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.

Horizontal Coordination of I-LNGOs in the Humanitarian Supply Chain: An Evolutionary Game Approach

“…For example, Nikkhoo et al verified that coordination between upstream manufacturers and downstream NGOs in procurement can improve the satisfaction level of the affected areas [39]. Joint decision-making of inventory by suppliers and relief organizations significantly minimizes the total cost and strengthens risk management in disasters [3]. A distribution model merging multiple humanitarian stakeholders' preferences can improve the reliability of the humanitarian supply chain [40].…”

“…Sustainable social development cannot be separated from a stable environment. To cut losses and accelerate the process of social recovery, there is a greater focus on the sustainability of the humanitarian supply chain, which consists of donors, host governments, international and local nongovernment organizations (INGOs and LNGOs), and the military and private sectors engaged in disaster relief activities [3][4][5]. Considering the aim of helping populations regain their life status and contribute to social, economic, and ecological development, the UN (2015) proposed that the concept of sustainable development be incorporated into the work of disaster relief [6].…”

“…(2) How should optimal behavior in the dynamic mechanism be adopted? (3) What are the interventions that affect coordination? This paper attempts to address this gap by developing an evolutionary game model to quantitatively analyze horizontal coordination strategies between INGOs and LNGOs.…”

Network analysis of Youtube videos based on keyword search with graph centrality approach

“…SNA is a methodology that emerged based on graph theory [6]. In addition, SNA has been successfully applied to many research domains [6]- [8].…”

Convening green to humanitarian logistics scene: a collaborative research approach for embedding environmental sustainability in humanitarian logistics

“…That is why recently Journal of Humanitarian Logistics & Supply Chain Management published a special issue on research methods in HSC, where the editors highlighted the need for innovative methods in HSC research (Kovacs et al, 2019). The special issue introduces and illustrated the applications of some of such innovative methodologies, namely social network analysis (Tacheva and Simpson, 2019), quantitative content analysis (Kunz, 2019), Delphi method (Gossler et al, 2019), gaming (Lukosch and Comes, 2019), and collaborative research (Sabri et al, 2019).…”