Balancing economic rationality and social responsibility under resource constraints
Findings on doctors’ knowledge and timing in medical device innovation
Bibliographic Data
| ID | 15736216 |
|---|---|
| Authors | Masaya Onuma (Yokohama National University, corresponding author), Tatsuya Kubota (0000-0003-4766-4771, Seijo University), Atsushi Tsumita (Seijo University) |
| Year | 2026 |
| Volume | 13 |
| Issue | 1 |
| Publication date | 2026-01-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Responsible Innovation (JOURNAL) |
| Journal identifiers | ISSN: 2329-9037 • E-ISSN: 2329-9460 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/23299460.2025.2611505 |
| OpenAlex | W7124131903 |
| Language | EN |
| References cited | 85 |
Resource constraints often hinder companies from balancing economic goals with Responsible Research and Innovation (RRI). In the medical device sector, doctors are critical stakeholders in addressing this challenge, as they integrate patient, clinical, and societal needs into medical innovation. This study examines doctors' knowledge types and the timing of their engagement in innovation. Drawing on a survey of doctors at a hospital that has successfully integrated RRI into medical device development, we analyze involvement across innovation stages using three types of knowledge: needs-knowledge, solution knowledge, and regulatory-knowledge. The results show that solution knowledge is particularly important for early-stage participation, whereas all three knowledge types are significantly associated with involvement in later stages. These findings suggest that companies can more effectively implement both RRI and User Innovation by strategically selecting partners based on stage-specific knowledge requirements. Overall, the study highlights the value of a dynamic partner selection strategy as innovation progresses
Health technology · Medical device · Rationality · Resource (disambiguation · Responsible Research and Innovation · Selection (genetic algorithm · Value (mathematics · Biomedical and Engineering Education · Biomedical Ethics and Regulation · Health Systems, Economic Evaluations, Quality of Life
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| Citation velocity | historical |
|---|---|
| Highly cited | No |