The Structure and Performance of Inter-organizational Relationships. within Public Service Delivery Networks 1

Transcription

1 The Structure and Performance of Inter-organizational Relationships within Public Service Delivery Networks 1 Elizabeth A. Graddy University of Southern California School of Policy, Planning, and Development Prepared for the Conference of the European Group of Public Administration Rotterdam, The Netherlands September 2008 ***DRAFT Please do not cite or quote without author permission*** Abstract/Summary A critical and under-studied dimension of inter-organizational relationships is the structure-performance relationship. This paper considers three aspects of this relationship in the operation of lead-organization public service delivery networks -- how governance functions are structured and managed, the nature of inter-organizational interdependencies in service delivery, and the intensity of interactions. Using data on 138 partnerships operating within 26 networks to provide family preservation services in Los Angeles County, we estimate a random-effects model of this relationship. After controlling for partner and network characteristics, we find that service delivery is positively impacted when roles and responsibilities are contractually defined, and by the extent to which decision making, information, and resources are shared. Network relational benefits also accrue when roles are contractually defined, and to the extent information and resources are shared. More sector diversity within the network is associated with less effective service delivery, but with an increase in subsequent ties among network organizations. Finally, group structures were not found to affect performance for any outcome. 1 I appreciate the helpful assistance of Bin Chen in this research. 21 July

2 Introduction Community based networks to deliver public services have become almost ubiquitous, and this phenomenon has received considerable attention in the public management literature (e.g., Milward and Provan, 1993; Agranoff and McGuire, 1998; Goldsmith and Eggers, 2004). The widely-held expectation is that such networks provide both organizational benefits to their member organizations and improve the effectiveness of service delivery for clients. But, the circumstances under which these benefits occur are still an unresolved question. While many have begun to explore various aspects of network effectiveness, the role of structural characteristics and especially governance 2 structure remain under-studied (Provan and Kenis, 2008). Structure has been used to describe a variety of properties of interorganizational relations. 3 In this paper, structure refers to the nature of the governance arrangements for network control, joint decision making, and the coordination of joint activities, as well as the intensity of interactions across organizations. The questions raised by the structure-performance relationship are multi-faceted. What determines the choice of structural characteristics? What implications do these choices have for how the network functions? Are some dimensions of structure more decisive than others in affecting outcomes? The answers to these questions are important. Some elements of network structure are often mandated, or implicitly created, by funding or oversight agencies. But 2 Governance refers broadly to the institutions and processes by which we make collective decisions, and can be addressed at multiple levels of analysis, ranging from individual organizations to societal policy making. 3 For example, Parkhe (1993b) lists comparative properties (homogeneity, domain consensus, stability, resource distribution, overlap in membership) and relational properties (formalization, intensity, reciprocity, and standardization) 21 July

3 the implications of these decisions may not be fully understood. Entities that selforganize into networks would benefit from a better appreciation of the circumstances under which some structural characteristics are likely to produce more effective outcomes. This paper will analyze three aspects of the structure-performance relationship. First, we consider how the governance function is structured and managed within the formal network structure. In particular, we consider the role of network-specific investments, which have been found to affect alliance success (Provan and Milward, 1995; Dyer and Singh, 1998). Second, we consider the formal arrangements that coordinate the primary cooperative activity service delivery. Finally, we consider the role of operational interactions across organizations, indicators of the intensity of interorganizational relations. Building on the existing literature, we develop the theoretical basis for the expected relationship between these inter-organizational structural characteristics and performance. The relationships we explore occur within a lead organization network structure, one of 3 major types of network arrangements described and analyzed by Provan and Kenis (2008). The choice of overall network structure and its consequences is an important one, but not a question we address here. Many service delivery networks are either explicitly or implicitly structured as lead organization networks by the funding agency. For example, the funding agency may contract with a lead agency to deliver a set of services and then encourage or require the lead organization to form a community-based network of service providers. Our focus is on the structural arrangements within the lead organization network structure. In these networks, the lead organization is the presumed focal point for network governance. Yet, there is variation in how service delivery is organized, how collective decisions are made, how 21 July

4 activities are coordinated, and in how intensive are the interactions. It is the variation in these processes and their consequences that we explore here. The paper proceeds as follows. We first discuss the structure of network interactions and the expected consequences of different arrangements. Then we explore the relationships empirically with data on family preservation services networks in Los Angeles County. We conclude with a discussion of the findings and their implications for our understanding of effective public service delivery networks. The Structure of Inter-organizational Relationships We are concerned here with a specific set of inter-organizational relationships -- cooperative and consequential networks of public, nonprofit and/or business organizations to deliver public services. By consequential we mean networks that produce outputs that are material to service provision and involve some degree of joint decision making (Graddy and Ferris, 2006). All purposeful inter-organizational arrangements face formidable challenges in managing collective action across independent entities. The mutual interdependence of network organizations makes each participant vulnerable to opportunistic behavior by the others. Often organizations within service delivery networks are competitors for the same government contracts. In addition to the distrust generated by competitive, and perhaps appropriation, concerns, this suggests there may be instability in these arrangements, or, alternatively, they may be viewed as episodic. For example, networks are formed in response to specific funding opportunities and then dissolved. 21 July

5 In this environment of uncertainty, inter-organizational structures that address issues of control and coordination are needed for conducting effective collective action. 4 Mechanisms are needed that delineate roles and responsibilities, and that facilitate and coordinate joint activities across the independent entities. When the purpose of the inter-organizational arrangement is public service delivery, these arrangements must also define task coordination in the delivery of services to clients. What then is the nature of structural arrangements that are likely to be associated with inter-organizational effectiveness? First, a clear delineation of roles and responsibilities is critical for effective collective action. Transaction costs decline and uncertainty is reduced when the roles of constituent parties are clearly delineated. In addition, some mechanism must direct resources to collective activities, rather than individual organizational goals. There are multiple mechanisms to achieve these governance goals contracts, written or unwritten inter-organizational agreements, or a network-level governing group. Contracts offer several advantages. They explicitly define roles and responsibilities, and address accountability concerns a priori. Moreover, contract specifications are enforceable thus reducing uncertainty. 5 Therefore, we expect inter-organizational arrangements governed primarily by legally-binding contracts to be more effective than those that rely on unwritten or written, non-binding, agreements. Group governance, however, also offers advantages. A governing committee that represents the collective goals of the network is a more adaptive governance structure as it can respond to ex post changes in the environment. Moreover this 4 Note that control and coordination mechanisms are required even in self-organizing networks built on trust (Park, 1996). 5 Overall transaction costs, however, may not be reduced with contract governance structures, since the contract writing stage is likely to involve substantial negotiation. 21 July

6 governance structure itself generates relation-specific assets, such as trust, respect, and shared values and norms through the communication and planning processes (Cohen and Levinthal, 1990). Moreover, there is some evidence that network-specific investments like group governance may be a key determinant of network success (Dyer and Singh, 1998; Provan and Milward, 1995). Of course, such group governance mechanisms can be ineffective as well. Power struggles and weak enforcement mechanisms can offset the relational advantages cited above. Nevertheless, we hypothesize that inter-organizational arrangements governed primarily by a networklevel governing group will be positively associated with effective inter-organizational performance. Shared activities must also be coordinated. This coordination function could be addressed with written or unwritten agreements, by dedicated network staff who are responsible for coordinating activities across organizations, or by inter-organizational committees that meet regularly to plan and coordinate network activities. The latter would seem to offer the most promise for effective collection action. An active on-going planning process, as noted above, creates opportunities to build relationships, generate trust, and create shared values. In addition to internal coordination, this mechanism provides on-going information management and a forum for addressing accountability issues. As in the case of group governance, a group coordination committee itself represents an investment in the network, which may reinforce commitment to network activities. Therefore, we expect the performance of inter-organizational relationships that rely on group committees to coordinate collective activities to be enhanced. Note that networks may rely on different combinations of contracts, agreements, and group governance processes to address the general roles, responsibilities, and steering function versus the coordination of network activities. 21 July

7 Consider now the mechanisms to manage task interdependence. Many organization theorists have argued that the choice of formal structure will reflect the nature of the interdependencies the organization is trying to manage (Thompson, 1967). In service delivery networks, these interdependences involve the service production process. How do clients flow through the network? Does the network operate largely as a referral structure where organizations make or accept referrals from each other but service clients independently? Alternatively, do organizations share case management such that clients are served by organizations whose treatment staffs have developed one treatment plan and who essentially constitute one intervention team? The latter approach represents highly integrated task activities and is the most difficult to manage and to monitor. Integrated case management requires joint decision making and entails high transactions costs, but it also offers the most promise for effective service delivery. By directly drawing on the expertise of multiple service providers, integrated case management can provide the hoped-for client benefits of network service delivery. Therefore, we expect networks with larger proportions of their client flow handled by an integrated case management structure to be more effective in service delivery. The impact of decision making structures, however, cannot be easily isolated from the interactions they engender. Differing network management structures encourage different levels of inter-organizational activity. Group-oriented governance structures should encourage more interactions, and it is through repeated interactions that partners develop trust and goal congruence (Ring and Van de Ven, 1994). Parkhe (1993a) argues that frequency of interactions increases transparency and thus strengthens inter-organizational relationships. The intensity of interactions is also an important indication of the nature and depth of the ties among network organizations. This complex interrelationship between decision making structures and the intensity of interactions makes it important that we consider both in our model. In particular, we 21 July

8 consider the extent to which participants in the network make joint decisions, and share information and resources. The intensity of these interactions is expected to be positively associated with performance. Although our primary focus is on the impact of governance and service delivery structures that control and coordinate network activities, the performance of any interorganizational arrangement will also depend on other factors, including the characteristics of the network and its constituent organizations. Therefore, we control for organization and network characteristics that are expected to impact effectiveness. The willingness of organizations to work together depends on trust and a shared sense of purpose. Organizations will collaborate with others only when interorganizational arrangements can achieve the objectives of the individual partners. Therefore, successful collaborations require an alignment or compatibility of goals across the partner organizations, and confidence that the members of the collaboration can and will deliver their expected contribution. Perceptions by the lead organization of the trustworthiness of members of the network, and the extent to which they share a common purpose are likely to affect both the governance characteristics of the group and its effectiveness. Thus we control for these characteristics of partner organizations. Characteristics of the network itself can also impact how the governance processes operate and the effectiveness of the group s activities. Many network scholars have argued that size is likely to have an important impact on performance (e.g., Provan and Kenis, 2008). Larger networks have higher coordination and control costs it is simply more difficult to manage more independent entities. The size of the lead organization may also be important. Large organizations, with their greater financial and human resources, are presumably better able to absorb the considerable costs of sustaining inter-organizational relationships. Small organizations, however, may 21 July

9 have greater need for the resources provided by the network, and may thus be more willing to dedicate scarce resources to their development and success. In addition, sector composition is likely to impact performance. Cross-sector networks offer the promise of more effective service delivery by introducing innovation and diversifying resources and expertise, but the coordination function is likely to be complicated by the presence of organizations from more than one sector. Managing across different organizational cultures, contexts, and constraints increases transactions costs (Graddy and Ferris, 2006; Herranz, 2008). Moreover, cross-sector participation that is mandated or associated with funding requirements can exacerbate these challenges (Herranz, 2008). Therefore we control for the sector composition of the network. To summarize, we expect the performance of inter-organizational relationships in public service delivery networks to be impacted by how governance functions are structured and managed, the nature of inter-organizational interdependencies in service delivery, and the intensity of interactions, as well as partner and network characteristics. Contractual and group governance structures are expected to improve performance, as is integrated management of service delivery. More intense interactions in decision making, sharing information, and sharing resources are also expected to improve performance. Effective Performance Inter-organizational performance is a complex and difficult to measure set of outcomes. In the public service delivery context, effectiveness can be assessed on multiple levels and from multiple perspectives (e.g., client, organization, network, 21 July

10 community, citizen). Our focus here is on the performance of inter-organizational relationships within lead-organization service delivery networks. In these interorganizational arrangements, accountability lies with the lead organization. Therefore, we consider effectiveness as reflected in the assessments of that organization. We consider here two dimensions of inter-organizational effectiveness indicators of improved service delivery to clients, and indicators of strengthened ties among network partners. Improved service delivery is the espoused rationale for public policies that encourage the formation of community-based service delivery networks. Thus this is an important dimension of effectiveness. Specifically, we consider improvements in the ability to serve clients and broadened views about service delivery in the lead agency. The second dimension captures potential collective and organizational outcomes. We consider improved working relationships among the organizations in the network, and increased subsequent ties. If the organizations in the networks work well together, they enhance the social capital of the communities served and improve community problem solving capacity. Subsequent joint activities such as new contracts, increased referrals, or joint program development yield direct organizational benefits. Governance structures that clearly define roles and responsibilities and coordinate collective action should enhance both dimensions of effectiveness. Integrated case management should directly improve service delivery, but the process of working together to create integrated treatment plans should also yield relational benefits. Similarly, increased operational interactions are likely to improve service delivery and to strengthen relational ties. 21 July

11 EMPIRICAL SPECIFICATION We empirically explore the structure-performance relationship within social service delivery networks in Los Angeles County. Here, we discuss our data and variable measurement. In the next section, we discuss the estimation methodology and the results. Study Population The population for this study is the social service agencies in the Family Preservation Program (FP) administered by the Los Angeles County Department of Children and Family Services (DCFS). In the context of rising foster care caseloads and increasing foster care costs, both federal and state governments became interested in time-limited, intensive home-based services to families in crisis. The aim of the interventions is to improve family functioning when children are at imminent risk of placement in foster care and to prevent this placement. The FP program in Los Angeles County is the largest of its kind in the United States, serving a county of 10 million people. Children and family-related social services are both substantial and diverse. The Family Preservation Program is based on a leadorganization network model. DCFS created 38 Community Family Preservation Networks (CFPN) in defined geographic areas throughout the county. For each area, DCFS contracts family preservation services to a lead agency through a Request for Proposal (RFP) process. The lead agency receiving the contract, which can be either a 21 July

12 public or a nonprofit organization, 6 is asked to form a community-based network of service providers to deliver a broad range of services to children and families. The data used in this study are from a comprehensive survey by Elizabeth Graddy and Bin Chen 7 of CFPN lead agencies and their network partners. In the Family Preservation Program, each service contract covers one CFPN in a specific geographic area. The Department allows an organization to bid on more than one service contract. As a result, DCFS granted multiple contracts to five lead agencies. Three of these agencies chose to manage their multiple contracts as one CFPN. Therefore, 35 lead agencies were slated for study. A 15-page survey, with sections on the lead agency, the network structure, the partner organizations, and on network management, was mailed to the executive director or the family preservation program manager in each of the lead agencies/networks. The response rate was 77% -- with 27 of the 35 lead agencies completing the survey. All the lead agencies except one are nonprofit social service providers (the exception is a public agency). The units of analysis for this study are the dyadic relationships operating within each network between a lead agency and each of its partners. One lead agency did not form a network and was dropped from the study. The remaining 26 lead agencies formed 138 partnerships within 26 networks to deliver up to eleven different family preservation services. The networks averaged 5 organizations, and ranged from 2 to 10. The component organizations included 14 public organizations (10%), 112 (81%) nonprofit organizations, and 12 (9%) for-profit organizations. 6 Only public entities or nonprofit social service organizations that are tax exempt under 501(c)(3) of the Internal Revenue Code are qualified to bid on the RFPs. 7 Details on the collection process are found in Graddy and Chen (2006). The survey instrument is available upon request. 21 July

13 The 26 networks had different sectoral compositions. All included nonprofit organizations, and twelve consisted solely of non-profit organizations. The remaining 14 networks were cross-sectoral in composition. Eight included nonprofit and public organizations; 3 included nonprofit and business partners, and 3 included organizations from all 3 sectors. This rich mix of sectoral compositions will allow us to compare crosssectoral and nonprofit networks. Variable Measurement The variables are measured from lead agency responses to the survey questions, and their descriptive statistics are summarized in Table 1. Performance Measures. The dependent variables the measures of performance -- were measured with the following 4 questions: Effective service delivery Broadened views Working relationships Subsequent ties/activities Overall, how effective is this collaboration in achieving the expected goals of serving Family Preservation Program clients? Overall, to what extent has your organization s view on how to better serve Family Preservation Program clients been broadened as a result of listening to this partner organization s views? Overall, how would you rate the quality of working relationships that have developed between your organization and this partner organization as a result of this collaboration? Overall, to what extent has your organization increased its interactions with this partner organization (e.g., increased referrals, service contracts, joint program development) as a result of this collaboration? The first two questions are measures of improvements in service delivery; the second two are measures of improvements in network relationships. Based on a 7-point Likert scale (with 1 representing Not At All and 7 representing Very Effective ), respondents were asked to select the number that best indicates their assessment of each outcome 21 July

14 with each network partner. As Table 1 reveals, the partnerships on average were viewed as effective in achieving these outcomes. Effective service delivery has the highest overall mean (and the lowest variance in responses) indicating this was the most successful outcome on average. The second measure of service delivery outcomes, Broadened views, was on average the least successful outcome (with the largest variance), but even this outcome was rated highly. Measures of the independent variables were formed by the responses of the lead organization to questions about the structure and operations of the network, and about the characteristics of partner organizations. Governance structures. Governance structures were captured with 3 variables. Contract governance is a dummy variable that assumes a value of 1 if a legally binding contract is the primary method that governs the relationship between the lead organization and a network partner. Network governance is a dummy variable that assumes a value of 1 if a governing group representing the network is the primary method that governs the relationships between the lead organization and its network partners. These two variables represent alternative governance structures. Both are expected to have a positive impact on performance compared to the omitted categories, which include other governance structures like MOUs or informal interagency agreements. The third governance variable, Inter-organizational coordination is the percentage of time the lead organization relies on a standing or ad hoc interorganizational committee to plan and coordinate joint activities. This coordination function is measured separately from the two governance control variables described above, and is expected to have a positive impact on performance. The summary statistics in Table 1 reveal that in our sample, 46% of the dyadic relationships between lead organizations and their network partners use a formal written 21 July

15 contract as their primary mechanism for role and responsibility definition, while 9% use a network committee for this purpose. An inter-organizational committee is used as a coordinating mechanism 16% of the time. While formal contractual arrangements are the most popular governance mechanism in our sample, there is sufficient use of group mechanisms to allow us to explore their role in performance. Service Delivery Characteristics. The nature of the organizational interdependences in service delivery is captured with two variables. Integrated case management, a network variable, is the percentage of clients flowing through the network served by one treatment plan developed by staff across organizations, and who constitute one intervention team. Joint contracting is a dummy variable denoted whether or not the lead organization jointly contracts the provision of one or more family preservation services with a particular network partner. Joint contracting requires more interaction and joint decision making than contracting with a partner for complete provision of a service. Integrated case management averaged 22% of the client flow in our sample networks, and 59% of all service delivery partnerships involve some joint contracting. Both service delivery variables are expected to have a positive impact on performance. Operational Interactions. The frequency and nature of the interactions among organizations in the networks is captured with 3 variables. Joint decision making measures the extent to which the lead organization brainstorms with a partner organization to develop solutions to problems faced in the network. Shared Information captures the extent to which the lead organization shares information with the partner that will strengthen their operations and programs. Shared resources captures the extent to which the two organizations combine and use each other s resources to mutual benefit. These variables are measured on a 7-point Likert scale (with 1 representing Not At All and 7 representing To a great extent ). The highest interactions in our 21 July

16 sample are in joint decision making and the lowest are in shared resources, but all 3 variables have relatively high means suggesting considerable inter-organizational interactions on all 3 dimensions. Increased interactions of all 3 types are expected to be positively associated with performance. Partner Controls. We include 3 controls for partner characteristics. Two measure inter-organization trust. Perceived trustworthiness measured on a 7-point Likert scale indicates the extent to which the lead organization can count on this partner to meet its obligations to the network. Same sector is a dummy variable denoting whether or not the lead organization and each network partner is in the same sector (public, nonprofit, business). This variable is included because many organizations are distrustful of the motives or capabilities of organizations in other sectors. The third variable, Congruent network goals, measures the extent to which the lead organization and each partner organization agree about the goals of the network. All 3 partner control variables are expected to have a positive impact on performance. Network Controls. Finally, we include 4 controls for network characteristics two measures of size and two of composition. We measure the size of the lead organization as Lead agency total revenue. Large lead organizations may have better managerial and financial capacity to handle the challenges of network management, but small ones may have greater need for the resources provided by the network. Since the two size effects could offset each other, obscuring the importance of organizational size, we capture both with a quadratic specification of Total revenue. Network size, the number of organizations in the network, is our measure of network size. The increased transactions costs associated with larger networks are expected to have a negative impact on performance Our model includes two measures of sector diversity. Percentage Cross- Sectoral is the percentage of the organizations in the network that are in a different 21 July

17 sector from the lead organization. Percentage Business captures the role of business firms in the network. The increased transactions costs associated with sector-diverse networks are expected to have a negative influence on performance. We turn now to the estimation and analysis of our model. Table 1. Descriptive Statistics (138 partnerships in 26 networks) N Mean SD Min Max PERFORMANCE MEASURES Effective service delivery Broadened views Working relationships Subsequent ties/activities INDEPENDENT VARIABLES Governance Structures Contract governance Network governance Inter-organizational coordination Service Delivery Characteristics Integrated case management Joint contracting Operational Interactions Joint decision making Shared resources Shared information Partner Controls Perceived trustworthiness Same sector Congruent network goals Network Controls Lead agency total revenue 26 $7.957M $7.847M $1.004M $27.490M Network size Percentage cross-sectoral Percentage business ESTIMATION and ANALYSIS The two-level structure of these data makes it inappropriate to apply a traditional linear regression analysis. The 138 dyadic relationships in our sample operate within July

18 networks. Relationships in the same network are presumably more similar than those in different networks. Thus, it is not reasonable to assume independence across pairs within a network. Dyadic relationships operating in the same network are likely to share values on several variables. Some of these variables will not be observed, and their presence in the error term would violate estimation assumptions in the classical multivariate regression model. Put in terms of variance component models, the error terms have a group (network) component and an individual (dyadic) component. Group components are assumed to be independent across networks, but correlated within networks. Some groups might be more homogeneous than others, so the variances of the group components can differ (Bryk and Raudenbush, 1992). Therefore, we estimate a random effects model. This model assumes that all dyadic relationships in the same network share a common random error in addition to the traditional random error that is homoskedastic and independent at the individual level. Because of the common error at the network level, pairs in the same network are correlated, but pairs from different networks are assumed to be independent (Bryk and Raudenbush, 1992; Singer, 1998). We estimate the model in two versions, with and without the percentage of business organizations in the network, in order to isolate the effect of sector differences generally from the unique costs associated with incorporating business organization in networks led by nonprofit or public organizations. The results are presented in Tables 2 and 3, reporting respectively service delivery outcomes and network relationship outcomes. Statistical significance is assessed based on a two-tailed test. The results reveal that the model is a good fit for the data for both types of outcomes. All 4 estimations are significant in their overall fit, and all 5 sets of variables impact one or more outcome measures. We now consider the service delivery and network-relationship outcomes separately. 21 July

19 Service Delivery Outcomes Table 2 presents the results of the estimations for the two service delivery outcomes, and we find evidence of impact of governance structures and operational interactions on these outcomes. Service delivery effectiveness for clients was found to increase when inter-organizational relationships had a contractually defined governance arrangement, and with more joint decision making and information sharing. These effects demonstrate the expected link between structure and performance. The impacts of the control variables are also interesting. As expected more trustworthy partners were found to yield more effective outcomes, but not partners most like the lead organization. Partners from the same sector and those with more congruent network goals did not produce better outcomes perhaps there were fewer potential gains from working together. As expected, networks with more sector diversity had worse outcomes, presumably reflecting their greater coordination costs. The structural variables have less impact on the second service delivery outcome, broadened views by the lead organization about service delivery. Increasing resource and information sharing broadens the lead organization s views on service delivery, as expected, but neither governance structures nor service delivery characteristics had an impact. Service delivery views are impacted when the partner is perceived as trustworthy, and, in contrast to client outcomes, when the partners were of the same sector. This difference in the role of sector is interesting. Perhaps network partners of different sectors bring unique expertise that is perceived to be valuable to clients, but organizational perspectives on service delivery are not changed. For the latter to occur, the greater trust that comes from working with organizations with a shared mission may be needed. Finally, mid-sized lead organizations were the most likely to experience this benefit to their service delivery views. 21 July

20 Service delivery characteristics had no impact on either measure of service delivery effectiveness, and sector diversity from business organizations did not have a unique impact. Table 2. Random Effects Estimation on Service Delivery Outcomes (n=138) Effective service delivery Broadened views Governance Structures Contract governance.25***.25*** Network governance Inter-organizational coordination Service Delivery Characteristics Integrated case management Joint contracting Operational Interactions Joint decision making.23***.23*** Shared resources ***.14*** Shared information.11***.10**.41***.42*** Partner Controls Perceived trustworthiness.401***.40***.26***.26*** Same sector -.23** -.21*.46***.45*** Congruent network goals -.11*** -.11*** Network Controls Lead agency total revenue 6.91e e e-07** 2.08e-.07** Lead agency total revenue squared 1.66e e e-15** -8.01e-15** Network size Percentage cross-sectoral ** *** Percentage business Wald (n IVs) 368(15) 372(16) 195(15) 194(16) Prob > Wald Chi-squared R-squared within R-squared between R-squared overall ***=.01, **=.05,*= July

21 Network relationship outcomes Table 3 presents the estimation results for the two network relationship outcomes improved working relationships and increased interactions. All 3 sets of structural variables had impacts on these outcomes. The quality of working relationships was found to improve when network governance was contractually defined. As expected, the clear role definitions that formal contractual arrangements provide yield better working relationships. Also as expected, when resources and information were more frequently shared, working relationships improved. Joint contracting, however, harmed working relationships. Evidently, the challenges inherent in joint production are not easily resolved. Not surprisingly, working relationships were enhanced with trustworthiness. The effect of network size is however surprising. The larger the network, the better is the improvement in working relationships. The second network relationship outcome, an increase in subsequent ties/activities, is impacted by only one structure variable more sharing of information. This suggests that structure variables are not the key determinant of future interactions among the partners. The effect of sector composition is however very interesting. As expected greater involvement of business organizations decreased this performance measure, but it was enhanced by the extent of cross sector partners. This combination suggests that diverse partners are a plus for expanding future ties, but not if there are too many business partners. Finally, mid-sized lead organizations are the most likely to see increases in future joint activities. This organization size finding, coupled with the same effect for the broadened views suggests that it is mid-sized firms that have the right combination of capacity and need to benefit from these inter-organizational relationships. 21 July

22 Table 3. Random Effects Estimation on Network-Relationship Outcomes (n=138) Working Relationships Subsequent Ties/ Activities Governance Structures Contract governance.16*.16* Network governance Inter-organizational coordination Service Delivery Characteristics Integrated case management Joint contracting -.18** -.18** Operational Interactions Joint decision making Shared resources.061**.061** Shared information.17***.17***.47***.49*** Partner Controls Perceived trustworthiness.57***.57***.55***.57*** Same sector Congruent network goals Network Controls Lead agency total revenue 2.40e e e-07* 1.74e-07* Lead agency total revenue squared -2.06e e e-15* -7.13e-15* Network size.063*.064* Percentage cross-sectoral *.039*** Percentage business ** Wald (n IVs) 368(15) 373(16) 97(15) 104(16) Prob > Wald Chi-squared R-squared within R-squared between R-squared overall ***=.01, **=.05,*= July

23 CONCLUSION This paper considered the role of several aspects of the structure of interorganizational relationships on their performance. Based on a sample of lead organization social service delivery networks, we find evidence that the use of formal contracts to define roles and responsibilities is positively associated with more effective service delivery and with the improved quality of working relationships across the organizations. We also find evidence that more intensive operational interactions in decision making, sharing resources, and sharing information are associated with more effective service delivery and network-relationship outcomes. The results with respect to group processes, however, are disappointing. Neither the use of a network governance group to steer the network relationships, nor the use of an inter-organizational coordination committee to coordinate group activities, nor the integration of service delivery through an integrated treatment team were associated with any of the 4 performance measures. This study, therefore, finds no support for the effectiveness of these decision-making processes in this service delivery context. Taken together these results provide support for the pervasive use of formal written contracts in defining the nature of inter-organizational arrangements to deliver publicly-funded services. Even controlling for trust characteristics that have been hypothesized to mitigate the need for written contracts (e.g., Lyons and Mehta, 1997), we find these mechanism to be effective. Alternative group-based processes, while offering the promise of greater flexibility and a mechanism for the development of shared values and norms among group members, do not seem, for this sample at least, to deliver on their promise. On the other hand, the expectations that more interactions will increase transparency, and strengthen relationships (e.g., Parkhe, 1993a) are supported by these findings. 21 July

24 The findings also raise intriguing questions about the role of sector in interorganizational relationships. As predicted by Graddy and Ferris (2006) and Herranz (2008), we find evidence that the greater transactions costs associated with crosssectoral networks can decrease their effectiveness. But we also find that cross-sectoral networks have positive impacts on some relational outcomes. We similarly find mixed results when dyadic partnerships cross sectors. When the lead organization and its partner are in the same sector, service delivery is found to be less effective, but the lead organization is more likely to broaden its views on service delivery. Clearly the role of sector in inter-organizational relationships is complex, and requires much more investigation. This study of course has limitations. The focus on effectiveness as perceived by lead organizations, while appropriate and informative for the study of lead-organization networks, provides only part of the effectiveness story. Hopefully future studies will be able to include and compare the assessments of all members of service delivery networks. In addition, our sample focuses on a specific type of services family preservation services (though they include 11 different services) and one location, Los Angeles County. Both these foci can limit the generalizability of this work. Nevertheless, this study makes an important contribution to our understanding of the structure-performance relationship and more generally to the determinants of effectiveness in lead-organization networks. 21 July

25 REFERENCES Agranoff, Robert and Michael McGuire (1998). Multinetwork Management: Collaboration and the Hollow State in Local Economic Policy. Journal of Public Administration Research and Theory 8:1, Cohen, Wesley M. and Daniel A. Levinthal (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly 35: Dyer, H. Jeffrey and Harbir Singh (1998). The Relational View: Cooperative Strategy and Sources of Interorganizational Competitive Advantage. Academy of Management Review 23:4, Goldsmith, Stephen and Williams Eggers (2004). Governing by Network: The New Shape of the Public Sector. Washington, DC: Brookings. Graddy, Elizabeth A. and Bin Chen (2006). Influences on the Size and Scope of Networks for Social Service Delivery. Journal of Public Administration Research and Theory 16:4, Graddy, Elizabeth A. and James Ferris (2006). Public-Private Alliances Why, When, and to What End? In N. Verma (Ed.), Institutions and Planning, Current Research in Urban and Regional Studies Series. Chapter 9. Oxford, U.K.:Elsevier, pp Herranz, Joaquin Jr. (2008). The Multisectoral Trilemma of Network Management. Journal of Public Administration Research and Theory 18:1, Lyons, Bruce and Judith Mehta (1997). Contracts, opportunism, and trust: self-interest and social orientation. Cambridge Journal of Economics 21, Milward, H. Brinton and Keith G. Provan (1993). The Hollow State: Private Provision of Public Services. In H. Ingram and S.R. Smith, eds, Public Policy for Democracy. Washington, DC: Brookings, Park, Seung Ho (1996). Managing an Interorganizational Network: A Framework of the Institutional Mechanism for Network Control. Organization Studies 17, 795. Parkhe, Arvind (1993a). Strategic Alliance Structuring: A Game Theoretic and Transaction Cost Examination of Interfirm Cooperation. Academy of Management Journal 36:4, (1993b). Partner Nationality and the Structure-Performance Relationship in Strategic Alliances. Organization Science 4:2, 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, July

26 Provan, Keith G. and Patrick Kenis (2008). Modes of Network Governance: Structure, Management, and Effectiveness. Journal of Public Administration Research and Theory 18:2, Ring, Peter Smith and Andrew H. van de Ven (1994). Developmental Processes of Cooperative Interorganizational Relationships. The Academy of Management Review 19:1, Thompson, James D. (1967). Organizations in Action: Social Science Bases of Administrative Theory. New York: McGraw-Hill. 21 July