Exploring the role of interaction in older-adult service innovation: insights from the testing stage

The paper frames older adulthood as a heterogeneous concept shaped by health, social roles, and economic factors, noting no universally accepted chronological threshold. It highlights growing, diverse needs among older adults and argues that service innovation must move beyond design and implementation to address the often-overlooked testing phase. Existing research has largely emphasized acceptance models and intergenerational collaboration, leaving gaps in understanding older adults’ interaction with service teams, learning curves, comfort, user experience, and long-term sustainability. The authors position the testing stage as critical and multifaceted, encompassing lifestyle, culture, and social networks, especially within public service platforms and digital co-design contexts. The study seeks to develop and validate a framework for the testing stage of service innovation for older adults, integrating insights from public service co-design and delivery to better tailor services and sustain engagement, trust, and value co-creation.

The review centers on value co-creation and its managerial mechanisms, emphasizing interaction as the cornerstone of co-creation where stakeholders act as resource integrators. Older adults’ consumption evolves with experience; they value safety, reliability, authenticity, and face technology-related challenges that require empathetic, skilled frontline employees and structured support. Public service platforms foster co-created, personalized service bundles and both virtual and in-person engagement. Technology-oriented approaches prioritize intuitive, user-centered solutions (e.g., health monitoring, voice assistance) and incorporate older adults’ perspectives into service design for resonance. Effective communication (e.g., in emergency care) and virtual co-design (especially during COVID-19) demonstrate the importance of involving older adults and adapting to digital environments. The testing stage is portrayed as a pivotal gatekeeper in service innovation, often underemphasized, and should assess perceptions, emotions, demand, and iteratively refine services. The stage can be segmented into preliminary, mid-term, adjustment, and post testing sub-stages, with methods such as video prototyping to simulate and evaluate service concepts. Overall, testing ensures alignment with market expectations, reduces waste, and supports ongoing refinement in service-dominant logic.

The study adopts an exploratory qualitative case study design using theoretical sampling. It focuses on two cases (L-Team and HC-Team) associated with a leading Beijing institution (2014–2017), covering R&D through service trials in aging communities. Data collection included field observation, multiple in-depth and semi-structured interviews with six department heads and frontline staff, archival and secondary sources (market reports, online and commercial reports), and informal follow-ups via email/phone. Triangulation was applied to enhance accuracy and credibility. The editing analysis method was used to code and reorganize textual data to identify categories and relationships. Evaluation criteria followed Golden-Biddle and Locke (Authenticity, Plausibility, Criticality). L-Team focuses on community, group-based services for healthy and mildly disabled/dementia older adults; HC-Team provides professional, one-to-one nursing for moderate to severe disability/dementia. The analysis tracks organizational learning mechanisms during the testing stage, including team dynamics, technology use, learning processes, value co-creation, and customer interaction across sub-stages.

- The authors propose and support a framework detailing how interaction among teams, technologies, learning, value co-creation, and customer engagement shapes testing performance in older-adult service innovation, while accounting for project and user characteristics (Fig. 1). - The testing stage functions as a multi-gate process across sub-stages (preliminary, mid-term, adjustment, post testing) to assess user perceptions, emotional responses, demand, and sustainability, enabling iterative refinement. - Differences between cases: L-Team (community/group services for healthier, socially active older adults) experiences fewer hurdles in preliminary community testing; HC-Team (home-based, one-to-one services for moderate/severe disability/dementia) faces larger challenges in access, communication, and trust-building, requiring repeated interactions and specialized care management. - Older adults’ characteristics significantly influence communication and engagement strategies: healthy, active individuals value diverse activities and face-to-face interaction; homebound, frail individuals may be reserved, with limited mobility and difficulty expressing preferences, shifting engagement toward family caregivers and trust-building over time. - Technology integration evolves across testing sub-stages: early reliance on market research and community pilots; later use of mobile apps, social media, service platforms, and data from devices (e.g., blood pressure and chronic condition monitoring) to extend reach, tailor services, collect feedback, and inform organizational learning (Table 3). - Team composition and capabilities adapt across stages: L-Team moved from IT-centric to service-enthusiastic frontline staff with pre-test community groups, training camps, standardized procedures, and cloud knowledge systems; HC-Team evolved toward medical professionals, standardization of care processes (iterative additions of assessment, planning, monitoring), and becoming professional service provider evaluators (Tables 4–5). - Experiential learning and trial-and-error are central: expert reviews, safety screening, satisfaction surveys, debriefings, and provider switching based on feedback are used to refine services and ensure quality. - Gatekeeping mechanisms (trial-and-error, screening, auditing) applied at different sub-stages prevent investment in nonviable services, drive continuous improvement, and ensure alignment with older adults’ needs (Tables 6–7). - Public service platforms and digital co-design environments support personalization, bottom-up group formation, and hybrid virtual–physical participation. - The framework underscores the necessity of not only attracting but also retaining older users through trust and sustained interaction, aligning short-term fit with long-term loyalty and sustainability. Quantitative statistics are not reported; findings are qualitative and process-oriented.

Findings substantiate the central research claim that interaction during the testing stage is pivotal to aligning services with older adults’ diverse needs. By segmenting the testing phase and embedding gatekeeping (trial-and-error, screening, audits), providers can iteratively refine service content, delivery methods, and team capabilities. Differences across user segments (healthy, socially active versus homebound with cognitive/physical limitations) mandate differentiated communication, engagement, and family involvement strategies. Technology supports, but does not substitute for, deep understanding and tailored delivery; its role is strongest when paired with data-informed learning loops and user-centered design. Team evolution toward balanced professional and empathetic competencies, supported by internal communication and knowledge systems, enhances service robustness. Overall, the framework advances service-dominant logic by demonstrating how interaction, co-creation, and organizational learning during testing drive sustainable innovation outcomes for older adults in both public platform and home-care contexts.

The study presents and validates a comprehensive framework for the testing stage of older-adult service innovation, highlighting the intertwined roles of professional expertise, customer engagement, and technological adaptation. It emphasizes sustainable gatekeeping across sub-stages to safeguard investments, ensure quality, and maintain long-term viability and value. The results show that aligning services to heterogeneous older-adult needs requires iterative testing, trust-building, and differentiated communication, supported by evolving team skills and technology-mediated feedback. Future research should integrate quantitative methods with qualitative insights, examine long-term outcomes, explore organizational culture and leadership influences, foster knowledge sharing and collaboration, conduct cross-country comparisons, and increase older adults’ involvement in co-design to deepen understanding and scalability.

The cases are drawn from specific industries and regions (Beijing, China), which may limit generalizability. The study emphasizes professional expertise and does not extensively analyze organizational culture or leadership dynamics. The predominantly qualitative approach lacks quantitative validation, potentially biasing perspectives on innovation processes and outcomes. Broader datasets, mixed-method designs, and longitudinal assessments are recommended to strengthen external validity and inferential power.