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University–industry collaboration for AI-driven service innovation

Alexandra Kriz et al.

Journal of Service Management2026https://doi.org/10.1108/josm-12-2024-0545article
AJG 2ABDC A
Weight
0.50

Abstract

Purpose Service innovation is undergoing a fundamental transformation due to digitization, sustainability imperatives, platformization and particularly the rapid diffusion of artificial intelligence (AI). AI-driven service innovation requires specialized and domain-specific expertise, which is increasingly derived from university–industry collaboration (UIC) contexts. However, UICs are often prone to failure, and new, uncertain and ambiguous characteristics of AI can further exacerbate these challenges. This paper, therefore, explores how UICs can be successfully managed to develop and deploy AI-driven service innovations. Design/methodology/approach A qualitative, illustrative case study research design featuring expert interviews and secondary data sources was employed to analyze multiple UIC contexts, with a focus on understanding AI-driven service innovation. The interpretive thematic analysis revealed inductive data-driven insights that were used to refine and augment deductive insights from the literature. Findings A framework for enhancing the success of UIC for AI-driven service innovation is proposed, based on illustrative case data, alongside theoretical perspectives from UIC, service-dominant logic and service ecosystems. The framework incorporates the mechanisms for thriving partnerships and defines how AI-driven stakeholder value can be enhanced. Therein, it identifies key enablers, processes/practices and outcomes/benefits of UIC for AI-driven service innovation. The conclusion brings together theoretical and practical implications from the framework and presents a future research agenda. Originality/value Prior literature has primarily examined UIC for technological, product and process innovation. To the best of the authors’ knowledge, this is the first study to focus on UIC collaborations for service innovation, specifically in AI contexts. It synthesizes existing literature and insights from successful UIC in the context of AI to develop a novel framework to guide further theory development, practical collaborations and policy design.

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https://doi.org/https://doi.org/10.1108/josm-12-2024-0545

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@article{alexandra2026,
  title        = {{University–industry collaboration for AI-driven service innovation}},
  author       = {Alexandra Kriz et al.},
  journal      = {Journal of Service Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/josm-12-2024-0545},
}

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University–industry collaboration for AI-driven service innovation

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.