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.
A burgeoning stream of sustainability research explores the role of companies’ top management team (TMT) characteristics in corporate sustainability efforts, while another stream investigates the effect of a company’s supply chain position on its likelihood of engaging in sustainability. This study shows the importance of integrating the two research streams by demonstrating that supply chain position moderates the relationship between TMT characteristics and sustainability and thus establishes boundary conditions for this relationship. By matching 758 corporate sustainability initiatives with control observations, our results show that the size of the top executive team and the average age of its members, two well-known predictors of corporate sustainability, are distinctly moderated by supply chain position. While business-to-business (B2B) companies are less likely to report a sustainability initiative compared to business-to-consumer (B2C) organizations, we found that B2B TMT size has a greater positive effect on sustainability initiative likelihood than B2C TMT size. Conversely, average B2C TMT age has greater predictive power in explaining sustainability initiative likelihood than average B2B TMT age. The implications of these findings in advancing corporate sustainability and organizational change are discussed.
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