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dc.contributor.author
Shrestha, Yash R.
dc.contributor.supervisor
von Krogh, Georg
dc.contributor.supervisor
Puranam, Phanish
dc.contributor.supervisor
Zhang, Ce
dc.contributor.supervisor
Ben-Menahem, Shiko
dc.date.accessioned
2019-03-21T10:02:21Z
dc.date.available
2019-03-20T15:26:21Z
dc.date.available
2019-03-21T10:02:21Z
dc.date.issued
2019-02
dc.identifier.uri
http://hdl.handle.net/20.500.11850/332700
dc.identifier.doi
10.3929/ethz-b-000332700
dc.description.abstract
Major developments in digital technology in recent decades have led to new ways of collaboration and knowledge creation, particularly the proliferation of open source software development communities. The unique characteristics of open source software development communities—especially the absence of employment contracts, fluidity of organizational boundaries and openness of knowledge collaboration—provide fruitful ground for advancing organizational theories. Anecdotal evidence has shown that new solutions to the problems of collaboration and coordination in the absence of a formal hierarchy and centralized organizational design have emerged in these communities. The last few years have also witnessed rapid progress in the development of predictive technologies based on artificial intelligence and machine learning algorithms that are efficient at providing fast, accurate and low-cost predictions. Such predictive algorithms show great promise as a methodological tool for organizational research. Moreover, as these algorithms increasingly facilitate decision-making tasks in organizations, they open up new opportunities for organizational design researchers to understand how organizational decision making can be better aided by applying machine learning. Accordingly, the goal of this thesis is two-fold. First, the thesis aims to extend the theoretical understanding of the following important but poorly understood questions: (1) How can organizations scale up without a formal hierarchy? and (2) How should decentralized organizations be designed? Second, condensing insights from the application of machine learning and artificial intelligence algorithms in organization science, I propose a framework that is useful for introducing machine learning algorithms into 1) organizational research methods and 2) organizational decision making. The first two studies of this thesis explore two important issues regarding organizing at scale in the absence of a formal hierarchy and centralized explicit mechanisms: (i) the efficient resolution of disputes and (ii) the delegation of tasks. These studies employ open source software development communities as a research context—specifically, the GitHub and OpenStack communities. In Study I, I find that dispute resolution in online communities features problem solving rather than bargaining and occurs in the absence of explicit global mechanisms. In these communities, larger and unconstrained discussions facilitate the switch from alternative-focused discussions to attribute-focused discussions, which increases the likelihood of dispute resolution. In Study II, I find that in the absence of perfect information on potential delegates and a formal hierarchy, delegators rely on experiential and vicarious learning from past delegation decisions when making current decisions. I also show that the decision to delegate authority significantly mediates the relationship between a delegators’ previous experience with delegation and the likelihood of future implementation. The last two studies in this thesis provide a framework for applying artificial intelligence and machine learning algorithms to organizational research and organizational decision making. In Study III, I present machine learning techniques as a useful new tool for organizational researchers pursuing inductive research. I propose that adding machine learning techniques to existing inductive research methods—for example, using robust and replicable “stylized” pattern detection—allows researchers interested in using qualitative data to both develop and test theories in a transparent and reproducible manner. Finally, Study IV provides a framework outlining prototypical decision-making structures for organizations in which human decision making can be blended with algorithmic decision-making. Overall, this thesis examines and advances our understanding of two underexplored aspects of online communities—i.e., dispute resolution and delegation (Study I and Study II, respectively)—and provides useful frameworks for integrating machine learning and artificial intelligence-based predictive technologies into organizational research and organizational decision making (Study III and Study IV, respectively).
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.title
Bridging Data Science and Organization Science: Leveraging Algorithmic Induction to Research Online Communities
en_US
dc.type
Doctoral Thesis
dc.date.published
2019-03-21
ethz.size
273 p.
en_US
ethz.code.ddc
DDC - DDC::3 - Social sciences::330 - Economics
ethz.identifier.diss
25808
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03719 - von Krogh, Georg / von Krogh, Georg
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03719 - von Krogh, Georg / von Krogh, Georg
en_US
ethz.date.deposited
2019-03-20T15:26:28Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Embargoed
en_US
ethz.date.embargoend
2022-03-21
ethz.rosetta.installDate
2019-03-21T10:02:29Z
ethz.rosetta.lastUpdated
2020-02-15T17:57:02Z
ethz.rosetta.versionExported
true
ethz.COinS
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