Presentation of the paper "A Framework to Investigate the Relationship Between Employee Embeddedness in Enterprise Social Networks and Knowledge Transfer" by Janine Viol and Caroline Durst in "Witold Pedrycz, Shyi-Ming Chen (Eds). Social Networks: A Framework of Computational Intelligence.
Studies in Computational Intelligence Band 526. Berlin: Springer. 2014." in course "Semantic Modelling" at HTW Berlin, major "Internationale Medieninformatik".
1. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Computational Intelligence
“A Framework to Investigate the Relationship Between
Employee Embeddedness in Enterprise Social Networks and
Knowledge Transfer” by Janine Viol and Caroline Durst
2. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Contents
● Introduction
● Knowledge Transfer
○ informal and formal
○ implicit and explicit
○ Limitation to enterprises
● Social Capital - bonding and bridging SN
● Measurement and data collection
○ Approaches to measure Knowledge Transfer
○ Strategies of Data Collection in SN
● Theoretical Framework
● Conclusion and Discussion
3. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Contents
● Introduction
● Knowledge Transfer
○ informal and formal
○ implicit and explicit
○ Limitation to enterprises
● Social Capital - bonding and bridging SN
● Measurement and data collection
○ Approaches to measure Knowledge Transfer
○ Strategies of Data Collection in SN
● Theoretical Framework
● Conclusion and Discussion
4. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Introduction
➢ Knowledge Transfer
● knowledge transfer influences productivity of an organization and is
“crucial for its survival”
● issues of the targeted research
○ knowledge transfer is often informal
○ to handle knowledge transfer in SN, it must be investigated and
understood
○ the process of knowledge transfer is difficult to be managed due to the
human factor (cultural differences, depth of social relationships, …)
5. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Introduction
➢ Research questions
● Research Questions
○ How are employees embedded in ESN?
○ How is this related to the company’s organigram?
○ How can social capital be identified and measured in ESNs using
methods of Computational Intelligence?
○ How is social capital associated with the knowledge transfer process of
individual employees?
○ How can the knowledge transfer get improved?
● Management Goals
○ Increasing productivity and team performance
○ Improving collaboration and reducing costs
6. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Contents
● Introduction
● Knowledge Transfer
○ informal and formal
○ implicit and explicit
○ Limitation to enterprises
● Social Capital - bonding and bridging SN
● Measurement and data collection
○ Approaches to measure Knowledge Transfer
○ Strategies of Data Collection in SN
● Theoretical Framework
● Conclusion and Discussion
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Knowledge Transfer
➢ Definition
“Sharing, interpreting, combining and storing of information.”
Argote et al.
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Knowledge Transfer
➢ Informal and formal
● informal
➢ knowledge transfer is not caused by business processes, but by
independent social interaction
● formal
➢ knowledge transfer is caused by business processes (e.g.
documentations, charts, business reports, ...)
➢ can be improved
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Knowledge Transfer
➢ Implicit and explicit
● implicit (tacit)
➢ knowledge transfer is caused by experience or observation
➢ “difficult to formalize and to communicate”
● explicit
➢ knowledge is transferred by a formalized, systematic representation
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Knowledge Transfer
➢ Limitation to enterprises
● entities in ESN are employees
● relations between entities can represent immaterial or material resources
● relation/tie strength is depended by
○ amount of time
○ emotional intensity
○ intimacy
○ reciprocal services
● influence of ESN on knowledge transfer
○ strong ties -> more efficient in transferring tacit knowledge ([Hansen])
○ both weak and strong -> explicit knowledge transfer
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Knowledge Transfer
➢ Differentiation of ESN
● formal ESN
○ management-directed and unit-based (e.g. wikis)
● informal ESN
○ emergent, user-directed and overlapping units
● working domain ESN
○ knowledge is directly related to the work
● non-working domain ESN
○ knowledge is indirectly related to the work (e.g. talks on company
excursions)
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Knowledge Transfer
➢ Differentiation of ESN
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Knowledge Transfer
➢ Seeking and giving advice
● 5 ways (according to Cross et. al.)
○ provision of solutions
○ meta-knowledge
○ problem reformulation
○ validation
○ legitimization
15. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Contents
● Introduction
● Knowledge Transfer
○ informal and formal
○ implicit and explicit
○ Limitation to enterprises
● Social Capital - bonding and bridging SN
● Measurement and data collection
○ Approaches to measure Knowledge Transfer
○ Strategies of Data Collection in SN
● Theoretical Framework
● Conclusion and Discussion
16. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Social Capital
➢ Definition
“Resources embedded in one’s social network, resources that can
be accessed or mobilized through ties in the network.”
Lin
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Social Capital
➢ Differentiation
● bonding networks
○ homogenous groups
○ similar interests
○ generation of emotional support
● bridging networks
○ heterogenous groups
○ non-redundant resources
○ better access to informational and instrumental resources
strong ties
weak ties
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Social Capital
➢ Examples
bonding
bridging
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Social Capital
➢ ESN influence
Ellison et al.:
● SN encourage both bonding and bridging social capital
● they allow people to generate new social capital (e.g. exchanging information
with new acquaintances)
● they allow to strengthen and maintain offline relationships
● large network of (weak) bridging ties improve the access to diverse
resources
➢ positive outcome
20. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Contents
● Introduction
● Knowledge Transfer
○ informal and formal
○ implicit and explicit
○ Limitation to enterprises
● Social Capital - bonding and bridging SN
● Measurement and data collection
○ Approaches to measure Knowledge Transfer
○ Strategies of Data Collection in SN
● Theoretical Framework
● Conclusion and Discussion
21. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Measurement
➢ Knowledge Transfer
Differentiation
● explicit knowledge is measured based on questionnaires or surveys
● implicit knowledge can be measured by observation or text analysis
Aspects
● sinks of knowledge
● sources of knowledge
● velocity
● viscosity
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Measurement
➢ Data Collection in SN
● approaches for measurements of implicit knowledge transfers
○ Movery et al. used citation patterns to detect the knowledge transfer
between companies
○ Huang and De Sanctis focused on forums and searched for the different
text patterns to categorize posts
➢ information seeking
➢ information providing
➢ explicit knowledge sharing
➢ implicit knowledge sharing
“Where can I find...?”
“Does anybody have...?”
“Do you know…?”
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Measurement
➢ Data Collection in SN
● assumptions in the paper
○ focus on explicit knowledge
○ knowledge transfer can occur in formal, informal networks in the
working and non-working domain
● steps for knowledge analysis
○ manually find knowledge areas (e.g. by functions)
○ (automatically) figure out levels of expertise by positions of employees
(extraction from SN profile pages)
○ find knowledge flow, sources and sinks e.g. by wall posts and comments
using pattern matching similar to the approach of the previous slide
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Measurement
➢ Data Collection in SN
● further steps
○ publishing of material (documents, links, ...) as an indicator for a
knowledge source
○ rating system for posted information to figure out usefulness and/or
quality
➢ What could be possible criterias for this? How could a reasoner
decide?
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Measurement
➢ Network-Based Social Capital
● short introduction into ego networks
ego
alter
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Measurement
➢ Network-Based Social Capital
network size
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Measurement
➢ Network-Based Social Capital
hierarchical positions/occupations in
ego’s network
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Measurement
➢ Network-Based Social Capital
measurement of social resources in ego’s network
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Measurement
➢ Network-Based Social Capital
● structural measures
○ How are actors in the network connected to each other?
○ analyze network size
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Measurement
➢ Network-Based Social Capital
ego
3
5
2
6
4
1
● small network size
● mostly egocentric
network
● network is constrained
by alters
bonding
31. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Measurement
➢ Network-Based Social Capital
● positional measures
○ reflect ego’s position in the (overall) network
32. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Measurement
➢ Network-Based Social Capital
ego
3
5
2
6
4
1
● ego is central
● ego has 5 connections,
one is bridging
➢ betweenness
● ego is close to each alter
● ego is well-connected to 2,
3, 5, 6
➢ eigenvector
● ego has two bridges, but
to single alters
33. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Measurement
➢ Network-Based Social Capital
ego
3
5
2
6
4
1
● example for a bridge to
other groups
➢ generate social capital
➢ diversity increases
opportunity of getting
new capital
34. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Measurement
➢ Network-Based Social Capital
● functional measures
○ focusses on network resources provided by specific ties
○ tie strength may be difficult to be extracted in ESN
➢ there are different approaches
35. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Measurement
➢ Network-Based Social Capital
● measuring tie strength - different approaches
○ Granovetter: measures amount of time, emotional intensity, intimacy,
reciprocal services
➢ How can we measure emotional intensity and intimacy?
○ other approaches include the analyzation of comments and messages in
SN
36. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Measurement
➢ Network-Based Social Capital
● measuring tie strength - fuzzy-logic approach
○ Arnaboldi et al.: analyzed twitter tweets using fuzzy logic with fuzzy set
➢ very low
➢ low
➢ medium
➢ high
➢ very high
○ on input parameters
➢ reply message percentage
➢ common follower percentage
➢ normalized mean reply delay
37. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Measurement
➢ Network-Based Social Capital
“Due to the similar functionalities of online social networks or
Twitter and ESN, the discussed tie strength indicators and the use
of a fuzzy logic approach can be assumed to be equally applicable
in ESN.”
Viol, Janine and Durst, Carolin
38. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Contents
● Introduction
● Knowledge Transfer
○ informal and formal
○ implicit and explicit
○ Limitation to enterprises
● Social Capital - bonding and bridging SN
● Measurement and data collection
○ Approaches to measure Knowledge Transfer
○ Strategies of Data Collection in SN
● Theoretical Framework
● Conclusion and Discussion
39. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Theoretical Framework
“Knowledge transfer processes are (...) the effects of different
social capital types”
Viol, Janine and Durst, Carolin
44. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Theoretical Framework
➢ Analysis of network structure
● high ratio means ego has access to many colleagues
➢ good access to social capital
54. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Theoretical Framework
➢ Analysis of network ties
55. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Theoretical Framework
➢ Analysis of network ties
● fuzzy-logic approach adapting Fazeen et al.
○ fuzzy-sets (very low, low, medium, high, very high)
○ four parameters
➢ common colleagues percentage
➢ shared project groups percentage
➢ reply message percentage
➢ normalized mean reply delay
○ best case: value of all parameters is very high
➢ high tie strength
60. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Theoretical Framework
➢ Analysis of social capital
● fuzzy-logic can be used
● indicators for bonding social capital
○ high network closure
○ strong ties
○ high degree of network homogeneity
● bridging
○ establish new links to more diverse resources and knowledge
62. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Theoretical Framework
➢ Knowledge transfer
● finding sinks and sources of knowledge
○ suggestion: text-mining approach by searching for patterns
○ Critics: Different people communicate differently.
“Where can I find...?”
“Does anybody have...?”
“Do you know…?”
63. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Theoretical Framework
➢ Final embeddedness
● embeddedness in the ESN influences social capital
● social capital influences knowledge transfer
● Han and Hovav: “bonding social capital positively affects knowledge sharing
and project performance”
64. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Theoretical Framework
➢ Achieved knowledge transfer
● approach is to calculate ratio of
○ number of information requests (information seeking posts)
○ number of responses (information providing posts)
● Can each response be seen as information providing?
65. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Contents
● Introduction
● Knowledge Transfer
○ informal and formal
○ implicit and explicit
○ Limitation to enterprises
● Social Capital - bonding and bridging SN
● Measurement and data collection
○ Approaches to measure Knowledge Transfer
○ Strategies of Data Collection in SN
● Theoretical Framework
● Conclusion and Discussion
66. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Conclusions
● framework enables researchers and practitioners to investigate knowledge
transfer process using social network analysis and methods of computational
intelligence
○ e.g.: identify “knowledge key individuals” and check if they match those in
the enterprise’s “key positions”
● outlook
○ analyze dynamics in ESN using swarm-intelligence and neuro-fuzzy
systems
67. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Conclusions
Thank you for your attention!
68. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
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A Framework to Investigate the Relationship Between Employee Embeddedness in Enterprise Social
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70. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann
Discussion
● What do you think? Do employees in higher positions tend to provide more
knowledge than others?
● What kind of social capital is more useful in software projects? Bonding or
bridging? Is the capital in the analyzed network the only important factor for
knowledge transfer?
● What do you think? Can you measure similarity between colleagues by
comparing their public ESN profiles?