Strategy Formulation Process and Interorganizational Collaboration

1 Strategy Formulation Process and Interorganizational Collaboration Minji Hong PhD Candidate Department of Public Management and Policy Andrew Young School of Policy Studies [email protected] and Benedict S. Jimenez Professor of Public Budgeting and Finance Department of Public Management and Policy Andrew Young School of Policy Studies [email protected] Abstract: The study examines how two major strategy formulation approaches – rational planning and logical incrementalism – influence the decision of city governments to collaborate with for-profit, non-profit, and other public organizations. Collaboration with governmental and non-governmental actors gives rise to varying levels of risks, and the choice of which type of organization to collaborate with is influenced by how distinct strategy formulation processes can help governments address those risks. Using data from a national survey of cities, we find that the strategy-making process can spur or hinder collaborative undertakings. The results of the regression analysis indicate that rational planning catalyzes cross-sectoral collaboration but is not associated with government-to-government collaboration. Logical incrementalism, in contrast, has a consistently negative relationship with collaboration regardless of sector. The findings indicate that collaboration can be limited by city governments’ capacity to undertake rational planning and their propensity to engage in incrementalist decision-making. Keywords: inter-organizational collaboration, rational planning, logical incrementalism Acknowledgment: Some data used for this study is based upon work supported by the National Science Foundation under Grant Number 2114770. 2 INTRODUCTION Governments have embraced diverse types of collaborative arrangements involving nonprofit, for-profit, and other public sector organizations to implement public policy and deliver public services (Agranoff & McGuire, 2003; Ansell & Gash, 2008; Bryson et al., 2006). This trend is driven by the realization that effectively and efficiently addressing complex policy and service delivery issues is often beyond the capacity of a single public organization (Alter & Hage, 1993; Huang & Provan, 2007; Kettl, 2015; Mandell & Keast, 2014; McGuire, 2006; Weber & Kahneman, 2008). There is no overarching term that has been used in the literature to describe the phenomenon of collaboration among organizations from different sectors. 1 Several terms (and definitions) have been used in the literature including networks (O’Toole, 1997), 2 public service organizational networks (Provan & Milward, 2001), 3 collaborative networks (Mandell & Keast 2014), 4 institutional collective action (Feiock 2007, 2009), 5 collaborative governance (Ansell & 1 Not a few scholars have lamented this condition. See, for example, Thomson and Perry (2006) or Emerson and Nabatchi (2015). 2 For O’Toole (1997, p. 45) networks are “structures of interdependence involving multiple organizations or parts thereof, where one unit is not merely the formal subordinate of the others in some larger hierarchical arrangement”. The term “network” is not the same as “networking.” Networking, according to Meier and O’Toole (2010, p. 1027), refers to “contacts with key actors in the environment for the purpose of identifying and implementing mutually acceptable, even attractive, jointly determined decisions.” In other words, networking is the “external behavior of public managers, specifically their efforts to establish ties with actors from organizations, units or programs located outside of their own organizations” (Jimenez, 2017, p. 451-52) 3 Provan and Milward (2001, p. 417) use the term to refer to “a collection of programs and services that span a broad range of cooperating but legally autonomous organizations.” 4 Mandell and Keast (2014, p. 256) write that “Collaborative networks are formed to deal with very complex problems that no one organization or group is able to deal with on their own. In addition, they are formed because the participants recognize that the way they currently operate is no longer working and new and innovative solutions are needed to address the problem(s) involved.” 5 Feiock’s (2007, p. 48) institutional collective action (or ICA) includes “Bilateral contracting and multilateral collective action are mechanisms by which two or more governments act collectively to capture the gains from providing or producing services across a larger area.” Although his initial conceptualization of ICA focuses on interlocal cooperation, Feiock (2009, p. 362) subsequently included private and non-profit organizations, writing “Although less recognized in the literature, non-profit and for-profit producers may also seek to manage and coordinate interlocal public service provision.” 3 Gash, 2008), 6 collaborative governance regime (Emerson et al., 2012), 7 and cross-sector collaboration (Bryson et al., 2006), 8 among others. In this study, we use the straightforward and easily understandable term interorganizational collaboration to describe the phenomenon of organizations working with other organizations from within the same sector (e.g., within the public sector) or across sectors (e.g., public, private, or non-profit sectors) to achieve individual and shared goals. This broader definition builds on a commonality among the different conceptualizations of collaboration (and similar phenomena) in the extensive literature in this area emphasizing joint action across organizations and jurisdictions that is not limited to the governmental sphere (Agranoff & McGuire, 2003; Ansell & Gash 2008; Bryson et al. 2006; Emerson & Nabatchi, 2015; Feiock 2009, 2013; Mandell and Keast 2014; Provan and Milward 2001). Our focus in this study is on local governments and we consider them to be the primary actors (Agranoff & McGuire, 2003; Feiock 2009, 2013), but as previously emphasized, we also recognize that collaborative arrangements often include organizations from other sectors (Bryson et al., 2006; Provan & Milward, 2001). Local governments and organizations from other sectors voluntarily participate in these arrangements because they perceive that their benefits will exceed their costs (Feiock 2009, 2013; Steinacker et al., 2010). We also assume that in collaborating, organizations retain their identity and autonomy – remaining distinct and separate legal and organizational entities (Agranoff & McGuire, 2003; Mandel 1999; Provan and Milward 2001). 6 For Ansell and Gash (2008, p. 544) the term refers to a “governing arrangement where one or more public agencies directly engage non-state stakeholders in a collective decision-making process that is formal, consensus-oriented, and deliberative and that aims to make or implement public policy or manage public programs or assets.” 7 Or “the particular mode of, or system for, public decision making in which cross-boundary collaboration represents the prevailing pattern of behavior and activity,” according to Emerson et al. (2012, p. 6) 8 Defined by Bryson, Crosby, and Stone (2006, p. 44) as “the linking or sharing of information, resources, activities, and capabilities by organizations in two or more sectors to achieve jointly an outcome that could not be achieved by organizations in one sector separately.” 4 These collaborative arrangements address administrative, shared service, fiscal, and policy problems among the participants using different mechanisms of coordinating their actions to achieve desired individual and shared outcomes (Feiock 2009, 2013; Mandel 1999; Mandell & Steelman, 2003). These mechanisms go beyond informal coordination and include joint action with legal underpinnings (Shrestha & Feiock, 2009), often manifested in the form of contracting or shared services, tax-base sharing, grant partnerships, and economic development joint ventures (see Brown & Potoski 2003; Dixon & Elston, 2019; Feiock et al., 2009; O’regan & Oster, 2000 among others). 9 These legally framed arrangements involve interdependent and collaborative problem-solving and implementation (Agranoff & McGuire, 2003; Feiock 2009, 2013; Provan and Milward 2001). Extant research has studied different factors explaining why organizations collaborate (see, among others, Ansell & Gash, 2008; Bryson et al., 2006; Emerson et al., 2012; Feiock 2007, 2009) but few, if any, published studies have examined the role of the strategy formulation process within organizations, specifically in the public sector. Collaboration is a strategic choice that public organizations make to improve performance (Agranoff & McGuire, 2001, 2003; Alter & Hage, 1993; Huang & Provan, 2007; McGuire, 2006; Romzek et al., 2012). In this study, we propose that the strategy formulation process, which focuses on a government’s attempts to better deliver public services (Pollitt & Bouchaert, 2017), influences decisions to collaborate with other organizations from across sectors – public, private, and non-profit. Strategy formulation, as defined by Boyne and Walker (2004), encompasses the processes through which strategies are developed within organizations. We explore the relationship between two major 9 Others offer a more expansive range of collaborative arrangements. Mandell (1999, p, 5), for example, conceives of collaboration as a continuum that ranges from “loose linkages” to “more lasting structural arrangements.” Within this collaboration range are “joint powers agreements, contracting out, or public-private partnerships…” (p. 5-6). 5 strategy formulation models – specifically formal strategic planning and logical incrementalism – and local governments’ decision to engage in interorganizational collaboration. Formal strategic planning is characterized by a structured and systematic approach that incorporates detailed environmental analysis, goal setting, and strategy evaluation (Bryson, 2018). In contrast, logical incrementalism involves a more adaptable approach where strategies gradually evolve through a series of incremental decisions and experimentation, which some argue allows for flexibility and responsiveness to changing circumstances (Quinn, 1980). Why should different models of strategy formulation influence a local government’s decision to collaborate with other governments and nongovernmental actors? Briefly, our theory builds on the concepts of organizational form and proximity and the appropriateness of the strategy formulation process for reducing collaboration risks. Interorganizational collaboration creates various types of risks for the participants (Feiock 2013). We argue that the degree of risks differs depending on the homogeneity (sameness) or heterogeneity (or diversity) of participants’ organizational forms (Romanelli, 1991). Form matters because it affects organizational proximity or interactions built on shared rules, routines, and belief systems that promote mutual understanding among actors (Torre and Rallet 2005; Knoben and Oerlemans, 2006). Homogenous forms share similar institutional and organizational contexts and goals that facilitate organizational proximity and shared understanding, reducing collaboration risks. Thus, a government collaborating with a similar organizational form (such as other governments) faces fewer risks than a government collaborating with a distinct organizational form (nongovernmental entities such as for-profit and non-profit organizations). We argue that the strategy formulation process matters because it can help minimize or exacerbate risks of collaborations among homogenous or heterogeneous organizational forms. 6 This study uses the results of a national survey that targeted midsized and large city governments in the U.S. to test the theory. The results of the regression analyses show that formal strategic planning is positively associated with nongovernmental collaboration (city governments collaborating with nongovernmental entities) but has no significant impact on governmental collaboration (city governments collaborating with other governments). In contrast, logical incrementalism is negatively associated with collaborating with both governmental and nongovernmental actors. LITERATURE REVIEW Interorganizational Collaboration Extant research has examined the rationale for governments’ increasing reliance on interorganizational collaboration to implement public policy and deliver services. One perspective suggests that societal problems, such as poverty, health care, and the environment, have become more intertwined than ever before, necessitating a more inclusive and adaptable form of organizations to address complex policy problems (Kettl, 2006; McGuire, 2006). Partnering with organizations with essential resources can mitigate the complexity and uncertainty of emerging issues (Lee et al., 2022). Other determinants of interorganizational collaboration include high levels of interdependence between organizations (Logsdon, 1991), the inclination to share risks among collaborators (Alter & Hage, 1993; Thomson & Perry, 2006), and prior experience with collaboration (Radin et al., 1996). As collaborative activities involve distinct actors, the characteristics of the actors also matter in whether collaboration takes root (Amirkhanyan, 2009). For example, public organizations focus on policy outcomes, while private for-profit firms aim for the financial bottom line (Dias & Maynard-Moody, 2007). 7 A different branch of the literature examines the determinants of successful collaboration. A key driver is the willingness of organizations to engage in mutual monitoring and ensure adherence to previously agreed-upon rules (Thomson & Perry, 2006). As collaborations involve joint decision-making between distinct organizations, monitoring and overseeing collaborative progress and assessing each other’s performance based on pre-determined goals are also important. Additional factors that contribute to successful collaboration include trust, mutual respect, and a shared understanding of goals (see Agranoff & McGuire, 2001, 2003; Ansell & Gash, 2008; Emerson et al., 2012; Romzek et al., 2012; Weber & Khademian, 2008). While previous studies have examined collaboration from various angles, we have faced limited success in finding published research on how a public organization’s strategy-making process can influence its decision to engage in interorganizational collaboration. Boyne et al. (2004, p. 333) suggest that rational planning is crucial to integrating and coordinating collaborative activities but do not empirically test the argument. Strategy refers to “a pattern of action through which [organizations] propose to achieve desired goals, modify current circumstances, and/or realize latent opportunities” (Rubin 1988, p. 88). Strategy is a means to sustain or improve organizational performance in an ever-changing environment (Amburgey et al.,1990). Collaboration is a deliberate strategy employed by governments to improve their capacity for policy implementation and service delivery (Agranoff & McGuire, 2001, 2003; Alter & Hage, 1993; Huang & Provan, 2007; McGuire, 2006; Romzek et al., 2012). It is thus essential to study whether the distinct models of strategy-making influence city governments’ decisions to collaborate with other governments as well as organizations from other sectors. Strategy Formulation Process 8 The strategy formulation process refers to how strategy develops within organizations (Boyne & Walker, 2004). Two predominant strategy formulation models have been identified in the literature: rational planning and logical incrementalism (Boyne & Gould-Williams, 2003; Elbanna, 2006). Rational planning is frequently equated with strategic planning. Rational planning is the “analytical, formal and logical processes through which organizations scan the internal and external environment and develop policy options which differ from the status quo” (Andrews et al., 2009, p. 3). 10 This definition is highly similar to that of formal strategic planning. Bryson and George (2020) describe strategic planning as a “deliberate approach to strategy formulation and typically includes such elements as analyzing the mandate, defining a mission and values, analyzing the internal and external environment” (p. 2). For this study, we adopt the term “formal strategic planning.” 11 Strategic planning presumes that a predictable future allows an organization to compare available strategy options and make choices that best align with its goals (Davies & Coates, 2005). The predictability of future events and prospective opportunities, and the ability to undertake a comprehensive overview of strategy options, can spur organizations to engage in activities that maximize performance (Andrews et al., 2009; Boyne, 2001). Thus, formal strategic planning requires a comprehensive and systematic approach to developing strategies, stressing detailed analysis, precise goal setting, and careful evaluation of the organization’s internal and external environments (Bryson et al., 2004). Studies examining whether formal strategic planning leads to improved organizational performance have produced mixed results. On the one hand, some research finds that strategic 10 Some descriptions of the rational planning model follow closely that of Simon’s (1957) portrayal of the rational decision-making model in classical economics (see, for example, Methe, Wilson and Perry 2000). Others emphasize that rational planning transpires under Simon’s (1957) bounded rationality (see Andrews et al. 2009a; Elbanna 2006). Whether completely rational or only limitedly so, rational planning is described as formal, analytical and logical (Boyne 2001; Andrews et al 2009a). 11 Based on the suggestion of an anonymous reviewer. 9 planning processes (such as the formulation of goals and the internal and external analyses) enhance organizational performance (Boyne & Gould-Williams, 2003; George et al., 2019), increase effectiveness and productivity in urban public transit system in the U.S. (Poister et al., 2013), offer a potential solution to financial challenges faced by municipalities (Zafra-Gomez et al., 2014), and allow local governments to successfully target the retrenchment of expenditures (Jimenez, 2014). In contrast, critics of strategic planning point to organizations’ turbulent and unpredictable future (Davies & Ellison, 1998) as well as technical problems, such as data accessibility and interpretation issues due to a lack of resources and expertise (Boyne et al., 2004). Quinn (1980) adds that the strategies of successful organizations are not produced through formal strategic planning but are developed through logical incrementalism. While formal strategic planning focuses on a systematic and technical approach to decision making aimed at a predetermined outcome, logical incrementalism recognizes the potential for strategies to evolve and transform in response to emerging information. Logical incrementalism involves a political approach where “actors within organizations may have conflicting views on the most appropriate ways to meet organizational goals” (Andrews et al., 2009, p. 4). This perspective emphasizes ongoing adjustment and experimentation, allowing decision makers to move carefully from broad ideas to more specific commitments (Quinn, 1980). It enables organizations to benefit from the best available options by allowing strategies to emerge slowly, facilitating incremental decision patterns and experimental adjustment of proposals, and avoiding premature commitment to specific policy options. Some argue that logical incrementalism can help employees accept change more readily with the gradual transition (Johnson, 1988). 16 uncertainties associated with nongovernmental collaboration. Because formal strategic planning seeks to control and analyze the actions of different organizations (Langley, 1988; Quinn, 1980), it can help secure a sense of direction and oversight of collaborative activities involving organizations from different sectors. While the conventional problems of rational planning are primarily technical (Boyne et al., 2004), the weakness of logical incrementalism is that it inherently involves political conflicts. Elbanna (2006, p. 7) argues that actors “may share some objectives, such as the welfare of the organization, but they have conflicting preferences and interests which arise from different expectations of the future, different positions inside the organization and clashes.” Such conflict encourages the construction and maintenance of coalitions to shape policy content and goals (Honey, 1979). Different groups can advocate for the same policy without agreeing on the final objectives (Lindblom, 1959), creating room for additional uncertainty and undisciplined changes in the already risky collaboration among heterogeneous organizational forms. Logical incrementalism, therefore, can aggravate the prevailing differences among heterogeneous organizational forms, making governments less likely to collaborate with nongovernmental actors. We expect that: H3: Formal strategic planning is positively associated with city governments collaborating with nongovernmental organizations. H4: Logical incrementalism is negatively associated with city governments collaborating with nongovernmental organizations. RESEARCH METHODOLOGY Data Collection 17 To test our hypotheses, we use data from the Municipal Fiscal Retrenchment and Recovery (MFRR) survey (directed by one of the authors), which focused on midsized and large cities in the United States (population of 50,000 or more). Implemented in 2015, the survey targeted appointed managers such as city managers, chief administrative officers, chief operating officers, city or business administrators, and budget or finance directors. The survey instrument was designed to gather information about several aspects of fiscal retrenchment and recovery in city governments that experienced a serious budget crisis during the Great Recession of 20072009 and years after up to 2014. The survey also gathered information on different management and organizational characteristics of the city governments. The MFRR project involved several steps to improve the accuracy of responses and minimize measurement error (see Dillman et al., 2009; Podsakoff et al., 2012). First, the survey assured the strict confidentiality of respondents by anonymizing the names and official titles of the respondents and the city. Second, it used concise and unambiguous language, clearly defined concepts or terms to ensure a similar understanding of the questions, and used negative and positive wordings to reduce the motivation to respond stylistically. Third, if respondents found a question unclear or confusing, the survey instructed the respondents to call or email the principal investigator directly. Fourth, for those questions that required expertise or knowledge about a specific aspect of the organization (e.g., budgeting), the survey requested appointed managers to consult with relevant department heads (e.g., budget directors) before answering the questions. Finally, if the manager and budget/finance officer were newly appointed, the survey instructed them not to answer questions about the budget crisis that occurred before their hiring. 18 The sampling frame includes all 674 municipal governments with a population of 50,000 or more, as listed in the 2007 Census of Governments. 16 A total of 268 cities participated in the survey, or a response rate of approximately 40%. Nine in ten survey respondents were appointed managers, and the remaining was a finance or budget director. Respondents spent an average of eight years in their current position but had been in the local government profession for an average of 23.5 years. The respondents were highly educated, with more than four-fifths having graduate degrees, mostly in public administration. We assessed if the responding cities were different from non-responding cities, using difference-of-means tests for continuous variables and Chi-Square tests for dichotomous variables. The results indicated that responding cities were not systematically different from nonrespondents in terms of key community characteristics such as expenditures, revenues, property tax dependence, income, population, government form, and access to sales or income tax. 17 Dependent Variable: Measuring Interorganizational Collaboration Our study focuses on the specific sectors of organizations that city governments collaborated with, including nongovernmental entities (for-profit, non-profit) and other public organizations (primarily other local governments). We also examine different types of interorganizational collaboration, including service delivery, grant seeking, policy lobbying, taxbase sharing, and joint economic development. We rely on the results from the survey item asking respondents, “In response to the most recent budget crisis faced by your local government, please indicate the extent to which your government engaged in the following collaborative arrangements.” The survey defines a budget crisis as “a severe reduction in the ability of the local government to pay for the costs of delivering services demanded by citizens, 16 At the time of survey planning, the 2012 Census of Governments had yet to be released. 17 Because of space consideration, the results are not presented here but are available from the authors. 19 and to meet other financial obligations such as debt servicing.” The responses range from “Not at all” (coded 0), “Only sparingly” (1), “Engaged moderately (2), and “Engaged intensively” (3). Table 1 provides information about the specific collaborative arrangements. For nongovernmental collaboration, a substantial percentage of cities engaged moderately in contracting out services to for-profit organizations (43.72%) and not-for-profit organizations (36.99%). Most cities also engaged moderately with governmental collaboration, explicitly focusing on shared services (34.51%), applying for federal grants (39.13%), and policy lobbying (43.7%). In contrast, a sizeable share of cities did not engage at all in regional tax base sharing (72.22%) and, to some extent, joint ventures for economic development (35.57%). [Table 1 here] We applied exploratory factor analysis (EFA) to the survey items to assess whether they measure a latent concept. EFA is a data reduction technique used when the number of factors and specific items that determine which factors are not known. 18 EFA reduces the number of survey items by estimating linear combinations of the items that summarize the information about the types of collaboration each city engaged in. We weigh the sample by population to ensure that any potential overor under-representation of some cities by population does not invalidate the analysis. Table 2 shows the results of the factor analysis. [Table 2 here] For the collaboration items, the analysis retains two factors with an Eigenvalue greater than one. The difference between the two factors is the sector of the organization that city governments choose to collaborate with. The first factor (Eigenvalue of 2.45) involves city 18 A different approach is confirmatory factor analysis (CFA) in which a researcher groups items, ideally informed by a theory postulating a relationship among items and the underlying construct. A potential issue with this approach is that it imposes a preconceived factor structure largely determined by the researcher rather than the data. 20 government collaboration with other public sector organizations, termed governmental collaboration. In contrast, the second factor (Eigenvalue of 1.78) focuses on collaboration with private and non-profit organizations and is thus called nongovernmental collaboration. We use factor scores to calculate both indices. The Cronbach's alpha indicates that both indices are internally consistent (0.67 and 0.73, respectively). Main Independent Variables: Measuring Organizational Strategy Formulation Process For strategy formulation, we borrow and modify the survey items originally employed by Andrews et al. (2009), which were also used by Jimenez (2018). The survey items use a fiveitem Likert scale to measure the level of agreement (1 – strongly disagree to 5 – strongly agree) to different statements capturing the essence of each strategy formulation process. The survey items explore dissimilarities in internal and external environmental scanning, strategy choice processes, and strategy evaluation, among others. The EFA for the strategy formulation survey items, weighted by population, identifies two factors as shown in Table 2. The Eigenvalue and Cronbach’s alpha are 3.08 and 0.86 for the factor “formal strategic planning” and 2.19 and 0.78 for the factor “logical incrementalism.” Examining the survey items, the focus of the formal strategic planning items is on the use of a structured process to scan the external and internal environment of the organization, develop and examine strategy alternatives, and regularly assess strategy implementation. Logical incrementalism items, in contrast, capture the fundamental attributes of ongoing adjustment processes in response to changes and negotiation with major stakeholders. We also use factor scores to develop the indices. Control Variables We control for the effects of several external environmental and internal organizational factors that might affect collaboration choices. For external factors, we include measures of 21 demographic and local economic conditions, differences in intergovernmental context, and political influence. For internal factors, we focus on government fiscal condition and the quality of city administration. For demographic and economic factors, we include population, ethnic fragmentation, and household income using data from the American Community Survey. A larger population indicates a higher demand for services, potentially necessitating collaboration. Ethnic diversity within a population may lead to conflicting perspectives on interorganizational collaboration (Feiock, 2013). Furthermore, cities with higher incomes have an enhanced ability to increase spending for services (Hendrick et al., 2011; Jimenez, 2014), reducing the need to collaborate with other organizations for budgetary relief. City service responsibilities and revenue authority are largely determined by their state governments. For intergovernmental factors, we include measures of state mandates, differences in revenue sources and service responsibility, and previous engagement with collaboration. To measure state mandates, we rely on the MFRR survey item that asked to what extent “State mandates to provide certain service or level of service” has “contributed to the most recent serious budget crisis faced by your local government.” Responses range from “Did not contribute” (0) to “Strongly contributed” (4). Providing mandated services can force city governments to collaborate with other organizations to ensure service delivery. Because property taxes remain the most important source of revenues for city governments, we measure city dependence on this tax by dividing total property tax revenues by total taxes. Property tax dependence is linked to slower growth in total revenue, constraining the government’s capacity to support rising expenditures (Pagano & Johnston, 2000) and forcing city governments to collaborate to reduce costs. To measure differences in service responsibilities, we include Clark 22 and Ferguson’s (1983) functional performance index, with higher values indicating that a city performs a broader range of functions. Cities that perform various functions likely engage more in interorganizational collaboration to meet their service responsibilities. To assess previous experience with collaboration, we include per capita spending and revenues received from other local government organizations. We expect those with higher interorganizational spending and revenues to engage more in collaborative activities. The data are from the Census of Governments. For political factors, we include measures of the frequency of the appointed manager’s interaction with key political stakeholders. We use responses to the MFRR survey item “How frequently do you interact with individuals from each of the following?” specifically the “Mayor’s office” and “City council.” The responses include Never (0), Once a Year (1), Twice a Year (2), Quarterly (3), Monthly (4), Weekly (5), and Daily (6). Without support from political principals such as the mayor and city council, it is unlikely that cities will engage in interorganizational collaboration. For internal organizational factors, we include the general fund unassigned balance to measure city fiscal condition. The unassigned fund balance functions as a reserve for a city government to help continue providing services amidst a sudden decline in revenues. A declining balance indicates a poorer ability to meet the city’s service responsibilities and can spur cities to participate in collaborative arrangements to reduce costs (Jimenez, 2022). We divide fund balance by general fund expenditures to ensure comparability across cities. Data on fund balance and expenditures are from cities’ Annual Comprehensive Financial Reports. To assess the quality of city administration, we focus on administrative capacity and local government form. Administrative capacity is crucial in successfully implementing government 23 initiatives (El-Taliawi & Van Der Wal, 2019; Pritchett et al., 2013) such as collaboration. To measure administrative capacity, we use the MFRR survey item “Please indicate how budget cuts have affected the administrative capacity of your organization to function effectively in the future.” Responses range from “Significantly weakened administrative capacity” (1) to “Significantly strengthened” (5). Council-manager governments can be more professional than mayor-council governments and are associated with adopting innovative management and service delivery approaches including collaboration (Nelson & Svara 2012; see Feiock et al. 2009 for a different perspective). We identify cities with council-manager government forms, with 1 indicating yes and 0 otherwise. The data are from the International City/County Management Association’s Municipal Government Form survey. Table 3 shows basic descriptive statistics for all variables. 19 [Table 3 here] RESULTS Main Findings We use ordinary least squares regression to estimate the models where the dependent variables are the governmental and nongovernmental collaboration indices. We cluster standard errors by state to address potential group error correlation and use robust standard errors to address heteroskedasticity. The final number of observations is 196 cities (from 42 states) after we dropped cities where respondents did not completely answer all strategy formulation and collaboration questions. Table 4 contains the results of the regression analysis. [Table 4 here] 19 The bivariate correlation analysis does not show any high correlations among the independent variables. The results are not presented here but are available from the authors on request. 24 Models 1 to 4 focus on governmental collaboration, whereas models 5 to 8 focus on nongovernmental collaboration. To track how estimates change across different model specifications, we include control variables only in model 1 (and 5), the strategy formulation variables only in model 2 (6), all variables in model 3 (7), and interaction terms for strategy formulation in model 4 (8). The results, specifically for the strategy formulation variables, are consistent, with or without control variables. We also find that there is no statistically significant interaction between the two strategy formulation indices. We focus on the results from models 3 and 7, which contain all variables. Model 3 shows that formal strategic planning has no systematic relationship with governmental collaboration (p > 0.10). Formal strategic planning neither systematically hinders nor facilitates collaboration among governmental organizations. In model 7, formal strategic planning is statistically significant (p < 0.05) and positively associated with nongovernmental collaboration. When city governments employ a formal strategic planning approach, they contract out services to forprofit and non-profit organizations. Models 3 and 7 also show that logical incrementalism has a statistically significant and negative relationship with both governmental and nongovernmental collaborations (p < 0.05 and p < 0.00, respectively). Figures 1 to 4 show the marginal effects of formal strategic planning and logical incrementalism on governmental and nongovernmental collaborations, holding control variables constant at their means. Because we used factor scores to measure the strategy formulation and collaboration indices, the marginal effects are expressed in standard deviation (s.d.). To better assess the magnitude of the relationships, we calculate the first difference, or the difference in marginal effects between a city that has the lowest and highest scores for the formal strategic planning index, hereafter called the least and most strategic city (for the logical incrementalism 25 index, the least and most incrementalist city). In figure 1, the most strategic city engages in governmental collaboration by one-third s.d. higher than the least strategic city. In figure 3, the most strategic city, compared with the least strategic, engages more in nongovernmental collaboration by more than one s.d. higher. In figures 2 and 4, the most incrementalist city engages in governmental collaboration by three-fourths s.d. lower, and in governmental collaboration by close to one s.d. lower, than the least incrementalist city. [Figures 1 to 4] For external control variables, higher median household income and ethnically fragmented local population are associated with reduced collaboration with other governmental organizations. State service mandate to city governments shows a significant and positive association with governmental and nongovernmental collaboration. Most of these results conform with our expectations. For nongovernmental collaboration, the coefficient for interorganizational spending is marginally significant and negative, indicating that governments that previously spent higher on collaboration with other governments partnered less with nongovernmental entities. For the internal organizational variables, managerial interaction with the mayor is associated with governmental collaboration, whereas managerial interaction with the city council is associated with collaboration with nongovernmental entities. One possibility is that mayors are more risk averse in adopting innovative strategies (Carr, 2015) and thus prefer collaborations with other governmental organizations, which entail lower risks. Councils are not directly responsible for executive action and are less likely to be held accountable by voters for the outcomes of collaborative activities. This means council members, potentially, are more open to collaborating with nongovernmental entities despite the risks involved. Finally, council-manager 32 Governments have been encouraged to engage in interorganizational collaborations to address complex public policy and service delivery issues, access previously untapped resources of different actors, expand administrative capacity, benefit from joint learning, reduce redundancy, and achieve economies of scale (see Agranoff & McGuire, 2001, 2003; Alter & Hage, 1993; Ansell & Gash, 2008; Emerson et al., 2012; Huang & Provan, 2007; McGuire, 2006; Romzek et al., 2012; Thomson, 2001; Vansina et al., 1998; Weber & Khademian, 2008). A sizeable body of literature has explored the factors that promote interorganizational collaboration, but no study has yet to assess how strategy making shapes the decision to collaborate (see, among others, Ansell & Gash, 2008; Bryson et al., 2006; Emerson et al., 2012; Feiock 2007, 2009). In this study, we show that formal strategic planning facilitates crosssectoral collaboration but is not associated with government-to-government collaboration. Incrementalist decision-making, in contrast, inhibits interorganizational collaboration regardless of sector. The practical implication of the findings in this study is that the occurrence of collaboration – and, by extension, the realization of its potential contributions to improving policy and service delivery performance – may be limited by city governments’ capacity to undertake rational forms of strategy making and their propensity to engage in incrementalist decision-making. Local governments seeking to collaborate with nongovernmental entities in providing a public service or addressing a public policy issue need to invest in comprehensive planning mechanisms, including formal goal setting, environmental scanning, and regular evaluation of strategies. 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Contract out services to for-profit vendors Not at all 47 19.03 19.03 Only sparingly 56 22.67 41.70 Engaged moderately 108 43.72 85.43 Engaged intensively 36 14.57 100.00 Total 247 100.00 Contract out services to not-for-profit vendors Not at all 63 25.61 25.61 Only sparingly 73 29.67 55.28 Engaged moderately 91 36.99 92.28 Engaged intensively 19 7.72 100.00 Total 246 100.00 Shared services with other local governments Not at all 44 17.25 17.25 Only sparingly 86 33.73 50.98 Engaged moderately 88 34.51 85.49 Engaged intensively 37 14.51 100.00 Total 255 100.00 Collaboration with other municipal governments to apply for federal or state grants Not at all 49.00 19.37 19.37 Only sparingly 85 33.60 52.96 Engaged moderately 99 39.13 92.09 Engaged intensively 20 7.91 100.00 Total 253 100.00 Collaboration with other local governments to lobby federal and state officials about aid Not at all 25 9.84 9.84 Only sparingly 69 27.17 37.01 Engaged moderately 111 43.70 80.71 Engaged intensively 49 19.29 100.00 Total 254 100.00 Regional tax-base sharing such as common-pool funds for neighboring jurisdictions Not at all 182 72.22 72.22 Only sparingly 36 14.29 86.51 Engaged moderately 30 11.90 98.41 Engaged intensively 4 1.59 100.00 Total 252 100.00 Joint ventures with other cities to encourage economic development Not at all 90 35.57 35.57 Only sparingly 70 27.67 63.24 Engaged moderately 68 26.88 90.12 Engaged intensively 25 9.88 100.00 Total 253 100.00 40 Table 2 Results of Exploratory Factor Analysis Factor Analysis of Collaboration Items Survey Items Factor Loadings Factor 1 Factor 2 Governmental Collaboration Index (Factor 1) “Shared services with other local governments.” (0-Not at all, 3-Engaged intensively). 0.580 0.390 “Collaboration with other municipal governments to apply for federal or state grants.” 0.684 0.230 “Collaboration with other local governments to lobby federal and state officials about aid” 0.722 0.103 “Regional tax-base sharing such as common-pool funds for neighboring jurisdictions” 0.748 0.081 “Joint ventures with other cities to encourage economic development” 0.741 -0.212 Number of cities with complete responses 234 Eigenvalue 2.454 Cronbach’s Alpha 0.665 Nongovernmental Collaboration Index (Factor 2) “Service contract with for-profit vendors.” (0-Not at all, 3-Engaged intensively). 0.015 0.865 “Service contract with not-for-profit vendors.” 0.140 0.875 Number of cities with complete responses 234 Eigenvalue 1.780 Cronbach’s Alpha 0.734 Factor Analysis of Strategy Formulation Items Survey Items Factor Loadings Factor 1 Factor 2 Formal Strategic Planning Index (Factor 1) “We regularly assess developments in the local community or economy that can affect our capacity to deliver services” (1-Strongly Disagree, 5Strongly Agree) 0.762 0.369 “We regularly assess the efficiency and effectiveness of service delivery” 0.834 0.261 “We follow a formal process to formulate strategies in response to issues faced by our local government” 0.801 0.110 “We assess the feasibility of different strategies” 0.756 0.395 “Once strategies are implemented, we follow a formal process to assess their results” 0.653 -0.466 Number of cities with complete responses 225 Eigenvalue 3.084 Cronbach’s Alpha 0.8617 Logical Incrementalism Index (Factor 2) “Strategies are made on an ongoing basis” (1-Strongly Disagree, 5-Strongly Agree) 0.059 0.793 “We adjust our strategies in response to initiatives and activities of stakeholders such as elected officials, public employee unions or business groups” 0.186 0.691 “Strategies develop through negotiations with stakeholders such as elected officials, public employee unions or business groups” 0.362 0.701 Number of cities with complete responses 225 Eigenvalue 2.185 Cronbach’s Alpha 0.78 Note: Only factors with Eigenvalue greater than 1 are retained. 41 Table 3 Basic Descriptive Statistics Variable Mean Std. Dev. Min Max Collaboration Governmental collaboration index 0.20 0.90 -1.45 2.98 Nongovernmental collaboration index -0.26 1.01 -2.48 2.13 Strategy Formulation Process Formal strategic planning index -0.10 0.95 -1.86 2.30 Logical incrementalism index -0.08 0.92 -1.99 1.85 Controls General fund balance (divided by general fund expenditures)a 0.21 0.19 -0.03 1.41 Median household incomea 38497.24 12704.62 18932.98 82920.77 Populationa 161139.70 349842.70 49220.75 3789093.00 Ethnic fragmentationa 1 − ∑(𝑅𝑎𝑐𝑒𝑖) 𝑗 𝑖2 (where Race i denotes the share of population identified as race i, including White, Black, Hispanic, Asian and Pacific Islander, and American Indian. Ranges from 0-1, with higher values indicating greater ethnic heterogeneity.) 0.50 0.14 0.10 0.74 Per capita interorganizational spendinga 0.02 0.04 0.00 0.27 Per capita interorganizational revenuesa 0.02 0.04 0.00 0.25 State service mandates 2.50 1.01 1.00 4.00 Functional performance indexa ∑(Fi Wi); where Wi =Ei / Ni or the weight for subfunction i, Ei is per capita expenditure in all cities for subfunction i, Ni is the number of cities performing subfunction i, Fi is performance of subfunction i, which is 1 if city performs subfunction i, and 0 if city does not perform subfunction i. 2.74 5.86 0.22 62.92 Property tax as % of total taxes a 56.74 23.30 0.00 99.89 Council-manager government 0.71 0.46 0.00 1.00 Administrative capacity 2.48 1.02 1.00 5.00 Managerial interaction with mayor 5.43 1.01 1.00 6.00 Managerial interaction with council 5.24 0.97 0.00 6.00 Note: a – for these variables, we use the average from 2009-2013 for greater accuracy. Thus, although the MFRR survey targeted cities with a minimum population of 50,000 based on the 2007 Census of Governments, the lowest value for population in the table will not reflect this floor. This is because in some years between 2009-2013, some cities lost population and went below 50,000.