What question did this study set out to answer?

This paper aims to clarify the variability in reported failure rates for improvement and transformation initiatives.

February 28, 2026Open Access

Beyond the 70% myth: What do we actually know about failure rates in improvement and transformation initiatives?

Key Points

This paper aims to clarify the variability in reported failure rates for improvement and transformation initiatives.
Conducted an integrative evidence synthesis of existing literature.
Coded failure-rate claims by type, analysis unit, denominator, time horizon, and rate form.
Appraised evidential robustness of these claims.
Evaluated managerial definitions of 'failure' and their impact on failure rate perceptions.
Failure rates vary significantly across studies and need appropriate context for comparison.
Many claims of '~70% failure' stem from secondary sources rather than original calculations.
Managerial definitions often inflate failure rates by including broader criteria.
Proposes a 'Failure-Rate Reporting Minimum' for improving future claim accuracy.

Abstract

This paper examines what is actually known about failure rates in improvement and transformation initiatives, and why the headline '~70% failure claim' travels across domains. An integrative evidence synthesis maps failure-rate claims in academic and managerial literatures. Each claim is coded by failure type, unit of analysis, denominator, time horizon and rate form, and appraised for evidential robustness. Reported rates vary widely and are not comparable unless definitions, denominators and horizons are specified. Much academic repetition of '~70%' relies on secondary citation or underspecified claims rather than recalculated rates. Managerial benchmarks frequently define 'failure' as the complement of a stringent top-box success threshold and/or as non-sustainment over multi-year horizons, yielding higher apparent failure prevalence than narrower endpoints. Overall, the evidence does not support treating '70% failure' as a universal planning parameter; failure prevalence is best understood as a distribution conditional on what is counted and for how long. This study reframes the '70% debate' as a measurement and reporting problem, integrates academic and managerial evidence using a failure-type taxonomy and robustness appraisal, and proposes a 'Failure-Rate Reporting Minimum' to make future claims more falsifiable and practically useful.

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