Can you tell a good open innovation failure from a bad one?

Purpose Open innovation (OI) failures are considered a uniform class of undesirable outcomes. This study emphasises that two classes of failure exist: “bad”, triggered by preventable causes such as negligence, lack of skills or inadequate procedure, and “good”, stemming from hard-to-predict causes inherent in innovation endeavours. This study aims to clarify the differences between good and bad failures, describe their causes and recommend countermoves to mitigate “bad” failures. Design/methodology/approach The authors conducted a single case study analysis examining OI in Leonardo, a multinational aerospace and defence company. This study leverages the experience of “innovation focal points” and senior managers in Leonardo, conducting 19 semi-structured interviews and an abductive thematic analysis. Findings The thematic analysis led to the identification of several causes leading to “good” and “bad” failures. The authors grouped the causes of good failures into three domains: lack of experience, unexpected market scenario and unsuitable or unprofitable technology. The authors grouped the causes of bad failures into five domains: internal causes of failures, risk management, partner selection, coordination and negotiation and intellectual property (IP). Practical implications This study provides actionable guidance to mitigate the causes of “bad” failures, allowing managers to reap the full potential of OI. Examples of countermoves discussed in this study include OI training and education programmes, implementing and maintaining a risk management process and IP protection mechanisms. Originality/value To the best of the authors’ knowledge, this is the first study to provide ontological insights distinguishing “good” and “bad” OI failures and suggest appropriate countermoves for “bad” OI failures. This study sets a new perspective that could trigger a new epistemological process.