Absorbing knowledge from an emerging field: The role of interfacing by proponents in big pharma

TECHNOVATION(2022)

引用 3|浏览11
暂无评分
摘要
Engaging with knowledge from an emerging field, with its promises and limitations still uncertain, can be challenging for incumbent firms. Proponents of novel technologies within these firms then play an important role in bridging the developments in the field with the rest of the firm. In this study, we explore how researchers in big pharma firms engaged with knowledge from a promising, yet once-speculative approach called fragment based drug discovery (FBDD). To set the context, we mapped the publications and drugs in clinical trials coming from this approach across big pharma firms. Through our unique access to researchers in two big pharma firms (an early and a late adopter), we conducted a case study to explore how researchers built the capability within their firms. We find that at each stage of knowledge absorption (search and recognize value, assimilate, acquire, transform and exploit), proponents perform both internal-and external-facing activities to overcome uncertainties with regards to the field. We find that the emphasis of these activities evolves over time as the field evolves. In the early days of the field, proponents of the approach motivated knowledge absorption by emphasizing the internal needs of the firm. As the field matured, with its value becoming increasingly known, proponents lean on the exciting developments in the field to motivate further the adoption of the technology. In general, we find that proponents strategically tuned their external activities to cater to their internal context and similarly, adapted their internal activities to reflect external developments. By exploring interfacing in an emerging field, this study provides insights into how practitioners in large incumbent firms can adapt to the emergence of novel technologies.
更多
查看译文
关键词
Absorptive capacity, Innovation adoption, Research and development, Pharmaceutical industry, Drug discovery
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要