Table 1

Illustrative UIC success case overviews

Illustrative success case contextService innovation (including classification of innovation novelty)UIC partnersData collection sources
Case I: Healthcare service industry: AI-driven diagnostic toolsDevelopment of AI-driven service innovations, such as an AI-driven toola for early disease detection.
This healthcare service innovation created value by not only improving patient outcomes but also reducing healthcare costs
(new to market/world innovations)
  1. University (medical researchers)

  2. Industry (e.g., service firm – hospital, corporate technology partner)

Interviewees: Two senior academics (i.e., university), with one of the academics interviewed multiple times
Secondary data sources: website material, institute videos
Case II: Hospitality service industry: algorithmic (AI) nudging toolDevelopment of sustainable hotel innovations based on AI algorithms and machine learning to drive profit for hotel chains through “green nudging” (von Zahn et al., 2025) to incentivize environmentally friendly customer behavior
(incremental innovation)
  1. University (business school researchers)

  2. Industry (e.g., service firm – hotel provider)

Interviewee: Senior academic (i.e., university)
Secondary data sources: Presentation transcript, presentation slides, website material
Case III: Healthcare service industry: AI-driven oral health toolsDevelopment of an AI-supported oral health innovation to support accuracy in scans and better diagnostic support for customers.
Partnership collaboration focused on activities such as validation and AI software enhancement, with a focus on the industry partner becoming an international leader in its offering. AI recognized as an essential tool to enhance customer experience and enhance service delivery
(new to market/world innovation)
  1. University (oral health and engineer researchers)

  2. Industry (SME software and hardware service provider for oral health and publicly funded research agency)

Interviewee: CEO (i.e., software and hardware service firm)
Secondary data sources: video documentary, website material, press release, public interview
Case IV: Healthcare and consulting service industries: AI-driven workforce productivity and wellbeing toolsDevelopment of machine learning and AI-driven, organizational-level innovations addressing intelligence solutions for healthcare worker productivity and decision-making, as well as psychological health innovations that provide assistive tools and diagnostics to enhance mental health/wellbeing for healthcare workers
(new to market/world innovation)
  1. University (interdisciplinary researchers within a research center)

  2. Industry (e.g., SME AI service firm)

Interviewee: CEO/co-founder/Managing Director SME AI service firm (ex-academic researcher) (i.e., industry)
Secondary data sources: website material, research center website
Note(s)
a

In the discussion of a “tool,” we concur with Vargo and Lusch's (2008) service dominant logic and rather than physical tools (or goods), our focus is on the activities and services derived from such tools and that require value co-creation

Source(s): Authors' own work

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