STUDYING SUPPLY CHAINS FROM A SOCIAL NETWORK PERSPECTIVE

• Date: 01 January 2011
• DOI: http://doi.org/10.1111/j.1745-493X.2010.03209.x
• Author: JOSEPH GALASKIEWICZ
• Published date: 01 January 2011

STUDYING SUPPLY CHAINS FROM A SOCIAL

NETWORK PERSPECTIVE

JOSEPH GALASKIEWICZ

University of Arizona

Social networks often exist among individuals who are boundary spanners in

an interorganizational network. These relationships are critical in explaining

why interorganizational networks are formed, disintegrate, and succeed or

fail. Trust is central in most theories of social network effectiveness, and it

should also be true in supply chain networks. This article proposes that

supply chain architects consider supply chains that exhibit small world

properties. This optimizes trust withinthe clique, but enables predictions and

innovations to ‘‘hop’’ across cliques to other regions in the supply chain. The

article concludes byproposing that networks should be studiedover time and

not as stagnant structures, and highlights a new methodology (SoNIA) for

this.

Keywords: social network analysis; supply chain management; small-world network;

dynamic network visualization

Macro-organizational theory has now fully embraced

network analysis, and the volume of research on networks

and organizations is staggering (Brass, Galaskiewicz, Greve

and Tsai 2004). Students of human service organizations

and corporate interlocks carried out the pioneering work

on the topic, and Tichy, Tushman and Fombrun (1979) is

often cited as the first management article to articulate a

network approach, but it was not until the late 1980s and

1990s, with the work of Burt (1983), Granovetter (1985),

Galaskiewicz and Wasserman (1989), Mizruchi (1989),

Powell (1990), Burt (1992), Gulati (1995), Uzzi (1997),

and many others on networks among business organiza-

tions, did the field ‘‘take off.’’ Before addressing questions

about how network analysis relates to interdisciplinary

supply chain research, I want to make some distinctions

that, I believe, will clarify some points.

First, there is an important difference between those

who study networksas a collection of arcs and nodesand

focus on the formal properties of different configurations

and those who study social networks. The latter have

usefully drawn on formal theory, but the explanations

for why networks benefitnodes or why networks change

or why some kinds of networks are more effective

than others are based, for the most part, on theories

derived from behavioral psychology, e.g., balance theory,

exchange theory, social comparison theory, social

contagion theory, etc. (Galaskiewicz 2007). That is,

researchers have taken micro theories of behavior and

have tested them using organizations as the units of

analysis and network methodology. This type of social

network analysis is what I will focus on here.

Second, there are those who study the flows of different

content through a network and those who look at the

meanings attached to different relational forms (see

Borgatti and Li 2009). The former might look at the speed

at which a rumor spreads throughout a network or how

efficiently a product moves through a supply chain. In

contrast, social network analysis focuses on different types

of social relationships and how they provide context for

action. On the one hand, there are weak ties, people we

might call acquaintances. Social obligations are minimal

and short-term self-interest is paramount. Interaction

often takes the form of negotiation. On the other hand,

there are strong ties, people who are family or friends.

These relationships (or relational forms) have certain

cultural meanings attached to them with an accompany-

ing set of rules, obligations and norms. Reciprocity is

often normative. Thus, an arc (or line) connecting two

nodes may simply represent content (e.g., money) pass-

ing from one node to another or it could represent a

friendshipthat spans nearly20 years. Here,I will only talk

about mapping relationships rather than content flows,

although network analysis can examine both.

Acknowledgments: I would like to thank Craig Carter and Skye

Bender-deMoll for their useful comments on this essay.

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