Making Innovation Visible

Computational Methods for Studying Organizations and Innovation

Seminar

Mehmet Gençer

mehmetgencer@yahoo.com

http://mgencer.com

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

Organization ...

● … is communicatively constituted

Weick, Luhmann, ...

Innovation● Goes against routine● Its inputs and outputs are explored in research,

process itself is less so

Outline

● Networks and Texts → Why analyze● Demonstrative studies: network analysis● Demonstrative studies: text mining● Epliogue: Computational methods and

management

Innovation Dilemma● Organizing for routine: formal structure & culture of

efficiency● Organizing for innovation: informal structure &

culture of creativity

Why analyze?● Network analysis → Structure of communication● Text Analysis → Content of communication,

Organizational discourse → Culture● Digital ethnography → study contemporary

organizations in situ

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

SNA Examples● From Lazer et al. (2009) review on computational

social science

SNA Applications● A typology of ties studied in social network

analysis. (source: Borgatti et al., 2009)

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

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

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.

Study 1 …

Inter-organizational Network of co-participation to sub-projects

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

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.

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

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

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

Study 3...Exploration

Network of Internet standards...

...and authors

Study 3 ...● Findings● System clustering increased, subsystems ● More authors&references per document (complexity) ● Episodic growth

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.

Study 4 ...Organizational network: Colors ↔ Organizational Functions Node size ↔ Betweenness centrality of individual

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

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...

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

Study 5 ...

Theoretical Model

Study 5...Frequencies of words over time (tf-idf normalized).

Significant change with org. changeprogram

But some elements of discourse is persistent!(strong culture)

Study 5 ...Correspondence analysis of word --tf-idf normalized-- frequencies (longitudinal, inconclusive)

Study 5...Demographics of change

Discourse Change

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.

Study 6 ...

Study 6

Word frequencies in innovation related documents

Study 6 ...2000-2004

2009-2013

Preliminary co-word analysis

Study 6 ...● Innovation in Turkish academia → outcome focused,

patents● Government perspective →

innovation = R&D

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

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

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.

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

~~~THE END~~~

Thanks for joining.

Presented work avail at: http://mgencer.com

Contact: mehmetgencer@yahoo.com

Questions?