The Palgrave Handbook of Humanitarian Logistics and Supply Chain Management

Gyöngyi Kovács, Karen Spens, Mohammad Moshtari, Gyöngyi Kovács, Karen Spens, Mohammad Moshtari

• Editorial: Palgrave Macmillan
• Año: 2017
• ISBN:
  9781137590992
• Categoría: Commerce
• Categoría: Opérations

Focusing on the specific challenges of research design and exploring the opportunities of conducting research in humanitarian logistics and supply chain management, this handbook is a significant contribution to future research. Chapters include extensive descriptions of methods used, highlighting their advantages and disadvantages, and the challenges in scoping, sampling, collecting and analysing data, as well as ensuring the quality of studies. Covering a wide variety of topics including risk and resilience and the impact of humanitarian logistics on capacity building, sustainability and the local economy, it also explores the need for scalability and co-ordination in the humanitarian network. Contributors provide important insight on future directions and offer crucial guidance for researchers conducting projects within the field.

Part I
Innovative Methods - not that Much Used Yet

© The Author(s) 2018

Gyöngyi Kovács, Karen Spens and Mohammad Moshtari (eds.)The Palgrave Handbook of Humanitarian Logistics and Supply Chain Managementhttps://doi.org/10.1057/978-1-137-59099-2_1

Begin Abstract

1. Social Network Analysis in the Context of Humanitarian Logistics

Natalie Simpson¹ , Zhasmina Tacheva² and Ta-Wei (Daniel) Kao³

(1)

University at Buffalo (SUNY), Buffalo, New York, USA

(2)

University at Buffalo (SUNY), Buffalo, New York, USA

(3)

University of Michigan-Dearborn, Dearborn, Michigan, USA

Natalie Simpson (Corresponding author)

Email: [email protected]

Zhasmina Tacheva

Email: [email protected]

Ta-Wei (Daniel) Kao

Email: [email protected]

End Abstract

Introduction

Increased attention to the adaptive and emergent properties of whole networks has been posited as vital to the future development of supply chain management (SCM) (Pathak et al. 2007), a perspective that is particularly critical to the humanitarian sector. Humanitarian operations are uniquely challenged to serve uncertain yet urgent demand in highly dynamic environments, where success relies on coordination within supply chains otherwise heavily influenced by circumstance. Advancing the field of humanitarian logistics depends in part on better understanding of why this process is more successful in some instances when compared to others, but this in turn depends upon an ability to capture and study complex and changing logistical relationships. One branch of emerging supply chain research employs social network analysis (SNA) to quantify complex supply networks, enabling holistic assessments of network structure to be empirically related to outcomes such as profitability and risk (Borgatti and Li 2009; Galaskiewicz 2011; Kim et al. 2011; Bellamy et al. 2014). As a methodology, SNA provides a new lens through which to study dynamic logistical networks in search for the antecedents of success in this context. In light of this potential, the objective of this chapter is to provide a state-of-the-art briefing on SNA, blending literature review with SNA tutorial, to ultimately argue humanitarian logistics as best positioned to lead network research in SCM.

SNA provides a methodology and related metrics for mapping relationships between members of a group, characterizing the individual entities as nodes connected by ties to create networks (Borgatti and Foster 2003). Many aspects of humanitarian relief are amenable to this approach, as demonstrated by our first example, a network of developmental assistance between nations pictured in Fig. 1.1. Here, ties represent recent official development assistance of at least 100 million US$ between nodes that represent OECD Development Assistance Committee (DAC) members and recipient countries. These nodes have been arranged such that recipients with similar sets of benefactors are grouped closer, reducing visual noise and demonstrating one powerful feature of this methodology, the visualization of data (Basole and Bellamy 2014). Rendering major donation data as a network highlights the politicized overtones of such relationships (Day 2014), as regional clusters can be seen where main donors have geographical or historical ties, such as Australia’s aid to Papua New Guinea, Timor-Leste, and the Solomon Islands, or France’s support of Guinea and Cameroon. This visualization also highlights the complexity of Japan in the lower left quadrant, a DAC member providing aid to developing countries such as Vietnam and Myanmar while simultaneously receiving funds from countries such as Thailand and Malaysia, reflecting the fact that Japan has traditionally been a top donor to Southeast Asia, likewise making it a top creditor in the region.

Fig. 1.1

Net 2013 official development assistance (ODA) of at least 100 million USD between Development Assistance Committee (DAC) members of the Organisation for Economic Co-operation and Development (OECD) and recipient countries

In the following section, we begin with a brief chronological survey of SNA and supply chain-related literature, starting from SNA’s early sociological origins and concluding with a discussion of recent humanitarian logistics research featuring this methodology. This is followed by tutorial discussion and demonstration of common SNA concepts and metrics appearing within that body of literature. This aspect of briefing is organized into two sections, starting with the node-level focus of Part III and proceeding to the whole network focus of Part IV. Throughout the discussion, content is demonstrated with humanitarian sector data, creating a series of illustrations that starts with Fig. 1.1. The purpose of this diverse set of examples is not to pursue any particular research hypothesis within the confines of this chapter, but rather to transform the abstract into the concrete, a powerful mechanism in any tutorial-style communication (Morrison et al. 2004). Furthermore, the illustrations have been prepared with a range of software tools to simultaneously serve as a gallery of technical options readily available to interested researchers. Consistent with an overarching goal of promoting research interest in the application of SNA to humanitarian logistics, the details required for obtaining both the example data and the software used in any chapter example are provided in an Appendix at the end of this discussion. The literature drawn into the first four sections then “sets the stage” for Part V, where we employ network visualization to clarify how the existing literature relates to itself, highlighting the fact that not only does SNA methodology offer multiple research opportunities in the context of humanitarian logistics, it is humanitarian logistics that may bridge a divide between the truly “social” applications of SNA and a growing body of related but “nonsocial” investigations of complex networks.

A Review of the Literature

SNA: Origins and Growth

Current interest in SNA in the context of supply chain research is arguably one branch of its uptake into multiple disciplines (Borgatti et al. 2009). However, sociologists have been modeling groups as networks of individuals for well over a century. SNA’s sociological foundations rely heavily on the seminal work of Georg Simmel (1955) and others (e.g., Kadushin 1966; Milgram 1967; Merton 1968), who first brought focus to the concept of a social circle and the ensuing dynamics of affiliation. The influence of this work spread across sociology and organizational science just as the field of graph theory was likewise gaining prominence, creating an intersection that would become the genesis of SNA. In this initial period, Granovetter (1973) introduced the theory of weak ties, postulating that the stronger the tie between two actors, the more likely their social worlds would overlap, and that aside from strong connections, weak ties can exist between communities, particularly important to the linking of macro- and micro-levels of society. Coleman (1988) proposed the term social capital as a resource derived from an actor’s pattern of links, value that is distinct from financial, physical, or human capital. Coleman (1988) offered this concept as a theoretical reconciliation between the socialized and the rational, self-interested lenses of sociology and economics, respectively, and further emphasized the public good aspect of social capital, describing closure as its main facilitating function. Burt (1987, 1992, 2000) problematized this theory, arguing social cohesion as weaker than structural equivalence in the social contagion process (Burt 1987), tie weakness as correlating to rather than causing value derived from bridging ties (Burt 1992), and the function of closure as at best complementing the value of brokering structural holes (Burt 2000) within a network of actors.

Using the constructs of graph theory to quantify complex affiliations among individuals, SNA evolved into an established field of research in the social sciences by the 1980s, with related network analysis diffusing into disciplines such as biology and physics starting from 1990s (Borgatti et al. 2009). In recent years, SNA research has been galvanized by the groundbreaking work of Barabási and Albert (1999), which provided a new perspective on network generation in many real-life scenarios, including the World Wide Web. Podolny (2001) took an in-depth look at market networks, uncovering two alternative models: one interpreting ties as conduits for resources, and another in which they serve as prisms through which actors are evaluated by potential partners on the basis of their market relations. Podolny (2001) also distinguished between egocentric and altercentric uncertainty and demonstrated that structural holes are helpful in resolving the former, whereas status can help alleviate the latter. Concurrently, the creation of the UCINET software platform as described in Borgatti et al. (2002) enabled convenient empirical testing of previous theory from works such as Granovetter (1973) and Burt (1992). Another factor accelerating growth in SNA-related research in this time period was the simultaneous advent of online social media platforms such as Twitter, LinkedIn, and Facebook, applications now so ubiquitous in daily life and such a ready source of SNA data that they have attained a misnomer status for the field of social networks as a whole.

SNA: Supply Chain Literature

Similar to SNA, SCM emerged as a distinct area of research in the 1980s, although early investigations often modeled a supply chain as the interorganizational equivalent of the older concept of a bill of materials. In a recent review, Bellamy and Basole (2013) identify De Toni and Nassimbeni (1995) as among the earliest to adopt the broader network analytic lens in a comparative study of two manufacturing sectors. Thadakamalla et al. (2004) demonstrate the relative robustness of certain SNA-related network archetypes in military supply simulations, being among the earliest to relate the role of network structure to supply chain resilience. Network-oriented supply chain research grew dramatically during this time period (Bellamy and Basole 2013), culminating in supply chains being argued as complex adaptive systems, or the emergent result of deliberate design and circumstance (Choi et al. 2001; Surana et al. 2005; Pathak et al. 2007). As organizations within a supply chain can be readily characterized as actors in a network, the potential of SNA as a new research methodology was recognized in this interval....