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dc.contributor.author
Shrestha, Yash Raj
dc.contributor.author
Fang He, Vivianna
dc.contributor.author
Puranam, Phanish
dc.contributor.author
von Krogh, Georg
dc.date.accessioned
2021-07-30T09:42:31Z
dc.date.available
2021-07-30T02:42:44Z
dc.date.available
2021-07-30T09:42:31Z
dc.date.issued
2021
dc.identifier.issn
1047-7039
dc.identifier.issn
1526-5455
dc.identifier.other
10.1287/orsc.2020.1382
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/498484
dc.identifier.doi
10.3929/ethz-b-000498484
dc.description.abstract
Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for predictions without explanations that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate algorithm supported induction, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Institute for Operations Research and the Management Sciences
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
machine learning
en_US
dc.subject
algorithmic induction
en_US
dc.subject
theory building
en_US
dc.title
Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-12-09
ethz.journal.title
Organization Science
ethz.journal.volume
32
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
Organ Sci
ethz.pages.start
856
en_US
ethz.pages.end
880
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Leading and Coordinating Knowledge Creation in Online Communities
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Catonsville, MD
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
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
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
ethz.grant.agreementno
169441
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projektförderung in Geistes- und Sozialwissenschaften (Abteilung I)
ethz.date.deposited
2021-07-30T02:42:47Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-07-30T09:42:37Z
ethz.rosetta.lastUpdated
2021-07-30T09:42:37Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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