Table 2 Integrated dimensions of... | Emerald Publishing

Table 2

Integrated dimensions of successful AI-driven service innovation through UIC

Dimension	Enablers	Processes/practices	Outcomes/benefits	Illustrative case examples
Mutuality ✓ Value co-creation ✓ Alignment ✓ Governance (e.g., Ankrah and Al-Tabbaa, 2015; Bruneel et al., 2010; Katirai and Nagato, 2024)	Clear IP management Actively managed ownership Supportive and responsive institutional and governance structures Information accuracy and aligned timing expectations Mutual understanding of AI's value for all stakeholders	Research centers focused on data and AI Industry professionals with advanced research training Practical ethical processes and frameworks Research collaboration agreements with industry AI service providers	Create new markets and technological opportunities for global differentiation for both parties Identifying reciprocal advantages Enhanced value creation for all partners	Case II: The value derived from this AI-driven service innovation was multifaceted, including enhanced personalization and a better customer experience for hotel guests. Value also stemmed from potential cost reductions for the hotel providers, while simultaneously enhancing brand recognition by targeting their sustainability profiles Case III: Timing alignment between partners, R&D benefits, trust, and clarity of ownership and responsibility were important themes in nurturing a successful partnership
Relational embeddedness ✓ Trust ✓ Communication ✓ Empathy (e.g., Ankrah and Al-Tabbaa, 2015; Bruneel et al., 2010; Cao et al., 2026)	Understand pain points and how AI could be leveraged to solve problems Empathize to understand mindsets around AI utility Orchestrate trusted partnerships Build open communication channels and a shared language Effectively boundary span	Maintain linkages with the research community Ongoing dialogue Establishment of affiliate and adjunct positions at universities and/or within research centers Crossover activities for AI capability development	Long-term engagement strategy Lower transaction costs Enhanced access to data and consumer populations to sustain value drivers	Case III: A deliberate and ongoing engagement strategy with universities was valued. Partnerships with senior university staff were highlighted as a key mechanism for effectively facilitating trust Case IV: The academic/university's natural proclivity for a “deep-seated distrust” was identified as a key barrier. However, the role of the former university researcher, who was involved in multiple companies and had extensive and well-established university networks, led to genuine trust and boundary-spanning opportunities
Complementary capabilities ✓ AI expertise ✓ Data access ✓ Absorptive capacity (e.g., Ankrah and Al-Tabbaa, 2015; Benoit et al., 2019; Verreynne et al., 2021)	Viewing AI as a research tool Deliberate joint PhD supervision and resource sharing Opportunities for talent development across the partnership Understanding the expertise and skills of each party	Data control in AI model development Alignment in relation to data access Validating the algorithms and ensuring interpretability checks	Future talent pool, joint publication and internships Invisible-by-design AI integration	Case I: The collaboration had historically led to patent protection for the research, which was critical for the healthcare provider by differentiating its products and services and enhancing its reputational assets in the global healthcare market. However, AI and new tools were only useful if the partner could integrate them to add commercial value Case IV: Managing data ownership was identified as a major UIC hurdle due to the university partner's differing mental models of data and the need for broader system and institutional change

Dimension	Enablers	Processes/practices	Outcomes/benefits	Illustrative case examples
Mutuality✓ Value co-creation ✓ Alignment ✓ Governance (e.g., Ankrah and Al-Tabbaa, 2015; Bruneel et al., 2010; Katirai and Nagato, 2024)	Clear IP management Actively managed ownership Supportive and responsive institutional and governance structures Information accuracy and aligned timing expectations Mutual understanding of AI's value for all stakeholders	Research centers focused on data and AI Industry professionals with advanced research training Practical ethical processes and frameworks Research collaboration agreements with industry AI service providers	Create new markets and technological opportunities for global differentiation for both parties Identifying reciprocal advantages Enhanced value creation for all partners	Case II: The value derived from this AI-driven service innovation was multifaceted, including enhanced personalization and a better customer experience for hotel guests. Value also stemmed from potential cost reductions for the hotel providers, while simultaneously enhancing brand recognition by targeting their sustainability profilesCase III: Timing alignment between partners, R&D benefits, trust, and clarity of ownership and responsibility were important themes in nurturing a successful partnership
Relational embeddedness✓ Trust ✓ Communication ✓ Empathy (e.g., Ankrah and Al-Tabbaa, 2015; Bruneel et al., 2010; Cao et al., 2026)	Understand pain points and how AI could be leveraged to solve problems Empathize to understand mindsets around AI utility Orchestrate trusted partnerships Build open communication channels and a shared language Effectively boundary span	Maintain linkages with the research community Ongoing dialogue Establishment of affiliate and adjunct positions at universities and/or within research centers Crossover activities for AI capability development	Long-term engagement strategy Lower transaction costs Enhanced access to data and consumer populations to sustain value drivers	Case III: A deliberate and ongoing engagement strategy with universities was valued. Partnerships with senior university staff were highlighted as a key mechanism for effectively facilitating trustCase IV: The academic/university's natural proclivity for a “deep-seated distrust” was identified as a key barrier. However, the role of the former university researcher, who was involved in multiple companies and had extensive and well-established university networks, led to genuine trust and boundary-spanning opportunities
Complementary capabilities✓ AI expertise ✓ Data access ✓ Absorptive capacity (e.g., Ankrah and Al-Tabbaa, 2015; Benoit et al., 2019; Verreynne et al., 2021)	Viewing AI as a research tool Deliberate joint PhD supervision and resource sharing Opportunities for talent development across the partnership Understanding the expertise and skills of each party	Data control in AI model development Alignment in relation to data access Validating the algorithms and ensuring interpretability checks	Future talent pool, joint publication and internships Invisible-by-design AI integration	Case I: The collaboration had historically led to patent protection for the research, which was critical for the healthcare provider by differentiating its products and services and enhancing its reputational assets in the global healthcare market. However, AI and new tools were only useful if the partner could integrate them to add commercial valueCase IV: Managing data ownership was identified as a major UIC hurdle due to the university partner's differing mental models of data and the need for broader system and institutional change

Source(s): Authors' own work