Complex adaptive information flow and search transfer analysis
(2014) In Knowledge Management Research & Practice 12(1). p.29-35- Abstract
- Studying information flow between node clusters can be conceptualised as an important challenge for the knowledge management research and practice community. We are confronted with issues related to establishing links between nodes and/or clusters during the process of information flow and search transfer in large distributed networks. In the case of missing socio-technical links, social networks can be instrumental in supporting the communities of practice interested in sharing and transferring knowledge across informal links. A comprehensive review of methodology for detecting missing links is provided. The proportion of common neighbours was selected as best practice to elicit missing links from a large health insurance data set.... (More)
- Studying information flow between node clusters can be conceptualised as an important challenge for the knowledge management research and practice community. We are confronted with issues related to establishing links between nodes and/or clusters during the process of information flow and search transfer in large distributed networks. In the case of missing socio-technical links, social networks can be instrumental in supporting the communities of practice interested in sharing and transferring knowledge across informal links. A comprehensive review of methodology for detecting missing links is provided. The proportion of common neighbours was selected as best practice to elicit missing links from a large health insurance data set. Weights were based on geographical arrangements of providers and the dollar value of transactions. The core network was elicited based on statistical thresholds. Suspicious, possibly fraudulent, behaviour is highlighted based on social network measures of the core. Our findings are supported by a health insurance industry expert panel. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4418922
- author
- Feczak, Szabolcs ; Hossain, Liaquat LU and Carlsson, Sven LU
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- socio-technical systems, organisational learning, networks, case study
- in
- Knowledge Management Research & Practice
- volume
- 12
- issue
- 1
- pages
- 29 - 35
- publisher
- Palgrave Macmillan
- external identifiers
-
- wos:000332352400003
- scopus:84893916559
- ISSN
- 1477-8246
- DOI
- 10.1057/kmrp.2012.47
- language
- English
- LU publication?
- yes
- id
- 5c3967c9-756e-4481-8bef-8a77ef511402 (old id 4418922)
- date added to LUP
- 2016-04-01 09:49:08
- date last changed
- 2022-01-25 08:59:03
@article{5c3967c9-756e-4481-8bef-8a77ef511402,
abstract = {{Studying information flow between node clusters can be conceptualised as an important challenge for the knowledge management research and practice community. We are confronted with issues related to establishing links between nodes and/or clusters during the process of information flow and search transfer in large distributed networks. In the case of missing socio-technical links, social networks can be instrumental in supporting the communities of practice interested in sharing and transferring knowledge across informal links. A comprehensive review of methodology for detecting missing links is provided. The proportion of common neighbours was selected as best practice to elicit missing links from a large health insurance data set. Weights were based on geographical arrangements of providers and the dollar value of transactions. The core network was elicited based on statistical thresholds. Suspicious, possibly fraudulent, behaviour is highlighted based on social network measures of the core. Our findings are supported by a health insurance industry expert panel.}},
author = {{Feczak, Szabolcs and Hossain, Liaquat and Carlsson, Sven}},
issn = {{1477-8246}},
keywords = {{socio-technical systems; organisational learning; networks; case study}},
language = {{eng}},
number = {{1}},
pages = {{29--35}},
publisher = {{Palgrave Macmillan}},
series = {{Knowledge Management Research & Practice}},
title = {{Complex adaptive information flow and search transfer analysis}},
url = {{http://dx.doi.org/10.1057/kmrp.2012.47}},
doi = {{10.1057/kmrp.2012.47}},
volume = {{12}},
year = {{2014}},
}