Social network analysis in humanitarian logistics research

Abstract

Purpose

The purpose of this paper is to promote social network analysis (SNA) methodology within the humanitarian research community, surveying its current state of the art and demonstrating its utility in analyzing humanitarian operations.

Design/methodology/approach

A comprehensive survey of the related literature motivates a proposed agenda for interested researchers. Analysis of two humanitarian networks in Afghanistan demonstrates the use and utility of SNA, based on secondary data. In the second case study, the use of random graphs to detect network motifs is demonstrated using Monte Carlo simulation to create the benchmark null sets.

Findings

SNA is an adaptable and highly useful methodology in humanitarian research, quantifying patterns of community structure and collaboration among humanitarian organizations. Network motifs suggesting distinct affinity between particular agencies within humanitarian clusters are observed.

Research limitations/implications

The authors summarize common challenges of using SNA in humanitarian research and discuss ways to alleviate them.

Practical implications

Practitioners can use SNA as readily as researchers, to visualize existing networks, identify areas of concern and better communicate observations.

Social implications

By making SNA more accessible to a humanitarian research audience, the authors hope its ability to capture complex, dynamic relationships will advance understanding of effective humanitarian relief systems.

Originality/value

To the best of knowledge, it is the first study to conduct a systematic analysis of the application of SNA in empirical humanitarian research and outline a concrete SNA-based research agenda. This is also a currently rare instance of a humanitarian study using random graphs to assess observed SNA measures.

Citation

Tacheva, Z. and Simpson, N. (2019), "Social network analysis in humanitarian logistics research", Journal of Humanitarian Logistics and Supply Chain Management, Vol. 9 No. 3, pp. 492-514. https://doi.org/10.1108/JHLSCM-06-2018-0047