Servitization in the service industry – AI-empowered opportunities for innovation in the bike rental business

Kuanchin Chen, Josip Marić () and Ya-Han Hu
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Kuanchin Chen: Western Michigan University [Kalamazoo]
Josip Marić: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
Ya-Han Hu: NCU - National Central University [Taiwan]

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Abstract: Purpose This study investigates the application of artificial intelligence (AI) in enhancing the servitization of the YouBike rental service, particularly addressing the challenges of service delivery risks and fostering service innovation. The research is centered around using AI to manage and predict bike rental shortages effectively and to innovate service delivery by adapting to customer needs and environmental conditions. This aims to transform the YouBike service from a product-centric to a service-centric approach, leveraging digital servitization. Design/methodology/approach The methodology involves analyzing the proximity of rental stations to significant locations, historical demand, environmental factors and regional dynamics to inform the development of AI models. Various machine learning (ML) models were evaluated to identify an optimized model capable of predicting bike rental shortages at different time intervals and pinpointing key factors influencing these shortages. The study uses comparative analysis to determine the most effective AI strategies for operational and service innovation challenges. Findings The research demonstrates that the optimized ML model can effectively predict bike rental shortages and identify critical variables that influence these events thereby mitigating service risks and optimizing resource allocation. This enables digital service innovation through both basic and add-on servitization in a way that addresses both operational and environmental risks. Our findings suggest that AI significantly enhances resource management and supports digital service innovation DSI through strategies like service bundling and geographic customization. Originality/value The originality of this research lies in its exploration of AI's role in both mitigating risks and fostering service innovation to enable the two categories of servitization for the service industry. Additionally, mitigation of operational and environmental risks has received only beginning attention, with most works being theoretical and descriptive. The servitization literature has called for further empirical evidence in this area. Our work not only fills this gap but also extends the discourse on digital servitization by integrating AI with operational strategies, providing a new perspective on enhancing service delivery and creating innovative service solutions in the bike rental industry.

Keywords: Digital servitization; Digital service innovation; Machine learning; Predictive analytics; Public bike rentals (search for similar items in EconPapers)
Date: 2025-07-01
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Published in Journal of Enterprise Information Management, 2025, ⟨10.1108/JEIM-04-2024-0207⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05399007

DOI: 10.1108/JEIM-04-2024-0207

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