Computational Methods for Studying Organizations and ...mgencer.com/files/IEU-Seminer-Sunum.pdf ·...
Transcript of Computational Methods for Studying Organizations and ...mgencer.com/files/IEU-Seminer-Sunum.pdf ·...
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Making Innovation Visible
Computational Methods for Studying Organizations and Innovation
Seminar
Mehmet Gençer
http://mgencer.com
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Bio● Coder since age 12● 1986, İzmir Fen Lisesi● 1990, Bsc, Bilkent Uni., Electronics Eng
(Phys.Minor)● 1991, Tampere Institute of Tech., Digital Image
Proc. Pr.● 1992-1994 Msc, Eastern Mediterranean Uni.,
Electronics (Digital Communication)● 1994-1999 Industry, ERP development, Istanbul● 1999-2002 Industry, Expert Systems, USA● 2002-current Bilgi Uni.● 2009 PhD, Bilgi Uni., Organization Studies
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Organization ...
● … is communicatively constituted
Weick, Luhmann, ...
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Innovation● Goes against routine● Its inputs and outputs are explored in research,
process itself is less so
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Outline
● Networks and Texts → Why analyze● Demonstrative studies: network analysis● Demonstrative studies: text mining● Epliogue: Computational methods and
management
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Innovation Dilemma● Organizing for routine: formal structure & culture of
efficiency● Organizing for innovation: informal structure &
culture of creativity
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Why analyze?● Network analysis → Structure of communication● Text Analysis → Content of communication,
Organizational discourse → Culture● Digital ethnography → study contemporary
organizations in situ
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Social Network Analysis (SNA)
● Also named relational sociology● Focuses on relations, rather than substance of a
system● Attribute data (income, gender, profession, ...) →
variable analysis● Relational data (A-married-B, C-co-authored-with-D)
→ relational analysis
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SNA Examples● From Lazer et al. (2009) review on computational
social science
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SNA Applications● A typology of ties studied in social network
analysis. (source: Borgatti et al., 2009)
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SNA Representation
A B C D E
A - 1 2 1 0
B 1 - 0 3 0
C 2 0 - 0 0
D 1 3 0 - 1
E 0 0 0 1 -
Relations among a single set ofsocial entities, weighted, undirected
club1 club2 club3
A 1 1 0
B 0 0 1
C 0 0 1
D 1 1 0
E 1 0 1
Two sets/Bipartite,unweighted, directed
DegreeSymmetric
Matrix
In-degree
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SNA Representation and Metrics
Preferred representation: Graph Theory A Network=(A set of People,A set of Ties between them).
Metrics at different levels:● Individual: degree centrality, betweenness● Groups: clustering coefficient● Network: density, diameter
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Study 1: Open Innovation in Software Industry
● My own PhD research. 2009.● Case study: Eclipse, a collaborative technology
developed by 149 firms, led by IBM.● Strengthened IBM's position in software industry.
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Study 1 …
Inter-organizational Network of co-participation to sub-projects
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Study 1 ...
Highlights:● Cross-boundary communication correlated with
organization's contribution.● Challenges stimulate extrovert communication for
organizations● New comer attitude in communities● Others, strategy related findings
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Study 2: Networks of Individuals in Software Innovation
● Presented: COLLNET 2011, International Conference on Webometrics, Informetrics and Scientometrics
● Aim: Dissecting structural effects with respect to the two types of knowledge processes in open source innovation: knowledge creation and knowledge brokerage
● Lave&Lenger’s Communities of Practice as theoretical basis
● Social structure is operationalized from e-mail exchanges in multiple mail-groups in the same software project.
● Example project: 9 years, 75.000 people, 600.000 mails, 150 mail-groups, 1.2 million bug solving records.
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Study 2 ...● Hypothesis:
(1) Knowledge creation performance ← deep access to variety within a domain of expertise(2) Knowledge brokerage performance ← shallow access to variety across heterogeneous domains
● Operationalization: directed, weighted graph productivity : bug- and issue-solving records brokerage performance: diversity: Herfindahl-Hirschmann Index of degrees
in−degreeout−degree
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Study 2 ...
SNA measurement combined with conventional GLM & Bayesian statistics for testing hypotheses, e.g.:
Hypothesis 1: Estimate Std. Error t value Pr(>|t|) (Intercept) -6.029e+01 1.529e+01 -3.942 8.15e-05 *** peer-diversity 1.632e+02 2.803e+01 5.822 6.01e-09 *** domain-divers. -8.174e+01 3.759e+01 -2.174 0.0297 * time-in-comm. 3.313e-09 2.007e-10 16.509 < 2e-16 ***
...and 2: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.73583 0.01580 109.89 <2e-16 *** peer-diversity -1.16924 0.02773 -42.16 <2e-16 *** domain-divers. 0.83635 0.03552 23.55 <2e-16 *** Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Study 3: Internet Technologies
● Article: The Evolution of IETF Standards and Their Production. International Journal of IT Standards and Standardization Research, 2012.
● Corpus of interrelated texts and people: cross references and co-authorship.
● 30 years, 6000 documents, 4000 people
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Study 3...Exploration
Network of Internet standards...
...and authors
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Study 3 ...● Findings● System clustering increased, subsystems ● More authors&references per document (complexity) ● Episodic growth
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Study 4: Corporate Network
● Commissioned study for a large Telecommunications company, 2014
● Peer review comments, collected over 6 years, was underutilized
● 4500 people, 150.000 comments● Peer selection as directed and weighted informal
network.
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Study 4 ...Organizational network: Colors ↔ Organizational Functions Node size ↔ Betweenness centrality of individual
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Study 4 ...
● Findings, from customer HR’s perspective:GHR, İdari İşler ve Academy’nin saha ekipleri dışında merkezde konumlanmıştır. Diğer fonksiyonlar ve özellikle grup şirketleriyle bağlantıları azdır.
GHR’dan köprü rolü olarak öne çıkan bir birey mevcut değildir.
GHR & GFN; NOR, Corporate, SOL ve Tower ile daha uzak konumlanmıştır.
● More general findings:Köprülük rolünün genç nesilde konumlanması yeni fikirlerin kabulünün ve hayata geçirilmesinde gizli bariyerler olduğunu gösterir.
Karar vericilerle inovatifler arasında ilişkiler zayıftır.
● Network method enabled HR to identify and invest on individuals with ‘bridge’ roles
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Text Mining
● “Computational turn” → a lot of digitized text● Text mining of a corpus:
- count word frequencies- count bi- or tri-gram frequencies- find word distances, correspondence or co-word analysis- topic clustering
● Applications:- text classification (spam, customer complaint, etc.)- sentiment identification (e.g. Twitter flood)- discourse analysis...
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Study 5: Corporate Discourse● Completed PhD research: “Studying Dynamics of
Organizational Change through Discursive Transformations”
● Same data in study 4● Focal firm is trying to move towards an
organizational culture more suitable for innovation
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Study 5 ...
Theoretical Model
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Study 5...Frequencies of words over time (tf-idf normalized).
Significant change with org. changeprogram
But some elements of discourse is persistent!(strong culture)
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Study 5 ...Correspondence analysis of word --tf-idf normalized-- frequencies (longitudinal, inconclusive)
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Study 5...Demographics of change
Discourse Change
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Study 6: Innovation Discourse in Turkey
● Ongoing research, early stages● Motivation: Can we be agnostic towards
descriptions of the social? (Callon, 1986)● Focus: innovation discourse among practitioners in
Turkey, and its change● Data: “Kapital” periodical's archives 2000-2014,
>7000 documents.
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Study 6 ...
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Study 6
Word frequencies in innovation related documents
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Study 6 ...2000-2004
2009-2013
Preliminary co-word analysis
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Study 6 ...● Innovation in Turkish academia → outcome focused,
patents● Government perspective →
innovation = R&D
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Other works ?
● 2016, Book chapter: “Ghost in the System: Critical Management Studies in Turkey”
● 2015, Conference paper: “Reclassifying the Classified: Practical Considerations in Mining HR Data”
● 2014, Book chapter: “Open Innovation Ecosystems in Software Industry”
● 2011, Article: “Organizing the digital commons: a case study on engagement strategies in open source”
● 2006-2009 FP6 project: creating realistic networks for simulation of European Economy
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Related Courses● Social Networks, undergraduate course● Big Data in CRM: graduate course in marketing
MSc program● Graph Theory: applied graduate course in
engineering MSc program● Innovation Management, PhD course
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Digital Methods ↔ Research● A computational turn in social sciences?● Can potentially “demarginalize the voice of
respondents” (Murty, 2008) in our accounts● Most methods not framed in theory● Not a replacement, for example of ethnographic
study (uncovering stories), but supports it.
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Digital Methods ↔ Education● Digital natives → Digital producers?● Statistics and data science teaching: Applied and
digital ● Lean and functional● Graduate programs for differentiated, analytical
roles, as a career option
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~~~THE END~~~
Thanks for joining.
Presented work avail at: http://mgencer.com
Contact: [email protected]
Questions?