If a Telephone Network Needs an Infrastructure, Why Not a Service-Delivery Network?

Eugene Bardach

Goldman School of Public PolicyUniversity of California, Berkeley

Conference on “Collaboration Among Government, Market, and Society: Forging Partnerships and Encouraging Competition,” Shanghai, May 25-26, 2013, sponsored by University of Maryland, Fudan University, and Association for Public Policy Analysis and Management

Abstract

Interagency collaboration on public services delivery (and regulation) is frequently looked at through a conceptual lens loosely, but conventionally, known as “network theory.” Although the network concept has proven useful in connection with studying interagency collaboration in service delivery, it is limited in important ways. One way is that it is too simple. Providing networks with an “infrastructure” is one way to remedy this. If such a commonplace phenomenon as a telephone network can have an “infrastructure,” why not services delivery networks?

Adding the idea of infrastructure to the prevalent conceptions of networks brings certain theoretical advantages. Three of the more important are:

Providing a theoretical home for such important variables as: resource stocks and flows; differentials of power, authority, and influence; and the competence and quality of service providers. Formal, architectural, features of networks, like degree of point-centrality or density of linkages, need supplementing by such substantive variables.

Affording theoretical space for process dynamics, that is, for the activities of network agents that accumulate and feed back into the way the system works or doesn’t work. An example is the way trust, when fed through positive feedback loops, might increase in a network as a function of itself.

Deepening the understanding of how apparent hybrids of multi-agent production processes work, as when networks and hierarchies both operate in a single system. These are often complements, the one compensating for the limitations or defects of the other.

The value of “infrastructure” as a potential addition to the conceptualization of networks cannot be tested empirically and proved either true or false. It is to be judged by its utility. It is useful if it can extend perceptions, resolve conundrums, and deepen understandings. The arguments offered here are theoretical but not “only” theoretical.

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If a Telephone Network Needs an Infrastructure, Why Not a Service-Delivery Network?

Eugene Bardach

Goldman School of Public PolicyUniversity of California, Berkeley

The “network” concept has become an influential tool in public management analysis and discourse. It still operates with many imperfections and limitations, however. Some of the most important ones have been nicely reviewed by Isett et al. in an early 2011 article in the Journal of Public Administration Research and Theory (Isett 2011) and there is no need to review them here. I want to discuss a conceptual problem, however, that Isett and her colleagues do not identify, but which deserves attention.

It is the problem of over-simplification. Granted that some degree of simplification is always necessary and usually desirable in social science research, one must balance the gains from simplification against the losses from departures from realism.

Isett and her colleagues find three conceptual and empirical domains where the “network” concept is used in the public management literature. I restrict my attention here, however, to only one, that of interagency collaboration for service delivery. I also restrict attention to the idea of whole networks (Provan, Fish, and Sydow 2007), that is, the network considered as an entity and not just the sum of its bilateral parts.

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This does not claim to be a literature review. I have familiarized myself with what I take to be the main sources of ideas and research in the limited areas I discuss, but I intend this essay mainly to be suggestive, albeit in a serious way. Perhaps one can find in the literature discussions of some of the issues I raise and that I have neglected to present here, and perhaps better solutions than mine. But one can offer a critique of central themes and approaches in a very large field, and offer some suggestions, without being obliged to find every last possible contribution.

I present my solution to the problem of over-simplification up front, and in one sentence: “A network needs an infrastructure.”

A Useful Analogy

Consider a telephone network. The infrastructure contains both hardware and software. The hardware includes telephone instruments, cables, satellites, relays, and the like. The software is people and their knowledge, habits, and dispositions. That is, they know how to use telephones, to reach other numbers, to reach other people through those numbers. Software also includes rules and protocols, e.g., the per-minute charges for connections and the norms about (not?) interrupting and the meaning of a busy signal.

A transportation network infrastructure includes roads, rivers, canals, airports, cars, signal lights, and oil companies. The softer aspect of the infrastructure includes drivers, owners, and pilots, and also the rules of the road, the level of toll charges, the norms of driver-to-driver and driver-to-pedestrian reciprocity, the auto insurance industry and its products and premiums, insurance regulators, and police presence on the roads and the training of the force.

A television network has a hardware infrastructure of TV sets, home computers, cables, transmitters, relays, satellites, switches. The network includes, in its softer aspect, viewers, program producers, script writers, actors, agents, advertisers, commercial designers, and countless others. These come packaged together with their tastes and preferences and, derived from them in large part, the styles of programming chosen and what various parties are willing to pay to consume them.

What, then, might be the analogues to such infrastructure are to be found in human services delivery networks?

Human Services Analogies

The infrastructure analogue in services delivery networks is composed mostly of software, in that it consists largely of network “agents” who are human beings and their expectations and perceptions of one another. It also incorporates as agents various institutions, which can be said, in the network context, to “have” expectations and perceptions as well. These are not just any expectations but have to do with their orientations to one another as individuals and towards the institutions that employ them and, in many cases, which they represent. Thus, Jack (or Agency A, for whom he works) believes, based on experience, that Jill’s Agency B, is a good service provider for clients whom Jack refers for service, and that Jill is particularly good at getting timely appointments for referrals from Agency A and happens to be especially good at helping Jack’s referrals. The agencies themselves are linked not only by a mutually well-

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regarded history of referral provision and service delivery but by memoranda of understanding about types of cases that would be appropriate and those that would not. They even have a contract stipulating conditions under which Agency A would pay cash for certain services provided by Agency B to certain kinds of cases.

The infrastructure software may be said to incorporate the capacities of institutions and individuals to act with greater or lesser competency with respect to certain tasks – and, of course, perceptions on the part of other agents of these capacities. Agency B’s competence level is relevant to the network’s functioning, and so is Agency A’s perception of this fact. Moreover, B’s understanding that A’s perception is important to both A and B is relevant as well. Hence, we will probably see that A and B try to manipulate (“spin”) the perceptions each has of the other. And once this is understood to happen, or even to have the potential for happening, both A and B are likely to be aware of deliberate manipulations and to adopt strategies for countering these. This structure of combined trust and mistrust can be elaborated even further, of course. And the whole structure of elaboration can be fit into the concept of network infrastructure, alongside similar structures between other pairs of agents and also agents and the network as a whole.

What might the infrastructure concept bring to theorizing and research about how networks work? While there might be many such contributions, I discuss here only three:

a conceptual home for “non-architectural” variables a conceptual home for process dynamics a way to integrate networks and other collective production arrangements

(hierarchies and markets)

A Conceptual Home for “Non-Architectural” Variables

Consider one of the foundational papers in this area, by Keith Provan and Brint Milward (Provan and Milward 1995). It is a careful empirical study the relative effectiveness of mental health services networks in four cities, Providence, RI, Tucson, AZ, Albuquerque, NM, and Akron, OH.. They characterize these networks largely in formal terms such as the agents (organizations, largely) who form the “nodes” of the network, the “linkages” among the nodes, the “density” of linkages throughout the network, the “centrality” of certain agents within each of the networks. Other studies of this genre use such concepts of network and subnetwork structure as “edges,” “holes,” “ties,” and “multiplexity” (the variety and number of linkages between agents). I call all such formal structural features “architectural variables.”

Perhaps the most important finding by Provan and Milward was that the most effective network, as measured by clinical outcomes, was the “most centralized” network – in Providence. This is conceived as the “point centrality” of individual network agents, in this case the “core” mental health agency in Providence. In turn, centrality is conceived essentially as a function of the number of linkages between agencies that pass through a given agent (forming a star-shaped pattern on a graph).1 A linkage is operationalized as referrals received and sent, case 1 A star-shaped graph of linkages indicating point centrality can indicate a “centralized” network in a limited sense, but not in the sense that the agent in the center controls or influences the agents to which the central agent is linked. The implication of the term “centralized” in describing networks has to be inferred from the context.

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coordination, joint programs, and service contracts (p. 11). What Provan and Milward call the “core” mental health agency in Providence was, by these measures, very central and overwhelmingly important. The interpretation was that a “centralized control of integration…allows the coordination of integration across many agencies as well as closer monitoring of services.” (p. 13)

The empirical conclusion is unexceptionable and so too is the reasoning that monitoring of services along with the notion that “coordination of integration across many agencies” might somehow be involved. Provan and Milward note that the core agency channeled all the state funds for the severely mentally ill to other providers, and had done so for many years. State oversight of the Providence system was simplified by having a single local agency providing so much oversight of other providers itself. (p. 17)

Although centrality of a core agency is correlated with network efficacy, is this correlation also causal? No. The workings of a centralized network might also produce the opposite results as well if the core agency were incompetent or self-serving or overbearing. Indeed, on the last point, many of the other agencies in the network described the agency as “bureaucratic,” “insensitive,” and “arrogant” (p. 16), though Provan and Milward do not report negative comments about the quality of the agency’s work. Is this, then, a case of spurious correlation? No, but neither is a star-shaped pattern of centralized linkages in a meaningful sense a direct cause of the Providence network’s relative success. To clarify this answer we require a word on the idea of causation and its dubious role in understanding how a system works.

Centralization (in the point-central sense) is not a direct cause in the sense that gravity can be said to cause objects to fall towards the earth. It is not a sufficient cause, since other conditions must co-occur with it to make its impact felt. It is not a necessary cause, inasmuch as many other conditions could substitute for it.2 In any case, it is not a spurious cause (like sunspots and stock market movements), since it almost certainly contributes, in a way, to an overall causal picture. For the same reason, it is not merely a convenient instrumental variable, that is, a proxy measure for other causes. Perhaps it can best be viewed as a “force-multiplier,” in the same sense that concentration of military forces can multiply the fighting power of a number of dispersed soldiers or a reputation for aggressive oversight can multiply the deterrent power of an agency’s regulations. Thus, it has little or no causal force by itself, but when combined with other features, those other features can be unusually efficacious.

If we take it to be the last, though, how should it be inserted into a conceptualization of networks and the way they work? As stated above, it, along with other architectural features, should be conceived as part of network infrastructure. As such, it is just one more infrastructure feature that contributes to the functioning of the network and it makes its contribution only in combination with other features of an overall system, just as the heart does in conjunction with all the other organs of the body.

Many other features might also find a conceptual home in a systemic infrastructure. What these might be will emerge in due course if and when other researchers begin to fill in empirical blanks and also to refine the conceptual understanding. Almost surely these would 2 In any case, in connection with causality in the real world, as opposed to the abstract theoretical language of mathematics, the language of necessary and sufficient conditions is misleading.

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have to do with the incentives that are available to agents in the network for effecting exchanges with other agents; power differentials among agents taken pairwise and among particular agents and the network as a whole; various capacities for performing relevant technical tasks (such as policing compliance or delivering effective therapy or finding a client a job) that map onto the several agents; and formal and informal accounting protocols that link levels of government for grant programs (Abramson 2013). This list is not exhaustive, of course, only suggestive. No analyst would ever think of analyzing the workings of hierarchies or markets without some attention to these features (even if only to explicitly ignore them for model-building purposes), and the same should be true of networks.

A Conceptual Home for Process Dynamics

A network does not work by infrastructure alone. It is also the home to dynamic processes involving the agents transacting among themselves, and making decisions about how to do so. They talk and they do. They listen, they learn, they share, and they debate. In a telephone network, friends call friends, businesses call businesses, fundraisers call just about everybody. Callers congest the lines to certain numbers, and they learn how to avoid the congestion by timing their calls differently. Back in the day when operator-assisted calls predominated, callers learned to expect slow and sometimes mistaken connections with the potential for eavesdropping. In time, telephone companies, and telephone callers, found ways to substitute station-to-station for operator-assisted calling. In a transportation network travelers choose when and where to go. They choose routes, stop at red lights, take or avoid toll routes. Managers in the network create diamond lanes, set bus schedules, choose modalities with which they will send freight. All these are examples of network-based processes. They are shaped largely by infrastructure (both hardware and software aspects) but are in part governed by their own features and dynamics.

The process, or dynamic, level of a network depends to a large degree on how feedback loops operate (Bardach 2006) and citations therein). In part, these can be thought of as situated in the software portion of the infrastructure – a rule that in effect says something like, “Whenever a quantity of X occurs, Y percentage of X feeds into a process that governs changes in Z, creating Z’, and Z feeds back into X, changing X into X’.” This in turn depends heavily on the flow of transactions among agents, particularly those transactions involving client and customers, information, and money. To illustrate, here are some sample hypotheses (not necessarily mutually consistent) about such transactions and possible feedback loops: an agency’s reputation for performance adequately reflects reality; reputation for performance spreads by word of mouth widely within the service provider community; referrals track reputations, which in turn provide client to receive high-quality services, which add to the agency’s reputation; money in the short term flows to agencies irrespective of the quality of service rendered but a worsening reputation shrinks referrals and revenue in the long run; referrals from one source encourage referrals from other sources. Building trust, especially in the earlier stages of a developmental process, depends on many features but almost certainly includes complicated feedback 3 loops (Johnston, Hicks, Nan, and Auer 2011).

3 A useful demurrer from a conventional assumption that over time growing familiarity among agencies will lead them to substitute informal ties for formal contractual relations can be found in (Isett and Provan, 2005).

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Of especial significance are emergent processes. These are volume-dependent (some would say “density-dependent”) and are sensitive to the interactions among subsystem outputs and feedbacks. The system-level effects are often unpredictable even if we have complete information about how subsystems work in isolation. A convenient example is the way just a few individual automobiles, entering an already dense but flowing stream of traffic, can create severe congestion near the point of entry and erratic stop-and-go movement even several miles from that point. In the context of public management collaboratives, bandwagon-type effects are emergent and sometimes very important. Most collaboratives start out small, weak, and in search of resources, which usually come from participating agencies themselves, often given very reluctantly. However, the stronger and more effective a collaborative becomes, the more successful it may be at attracting resources from hitherto skeptical agencies; and over the long run it may transform itself from a relatively egalitarian network among agents to a more hierarchical organization with a budget of its own and the power to bring agencies into line.

Consider too the increasing legitimacy of the governance cadre in a collaborative, if there is one, and a possible paradoxical result. The longer a collaborative goes on, and the more it appears to accomplish, the more legitimacy its leadership is likely to acquire. But the more legitimacy, the more power, hence the more conspiring there is to seize control of what will have become a valuable asset, and the emergence of a new political vulnerability where none had been before.

Another emergent feature is a process to make an appropriate match of an agent with a specific need at a specific time with another agent who has a reasonable chance of meeting it. Thus, a telephone caller who feels a need to speak to a particular friend need only dial the friend’s number – and leave a voice-mail message if the friend is not available just then. The caller makes use of a host of cables, satellites, relays, and switches, but the process is very speedy, and the end result extremely reliable. In the service-delivery world, one of many possible analogues would be an agency looking for assistance on behalf of a client who has just showed up with a problem. The nature of the assistance might be a referral for possible service or simply a question about available resources or possible protocols. The matching is not as swift or reliable as dialing a friend’s telephone number, and it may take half a dozen contacts before a resolution is reached. However, the matching attempts are much better than random and they are typically numbered in single digits and not hundreds. In this case the emergent features that accompany search are more sophisticated information and search strategies on the part of the searcher and a shrinking pool of plausible targets in the searcher’s environment.

A good infrastructure helps accomplish this, and that probably means that knowledge is fairly widely distributed and is fairly accurate and up-to-date. It also means probably means that there are agents who in effect are specialists in gathering and disseminating knowledge and information and who are accessible, directly or indirectly, to many or most other agents.

Finally, we may mention the “groupware” referenced by Agranoff and McGuire (Agranoff 2007) an example of which is the capacity for what might be thought of as network self-awareness, that is, the capacity of the aggregate of agents to think of a trans-agental entity, itself, as in “Our Lake Tahoe management network needs to bring certain shoreline towns in line.” Presumably, this is the sort of self-awareness that Agranoff and his colleagues find in their studies of rural-development and urban-development collaboratives (Agranoff 2007; Agranoff and McGuire 2003; Radin, Agranoff, Bowman, Buntz, Ott, Romzek, and Wilson 1996).

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Hybrids Incorporating Networks, Hierarchies, and Markets

Based on the foregoing discussion, therefore, we can say that a network has two tiers, the infrastructure and the processes that are partly engendered and constrained by the infrastructure but also have a dynamic of their own. This two-tier perspective on a network is helpful in understanding apparent hybrids of networks and hierarchies (and also each of these and markets, which I touch upon only briefly).

For instance, often money moves among agents in the network in exchange for various resources, giving it the flavor of a market. Sometimes the more-or-less collaborative relationships among agents is mediated by power and authority differentials, as one could say of the Providence mental health network, giving it the flavor of a hierarchy. Provan and Kenis (2008) speak of the “governance” of networks and of networks characterized by some hierarchical features, lead agencies and what they call Network Administrative Organizations. They remark that some researchers do not think that such entities should be called “networks” at all. Provan and Kenis disagree, but the skeptics have a point. As we shall see, the point is semantic rather than substantive, but it is an important point nonetheless.

The case described by Moynihan (Moynihan 2008) of a number of agencies in California banding together to attack an epidemic among poultry of Exotic Newcastle Disease (END) is an unusually interesting specimen of a seeming hybrid. This collaborative effort involved inspecting quite a lot of commercial and backyard poultry-raising facilities in several counties of Southern California and the destruction of some three million birds.4 The main agencies were the federal Animal and Plant Inspection Service (APHIS) of the U.S. Department of Agriculture, the Animal Health and Food Safety Services (AHFSS) of the California Department of Food and Agriculture), and state and federal forest agent officials. All told some 7,000 individuals were involved in the planning and execution of the effort. These various agencies plus some lesser ones were organized according to an incident command system (ICS) framework, which had originally been developed some years earlier by firefighting agencies in California. The ICS framework calls for an “incident commander” responsible for “organizing the basic managerial functions required for most crises: operations, logistics, planning, finance and administration.” (p. 356)

Moynihan recognizes that, analytically, the blend of network and hierarchical relationships in the ICS framework needs special handling. He tips his hat to the hierarchy perspective: “Consistent with the partnership that AHFSS and APHIS had developed, they formed a joint command to run the ICS headed by one commander from each organization.” But it is the network perspective that in the end is more fundamental: “The ICS essentially overlays a hierarchical structure on a network, using a central command to manage conflict, coordinate action, and reduce classic network characteristics, such as a reliance on consensus.” (p. 356). And he comments that the ICS “appeared to work well, and network members saw the ability of the key agencies involved to work together as a major success factor…” (p. 356)

One could wonder, though, if his report and analysis would have differed had he looked primarily through the hierarchy lens rather than the network lens, that is, had he written instead: “The ICS essentially overlays a network structure on a hierarchy, using decentralized initiative

4 http://publichealth.lacounty.gov/vet/newcastle.htm

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and informal and formal channels to reduce classic hierarchical characteristics, such as a reliance on communications up and down through the chain of command.” Moynihan’s account makes the network primary and the hierarchy secondary, whereas the latter formulation reverses these two positions.

Perhaps this hierarchy-first perspective would have made a big difference in how we see the workings of the ICS, and perhaps not. In support of the latter position, one notes that each of the six prescriptions for “Learning in Emergency Networks,” (box, p. 357) such as “Ensure that information is examined and discussed on a regular basis and that it shapes operational decisions,” could have been directed to managers of a hierarchy as well. On the other hand, the hierarchy lens would have drawn more attention to probably important causal elements of the system that are not extensively discussed in the paper, particularly those having to do with the quality and activities of hierarchical leadership, e.g., its role in forestalling or settling disputes, setting up trainings for volunteers and newcomers, strategically deploying equipment and personnel, and insuring and editing collective memory and documentation – and, of course, seeing to it that the network aspects of the ICS were protected from the excesses of hierarchy.

We are all nominalists now, so we would never think to ask, “But which one is it really, a network or a hierarchy?” It is, in a sense, both of these, and it is reasonable to analyze its workings by using two conceptual lenses in turn, as Graham Allison did in his well-known analysis of the Cuban missile crisis (Allison 1971). But this is not the end of the story. It may be possible to synthesize a single unified perspective out of the two. To do so we first need to explain how the generic hierarchy and the generic network are similar and also how, within that similarity, they differ from each other. We can then return to the Moynihan ICS and see how that fits in.

Generic types of production sytems. Networks, hierarchies, and, for good measure, markets can usefully be understood as generic types of a productive arrangement that helps coordinate the actions of multiple agents in such a way that these actions add overall value to what they could achieve separately (Powell 1990). (Just who appropriates the added value – the agents themselves or the broader public – need not be specified.) These arrangements have in common that they work by facilitating exchanges among agents whose potential or actual cooperation is believed to generate complementarities. These exchanges support various functions, approximately captured by the mnemonic POSDCORB of public administration scholars Luther Gulick and Lyndall Urwick: planning, organizing, staffing, directing, coordinating, and reporting. Hierarchies and networks perform such functions in their own ways to some extent,5 though these often overlap (McGuire 2003).

Exchanges, however, mean transaction costs, e.g., time spent in finding suitable partners and negotiating with them the terms of exchange, time spent (and emotions roiled) in monitoring the execution of agreements, suspicions aroused among parties that are aware of but not participant in the exchange. Although these three arrangements all reduce these potential transaction costs, they differ in how they do this. Markets typically use prices and contracts. Hierarchies rely on institutionally set norms about the division and integration of labor, role relationships that express these norms, salaries and perquisites, and information flows reflecting

5 Even competitive markets do these things, but most of the functions are handled by decentralized and profit-seeking firms whose actions are then coordinated by prices and the invisible hand.

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actors’ role-relevant activities. The terms of exchange are embedded in expectations about all of these and tend to remain relatively stable in the short run, and sometimes the longer run too. Networks rely on informal relationships based on a wide array of dimensions from kinship to propinquity to previous experiences of mutuality. The terms of exchange are influenced by reciprocity norms that often accompany these relationships and by forms of leverage that these relationships often provide. They are also heavily influenced by the expectations that parties hold about the short- or long-livedness of these relationships. The motives for exchange in all three types of collaborative arrangements can be quite varied, including the idealistic as well as the self-centered and utilitarian.

Consider too that each of these approaches comes not only with such generic, POSDCORB-related, transactions costs but also comes with costs that it creates as a byproduct of its particular, form-specific, methods of reducing such costs. For instance, communications within hierarchies that follow the chain of command may be slow and/or risky. The division of labor may have been efficient when first put in place but may have become less so as time and technology go on. Overhead units within the organization, committed to standardization and economies of scale for their own sake may fall out of touch with the particular needs of the several and diverse programmatic units. In a network, the ties and shared experiences that build mutuality and trust might also exclude agents who would otherwise be making positive contributions to the network. The relative informality of exchange relationships may be workable for the great majority of them but would add to confusion and mistrust in cases of perceived unreliability and conflict. The spirit of voluntary cooperation may not be enough to motivate contributions from agents that control critical resources.

It frequently happens in such cases that the primary form of productive organization can add as a complement features associated with an alternative form. In a well-functioning hierarchy, say, we might find both infrastructure and process that facilitate network-like communications such as: “Hi there, Scott, Mary here in Community Education, we have a concern about that executive order that came down yesterday concerning hiring that might affect our program. Who might I talk to over your way in Personnel?” The same is true when information needs to go across organizational lines. “Hi there, Scott, Mary here in Urban Renewal. Is it really true that you in Public Works are going to tear up Frontage Street beginning in two weeks?”

By the same token, networks can be bolstered by hierarchical infrastructure and processes, as in the cases of lead agencies and NAO’s (Provan and Kenis, 2008). A comparable conversation in such a case might sound like this: “Scott, Mary here in Urban Renewal. I’m afraid Public Works will have to delay the work on our Frontage Street development for at least a month because we are planning a big project opening ceremony there sometime about three weeks from now.”6

6 Similarly, in markets, the informal reciprocal expectations characteristic of networks can fill in where contracts are necessarily incomplete (due to fast-changing circumstances, say) or may call for adjustment because surprises turn up that make the original terms of exchange seem unfair. “I know we’re awfully late sending you last quarter’s reimbursement, Sam, but the State was abnormally slow in paying us, and therefore we were slow in paying you, but we’ll try to make it up to you in the next round.” Normally these are either the high costs that would be involved if transactions were to proceed according to the conventional template or else the relatively unsatisfactory results that would occur if the transactions were not to occur at all.

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Thus, a well-functioning network may be integrated into a well-functioning market and a well-functioning hierarchy, and vice versa. When we see hybrids, therefore, we usually see generic types that are combined so that one of them (the “secondary” one) may perform a compensatory role for the limitations of the other (the “primary” one).

One infrastructure, two dynamics. Now let us return to the matter of how this integration works in the ICS case described by Moynihan and, possibly, in other similar cases.

Like a network, with its two tiers of infrastructure and process dynamics, a hierarchy can be conceived similarly. (Detailed discussion of these tiers is beyond the scope of this paper.) Thus, the Incident Command System put in place to respond to Newcastle Disease had an infrastructure that potentially could act as either a hierarchy or as a network; which of these types was expressed at any given time was a function of the nature of the task that had to be done. When information needed to be rapidly shared among field units, network processes were activated. But when conflicts had to be resolved, hierarchical processes turning on the pre-planned distribution of authority were activated. Thus, one infrastructure, two process dynamics.7

All depends on context, therefore. The situation is like that of a cell with many genes, many of which are latent most of the time. They are only activated (and others suppressed) when environmental cues, often proteins, signal them. And so it is with coordinative infrastructures. They may most of the time activate, say, hierarchical processes. But when the task calls for a switch to, say, network processes, the infrastructure and its complement of dynamic processes oblige. Just how this switch is effected is beyond the scope of this paper and is an intriguing question for future research.

A semantic problem. If this substantive approach to the analysis of hybrids is followed, it leaves us with a problem of what to call the phenomenon. It is a system that behaves as one type at one time and another at another time, or perhaps even simultaneously. Moreover, the system might evolve over time, so that in its early years it looks a lot like a network but as time goes on and it grows larger it in effect morphs into what is predominantly a hierarchy.

What to call such a system? It may be best to avoid the labels “hierarchy” and “network” (and “market”) most of the time, since using them might prejudice one’s ability to understand the complexities and evolutionary dynamics of the system. The term “collaborative” might do instead, but this might prejudice observations in favor of finding cooperation and harmony but little or no conflict, which distorts understanding as well. Bardach (Bardach 1998) has used “interorganizational collaborative capacity,” or ICC, in order to minimize theoretical prejudice, but this term lacks felicity and has not found favor in the research community. The semantic problem therefore awaits resolution.

Summary and Conclusion

Interagency collaboration on public services delivery (and regulation) is frequently looked at through a conceptual lens loosely, but conventionally, known as “network theory.” Although the network concept has proven useful in this connection, it is limited in important

7 No doubt certain market-like processes were activated as well, since the infrastructure would likely have contained understandings, MOU’s, and contracts pertaining to cost-sharing and reimbursements.

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ways. One way is that it is too simple. Providing networks with an “infrastructure” is one way to do remedy this. After all, if such commonplace phenomena as communications and transportation and TV networks have an “infrastructure,” why not services delivery networks?

Network infrastructure in these physically based networks has both hardware and software components, although in the case of service delivery networks, the infrastructure is mostly social (organizational capacity) and psychological (expectations and perceptions) and economic (resource flows) and therefore predominantly software.

Adding the idea of infrastructure to the prevalent conceptions of networks creates certain theoretical advantages. Three of the more important are:

Providing a theoretical home for such important variables as resource stocks and flows; differentials of power, authority, and influence; and the competence and quality of service providers. Formal, architectural, features of networks, like degree of point-centrality or density of linkages, need supplementing by such substantive variables.

Affording theoretical space for process dynamics, that is, for the activities of network agents that accumulate and feed back into the way the system works or doesn’t work. An example is the way trust, when fed through positive feedback loops, might increase in a network as a function of itself.

Deepening the understanding of how apparent hybrids of multi-agent production processes work, as when networks and hierarchies both operate in a single system. These are often complements, the one compensating for the limitations or defects of the other.

The value of “infrastructure” as a potential addition to the conceptualization of networks cannot be tested empirically and proved either true or false. It is to be judged by its utility in extending perceptions, resolving conundrums, and deepening understandings. The arguments offered here are theoretical but not “only” theoretical.

References

Abramson, Alan, Lehn Benjamin, Timothy Conlan, Sheldon Edner, Paul Posner, Priscilla Regan, Stefan Toepler. 2013. "The Implementation of the Recovery Act: Networks Under Stress." edited by P. Posner: Center on the Public Service, Department of Public and International Affairs, George Mason University.

Agranoff, Robert. 2007. Managing Within Networks: Adding Value to Public Organizations. Washington DC: Georgetown University Press.

Agranoff, Robert and Michael McGuire. 2003. Collaborative Public Management: New Strategies for Local Governments. Washington DC: Georgetown University Press.

Allison, Graham. 1971. Essence of Decision: Explaining the Cuban Missile Crisis. Boston: Little, Brown.Bardach, Eugene. 1998. Getting Agencies to Work Together: The Practice and Theory of Managerial

Craftsmanship. Washington, DC: Brookings Institution.—. 2006. "Policy Dynamics." Pp. 336-366 in The Oxford Handbook of Public Policy, edited by M. Moran,

M. Rein, and R. E. Goodin. Oxford, UK: Oxford University Press.

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Isett, Kimberley R., Ines A. Mergel, Kelly LeRoux, Pamela A. Mischen, R. Karl Rethemeyer. 2011. "Networks in Public Administration Scholarship: Understanding Where We Are and Where We Need to Go." Journal of Public Administration Research and Theory 21:157-173.

Johnston, Erik W., Darrin Hicks, Ning Nan, and Jennifer C. Auer. 2011. "Managing the Inclusion Process in Collaborative Governance." Journal of Public Administration Research and Theory 21:699-721.

McGuire, Michael. 2003. "Is It Really So Strange? A Critical Look at the "Network Management is Different from Hierarchical Management" Perspective." in Public Management Research Association. Washington, D.C.

Moynihan, Donald P. 2008. "Learning under Uncertainty: Networks in Crisis Management." Public Administration Review:350-365.

Provan, Keith G., Amy Fish, and Joerg Sydow. 2007. "Interorganizational Networks at the Network Level: A Review of the Empirical Literature on Whole Networks." Journal of Management 33:479-515.

Provan, Keith G. and H. Brinton Milward. 1995. "A Preliminary Theory of Interorganizational Network Effectiveness: A Comparative Study of Four Community Mental Health Systems." Administrative Science Quarterly 40:1-33.

Radin, Beryl A., Robert Agranoff, Ann O'M. Bowman, C. Gregory Buntz, J. Steven Ott, Barbara S. Romzek, and Robert H. Wilson. 1996. New Governance for Rural America: Creating Intergovernmental Partnerships. Lawrence, KA: University of Kansas.

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