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Chain of custody · waved through on who handled it

Pro-Innovation Bias

The tendency to assume that an innovation should be adopted by all members of a social system, diffused rapidly, and neither re-invented nor rejected, leading to systematic neglect of an innovation's potential downsides.

Entered into evidenceThe pro-innovation bias is the implication of most diffusion research that an innovation should be diffused and adopted by all members of a social system, that it should be diffused more rapidly, and that the innovation should be neither re-invented nor rejected.

→ follow the claim down the corridor, hand to hand

Filed · origin

Everett M. Rogers, 1962

Diffusion of Innovations

Entered

Handler 01 · study

Rogers' meta-analysis of diffusion research

The field's research designs, innovation selection, and interpretations consistently favored rapid and complete adoption, creating a blind spot around unsuccessful diffusion and negative consequences.

Rogers, 2003 · not reported

Cleared

Handler 02 · study

Technology adoption without due diligence

Investors and partners adopted Theranos's technology despite lack of scientific validation, demonstrating how pro-innovation bias in Silicon Valley's 'move fast' culture can lead to adoption of harmful innovations.

Straker, Nusem, & Islam, 2021 · not reported

Cleared

Final disposition

No chain of custody

Pro-innovation bias is more of a field-level critique and conceptual framework than a single laboratory effect. It has been widely applied across disciplines to explain premature technology adoption and has been supported by numerous case studies of failed innovations. However, no single experimental paradigm has been systematically replicated across labs.

folk-concept
No chain of custody

Replication — folk-concept. Pro-innovation bias is more of a field-level critique and conceptual framework than a single laboratory effect. It has been widely applied across disciplines to explain premature technology adoption and has been supported by numerous case studies of failed innovations. However, no single experimental paradigm has been systematically replicated across labs.

Handling note · how it gets waved through

Pro-innovation bias arises from multiple converging factors: research designs that select only successful innovations for study, organizational cultures that reward early adoption and penalize skepticism, and change-agent incentives that promote rapid diffusion. Rogers identified the bias as endemic in diffusion research because scholars typically studied innovations after they had already diffused successfully, creating a survivorship bias that normalized the assumption that all innovations should be adopted.

Competing account — Some scholars distinguish pro-innovation bias from status quo bias, noting they can operate in tension—the former drives premature adoption of unproven technologies while the latter resists beneficial change.

Seen in the field

  • medicine · 2014-2018

    Theranos blood-testing technology adoption

    Investors and partners adopted Theranos's unvalidated blood-testing technology, valuing the company at $9 billion before investigative reporting revealed the technology produced inaccurate results. The case exemplifies pro-innovation bias in Silicon Valley's culture of prioritizing disruptive innovation over scientific validation.

  • government · 2002-2011

    UK National Programme for IT in the NHS

    The UK government spent approximately £12.7 billion on a centralized electronic health record system that was ultimately abandoned, driven by assumptions that the new IT infrastructure would inevitably improve healthcare delivery without adequate consideration of implementation challenges and user needs.

To break the chain

Implement a 'red team' or pre-mortem process before adopting innovations, requiring explicit identification of potential failure modes and adoption risks. Rogers recommended studying unsuccessful innovations alongside successful ones and investigating the broader context of diffusion decisions.

Pre-mortem analyses have been shown to improve decision quality by forcing consideration of failure scenarios that might otherwise be ignored due to optimism and pro-innovation bias.

Catch it in the act

Watch for situations where teams or organizations adopt new technologies simply because they are new, without rigorous evaluation. Warning signs include: dismissing concerns about a new tool as 'resistance to change,' ignoring pilot data showing problems, and pressuring holdouts to adopt without addressing legitimate concerns.

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