Efficiency assessment using network analysis tools

Sebastián Lozano and Laura Calzada-Infante

Journal of the Operational Research Society, 2018, vol. 69, issue 11, 1803-1818

Abstract: In this paper, some of the network analysis techniques generally used for complex networks are applied to efficiency assessment. The proposed approach is units invariant and allows the computation of many interesting indexes, such as node specificity, benchmarking potential, clustering coefficient, betweenness centrality, components and layers structure, in- and out-degree distributions, etc. It also allows the visualisation of the dominance relationships within the data-set as well as the potential benchmarks and the gradual improvement paths from inefficient nodes. A number of useful filters (bipartite subgraph, ego networks, threshold networks, skeletonisation, etc.) can be applied on the network in order to highlight and focus on specific subgraphs of interest. The proposed approach provides a new perspective on efficiency analysis, one that allows not only to focus on the distance to the efficient frontier and potential targets of individual units but also to study the data-set as a whole, with its component and layer structure, its overall dominance density, etc.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2017.1409866 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:69:y:2018:i:11:p:1803-1818

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2017.1409866

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().