Administrative Science Quarterly 2019, Vol. 64(1)87–123 Ó The Author(s) 2018 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0001839217751692 journals.sagepub.com/home/asq Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail Matthew Beane Abstract I explore here how trainees in a community of practice learn new techniques and technologies when approved practices for learning are insufficient. I do so through two studies: a two-year, five-sited, comparative ethnographic study of learning in robotic and traditional surgical practice, and a blinded interview- based study of surgical learning practices at 13 top-tier teaching hospitals around the U.S. I found that learning surgery through increasing participation using approved methods worked well in traditional (open) surgery, as current literature would predict. But the radically different practice of robotic surgery greatly limited trainees’ role in the work, making approved methods ineffective. Learning surgery in this context required what I call ‘‘shadow learning’’: an interconnected set of norm- and policy-challenging practices enacted exten- sively, opportunistically, and in relative isolation that allowed only a minority of robotic surgical trainees to come to competence. Successful trainees engaged extensively in three practices: ‘‘premature specialization’’ in robotic surgical technique at the expense of generalist training; ‘‘abstract rehearsal’’ before and during their surgical rotations when concrete, empirically faithful rehearsal was prized; and ‘‘undersupervised struggle,’’ in which they performed robotic surgi- cal work close to the edge of their capacity with little expert supervision—when norms and policy dictated such supervision. Shadow learning practices were neither punished nor forbidden, and they contributed to significant and troubling outcomes for the cadre of initiate surgeons and the profession, including hyper- specialization and a decreasing supply of experts relative to demand. Keywords: learning, technology, communities of practice, work, deviance, robotic surgery, training We have known for decades that the world of work is changing and that com- munities of practice must cultivate new skills to stay relevant (Barley, 1996; Anteby, Chan, and DiBenigno, 2016). New business models (Barley and Kunda, 2006), collaborative practices (Leonardi, 2011), and technologies (Bailey, 1 University of California, Santa Barbara