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Granularity of Personal Intelligence in Social Networks

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Book cover Social Networks: A Framework of Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 526))

Abstract

This chapter introduces and exposes the latest development of agent-modeling framework that emerges from understanding the human behaviour of personal knowledge management (PKM) in social networks. The growing interest in personal knowledge management in exposing personal intelligence has brought out the postulation of GUSC model, which becomes the basis of agent-mediated PKM. In micro view, this GUSC model is the significant tool in understanding the granularity of personal intelligence in social networks. It eventually leads to a theoretical treatment on the bottom-up manifestation of organisational knowledge management from individual PKM processes.

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Notes

  1. 1.

    Avery et al. (2001) [4]; Pettenati et al. (2007) [5]; Grundspenkis (2007) [3]; Razmerita et al. (2009) [2]; Jarche (2009) [6]; Jarche (2010) [7]; Martin (2000) [8].

  2. 2.

    (Ismail and Ahmad, 2012) It does not constitute that all cognitive enablers should exist in full form in order for each PKM process to work efficiently. In fact, there is a possibility of having the cognitive enablers carrying different percentages of weight in terms of importance and necessity, to support each PKM process. This may also varies across different background of individual knowledge workers, especially those from different industries. Yet, the whole concept of having these enablers and processes is valid across all individuals and industries. Hence the way the model is drawn is in a dynamic shape, to show fluidity of the existing elements within the concept.

  3. 3.

    The score sheet is based on the GUSC Model (that covers the PKM processes), but at the same time tries to analyse the cognitive enablers.

  4. 4.

    This is an unpublished work as it is still in progress. The full result will include all the four GUSC relationships in social network analysis (SNA) diagram.

  5. 5.

    This case study is based on the preliminary findings from a data triangulation result, which is the base of the agent-mediated PKM framework formulated by Ismail and Ahmad (2011) [13].

  6. 6.

    From this point to Fig. 8 and equation (1): This section on nodal approach is adapted from Ismail, Ahmad and Hassan (2012) [24], to fit the purpose of understanding the concept before applying the GUSC processes to role agents.

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Author information

Authors and Affiliations

  1. Malaysian Institute of Information Technology, Universiti Kuala Lumpur, 1016 Jalan Sultan Ismail, 50250, Kuala Lumpur, Malaysia

    Shahrinaz Ismail

  2. College of Information Technology, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia

    Mohd Sharifuddin Ahmad & Zainuddin Hassan

Authors
  1. Shahrinaz Ismail
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  2. Mohd Sharifuddin Ahmad
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  3. Zainuddin Hassan
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Corresponding author

Correspondence to Shahrinaz Ismail .

Editor information

Editors and Affiliations

  1. Electrical & Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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© 2014 Springer International Publishing Switzerland

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Ismail, S., Ahmad, M.S., Hassan, Z. (2014). Granularity of Personal Intelligence in Social Networks. In: Pedrycz, W., Chen, SM. (eds) Social Networks: A Framework of Computational Intelligence. Studies in Computational Intelligence, vol 526. Springer, Cham. https://doi.org/10.1007/978-3-319-02993-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-02993-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02992-4

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