Before ChatGPT: Open Source Innovation at NASA
This piece complements Bridges' by exploring what makes some professionals open to innovation and shifting knowledge boundaries while others resist. Lifshitz-Assaf's methodology—moving from individual accounts to organizational "thick description"—also models the close listening and multi-year analysis required to understand how faculty are actually experiencing AI transformation
The history of science and technology is filled with cases of rejected innovations coming from outside the knowledge boundaries of a given professional community: from germ theory by Pasteur (a chemist, not a medical professional) to the theory of energy conservation by Helmholtz (a physician, not a physicist) to principles of inheritance from Mendel (an amateur biologist who relied on mathematics) (Barber, 1961; Campanario, 2009). Knowledge is embedded and invested in R&D professionals’ work processes and practices and therefore is “at stake” for those actors who have developed it (Carlile, 2002). Moreover, open innovation platforms attract individuals on the margins of knowledge boundaries either socially or knowledge-wise (Jeppesen and Lakhani, 2010), posing a threat to specialized knowledge developed in an organization or a discipline. Therefore, beyond the notorious not-invented-here syndrome (Allen, 1977; Katz and Allen, 1982), R&D professionals have unique reasons for rejecting knowledge from open innovation platforms.