Semantic Modelling - Paper presentation

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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".

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Semantic Modelling - Paper presentation

  1. 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. 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. 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. 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. 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. 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
  7. 7. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Knowledge Transfer ➢ Definition “Sharing, interpreting, combining and storing of information.” Argote et al.
  8. 8. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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
  9. 9. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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
  10. 10. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Knowledge Transfer ➢ Table
  11. 11. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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
  12. 12. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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)
  13. 13. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Knowledge Transfer ➢ Differentiation of ESN
  14. 14. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Knowledge Transfer ➢ Seeking and giving advice ● 5 ways (according to Cross et. al.) ○ provision of solutions ○ meta-knowledge ○ problem reformulation ○ validation ○ legitimization
  15. 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. 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
  17. 17. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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
  18. 18. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Social Capital ➢ Examples bonding bridging
  19. 19. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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. 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. 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
  22. 22. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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…?”
  23. 23. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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
  24. 24. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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?
  25. 25. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Measurement ➢ Network-Based Social Capital ● short introduction into ego networks ego alter
  26. 26. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Measurement ➢ Network-Based Social Capital network size
  27. 27. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Measurement ➢ Network-Based Social Capital hierarchical positions/occupations in ego’s network
  28. 28. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Measurement ➢ Network-Based Social Capital measurement of social resources in ego’s network
  29. 29. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Measurement ➢ Network-Based Social Capital ● structural measures ○ How are actors in the network connected to each other? ○ analyze network size
  30. 30. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  40. 40. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework
  41. 41. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework
  42. 42. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Analysis of network structure
  43. 43. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Analysis of network structure
  44. 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
  45. 45. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Analysis of network structure
  46. 46. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Cliques and components ego 3 5 2 6 clique
  47. 47. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Cliques and components ego 3 5 2 6 component
  48. 48. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Analysis of network structure
  49. 49. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Brokerage ego 3 5 2 6 ● not connected ○ (2, 5) ○ (5, 6) ○ (3, 6) ● brokerage = 3
  50. 50. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Analysis of network structure
  51. 51. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Effective size ego 3 5 2 6 ● number of colleagues: 4 ● average number of alter ties: ● effective size: 3.25
  52. 52. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Analysis of network structure
  53. 53. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework
  54. 54. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Analysis of network ties
  55. 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
  56. 56. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework
  57. 57. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework ➢ Analysis of network members ➢ find similarity between colleagues
  58. 58. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework
  59. 59. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework
  60. 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
  61. 61. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Theoretical Framework
  62. 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. 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. 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. 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. 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. 67. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Conclusions Thank you for your attention!
  68. 68. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Sources ● [Primary] [tables on slides 11, 14, 27-30, 32, 35, 41-46, 49, 51, 53, 55, 57-60, 62] A Framework to Investigate the Relationship Between Employee Embeddedness in Enterprise Social Networks and Knowledge Transfer. Janine Viol and Carolin Durst. pp. 259–287 in Witold Pedrycz, Shyi-Ming Chen (Eds). Social Networks: A Framework of Computational Intelligence. Studies in Computational Intelligence Band 526. Berlin: Springer. 2014. ● http://www.britannica.com/EBchecked/topic/287895/information-system, retrieved on: 25.06.2014. ● Konar, Amit: Computational Intelligence : Principles, Techniques and Applications. Berlin Heidelberg: Springer Science & Business Media, 2006. ● http://www.unc.edu/~sunnyliu/inls258/Introduction_to_Knowledge_Management.html, retrieved on: 24.06.2014. ● Wasserman, Stanley ; Faust, Katherine: Social Network Analysis : Methods and Applications. New.. Cambridge: Cambridge University Press, 1994. ● [Hansen] Hansen, M.T.: The search-transfer problem: the role of weak ties in sharing knowledge across organization subunits. Adm. Sci. Q. 44(1), 82 (1999). ● [Boyd and Ellison] Boyd, D.M, Ellison, N.: Social network sites: definition, history, and scholarship. J. Comput.-Mediat. Commun. 13(1), 210-230 (2007) ● [Argote et al] Argote, L., Ingram, P.: Knowledge transfer: a basis for competitive advantage in firms. Organ. Behav. Hum. Decis. Process. 82(1), 150-169 (2000). ● [Lin] Lin, N.: Social Capital: A Theory of Social Structure and Action. Cambridge University Press, Cambridge (2001) ● [Ellison et al.] Elisson, N., Steinfield, C., Lampe, C.: The benefits of facebook friends: social capital and college students use of online social network sizes. J. Comput.-Mediat. Commun. 12(4), 1143-1168 (2007). ● [Steinfield et al.] Steinfield, C., DiMicco, J., Elisson, N.: Bowling online: Social networking and social capital within the organization. In: Proceedings of the Fourth Communities and T echnologies Conference, pp. 245-254 (2009).
  69. 69. HTW - WT2 Semantic Modelling - SoSe 2014 Sascha Feldmann Sources ● [Fritsch and Kauffeld-Monz] Fritsch, M., Kauffeld-Monz, M.: The impact of network structure on knowledge transfer: an application of social network analysis in the context of regional innovation networks. Ann. Reg. Sci. 44(1), 21-38 (2008). ● [Chan and Liebowitz] Chan, K. Liebowitz, J.: The synergy of social network analysis and knowledge mapping: a case study. Int, J. Manag. Decis, Mak. 7(1), 19 (2006). ● [Helms and Buijsrogge] Helms, R., Buijsrogge, K.: Knowledge network anlysis: A technique to analyze knowledge management bottlenecks in organizations. In: 16th international workshop on database and expert systems applications (DEXA ‘05), pp. 410-414. IEEE (2005). ● [Granovetter] Granovetter, M.S.: The strength of weak ties. Am. J. Sociol, 78(6), 1360-1380 (1973). ● [Marsden and Campbell] Marsden, P.V., Campbell, K.E.: Measuring tie strength. Soc. Forces 63(2), 482-501 (1984). ● [Matthews] Matthes, K.M., White, M.C., Long, R.G:: Soper, B., Von Bergen, C.W.: Association of indicators and predictors of tie strength. Psychol. Rep. 83(3), 1449-1469 (1998). ● [Hassan] Hassan, S., Salgado, M., Pavón, J.: Friendship dynamics: modelling social relationships through a fuzzy agent- based simulation. Discret Dyn Nat Soc. 2011, 1-19 (2011). ● [Xiang et al.] Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web - WWW’ 10, p. 981 (2010). ● [Arnaboldi et al.] Arnaboldi, V., Guazzini, A., Passarella, A.: Egocentric online social networks: Analysis of key features and prediction of tie strength in Facebook. Comput. Commun. 36, 1130-1144 (2013).
  70. 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?

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