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structure. A matrix is most suitable when the demands of competing dimensions are roughly equivalent and difficult to address sequentially. If one dimension was clearly more important, then the preferred structure would be multidivisional, with the dominant dimension being “higher” in the firm’s structure than the other and receiving greater priority from top managers. If decisions regarding structural demands can be addressed sequentially, then it may be possible to address big issues early on and at a high position in the hierarchy. Less important issues can be addressed later on and further down in the hierarchy.

Matrix or Division? A Model of Optimal Structure

David Baron and David Besanko developed an economic model of shared incentive authority to further explore the optimal choice of structure. They focus on firms that face organizing demands on both product and geographic dimensions.15 The optimal structure to use emerges from the interplay of spillovers within product lines and geographies and the interrelationships among multiple activities that local units perform.

Baron and Besanko see two considerations driving structural choice. The first is whether demand-enhancing activities, such as advertising or product promotion, and cost-reducing activities, such as downsizing or production rationalization, are profit complements or substitutes. Demand-enhancing and cost-reducing activities are complements when an increase in the level of one activity increases the marginal profitability of the other. For example, this would occur when managers redesign their products and in the process also reduce their defect rates. Demand-enhancing and cost-reducing activities are substitutes when an increase in the level of one activity reduces the marginal profitability of the other. This would occur, for example, when managers allocate scarce resources (e.g., managerial attention) to some activities, such as new business development, at the expense of others, such as control.

The second consideration driving a choice is whether spillovers of know-how are positively or negatively correlated. Spillovers refer to the transfer of knowledge within the firm that occurs when a given activity is performed. The spillovers that are available to a firm in a given situation depend on the firm’s capabilities at that time. Spillovers in two activities are positively correlated if they both primarily benefit a single dimension. This would occur if the introduction of a new product in one market helps the firm produce or sell the product in other markets. They are negatively correlated if spillovers from some activity positively benefit one dimension (e.g., products) but reduce benefits on another dimension (e.g., geography).

The problem for top managers in a decentralized multiproduct and multilocation firm is to shape incentives for local managers so that they perform appropriately on product and geographic dimensions. Doing this counteracts the free-rider problem that arises when local managers fail to internalize the benefits that their activities generate for the rest of the firm. The choice of an appropriate organization design can shape local managers’ incentives to perform optimally.

Baron and Besanko identify the conditions in which a matrix is never optimal and those conditions in which it can be optimal. A matrix will never be optimal when spillovers are positively correlated and activities are profit complements. When activities are profit complements but spillovers are negatively correlated, a matrix can be optimal if spillovers do not disproportionately favor one dimension over another. If activities are profit substitutes and spillovers are positively correlated, then a matrix can be optimal if the activities are strong substitutes. Otherwise, a

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product or geographic structure is optimal. Finally, if spillovers are negatively correlated and activities are profit substitutes, a matrix will be optimal if spillovers are strongly product-specific in one activity and strongly specific to geography in the other activity.

To see if (and how) real global firms acted on these considerations of their model, Baron and Besanko looked at how Citibank reorganized as it adopted a global approach to its business that required a balance between product and local market demands.16 They examined the formation of the Global Relationship Bank (GRB) in 1994 and the creation of the Global Markets unit in 1997 to bridge between the GRB and Emerging Market units within Citibank. They concluded that Citibank’s reorganizations were consistent with a need to balance customer and geographic orientations within a global framework.

Network Structure

Recall from Chapters 3 and 4 that the value chain can be arranged along a continuum from full integration to arms-length. We observed that when organizing the value chain, it is important to think about the relationships among the individual components. Firms need to be flexible, “making” some components themselves while “buying” others from the market. The network structure (see Figure 13.4) places a similar emphasis on the relationships among workers and the benefits of flexibility. Workers, either singly or in combination, can contribute to multiple organizational tasks and can be reconfigured and recombined as the tasks of the organization change.

Gary Hamel describes a network structure in operation at the Morning Star Company in California. Morning Star is a privately held company that is the largest tomato processor in the world, with 400 full-time employees, $700 million in annual sales, and an annual share of 25 to 30 percent of the tomatoes processed in the United States each year. Morning Star has a well-articulated company mission, some procedural rules of order, but no formal bosses, no titles or formal job descriptions, and peer-based compensation. Each individual’s tasks, responsibilities, and obligations to other workers are negotiated in discussions between the employees involved and summarized in a Colleague Letter of Understanding (CLOU). The network that arises out of the dyadic relationships summarized in the CLOUs is what constitutes the firm’s structure. There are occasional disputes among colleagues, which if not settled informally can be subject to peer review for resolution with a possible appeal to the firm’s owner. There are 23 separate business units with the firm, each with its own profit and loss responsibility. These business units also manage their buyer-supplier relationships with each other by negotiating binding agreements with each other, similar to the process for developing CLOUs.17

At a more aggregated level of analysis, networks develop from the patterned relationships of units that may or may not be part of a single integrated organization. Networks of small autonomous firms can even approximate the behavior of larger firms, earning them the name “virtual firms.” This can occur as networks grow out of dyadic business-to-business relationships among firms or as part of a larger consortium effort, such as with the original Airbus consortium.18

Network structures and their component work groups can be organized into cross-cutting teams on the basis of task, geography, customer, or other bases. However these structures are formally organized, the actual relationships between work groups are frequently governed more by the often-changing requirements of common tasks than by the formal lines of authority.19 A network is preferable to other structures

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when the substantial coordination costs of employing it are compensated for by technical efficiency, innovation, or cooperation. The Japanese keiretsu structure discussed in Chapter 4 is a type of network in which informal ties between members also facilitate coordination and reduce agency problems. The same could be said for most Asian business group structures in which nominally independent autonomous firms could be incorporated into a network structure through such means as bilateral contracts, board interlocks, and patterns of cross-ownership among members of the larger business group.20

The interrelationships among firms in the biotechnology industry provide an example of network structures that facilitate information flows. Such flows are necessary because these technologies have applications in such diverse areas as seeds, pharmaceuticals, and beer. Observers have seen these networks as a principal reason for the industry’s historically high level of new product development. This setting provides an example of an alliance network in which separate firms can act collectively through a combination of informal relationships, bilateral contracts, and more complex joint venture agreements.21

Network structures become more popular as their organizational costs decline. The spread of the Internet has provided an infrastructure with which networks can form and continue operating at a much lower cost than was the case for more traditional relationship-based infrastructures or for more dedicated but less flexible coordination schemes such as Electronic Data Interchange. In this sense, networks have always been possible modes of organizing but have been too costly to rely on in most environments. There are exceptions, of course. For example, the world diamond industry has long been characterized by extensive informal networks that routinely handle large amounts of expensive product, relying almost solely on interpersonal trust. Those informal networks have also rested on the more formal contractual networks of the world diamond cartel maintained by DeBeers in conjunction with its suppliers and buyers.22

“Modular organization,” an alternative to network structure, involves relatively self-contained organizational units tied together through a technology standard. Network externalities, described in Chapter 11, provide opportunities for modularity. For example, consider the individuals and firms that work closely with Apple to develop applications (“Apps”) for the iPhone and the iPad. These are largely independent contractors who produce their products for Apple as they wish, provided that they meet Apple’s rigid technical specifications. This permits Apple to outsource the development of software applications and capitalize on their innovative capabilities while at the same time maintaining the controls over their products that would traditionally be expected to require inclusion within a firm’s authority system. Boeing has also moved into modular design and manufacturing with its highly publicized 787 Dreamliner, most of whose component systems are outsourced to contractors while Boeing serves as the designer and ultimate assembler of this complex aircraft. This proved to be a challenge, as we discussed in Example 3.4.

Although modular organization limits some opportunities for scope economies, it also helps networks to grow by fostering modification of the network through the addition or subtraction of subunits without significant disruption to existing relationships. Modularity also facilitates adaptation to technological change by diffusing R&D among many firms, each of which is free to pursue its own avenues for innovation. The long-term relationships established by the firms in a modular organization allow them to adapt to changes in the unifying technology. The overall effect of modularity