Designing Supply Networks in Manufacturing Industries
Read Sections 1 and 2 of this article. The study investigates how automotive original equipment manufacturers (OEMs) design supply networks. In Table 2, notice how personal ties and contractual, transactional, and professional network ties play a role.
Theoretical Background and Hypotheses
Indices for Network Characterization
To demonstrate different supply network architectures consisting of the four aforementioned heterogeneous supply network ties (i.e., contractual, transactional, professional, and personal ties), this study adopts social network analysis (SNA), which has long been used in analyzing any social network as a set of interrelated actors and ties. The field of SCM has stressed the potential applicability of SNA in a supply network context. For instance, Carter et al. proposed SNA as a valuable complement to traditional methodologies which may be used to advance current knowledge on various relationships existing within and beyond the supply chain. This view was echoed by Borgatti and Li who pointed out that supply chain settings are particularly suitable for SNA indices, which have proven "highly portable" across other disciplines from economics to physics. More recently, Galaskiewicz also noted that SCM theories mostly captured at the local level (e.g., dyad or triad) can be tested by using a supply network as the primary unit of analysis.
Despite repeated calls for such approach, there are still very few SCM studies that use SNA. Moreover, the vast majority of existing studies on supply network are case-based research that uses SNA measures defined for binary (i.e., "1" if a tie exists between two supply network entities, "0" otherwise) and non-directional ties (i.e., if one supply network entity perceives a tie, its counterpart's perception of the existence of the tie is automatically assumed). This is commonly referred to as the binary network approach, and most of the existing SNA indices have been devised solely based on this approach. The binary network approach specified by a symmetric adjacency matrix is conceptually and computationally straightforward and especially appropriate when a researcher focuses on cognitive ties (e.g., who knows whom). An important limitation of this approach, however, is that it involves an unrealistic premise - all ties are completely homogeneous and symmetrical - which contradicts previous findings in the literature. For instance, strong social ties strengthen interpersonal obligations, facilitate change in the face of uncertainty, and help to develop relationship-specific heuristics. Therefore, by using the binary network approach, network researchers can inevitably overlook important information about network properties embedded in network ties and consequently arrive at limited or even misleading implications for network architecture.
We thus
adopted a directed valued network approach represented by an asymmetric
adjacency matrix to overcome the aforementioned shortcomings of the
binary network approach. This approach takes into account the
direction and strength (or magnitude) of each tie between different
network entities. In network terms, a directed valued network consists
of a set of actors (or nodes) , a set of arcs (i.e.,
directional ties or links)
, and a set of values
attached to the arcs, subject to
where
is not necessarily equal to
. This is a more useful and realistic approach for exploring
supply network phenomena since it allows for the possibility that a
focal firm and its suppliers may view the strength (or even the
existence) of their ties differently. In this sense, there has been a
growing need for SNA indices that can be used in the directed valued
network setting when it is based on a different adjacency matrix.
More specifically, this study focuses on four socio-centric network indices (i.e., betweenness centralization, in-degree centralization, out-degree centralization, and global clustering coefficient), which describe the overall pattern of multiple actors within a single, bounded network. While ego-centric indices, such as centralities, deal with a particular actor's (i.e., ego's) position within the network, they provide a better understanding of the directed valued network in that the network architecture from one ego's viewpoint can be markedly different from those of others linked directly or indirectly. They also fit perfectly with the purpose of this study to explore the association between an OEM's strategic orientation and the supply network architectures it creates based on different types of supply network ties. Table 2 proposes a new framework for the supply network implications of the socio-centric SNA indices for the directed valued networks used in this study for four types of supply network ties.
Table 2. Socio-centric indices, conceptual definitions, and interpretations by supply network tie.
Socio-Centric SNA Index | Conceptual Definition | Tie Type | Implications for Directed Valued Supply Network |
---|---|---|---|
Betweenness centralization (BTC) | The extent to which particular network actors serve as hubs relative to the rest of the network | Contractual | The
extent to which there exist particular focal firms that have more or
less complete (or specific) contract terms than other supply network
members.
|
Transactional | The
extent to which there exist particular focal firms that have a higher
or lower percentage of monetary exchanges than other supply network
members (i.e. distribution of sales and spending in the network).
|
||
Professional | The
extent to which there exist particular focal firms that have more or
less work-related interactions than other supply network members.
|
||
Personal | The
extent to which there exist particular focal firms that have more or
less non-work-related interactions than other supply network members.
|
||
In-degree centralization (IDC) |
The extent to which network resources are converged on particular network actors | Contractual | The
extent to which particular focal firms obtain more complete (i.e. less
favorable) contract terms from the other supply network members.
|
Transactional | The
extent to which particular focal firms take up a greater percentage of
the monetary exchanges occurring inside the supply network than others.
|
||
Professional | The
extent to which particular focal firms obtain more incoming
work-related interactions from the rest of the supply network members.
|
||
Personal | The
extent to which particular focal firms obtain more incoming
non-work-related interactions from the rest of the supply network
members.
|
||
Out-degree centralization (ODC) |
The extent to which particular actors disseminate network resources to others | Contractual | The
extent to which particular focal firms provide more complete (i.e. less
favorable) contract terms for the rest of the supply network members.
|
Transactional | The
extent to which particular focal firms generate higher percentages of
the monetary exchanges occurring inside the supply network than others.
|
||
Professional | The
extent to which particular focal firms have more outgoing work-related
interactions to the rest of the supply network members
|
||
Personal | The
extent to which particular focal firms generate more outgoing
non-work-related interactions for the rest of the supply network members
|
||
Global clustering coefficient (GCC) |
The extent to which the network as a whole is cliquish (or tightly knit) (i.e. the degree to which all the network actors tend to cluster together) | Contractual | The extent to which members of the entire supply network are directly connected by contract relations
|
Transactional | The extent to which the members of the entire supply network are directly connected by monetary exchanges
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Professional | The extent to which all the supply network members freely communicate work-related subjects across firm boundaries
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Personal | The extent to which all the supply network members freely communicate non-work-related subjects across firm boundaries
|