Theory of errors in innovation failure and strategic management of winning by failing

ABSTRACT

The risk of failure in innovation projects affects organisational behaviour and the performance of firms. This study develops a theory of errors in innovation project-level failure. Error is a set of faults that generates deviations in phases of the R&D process in projects, decreasing the expected results. From a systemic perspective, innovation failure in a project is due to a set of errors that generates a negative outcome. The proposed theory clarifies the general determinants of innovation failure in projects that are due to errors in design, execution and market orientation. Case studies in the drug discovery industry and pharmaceuticals validate the consistency of the proposed theory, giving examples of errors in projects leading to innovation failures. The payoff matrix of success/failure occurrences predicts that the probability of failure in innovation projects can double by setting a difficult goal compared to an easy one. Inductive implications of the proposed theory are that organisations reduce innovation failure in projects with better adaptedness to changes that minimise errors and learn from them. The proposed theory can guide R&D managers, designers, etc. in detecting critical errors in the R&D process to apply strategies of problem solving based on a winning approach by learning from errors that lead to innovation failure in projects to enable the organisation to adapt, take advantage of important opportunities and cope with consequential environmental threats for achieving goals and sustaining the competitive advantage in turbulent markets.

KEYWORDS:

• Innovation failure
• goal failure
• Concorde failure
• innovation management

JEL CODES:

• O31
• O32
• O33

Acknowledgements

I would like to thank Prof. Robert G. Cooper (McMaster University Business School, Canada and Smeal Business School, Penn State University, USA), Prof. Alessandro Sterlacchini (Università Politecnica delle Marche, Italy), participants at the XXI Workshop SIEPI (University of Naples-Federico II, 15–16 June 2023), colleagues of the Arizona State University (ASU)-School of Complex Adaptive Systems and three reviewers for manifold and helpful suggestions to improve this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The equal-likelihood model is based on a finite number of alternative outcomes, all of which have equal probability: 1 indicates the certainty and 0 indicates the impossible event.

Additional information

Funding

I thank the CNR-National Research Council of Italy for funding Short Term Mobility Program 2023 [n. 224705-CNR0A00-0224705] to develop this study at the ASU.

Notes on contributors

Mario Coccia

Mario Coccia is a research director at the National Research Council of Italy and Visiting Scholar at the Arizona State University-School of Complex Adaptive Systems. He conducted research work at the Max Planck Institute of Economics, Georgia Institute of Technology, Yale University, United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), RAND Corporation (Washington DC), University of Maryland (College Park), Bureau d’Économie Théorique et Appliquée (Strasbourg), Munk School of Global Affairs (University of Toronto) and Institute for Science and Technology Studies (University of Bielefeld). He is the Editor-in-Chief and a member of the editorial board of manifold International Journals, such as Sensors, Discover Sustainability, Economics and Business Letters, The Economics of Science, HighTech and Innovation Journal. He published many papers in the research fields of economics of science and innovation, technometrics, technological forecasting, sociology of innovation, sources-evolution-diffusion of technology and innovation and political economy of science and technology. He is also in the editorial borad of manifold international journals of several disciplines.