Integrating Problem Solvers from Analogous Markets in New Product Ideation

Who provides better inputs to new product ideation tasks: problem solvers with expertise in the area for which new products are to be developed, or problem solvers from "analogous" markets that are distant but share an analogous problem or need? Conventional wisdom appears to suggest that target market expertise is indispensable, which is why most managers searching for new ideas tend to stay within their own market context even when they do search outside their firms' boundaries. However, in a unique symmetric experiment that isolates the effect of market origin, we find evidence for the opposite: Although solutions provided by problem solvers from analogous markets show lower potential for immediate use, they demonstrate substantially higher levels of novelty. Also compared to established novelty drivers, this effect appears highly relevant from a managerial perspective: we find that including problem solvers from analogous markets vs. the target market accounts for almost two thirds of the well-known effect of involving lead users instead of average problem solvers. This effect is further amplified when the analogous distance between the markets increases, i.e., when searching in far vs. near analogous markets. Finally, results indicate that the analogous market effect is particularly strong in the upper tail of the novelty distribution, which again underscores the effect's practical importance. All this suggests that it might pay to systematically search across firm-external sources of innovation that were formerly out of scope for most managers. (authors' abstract

The value of scientific knowledge dissemination for scientists:A value capture perspective

Scientific knowledge dissemination is necessary to collaboratively develop solutions to today’s challenges among scientific, public, and commercial actors. Building on this, recent concepts (e.g., Third Mission) discuss the role and value of different dissemination mechanisms for increasing societal impact. However, the value individual scientists receive in exchange for disseminating knowledge differs across these mechanisms, which, consequently, affects their selection. So far, value capture mechanisms have mainly been described as appropriating monetary rewards in exchange for scientists’ knowledge (e.g., patenting). However, most knowledge dissemination activities in science do not directly result in capturing monetary value (e.g., social engagement). By taking a value capture perspective, this article conceptualizes and explores how individual scientists capture value from disseminating their knowledge. Results from our qualitative study indicate that scientists’ value capture consists of a measureable objective part (e.g., career promotion) and a still unconsidered subjective part (e.g., social recognition), which is perceived as valuable due to scientists’ needs. By advancing our understanding of value capture in science, scientists’ selection of dissemination mechanisms can be incentivized to increase both the value captured by themselves and society. Hence, policy makers and university managers can contribute to overcoming institutional and ecosystem barriers and foster scientists’ engagement with society

Genetic newborn screening and digital technologies: A project protocol based on a dual approach to shorten the rare diseases diagnostic path in Europe.

Since 72% of rare diseases are genetic in origin and mostly paediatrics, genetic newborn screening represents a diagnostic "window of opportunity". Therefore, many gNBS initiatives started in different European countries. Screen4Care is a research project, which resulted of a joint effort between the European Union Commission and the European Federation of Pharmaceutical Industries and Associations. It focuses on genetic newborn screening and artificial intelligence-based tools which will be applied to a large European population of about 25.000 infants. The neonatal screening strategy will be based on targeted sequencing, while whole genome sequencing will be offered to all enrolled infants who may show early symptoms but have resulted negative at the targeted sequencing-based newborn screening. We will leverage artificial intelligence-based algorithms to identify patients using Electronic Health Records (EHR) and to build a repository "symptom checkers" for patients and healthcare providers. S4C will design an equitable, ethical, and sustainable framework for genetic newborn screening and new digital tools, corroborated by a large workout where legal, ethical, and social complexities will be addressed with the intent of making the framework highly and flexibly translatable into the diverse European health systems

Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science (SUF edition)

Full edition for scientific use. As part of a study on factors influencing researcher data reuse and the mechanisms by which these factors are activated, the research team conducted semi-structured oral interviews with a purposive sample of 24 data reusers and intermediaries. This dataset includes de-identified transcripts of 21 of the interviews, as well as written follow-up responses from 8 of the study participants

How will Artificial Intelligence (AI) influence openness and collaboration in science?

Artificial intelligence (AI) is increasingly contributing to scientific breakthroughs in many fields. It is also clear that openness and cross-disciplinary collaboration are becoming key features of the process of modern science. Yet, we know little about the intersection of these two developments - whether and how AI may shape openness and collaboration in research. We, a group of scholars in the fields of science and innovation studies, engaged in an experiment as part of the 2022 OIS Research Conference: we collaboratively developed research ideas for a grant proposal, first without and then with the help of an AI tool. The results from the experiment indicate that AI is potentially useful for identifying and sharing relevant knowledge and for collaborating with knowledge actors outside the boundaries of scientists' own scientific fields, but it is not (yet) able to fully leverage this potential. At the same time, using AI tools may change the nature of scientific collaborations in subtle ways and lead to behavioral and cognitive challenges for scientists. The experiment raises new research questions regarding how collaborations between human actors and AI can be organized in a productive way