BMs through cluster analysis, simplifying the complexity of these models and highlight key configurations and their underlying generic logics. These and additional contributions are valuable for both research and industrial practice. For researchers, the taxonomy and archetypes provide a structured and robust framework for systematically exploring, classifying, and analysing ARRS-BMs. By adopting the taxonomy as a reference, future studies can ensure consistency and comparability in the analysis of ARRS-BMs. For practitioners, the thesis offers actionable insights into designing and managing ARRS-BMs, including guidance on evaluating AR suitability for service tasks, selecting between AR devices such as smart glasses and smartphones, and making informed decisions about business model configurations based on the person being supported. The study also acknowledges its limitations, including the predominantly qualitative research approach, the largely German company sample, and the ongoing technological advancements in the field of AR. To address these limitations, future research is encouraged to validate the findings through large-scale quantitative surveys and to explore regional variations in ARRS-BM design.
| Date of Award | 26 Aug 2025 |
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| Original language | English |
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| Supervisor | Rakesh Mishra (Main Supervisor) & Artur Jaworski (Co-Supervisor) |
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