Designing Supply Networks in Manufacturing Industries

Introduction

Faced with a changing business environment in which the coordination of the complex global networks involved in a firm's activities is becoming a prime source of competitive advantage, more supply chain management (SCM) researchers have focused on the simultaneous interactions among multiple supply chain entities within the supply network, rather than searching for dyadic or triadic ties between one original equipment manufacturers (OEM) and its immediate supplier(s). Network thinking and analysis were originally regarded as a subtype within the general framework of structural sociology. In certain respects, the field of sociology viewed the emergence of networks - the collection of interpersonal ties (e.g., kinship, friendship, communication, co-membership, etc.) - as a social and psychological phenomenon occurring among non-predetermined individuals through face-to-face conversations. In business settings, however, these networks do not spontaneously emerge from face-to-face interaction among non-predetermined individuals; rather, corporate managers strategically orchestrate these relationships by acting as network architects who designate the member companies of the network and its objectives. In this vein, business academics have predominantly explored how a firm can manage its portfolio of multiple simultaneous alliances. Yet an important question still remains unclear: "What determines different network architectures"; in other words, what are the strategic antecedents of network properties? Working from a strategic network perspective emphasizing the importance of network design in achieving a firm's strategic objectives, Doreian hints at the existence of antecedents of network architecture by asserting that the first principle of network formulation is that "networks have instrumental character for network members as these members have structured goals and some goals are achieved through network choices".

In the above vein, SCM researchers and practitioners have also conjectured the existence of antecedents for heterogeneous supply network architectures. For instance, facing a turbulent business environment, firms need to build and maintain multiple supply bases which are "the portion(s) of the (bigger) supply network that is within the managerial purview of the focal company". While it is obvious that the strategic network perspective should be considered an integral component of theory in SCM because researchers consider multiple entities commonly composed of large numbers of firms from multiple interrelated industries, empirical SCM research has confined itself to simple descriptions of supply network characteristics. Without considering the possible antecedents of network formulation, studies may give misleading answers about how different supply networks across various contexts should be managed. Goal conflicts are also more likely to arise in a supply network setting that essentially consists of multiple tiers of legally separate profit-making organizations with their own strategic goals; in other words, an OEM cannot attain supply chain success without deliberately designing its entire supply network in accordance with different strategic intents. Therefore, in exploring supply network phenomena, it is reductive to rely on the sociological viewpoint, which characterizes networks in terms of spontaneous and informal face-to-face conversations among non-predetermined individuals. Rather, a supply network should be viewed as a systematic outcome which is intentionally and strategically designed, implemented, and maintained in service with the OEM's strategic intent(s).

In line with this argument, this study attempts to address a theoretical and empirical gap in supply network research by exploring the unknown strategic antecedents of different supply network architectures. Specifically, it looks into the following questions:

(1) Are an OEM's strategic intent choices associated with supply network architecture; and

(2) If so, what differential effects do those strategic intents have, and which architectural properties of the supply network are effected? To categorize strategic intents, this study borrows from Fisher's supply chain design considerations where strategic intent is categorized by focus on cost leadership or market responsiveness. Drawing upon a unique dataset which allows analyses of multiple directed valued supply networks, this research sheds lights on the unresolved question of the supply network antecedents in a directed valued network setting and, consequently, offers a strategic supply network perspective. The remainder of this study is structured as follows: Section 2 provides the theoretical background and develops the hypotheses; Section 3 reviews the data, measures, and research methods used to test the proposed hypotheses; Section 4 provides the key results and interpretations; Section 5 presents the results of additional field investigations which provide further insights into the quantitative and qualitative findings, and is followed by Section 6 that discusses the contributions of this research and directions for future work.

Source: Myung Kyo Kim and Ram Narasimhan, https://www.mdpi.com/2227-9717/7/3/176/htm
This work is licensed under a Creative Commons Attribution 4.0 License.

Theoretical Background and Hypotheses

Network Resource and Strategic Intent

Many sociologists traditionally viewed the emergence of social networks as the outcome of spontaneous and informal face-to-face conversations among non-predetermined individuals. On the other hand, a stream of strategic alliance literature has adopted a different view in which firms utilize strategic alliances to access partners' knowledge or skills to hedge their performance risk, or to enter a certain foreign market within interfirm dyad settings. This view has been anchored in the network resource theory (also known as social resource theory), one of the most popular theories in social network research. The theory, mainly developed by Lin et al., argued that interpersonal contacts enable better access to, and mobilization of, resources embedded within and outside one's social network, such as valuable information and prestigious connections. However, relatively little is known about what specific motives drive interfirm network actors to interact with one another and attempt to form a specific network architecture.

The concept of strategic intent initially suggested by Hamel and Prahalad has been useful throughout various business disciplines in accounting for managerial motives behind the strategic alliance or joint venture formulation. While a vision is commonly developed and held by top management teams, strategic intent is more than just a vision or ambitious target of top management in that it is shared and implemented at multiple levels of the organization. For instance, Koza and Lewin proposed a framework emphasizing that a firm's strategic partnership structure varied by its strategic intent (exploitation or exploration). DiRomualdo and Gurbaxani also highlighted the importance of alignment between the strategic intent and supplier relationships to achieve outsourcing success. Ryall more recently espoused this view in his argument that an OEM should utilize different strategic intents (competitive or persuasive) in garnering the resources and capabilities possessed by non-immediate members of its value network. Extending the aforementioned conceptual arguments to the SCM domain, the OEM's strategic intents may serve as pivotal reference points for managing its supplying partners across multiple tiers, which result in different architectural properties of the formed supply network. Very little empirical research, however, has been done to test this conjecture. This study investigates the strategic antecedents of different supply network architectures by incorporating Fisher's supply chain design considerations (i.e., cost leadership and market responsiveness) as shown in Figure 1, and as a result, aims to provide a strategic supply network perspective.

Figure 1. Strategic intents and corresponding supply network types.

Supply Network Tie Types

Ties across interfirm networks serve as conduits for network actors to access, transmit, or exchange critical organizational resources. Interestingly, the same supply network can have multiple different architectural properties with regard to types and attributes of network ties (i.e., network resources), which is commonly referred to as "multiplexity". Thus, in accounting for interfirm network phenomena, such as supply networks, it is essential to take a network multiplexity approach in order to find "hidden" network architectures. This study considers four different supply network tie types - contractual, transactional, professional, and personal ties - which interlink supply network partners. The first two types, contractual and transactional ties, represent visible network ties for the exchange of tangible network resources, such as goods and services, whereas the remaining two, professional and personal ties, capture the invisible (and mostly intangible) exchange of network resources between supply network partners.

Obviously, a supply network consists of visible ties, such as contracts or deliveries and receipt of goods and services. Contractual ties are written agreements that seek to regulate interfirm transactions by specifying a detailed set of legally binding guidelines on operational requirements, quality monitoring and control, warranty policies, penalties, expected service level, etc. Another type of visible network ties considered is a transactional tie reflecting the amount of monetary exchanges, which have been regarded as a simple but clear manifestation of the economic transactions occurring within interfirm networks. Transactional ties represent the economic interdependence between network members. In other words, a buying firm becomes more dependent on the supplier as the percentage of its total payments to a specific supplier relative to other suppliers increases while the same occurs to the supplier when a greater percentage of its total sales comes from a specific buying firm relative to others. Although the most fundamental element of economic exchanges between supply chain partners, a contractual tie (i.e., a formal written contract between one supply network actor's sourcing partner) by its very nature can both foster and hinder commitment between buyers and sellers. For instance, a stronger contractual tie (i.e., more complete contract) including explicit work-related provisions and prescriptions, can protect buyers from the opportunistic behavior of their counterparts. Viewed from a supplier's standpoint, on the other hand, a strong contractual tie specifying more control and legal rules can serve as a threat when buyers opportunistically utilize it to impose unreasonable terms and conditions on the supplier. In this vein, a transactional tie (i.e., the actual exchange of goods and services) can be established without a formal written contract when both parties share relational norms such as reciprocity, solidarity, and information sharing. This study thus regards the above two visible supply network ties (i.e., contractual and transactional ties) as separate types in which a stronger contractual tie does not necessarily imply more or less economic transactions and vice versa.

Prior network research has pointed out that much of an interorganizational commitment is often formalized at a personal, rather than organizational level, and hence, the arrangement can offer exclusive access to network resources. However, interpersonal and thus invisible ties in supply networks have received relatively less research attention, whereas visible network ties representing economic exchange have been actively discussed in the literature. Thus, this study also considers two invisible network ties (i.e., professional and personal ties) that bridge the supply chain personnel of partnering firms. Professional ties are normally task-oriented and focus on achieving assigned objectives, while personal ties deal more with the social/emotional side of non-work-related interactions and focus on interpersonal likeability. In an SCM context, these invisible ties between purchasing and supply managers play a crucial role in facilitating buyer-supplier cooperation, trust, reputation and image, and subsequent organizational performance. When incorporated with social network analysis, this consideration further enables the inter- and intra-comparisons of different tie types and comparable network indices and consequently can provide invaluable insights concerning the underlying network architecture. Table 1 provides conceptual definitions of the four supply network tie types under consideration and their measurement items used based on the literature.

Table 1. Conceptual definitions, item measures, and related literature for supply network tie types.

Tie Type	Conceptual Definition	Measurement Items
Contractual	The extent to which a supply network entity perceives that it has a 'complete' formal written contract with its immediate counterpart	We have a formal written contract(s) detailing the operational requirements. We have a formal written contract(s) that detail(s) how performance will be monitored. We have a formal written contract(s) detailing warranty policies. We have a formal written contract(s) detailing how to handle complaints and disputes (e.g., penalties for contract violations). We have a formal written contract(s) detailing the level of service expected from this supplier.
Transactional	The amount of 'monetary' exchange (in percentage points) between a supply network entity and its immediate counterpart(s)	For original equipment manufacturers (OEMs) (i.e., tier-0 firms): A percentage of total spending for each tier-1 supplier of the selected component. For tier-(N) (i.e., intermediate) suppliers where N = 1 or 2: Percentages of total sales derived from the tier-(N − 1) buyer AND total spending for each tier-(N + 1) supplier in dealing with the OEM's selected component. For tier-3 (i.e., end-tier) suppliers: A percentage of total sales derived from tier-2 suppliers in dealing with the OEM's selected component.
Professional	A supply network entity's perceptions of the strength of the interactions with its immediate counterpart in performing 'work responsibilities'	We regularly communicate (via face-to-face interaction, conference calls, e-mails, etc.) on work matters. We widely share and welcome each other's ideas or initiatives via open communication (e.g., joint workshops, etc.). Communication between us occurs at different levels of management and cross-functional areas. I (or our executives) receive periodic feedback (via face-to-face, conference calls, e-mail, etc.) on progress, problems, and plans from this supplier's counterparts. I (or our executives) do periodic on-site visits to this supplier's plants.
Personal	A supply network entity's perceived strength of the interactions 'not directly related to work' with its immediate counterpart	We always invite each other to participate in various activities to socialize. We do personal favors for each other. We voluntarily exchange something of a personal nature to each other on appropriate occasions (e.g., birthday cards, congratulations, condolences, etc.). We often communicate (via face-to-face, phone calls, e-mails, social network services, etc.) during non-working time. We often communicate (via face-to-face, phone calls, e-mails, social network services, etc.) outside work places.

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) ${n_1, n_2, ⋯, n_g}$ , a set of arcs (i.e., directional ties or links) ${l_1, l_2, ⋯, l_L}$ , and a set of values ${v_1, v_2, ⋯, v_L}$ attached to the arcs, subject to $l_k=≠l_m=$ where $v_k$ is not necessarily equal to $v_m$ . 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. - The lower the index, the more firms there are which have more equally complete contract terms with their supply network counterparts. - The higher the index, the more firms there are which have more unequally complete contract terms with their supply network counterparts.
		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). - The lower the index, the more firms there are which have equal percentages of monetary exchanges with their supply network counterparts. - The higher the index, the more firms there are which have higher or lower percentages of monetary exchange with their supply network counterparts.
		Professional	The extent to which there exist particular focal firms that have more or less work-related interactions than other supply network members. - The lower the index, the more firms there are which have an equal amount of work-related interactions with their supply network counterparts. - The higher the index, the more firms there are which have more or less work-related interactions with their supply network counterparts.
		Personal	The extent to which there exist particular focal firms that have more or less non-work-related interactions than other supply network members. - The lower the index, the more firms there are which have an equal amount of non-work-related interactions with their supply network counterparts. - The higher the index, the more firms there are which have more or less non-work-related interactions with their supply network counterparts.
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. - The lower the index, the more firms there are which have fair contract terms with their supply network counterparts. - The higher the index, the fewer particular focal firms possess less favorable contract terms with their supply network counterparts.
		Transactional	The extent to which particular focal firms take up a greater percentage of the monetary exchanges occurring inside the supply network than others. - The lower the index, the more firms there are which have equal percentages of the monetary exchanges. - The higher the index, the fewer particular focal firms account for higher percentages of the monetary exchanges than the others.
		Professional	The extent to which particular focal firms obtain more incoming work-related interactions from the rest of the supply network members. - The lower the index, the more equal the amount of work-related interactions between supply network members. - The higher the index, the more work-related interactions among supply network members is focused on fewer particular focal firms.
		Personal	The extent to which particular focal firms obtain more incoming non-work-related interactions from the rest of the supply network members. - The lower the index, then each of the supply network members has a more equal amount of non-work-related interactions with one another. - The higher the index, the more non-work-related interactions among supply network members is focused on fewer particular focal firms.
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. - The lower the index, the more firms there are which have fair contract terms with their supply network counterparts. - The higher the index, the fewer particular focal firms yield less favorable contract terms for their supply network counterparts.
		Transactional	The extent to which particular focal firms generate higher percentages of the monetary exchanges occurring inside the supply network than others. - The lower the index, the more firms there are which have equal percentages of the monetary exchanges. - The higher the index, the fewer particular focal firms send out higher percentages of the monetary exchanges for the rest of the supply network members.
		Professional	The extent to which particular focal firms have more outgoing work-related interactions to the rest of the supply network members - The lower the index, the more equal the amount of work-related interactions between each of the supply network members and the others. - The higher the index, the fewer particular focal firms initiate most of the work-related interactions with 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 - The lower the index, then each of the supply network members has more equal amount of non-work-related interactions with one another. - The higher the index, the fewer particular focal firms make more 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 - The lower the index, the lower the proportion of all supply network members that are directly connected by contract relations (i.e. the supply network has a more ‘hierarchical’ architecture as a whole). - The higher the index, the higher the proportion of supply network members that are directly connected by contract relations (i.e. the supply network has a more ‘lateral’ architecture as a whole).
		Transactional	The extent to which the members of the entire supply network are directly connected by monetary exchanges - The lower the index, the more the supply network as a whole has a “hierarchical” architecture in the monetary exchanges among supply network members. - The higher the index, the more the supply network as a whole has a “lateral” architecture in the monetary exchanges among supply network members.
		Professional	The extent to which all the supply network members freely communicate work-related subjects across firm boundaries - The lower the index, the more “hierarchical” the architecture of non-work-related interactions among members in the supply network as a whole. - The higher the index, the more the supply network as a whole has a “lateral” architecture for work-related interactions among supply network members.
		Personal	The extent to which all the supply network members freely communicate non-work-related subjects across firm boundaries - The lower the index, the supply network as a whole has a more ‘hierarchical’ architecture of non-work-related interactions among supply network members. - The higher the index, the more “lateral” the architecture of non-work-related interactions among members in the supply network as a whole.

First, betweenness centralization (BTC) represents whether most network actors are equally central, or some actors (i.e., hubs) are much more central than others. This index can be calculated by dividing the variation in the betweenness centrality by the maximum variation in betweenness centrality scores possible in a network of the same size. Betweenness centrality is an ego-centric index indicating how often an actor lies on the shortest path between all combinations of pairs of other actors. The higher an actor's betweenness centrality, the more its immediate counterparts depend on this actor to reach out to the rest of the network. This index focuses on the role of an actor as an intermediary and posits that the dependence of others makes the actor central in the network. BTC, a socio-centric version of betweenness centrality, ranges from 0 where all network actors have the same betweenness centrality, to 1, where there exists one single actor connecting all the other actors. This study calculates the BTC of a directed valued supply network by adopting the formula suggested by Opsahl et al. for betweenness centrality (

$C^{wα}_{B}(n_i)$ ) for network actor

$n_i$ , defined as:

$C^{wα}_{B}(n_i)=\dfrac{g^{wα}_{n_jn_k}(n_i)}{g^{wα}_{n_jn_k}}$

where

$g^{wα}_{n_jn_k}$ is the total number of geodesics between two actors (

$n_j$ and

$n_k$ ),

$g^{wα}_{n_jn_k}(n_i)$ is the number of geodesics passing through actor

$n_i$ ,and

$α$ is a positive tuning parameter that is set to the benchmark value of 0.5 to equally value both the number of ties and their strengths (

$w$ ). Thus, BTC can be formally expressed as:

$C_B=\dfrac{∑_{i∈G}{C^{wα}_{B}(n^∗)−C^{wα}_{B}(n_i)}}{max∑_{i∈G}{C^{wα}_{B}(n^∗)−C^{wα}_{B}(n_i)}}$

where

$C^wα_{B}(n^∗)$ is the largest value of the betweenness centrality that occurs across the network

$G$ ; that is,

$C_{B}^{w \alpha}\left(n^{*}\right)=\max _{i} C_{B}^{w \alpha}\left(n_{i}\right)$ .

In the case of a directed network, two additional degree indices are defined: in-degree, or the number of links terminating at the actor

$(k^{in}_{n_i})$ , and out-degree, or the number of ties originating from the actor

$(k^{out}_{n_i)}$ . In-degree centralization (IDC) calculates the dispersion of or variation in in-degree centrality, and the extent of an individual actor's influence on other actors; thus, high IDC indicates the incoming flows of different network resources are focused on a small group of actors in the overall network. In the same sense, high out-degree centralization (ODC) indicates that a small number of actors send out most of the network resources to the rest of the network actors. This study derives IDC and ODC of a supply network from in-degree centrality (

$C^{wα}_{D-in}(n_i)$ ) and out-degree centrality (

$C^{wα}_{D-out}(n_i)$ ) for actor ni of a directed valued network using the following equations:

$C^{wα}_{D-in}(n_i)=k^{in}_{n_i}×(\dfrac{s^{in}_{n_i}}{k^{in}n_i})^α$

$C^{wα}_{D-out}(n_i)=k^{out}_{n_i}×(\dfrac{s^{out}_{n_i}}{k^{out}_{n_i}})^α$

where

$s^{in}$ and

$s_{out}$ are the total strengths attached to the incoming and outgoing ties, respectively. Therefore, the general IDC and ODC ranging from 0 to 1 are respectively defined as:

$C_{D-in}=\dfrac{∑_{i∈G}{C^{wα}_{D-i_n}(n^∗)−C^{wα}_{D-in}(n_i)}}{max∑_{i∈G}{C^{wα}_{D-in}(n^∗)−C^{wα}_{D-in}(n_i)}}$

$C_{D-out}=\dfrac{∑_{i∈G}{C^{wα}_{D-out}(n^∗)−C^{wα}_{D-out}(n_i)}}{max∑_{i∈G}{C^{wα}_{D-out}(n^∗)−C^{wα}_{D-out}(n_i)}}$

where

$C^{wα}_{D-in}(n^∗)$ and

$C^{wα}_{D-out}(n^∗)$ are the largest in-degree and out-degree centrality values in the network

$G$ .

Lastly, this study uses a global clustering coefficient (GCC) varying from 0 to 1 to measure the overall level of cohesion among network actors. In social network terms, this indicates the probability that network actors

$n_j$ and

$n_k$ are also connected to each other when

$n_i$ is connected to both of them, collectively represented as

$(n_i;n_j,n_k)$ . In a directed valued network setting, this socio-centric index is defined as the total value of closed triplets (i.e., triples of network actors where each actor is connected to the other two;

$τ_Δ$ ) divided by the total value of triplets (i.e., triples where at least one actor is connected to the other two;

$τ$ ). Triplet value (

$ω$ ) calculation is based on the geometric mean of the tie values for the nodes comprising the triplet in that it: (1) Captures differences between tie strengths, and (2) is robust to extreme tie strength. Thus, the general GCC (

$C_g$ ) can be formally stated as:

$C_g=\dfrac{1}{N}∑_{i,j,k∈G}\dfrac{{∑_{(ni;nj,nk)∈{τ_Δ}}ω_{τ_Δ}(ni;nj,nk)}}{{∑(_{ni;nj,nk)∈{τ}}ω_τ(ni;nj,nk)}}$

where

$N$ is the number of possible triplets in network G. Readers can refer to the recent study of Opsahl and Panzarasa for more details on this technique.

Because SNA indices have been developed and used within a sociological context, they cannot be directly applied and interpreted within an interfirm supply network context. Table 2, consequently, proposes a new framework for the supply network implications of the socio-centric SNA indices for directed valued networks used in this study for each of the four tie types previously defined in Table 1.

Hypotheses

A firm has a power advantage when it is relatively less dependent upon the resources of its counterpart(s), and it often leverages this power over others to achieve intended strategic goals. Social network studies have adopted betweenness centrality to measure an individual actor's power, and the extent to which it controls the resource flows in its network. As a socio-centric measure indicating the variation of the betweenness centralities of all network actors, BTC characterizes the extent to which the overall network is built around a particular group of actors serving as hubs relative to the rest of the network. A low BTC score indicates that the network resources running through various types of ties are almost equally distributed across the entire network, whereas a high score indicates that there exist particular focal firms possessing more network resources. This measure can be interpreted differently for different supply network tie types. For instance, an OEM pursuing a cost leadership strategy will try to make supply contracts as complete and detailed as possible to reduce any uncertainty, which may translate into cost savings and, as a result, the OEM will pursue unequal contracts with its supply network members. On the other hand, the lack of predictability of market changes prevents OEMs from designing complete supply contracts when they pursue market responsiveness, and this will result in cooperative, but loose, contracts containing rather general information. Regarding transactional ties, an OEM pursuing a cost leadership strategy can exploit economies of scale by focusing on a relatively small number of supply network members, whereas if that OEM is interested in responding promptly within an unsteady market, it would diversify its supply sources. This distinction is also observed for the supply networks consisting of professional and personal ties. In other words, a supply network actor devoted to cost leadership may tend to interact with a smaller range of counterparts, while it establishes a broader array of professional and personal interactions with other supply network members to be more responsive to market-led changes. Personal and professional ties in a market-responsiveness focused supply network, especially, might be expected to lead to more frequent interactions involving a greater number of actors to allow them more options in responding to an unstable environment. In this environment, information-seeking and problem-solving behaviors can be expected to dominate, resulting in more interactions with a greater number of network partners. Based on this line of reasoning, the following set of hypotheses is proposed:

Hypothesis 1A. An OEM's strategic intent of pursuing cost leadership is positively associated with the BTCs of its supply networks consisting of contractual, transactional, professional, and personal ties.

Hypothesis 1B. An OEM's strategic intent of pursuing market responsiveness is negatively associated with the BTCs of its supply networks consisting of contractual, transactional, professional, and personal ties.

A firm with more power over their counterparts also can more easily draw and absorb network resources from the rest of its network by exerting coercive or punitive pressure and, consequently, can achieve its strategic goals. In social network research, this power of an individual network actor is commonly measured by in-degree centrality, which represents the total number of ties pointing toward the actor. IDC, derived by the variation in individual actor's in-degree centrality at the network level, indicates the extent to which network resources are concentrated in particular actors. From a supply network perspective, an OEM trying to achieve cost leadership by pursuing economies of scale will have a network architecture with a relatively small group of members, which brings in more transactional, professional, and personal inflows from the rest of the network. A firm seeking market responsiveness, in contrast, will try to hedge against unexpected market changes using a diversification strategy and, as a result, will have a supply network architecture demonstrating relatively equal distributions of transactional, professional, and personal inflows across network members. The supply network in-degree centrality accounting for contractual ties may need more cautious interpretation because complete contract terms can impose institutional constraints on interorganizational transactions. More inflows of complete contracts (i.e., high in-degree centrality) thus indicate that a network actor receives the less favorable (or more restrictive) terms and conditions from its counterpart(s). In this sense, OEMs which need tight cost controls may build supply networks where a few focal firms take up more favorable (i.e., less complete) contracts showing low IDC, whereas their strategic intent of achieving market responsiveness drives the opposite consequence (i.e., supply network members have mutually favorable - that is, equally complete - contracts with others). The preceding discussion leads to the following hypotheses:

Hypothesis 2A. An OEM's strategic intent of pursuing cost leadership is positively associated with the IDCs of its supply networks consisting of transactional, professional, and personal ties, while being negatively associated with the supply networks consisting of contractual ties.

Hypothesis 2B. An OEM's strategic intent of pursuing market responsiveness is negatively associated with the IDCs of its supply networks consisting of transactional, professional and personal ties, while being positively associated with the supply networks consisting of contractual ties.

In addition, a firm may relax its own institutional constraints on other exchange partners expecting reciprocal behavior, and in doing so may make advances toward cost leadership or market responsiveness. This is true especially when both parties have complementary resources or similar sources of uncertainty and can provide more useful feedback to refine their efforts for their own benefit. Companies such as Dell and Whirlpool, for example, transformed themselves into "virtually integrated" organizations by sharing their information and knowledge on inventory level and sales forecasting with other supply network members. A network actor's use of this kind of influence on its exchange partners has been measured by out-degree centrality that denotes the number of network ties originating from the actor. As a socio-centric measure indicating the variation of the out-degree centralities of all network actors, ODC explains the extent to which particular actors distribute transactional or relational network resources to others. In other words, a high ODC score indicates that a few particular focal firms disseminate most of the transactional or relational network resources for the rest of the members, whereas a low score indicates that each member of the network has a more equal amount of those resources. This measure would be interpreted differently for each type of supply network tie. For instance, when OEMs seek cost leadership, their supply networks will have an architecture consisting of a small group of firms that send out more complete (i.e., less favorable) contract terms and a greater number of monetary exchanges for the rest of the network. They will not be very interested in establishing reciprocal professional and personal ties since those relationship-specific investments can increase switching cost as well as prevent their search for lower cost suppliers. On the other hand, OEMs pursuing market responsiveness will be more willing to initiate more professional and personal interactions with other network partners to detect potential market changes while maintaining a balanced approach toward contract completeness and quantity. This reasoning leads to the following two hypotheses:

Hypothesis 3A. An OEM's strategic intent to pursue cost leadership is positively associated with the ODCs of its supply networks consisting of contractual and transactional ties, while being negatively associated with the supply networks consisting of professional and personal ties.

Hypothesis 3B. An OEM's strategic intent of pursuing market responsiveness is negatively associated with the ODCs of its supply networks consisting of contractual and transactional ties, while being positively associated with the supply networks consisting of professional and personal ties.

Direct contacts and connections between a firm and its customers/suppliers also facilitate the exchange and distribution of organizational resources and subsequently contribute to the strategic goals and competitive advantage of the involved actors. For instance, Japanese automobile manufacturers such as Toyota and Nissan have endeavored to maintain direct connections with non-immediate suppliers by means of different supplier associations and considerable owner interest in their suppliers. Those efforts enabled them to oversee the whole supply network by supplementing the potential shortcomings of a hierarchical supply network, characterized by a reliance on a limited number of first-tier suppliers. In social network research, such connectivity among network actors has been measured by GCC that denotes how cliquish (or tightly knit) a network is as a whole. In plain terms, it measures the probability that the friend of John's friend is also John's friend. In a supply network with a low GCC, network actors interact with only a few contiguous others which results in a hierarchical (i.e., more cliquish) architecture as a whole. A high coefficient value, in contrast, suggests more actors are directly connected to one another manifesting lateral (i.e., less cliquish) network architecture. From a supply network perspective, an OEM's intent to acquire cost leadership will drive it to build a hierarchical supply network which allows for easier and more thorough control on a limited number of major suppliers. A firm interested in achieving market responsiveness, on the other hand, will try to establish direct connections with as many down-tier suppliers as possible in order to perceive and respond to changing market circumstances, which subsequently leads to lateral supply network architecture. Accordingly, this study investigates the following set of hypotheses:

Hypothesis 4A. An OEM's strategic intent of pursuing cost leadership is negatively associated with the GCCs of its supply networks consisting of contractual, transactional, professional, and personal ties.

Hypothesis 4B. An OEM's strategic intent of pursuing market responsiveness is positively associated with the GCCs of its supply networks consisting of contractual, transactional, professional, and personal ties.

Site:	Saylor Academy
Course:	BUS606: Operations and Supply Chain Management
Book:	Designing Supply Networks in Manufacturing Industries

Printed by:	Guest user
Date:	Wednesday, 9 April 2025, 2:53 PM

Designing Supply Networks in Manufacturing Industries

Description

Table of contents

Introduction

Theoretical Background and Hypotheses

Network Resource and Strategic Intent

Supply Network Tie Types

Indices for Network Characterization

Hypotheses