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dc.contributor.advisorWanda Orlikowski.en_US
dc.contributor.authorBeane, Matthew I. (Matthew Ian)en_US
dc.contributor.otherSloan School of Management.en_US
dc.date.accessioned2018-03-02T22:20:30Z
dc.date.available2018-03-02T22:20:30Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/113956
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 137-149).en_US
dc.description.abstractThough a 2.5-year mixed-method study comparing robotic surgical practice to traditional surgical practice, I explore how crucial outcomes require productive deviance: norm- and policy-challenging practices that are tolerated because they produce superior outcomes in the work processes governed by those norms and policies. My empirical focus was fortunate - I show that productive deviance is likely especially important in the first ten to twenty years of significant technical reconfiguration of surgical work. I open my dissertation through a comparative empirical introduction to my context and a review of the literature on deviance in organizations. The second chapter of my thesis is a history of how the surgical profession has relied on productive deviance for integrating new technologies since the early 1800s, ending with a deeper treatment on robotic surgery. My third chapter focuses on how only a very few surgical residents managed to gain confidence and competence with robotic surgical methods given significant barriers to such learning. In contrast to what the standing literature on learning would predict and in tension with the norms for learning within the surgical profession, these residents engaged in a suite of practices I call "shadow learning" - involving premature specialization, abstract rehearsal and undersupervised struggle. I explore how each of these practices both allow progress and create unintended negative consequences for the profession. My fourth chapter explores a case in which surgical teams routinely used new, well-maintained robotic surgical devices and occasionally faced the stressful and practically difficult task of using an under-maintained, unreliable surgical robot. In this chapter, I show quantitatively that patients did just as well on the degraded robot, and I outline the often invisible, undervalued "compensatory work" that professionals did to ensure such outcomes. The main contribution here is to explicitly treat affect as integral to coordinated work that is grounded in suboptimal material arrangements. Through these studies, I solidify and enrich our conception of productive deviance and show how it is critical for a range of professional and organizational outcomes.en_US
dc.description.statementofresponsibilityby Matthew Ian Beane.en_US
dc.format.extent159 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.titleOperating in the shadows : the productive deviance needed to make robotic surgery worken_US
dc.title.alternativeProductive deviance needed to make robotic surgery worken_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1023435140en_US


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