Inbound open innovation for pharmaceutical markets: a case study of an anti-diabetic drug in-licensing decision

This paper aims to increase understanding of how firms can more effectively identify valuable and profitable innovations in the pharmaceutical industry and to identify the issues and challenges posed by current managerial decision-making practices.

A case study of a single project is presented: a drug in-licensing decision made by a team of three managers in a large Australian pharmaceutical firm. Using participant-observation, interviews and archival analysis, the authors followed the managers as they identified and evaluated 122 late-stage anti-diabetic drug variants for further development.

The managers used decision heuristics to arrive at a short list of three drugs from a choice set of 122. While the process was ostensibly rational and systematic, there was evidence of data quality issues, misleading mental models and cognitive bias. The authors concluded a high probability of accepting a poor candidate or rejecting a stronger candidate (i.e. making Type I and II errors).

This paper focuses on initial market and technology evaluation stage only (i.e. not commercialization) and is a single case study design; therefore, care should be taken in generalizing to other decisions or other contexts. This paper highlights the need for further research integrating organizational decision-making and open innovation from a multi-disciplinary perspective.

This paper raises awareness of potential decision-making pitfalls and includes a detailed audit framework to support improved managerial decision processes and double rather than single loop learning.

The findings support better decision-making and therefore supports higher quality drug selection and development, leading to improved population health outcomes.

Multi-disciplinary, draws attention of marketing and new product development scholars to open innovation research. It adds to knowledge about open innovation practices at the project level. It also provides an extended model of market opportunity analysis for high technology markets.