NETWORK EFFECTIVENESS: COLLABORATING TO COMBAT HUMAN TRAFFICKING

Transcription

1 NETWORK EFFECTIVENESS: COLLABORATING TO COMBAT HUMAN TRAFFICKING Abstract Networks, collaborations of more than three agencies working to achieve shared goals, hope to attain collective gains through partnerships, sharing resources, and/or providing more comprehensive services. The goal of many of these networks is to tackle wicked problems, those too complex, systemic, or challenging to end without a broader effort. There is still little empirical evidence that these efficiencies are achieved as often as they are touted or that network governance is effectively structured to provide public goods and services. Network effectiveness, the ability of multiple organizations to trust each other, work together, share information, socialize, achieve goals, and resolve conflict is examined across 134 networks who are all working to combat human trafficking. Ending human trafficking, a form of slavery that preys on vulnerable people living with the problems of inequality, will require effective collaborative governance. Sampling networks seeking to achieve similar goals allows for comparison where similar contextual and structural challenges likely exist. Through factor analysis, two distinct concepts emerged from the data: effectiveness and dysfunction. The specific items which comprise these factors are useful for inclusion in future research, for more deeply understanding the differences between client, community, and network level effectiveness, and encourage network formation and maintenance that attends to both increasing effectiveness and decreasing dysfunction. Key Words: Network effectiveness; Nonprofit management; Human trafficking Foundations, governments, private entities and non-profits are increasingly developing networks that can improve service delivery, make a collective impact greater than they could do alone, and share resources like scarce funding. As agencies increase their interdependencies, they also increase the layers of bureaucracy, administration, and authority. Due to these increasing cognitive challenges related to additional layers of personnel as well as competing strategic goals for ending wicked problems, the ability of the networks to achieve goals, resolve conflict or develop trust may suffer. Some networks seem to overcome challenges more effectively than others or manage to find clarity where uncertainty previously ruled. By developing and testing a theory of network effectiveness, this study enhances understanding of the management and administration of networks.

2 The vast literature on networks can be understood as developing in three research traditions and those traditions are now conceptually different ways to understand the study of networks. The three traditions of network research; sociology, political science, and public management, produced innovations in social network analysis, policy innovation, change, and agenda setting, and development of managerial structures and behaviors as well as network performance and outcomes (Berry et al., 2004). These traditions built on each other, intertwined, and provided powerful insights like the concept of structural holes (Burt, 1992), density (Marsden, 1990), capacity to end wicked problems through partnerships (van Bueren, Klijn, & Koppenjan. 2003; Head, 2008), and the capacity for policy innovation and knowledge transfer across a network (Rhodes, 1997; Robinson, 2006; Weber & Khademian, 2008). Through a process of tracing citation relationships, Lecy, Mergel, and Schmitz (2014) argue that network scholars do a poor job of defining the way in which they conceptualize the network under consideration, the research tradition influencing the work, or the research program. The literature tracing process led those scholars to structure the public administration literature on networks in three conceptual areas: policy formation, governance and implementation (Lecy, Mergel, and Schmitz, 2014). The major development provided in this research is in the implementation of networks vein and focuses specifically on comparing the capacity of whole networks to solve wicked problems. Wicked Problems and Human Trafficking Poverty, human rights abuses, unemployment, homelessness, and violence prevention are all forms of wicked problems. These problems share the attributes of being sustained in communities over long periods of time, having multiple and competing

3 problem definitions, requiring public and administrative attention, and requiring solutions that may be contested by different segments of the population (i.e. the solutions are often politicized). These problems plague society because of the immense cognitive and strategic uncertainty that surround both the problem and the solutions (van Bueren, Klijn, & Koppenjan. 2003). Roberts (2000) provides a conceptual distinction around various types of problems. The first type, simple problems, may initially present a challenge but when expertise is applied, there is a clear definition of the problem and systematic ways to solve that problem. Complex problems present as ones where there is consensus on what the problem is. Citizens and policy makers can agree that the status quo is the problem and that something needs to be done. However, complex problems have multiple solutions. The final type of public problems are considered wicked because both the problem identification and possible solutions are contested. Citizens and government officials disagree about the root causes or identification, and the problem persists after numerous attempts to combat the issue. Human trafficking is one example of a contemporary wicked problem with global implications. As borders become more porous, information about the quality of life in other places is more easily shared, and the pace of economic globalization grows, so does the demand for labor, personal safety, or freedom. Trafficking in humans increases in conflict areas and vulnerable populations are especially at risk. Trafficking spans various types of crimes and impacts adult men as well as women and children. Too often, bureaucracies may oversimplify the problem as a sex crime only affecting youth and children. Head (2008) suggested this simplification is a direct result of the growth in needing to establish

4 targets and goals that can be met by bureaucrats and managers in the public sector. Simplification will allow for quantification of some aspects of this crime, but may lead to negative externalities for other potential victims and survivors of trafficking. Wicked problems like human trafficking appear intractable because they are complex, require participation from multiple sectors, and solutions will likely vary among communities. Human trafficking represents both a criminal offense (federal statute created by the Trafficking Victims Protection Act of 2000) and a violation of an individual s human rights. Trafficking is defined by the fact that victims agency and other human rights are violated through force, fraud, or coercion (Foot, 2016, p 2). This violation primarily comes as a result of some form of vulnerability. Women and children, the most frequent victims of trafficking, are typically more marginalized in societies across the globe or more likely to be perpetrated against (Roberts, 2008). Men, especially those who feel they must provide for themselves and their families are more frequently trafficked to provide labor. While vulnerable populations are more likely to be forced, coerced, or fraudulently deceived, trafficking is also viewed today as any modern form of slavery. The root causes of contemporary slavery are debatable and likely vary depending upon the state, region, or country. This complexity leads to the cognitive and strategic uncertainty that defines wicked problems. Human trafficking is particularly problematic as combating it will require solutions, collaborations, and partnerships across many levels of governance and jurisdictions. A truly wicked problem, according to Roberts (2000) is one where nothing really bounds the problem solving process it is experienced as ambiguous, fluid, complex, political, and frustrating as hell (p. 2). Current efforts to end trafficking, particularly those funded by the

5 United States government and the United Nations, have political will to end child sex trafficking. However, labor trafficking, border smuggling, drug running, and other forms of coercion, while less flashy, also require time, attention, funding, and political will if they are to end. Trafficking prevalence shifts geographically as conflict situations, black markets, and labor needs change globally. When a single municipality, county, state, or country decreases trafficking, the crime may easily shift into jurisdictions that are more permissive just as a markets shift with changes in supply and demand. This situation further complicates problem identification, jurisdictional authority, and potential solutions. Ultimately, human trafficking is both wicked and frustrating as hell. Many communities recognize the need to combat this problem, to provide attention and energy to the root causes by finding ways to prevent the crime, protect survivors, and prosecute the offense. Combating the multiple causes and outcomes related to trafficking will also require effective communication across agencies and governments, timely information sharing, and prevention, protection, and prosecution efforts that build on the work of each other. Networks are arrangements that allow organizations and individuals from different sectors, problem orientations, and service provision areas to come together and work to end wicked problems from multiple angles. Human trafficking represents an example of what Robert s (2000) describes as a truly wicked problem. She identifies three possible strategies for coping with wicked problems: authoritative, competitive and collaborative. The focus of this research is to examine how whole networks function to produce outcomes aimed at ending human trafficking. Whole Networks

6 Since networks have the collaborative capacity to combat wicked problems, scholars typically view them through a positive lens (Padgett & Ansell, 1993; Provan & Milward, 1995; Klijn & Koppenjan, 2000; Meier & O'Toole, 2003; Ansell & Gash, 2008; Koliba, Meek, & Zia, 2010). One indictment of the broadly and quickly expanding literature on networks is that the term networks is increasingly nebulous, represents a different concept to different people, and is inconsistently applied (Borgatti & Foster, 2003). This section identifies a conceptual definition of networks and connects that concept to the operationalization of networks in the survey from which the data for this project was drawn. The notion that several organizations or agencies work together toward achieving a common goal is the heart of understanding these network arrangements. O Toole and Meier (2004) argued that networks are an interdependent group of multiple organizations or parts of organizations whereby the connections among the actors are not arranged as superior to each other. This definition suggests that members of the network are not subordinate or report to one another. Provan and Kenis (2008) specifically defined networks as groups of three or more legally autonomous organizations that work together to achieve not only their own goals but also a collective goal (p 231). Provan and Kenis (2008) also argued that network configurations often require lead organizations or network administrators to provide an effective governance mechanism for positive outcomes. The structure of networks, leadership of the network and goal-directed behavior are all considered here. Specifically, this article addresses the research questions from the network as a form of governance approach as opposed to a network analytical approach (Provan & Kenis, 2008). The network as governance focus pays special attention to meta-

7 level characteristics of the network. The network leadership, funding, structure, stability, and size are all important considerations in this perspective. The Laboratory to Combat Human Trafficking, the community partner connected to this project, defines partnership research as: Partnership measures acknowledge that combating human trafficking requires a comprehensive response through the cooperation of multiple sectors. Partnerships bring together diverse experiences, amplify messages, and leverage resources. For the purpose of this survey, an anti-human trafficking partnership refers to a [e.g. a cooperative relationship between two or more organizations established for the purpose of jointly combating human trafficking in some way.] This language, while utilized specifically for the survey tool, suggests ways in which a group of organizations may band together to achieve their stated goals related to human trafficking. More specifically, this study examines various whole public management networks. Public management networks include agencies involved in public policy making and/or administrative structure through which public goods and services may be planned, designed, produced, and delivered (and any or all of the activities) (McGuire & Agranoff, 2007). O Toole (1997) and Agranoff and McGuire (2001) led the call for focused and continued efforts to study public management networks. Some of the whole networks included in this study are mandated, formal and directly connected to federal funding targeted at combating trafficking. Other networks are informal, lack funding mechanisms for combating trafficking, and have little regulatory authority to prosecute or criminalize trafficking in their local communities. Central to this article is the following research question: How can we measure and understand network level effectiveness? Theory

8 A comprehensive theory on network effectiveness does not currently exist (Turrini et al., 2010; O Toole, 2015). However, based on previous work related to network effectiveness (Provan & Milward, 1995; Provan & Sebastian, 1998; Turrini et al., 2010), the structural, functional and contextual descriptions of networks (Klijn & Koppenjan, 2000; Provan & Milward, 2000; Klijn, 2005; Provan & Kenis, 2008), and the work of the Laboratory to Combat Human Trafficking (LCHT), the aim of this paper is to explore the relationships among indicators of network performance. The most comprehensive attempt to identify a framework for understanding network effectiveness comes from Turrini et al. (2009). They posit the following: Figure 1: Turrini et al. (2010) Network Effectiveness Framework Overview The Turrini et al. (2010) framework is a way to conceptualize the determinants of network effectiveness. Of the limited studies examining whole networks, the definition of network effectiveness varies (Turrini et al., 2010). Three types of effectiveness: client level, community level, and network level were identified by Provan and Milward (2001) as indicators of network success (Turrini et al., 2010). Network level effectiveness refers to

9 the sustainability, legitimacy, and maintenance of the network. Ferlie and Pettigrew (1996) examined a single network for duration and effectiveness and a handful of other studies have examined the ability of networks to achieve stated goals, innovate, and remain sustainable, but empirical work has failed to address all of these types of effectiveness. The conceptual definition of network effectiveness utilized for this study is the network level capacity of the actors to work together to achieve goals related to combating human trafficking. Achieving goals is a key tenant in the definition but working together may not be as easily operationalized. Working together represents the network s ability to communicate, build trust, and resolve conflict. To be clear, effectiveness ought not be confused with efficiency or utility. Simon (1965) with interpretation from Baldwin (1985) both clarify the notion that efficiency and utility imply a relationship between costs and outputs whereas effectiveness is solely a conception of the benefits, outputs, or outcomes of the work. This analysis only attends to the observable outcomes of working together and achieving goals, not the costs imposed on the actors to achieve those outcomes. Future research will begin to address the relationships among the hypothesized determinants and network performance, but first, an empirical operationaliztion of network effectiveness is necessary. The following section discusses the results of a factor analysis of items collected from a national survey of networks seeking to combat human trafficking. Methodology In 2013, organizations who seek to combat human trafficking completed a survey conducted by a self-identified, backbone organization that seeks to end this wicked social problem. The Laboratory to Combat Human Trafficking (LCHT) supports research,

10 response, and reporting efforts in Colorado and works closely with organizations across the state that prevent the crime, protect survivors, and prosecute perpetrators. Two surveys were administered: one was a national sample and the other went out to organizations within a single state 1. Since the surveys were administered simultaneously, this study utilizes only the national survey at this time as a sample of the population of networks. The national survey is larger, likely more representative of unique, whole networks, and covers much broader geographic areas. The national respondents were selected through purposive and convenience sampling which lead to a sample of 453 organizations who received the survey. One hundred and eighty-six organizations returned surveys and the executive or program director in the agency completed the survey the majority of the time (55 per cent). Further details about the methodology of the survey or the types of organizations included in the sample is available at The following section details the use of factor analysis as it relates to determine how the items in the survey represent network effectiveness. Factor Analysis Factor analysis, a technique in which a researcher must determine how to rotate and group items tapping a single latent construct (DeVellis, 2012), was utilized to explore survey items believed to influence the functioning, performance, and overall effectiveness of the networks. The survey contained a question battery asking the respondent to consider several statements in regard to the partnership they opted to describe. Respondents who belong to multiple partnerships were asked to think of the most diverse 1 Complete access to survey questions, methodology, and sampling can be found at

11 partnership in terms of sector membership. They were also allowed to complete the battery multiple times for each partnership, however, only the most diverse partnership is included in this data set. The battery includes items on a level of agreement scale about a statement that refers in someway to the partnership. For example, one items states, there is a great deal of trust among members and the respondent can select strongly disagree, disagree, neutral, agree, or strongly agree 2. There are a total of twelve statements included in the battery (see Table XX for the exact statements) and the goal achieved through factor analysis is to determine if these items may effectively represent fewer, underlying constructs (Jolliffe, 2002; Brown, 2015). Based on the statements, generally positively or negatively related to the functioning of the network, the researcher hypothesized that there would be two distinct factors and that exploratory factor analysis (EFA) through principle axis factoring and rotation is most appropriate. As there is little empirical evidence or measures currently in use for network effectiveness, this will proceed as exploratory factor analysis rather than confirmatory. Brown (2015) suggests that confirmatory factor analysis (CFA) is only appropriate when there are specific theoretical priors for the communality estimate, the structure of the variables, matrices, and variance, and how the indicators are related to the factor or construct. Exploratory factor analysis is the most appropriate technique based on the state of theory on network effectiveness and the oft-unreliable nature of confirmatory factor analysis (Borsboom, 2006). 2 A quick note on the missingness in this battery. There are 186 respondents for the larger survey and 122 report on membership in a network. Of those 122, 95 respondents report that they are, indeed, part of a network combatting human trafficking. A factor analysis program will generally use casewise deletion leading to a total N value of 71 for the factor analysis.

12 Specification of the exploratory factor analysis is arguably one of the most important considerations when attempting to identify the factors that represent suspected latent concepts (Fabrigar et al., 1999; Preacher & MacCallum, 2003). These considerations include selecting the matrix of coefficients to be analyzed, the number of factors to be extracted, the extraction method, if the factors should be rotated, and the interpretation of the resulting factor scores (Thompson, 2004). The following section will outline each of those choice points. Since the statements are generally written in positive terms or negative terms, a theoretical prior is that the positive items will likely be correlated and the more negative statements will likely also have a correlated relationship. Additionally, trust, communication, and achieving goals together are implicated as ways in which networks can be viewed as effective (Provan & Milward, 2000; Provan & Kenis, 2008; Turrini et al., 2009). Therefore, Table 1: Hypothesized Factor Loadings below suggests the researcher expects two factor loadings, one that is the latent representation of effectiveness and the other appears to represent network dysfunction: Table 1: Hypothesized Factor Loadings Effectiveness Dysfunction There is a great deal of trust among members Member sometimes socialize together There is good communication among members Conflict can arise among members because of the different agency/organizational missions Conflict arises among members because of competing definitions of human trafficking There is competition among members that work within the same communities

13 The partnership will remain strong if the current leader leaves There is low turnover of members in the partnership The members of the partnership are able to resolve conflict effectively The partnership has achieved it annual goals over the past year There are difficulties sharing information among members about victims of trafficking due to confidentiality policies Several different techniques for extracting the factors are available to researchers looking to explore the relationship between multiple variables which may have a relationship to an underlying construct. Generally, researchers would consider principle component analysis and common factor analysis (Warner, 2008; Jolliffe, 2002; Galbraith, Moustaki, Bartholomew & Steele, 2002). Principle component analysis (PCA) is a sum of the variance of the variables the factors are created directly from the original data and represent essentially a sum of variance from the variables included in the factor under consideration (Warner, 2002; Galbraith, Moustaki, Bartholomew & Steele, 2002). Fabrigar et al. (1999) caution that researchers may not realize that PCA is a data reduction technique and does not account for the individual variance and error terms of each variable in the factor analysis. However, principle factor analysis or common factors, is a multiple regression technique that utilizes the shared variance of a set of variables to determine a communality estimate and this estimate is the variance that is predicted as a result of the correlation among the variables which comprise the factor (Warner, 2002). This communality value is replaced in the R matrix and several statistical packages can run this estimate over many iterations to improve the accuracy and reliability of the estimate

14 (Fabrigar & Wegener, 2012). Another distinction between PCA and the common factoring reported here is that PCA does not distinguish between common and unique variance. Common factoring is preferable here as it is likely that trust, communication, socializing, and other items that likely compose one of the factors do not have similar error terms or distributions (Fabrigar & Wegener, 2012). Therefore, the common factoring is utilized here. Both maximum likelihood extraction and the principle factors extraction techniques estimate parameters of unique variance and factor loadings (common factoring), but maximum likelihood (ML) techniques allow for statistical testing of correlations and factor loadings (Fabrigar, Wegener, MacCallum, and Strahan, 1999). Therefore, ML estimations must assume that the included variables are normally distributed and do not violate other normality assumptions. Principle factors allow for non-normality but are often a challenge to report and compare the reliability or validity of results (Preacher & MacCallum, 2003). Principle factoring can be iterative; the iterative process utilizes the sample correlation matrix initially then estimates new communality estimates in the matrix until the process reaches convergence (Preacher & MacCallum, 2003). Based on findings for exploratory factor analysis, only PAF will be utilized here as it appears to outperform ML and inferential statistics are not necessary (or reliable) when conducting exploratory analysis (Costello & Osborne, 2009; De Winter & Dodou, 2012). A decision of rotating the axis or not must also be considered (Fabrigar & Wegener, 2012). Rotating the factor analysis allows for the axis of each factor to shift in order to describe a more elegant and simple analysis for interpretation (Fabrigar & Wegener, 2012; StataCorp, 2012). Orthogonal rotation will ensure the assumption that the factors that are

15 uncorrelated remain in place whereas oblique rotation allows for factors to violate that assumption and be correlated. Since this is exploratory factor analysis, the researcher opted to utilize oblique rotation. There is no evidence to suggest that each of the items in the battery are uncorrelated, and if fact, it is likely that the ability to resolve conflict effectively is somewhat correlated to the relevance of conflicts that arise out of mission differences or from varying definitions of human trafficking. Additionally, not only could the items be correlated, the dysfunction of networks, one of the hypothesized factors may not may not be correlated with the effectiveness of networks. Theoretically, a network may remain effective and at the same time have dysfunction. For example, it is not unrealistic to expect that network members may disagree on the definitions of the wicked problem, to have conflicts as a result of differing missions or to have challenges related to sharing information and yet, with dysfunction present, the network will still have some level of effectiveness that can be measured and evaluated. The dysfunctions in the network may or may not have a relationship with the effectiveness of the network and further studies will need to explore the relationship between network determinants, dysfunction, and effectiveness. As such, oblique rotation seemed the most appropriate selection. Now that the methodological choices of the factor analysis are clearly presented and discussed, the results of the factor analysis are explored in the following section. Results In order to utilize the iterative option of principle factors, the first step was to examine non-iterative models to determine that indeed, two factors were likely present. Theory supported this hypothesis, but it was also necessary to test. In both Kaiser criterion, screeplot and parallel analysis, two factors were the best fit. The eigenvalues reported

16 below support both a scree test and parallel analyses of the iterative principle factor extraction 3. Typically eigenvalues greater than one would suggest that a distinct factor is present and this is often referred to as the Kaiser criterion (Fabrigar & Wegener, 2012; DeVillis, 2012; Dillon and Goldstein, 1984). The eigenvalues for factor 1 and factor 2 are greater than one and the scree plot appears to drop off considerably after the second factor. Parallel analysis results also suggest that two factors are present. Both the graph for the screeplot and the parallel analysis are located in Appendix 1. Based on all three of these results, the hypothesis is generally supported that the rotated, iterative principle factor model of two distinct factors suggests that while two factors are present, several items may not be a good fit for the resulting factors. Table 2: Iterated Principle Factors Analysis Variable Factor1 Factor2 Uniqueness Trust Socialize Compete for Funding Good Communication Mission Differences lead to Conflict Definition of HT leads to Conflict Competition in Same Community New Leadership Low Turnover Difficulty Sharing Information Resolve Conflict Effectively Achieved Past Goals The loading plot, the item communalities and an exploration of the Chronbach s alpha leads to the conclusion that several of the included items from the battery do not strongly load onto either factor 1 or factor 2. The item communalities reported above 3 Images of Scree Test and Parallel Analysis provided in Appendix XX.

17 suggest that socializing, mission differences lead to conflict, and a strong network with a leadership change do not meet general social science guidelines; there is not a hard and fast cut-point but current research standards suggest that values between.32 and.7 may be considered when the item theoretically fits the factor and items above.7 should generally be included (Costello & Osborne, 2009; Velicer & Fava, 1998). Uniqueness is generally considered to represent some level of measurement error as well as the communality of a variable in relation to a factor; uniqueness values above.6, considered high, are variables that are not closely correlated with the factors (StataCorp, 2012). Based on these criteria, factor 1 will be further explored with five items and factor 2 with four items. Table 3: Iterated Principle Factor Items details which items appear to load on factor 1, factor 2, or do not load on either. Table 3: Iterated Principle Factor Items Factor 1 Factor 2 Does Not Load Effectiveness Dysfunction (Eigenvalue = 3.82) (Eigenvalue = 1.55) Proportion of variance attributed to this factor:.68 Proportion of variance attributed to this factor:.35 There is a great deal of trust among members Conflict arises among members because of competing definitions of human trafficking The partnership will remain strong if the current leader leaves There is good communication among members There are difficulties sharing information among members about victims of trafficking due to confidentiality policies Conflict can arise among members because of the different agency/ organizational missions

18 There is low turnover of members in the partnership Partners compete for funding Member sometimes socialize together The members of the partnership are able to resolve conflict effectively There is competition among members that work within the same communities The partnership has achieved it annual goals over the past year Factor 1, titled network effectiveness as it appears to represent positive, whole network outcomes, explains approximately two-thirds or 68 per cent of the variance in the matrix. Network dysfunction, factor 2, describes 35 per cent of the variance in the test battery and the items that appear to load on this factor have lower communalities and higher uniqueness values. As a result of the less robust findings for factor 2, it will be further explored in future scholarship and for the remainder of this particular research, network effectiveness, factor 1, is the primary factor under consideration. Effectiveness more strongly demonstrates items that meet appropriate standards for inclusion based on theory, interitem correlations, and uniqueness values. Network Effectiveness Network effectiveness, the central concept under consideration here, is worthy of further, detailed attention. Since prior research on network effectiveness primarily considers client or community level effectiveness (Provan & Milward, 1995, 2001; O Toole, 2015; Turrini et al., 2010), the following results demonstrate how items load on factor 1, network effectiveness.

19 Table 3: Factor 1 (Network Effectiveness) Chronbach Alpha of Items Item Obs Sign item-test correlation item-rest correlation average interitem correlation alpha Trust Good Communication Low Turnover Resolve Conflict Achieved Past Goals Test scale Table 3 highlights the results of a chronbach alpha correlation test on the items that appear to load on network effectiveness. Based on the results included in Table 3, most of the itemrest correlations among the variables are very close to each other meaning that the item correlates with the other items comprising the factor, however, the low turnover variable appears to be much lower than the other included items (Velicer & Fava, 1998). When this item is dropped from the factor, the average interitem correlation increases to.73 and the overall alpha increases to Generally values greater than.6 on the average interitem correlation are preferable and suggest a reliable indicator, but alphas above.8 are often acceptable (Statacorp, 2012). Therefore, dropping the turnover item may best be considered a theoretical choice for future researchers. Discussion Establishing the construct of network effectiveness and the construct of dysfunction are a useful advancement in the network effectiveness literature for several reasons. First, the two constructs may now be utilized to develop empirical variables. Now that these two distinct latent concepts have been identified through exploratory factor analysis, future 4 See Table 4: Chronbach s Alpha Without Low Turnover in the Appendix for complete details.

20 studies can further develop the measurement of each factor through confirmatory factor analysis, and ultimately, structural equation modeling techniques can be utilized to explore relationships between contextual, managerial, functional, and structural network determinants that may lead to effectiveness. Conceptually, the two factors reflect the effectiveness of the network as a combination of developing trust, achieving shared goals, good communication, low membership turnover, and conflict resolution. Somewhat surprisingly, a change of leadership did not correlate with effectiveness. Leadership, and the turnover of leadership, may more effectively represent a determinant of effectiveness as hypothesized earlier rather than correlate as a function of effectiveness. Additionally, differences in organizational missions and the willingness of participants to socialize together do not appear to correlate strongly enough to comprise the network effectiveness factor either. Socializing, much like leadership change, may instead be predictors of effectiveness rather than a key component of effectiveness. Mission differences that create conflict across organizations may not represent effectiveness or dysfunction for several reasons. A third (33%) of respondents reported that they were neutral about the statement that conflict can arise as a result of differing missions it is not unrealistic to expect that respondents are neutral as they might be able to identify agencies within the network that make the network less effective as a result of differing missions as well as agencies who make the network more effective regardless of mission differences. Since it is currently unclear why so many respondent report neutral feelings, this item is not clearly an aspect of network effectiveness.

21 Future work may need to more closely connect network effectiveness to indicators that represent the outcomes of human trafficking networks actions. For example, only achieving past goals represents the ability of these networks to achieve community or client level outcomes. In order to determine how network effectiveness will actually decrease human trafficking, or directly influence the root causes of wicked problems, will require further operationalization of network effectiveness concepts and measures. Additional work can be done to support the construct validity of each factor as this will enhance the and guide the development of empirical analysis of network-level effectiveness. Provan and Kenis (2008) argue that trust and goal consensus appear to be important aspects related to network effectiveness and Provan and Milward (2001) suggest that network level effectiveness must address the ability of organizations to work together, to fund and maintain information sharing as wells as member commitments to network goals. The results provided here suggest that trust, low turnover, achieving goals and communicating effectively are all important dimensions of effectiveness. The addition of the concept of conflict resolution is new to the theoretical discussion of effectiveness. Little attention in current network literature attends to conflict resolution developments across networks suggesting this is an area fruitful for further application and consideration. O Toole (2015) asserts that developing opportunities to study networks as dependent and independent variables is a much needed development for the advancement of network theory. This study directly develops in that vein. Practically, the development of this network effectiveness variable will benefit grantors, grantees, community agencies,

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26 Scree plot of eigenvalues: IPF Eigenvalues Number Parallel Analysis: IPF Eigenvalues Factor Factor Analysis Parallel Analysis Table 4: Chronbach s Alpha Without Low Turnover

27 Item Obs Sign item-test correlation item-rest correlation average interitem correlation alpha Trust Good Communication Resolve Conflict Achieved Past Goals Test Scale