Collaboration within the Puget Sound Marine and Nearshore Science Network

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Collaboration within the Puget SoundMarine and Nearshore Science NetworkKristin Hoeltinga, Beth Moorea, Richard Pollnacb & Patrick Christieac

a School of Marine and Environmental Affairs, University ofWashington, Seattle, Washington, USAb Department of Marine Affairs, University of Rhode Island, Kingston,Rhode Island, USAc Jackson School of International Studies, University of Washington,Seattle, Washington, USAPublished online: 27 Jun 2014.

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Coastal Management, 42:332–354, 2014Copyright © Taylor & Francis Group, LLCISSN: 0892-0753 print / 1521-0421 onlineDOI: 10.1080/08920753.2014.923141

Collaboration within the Puget Sound Marineand Nearshore Science Network

KRISTIN HOELTING,1 BETH MOORE,1 RICHARD POLLNAC,2

AND PATRICK CHRISTIE1,3

1School of Marine and Environmental Affairs, University of Washington, Seattle,Washington, USA2Department of Marine Affairs, University of Rhode Island, Kingston,Rhode Island, USA3Jackson School of International Studies, University of Washington, Seattle,Washington, USA

This article presents results of a study intended to paint a broad picture and uncovergeneral trends in collaboration within the Puget Sound marine and nearshore researchcommunity. Survey results showed that natural scientists dominate the network, repre-senting 80% of all actors in the sample. Relational contingency analysis revealed highinternal rates of collaboration among social scientists and among interdisciplinaryscientists. The lowest rates of collaboration were observed between natural scientistsand social scientists (p < .001). Cohesion metrics were examined within sub-networksof individuals working on a variety of topical focus areas. In general, sub-networksfocused on human dimensions–related topics had higher fragmentation scores (lowercohesion) than sub-networks focused on ecological, biological, or physical processes.These less cohesive sub-networks are identified as areas of opportunity for strategicnetwork interventions to support and foster new collaborations. Results of qualitativeanalysis highlight factors that facilitate or inhibit success of collaborative researchefforts, such as leadership, incentives, and long-term adequate funding. Additionally,the degree to which a collaborative research model can be linked to “high-impact,”policy-informing research outcomes is addressed.

Keywords collaboration, incentives, Puget Sound, social network analysis

Introduction

The Puget Sound Partnership (PSP or Partnership) is a Washington State agency tasked withrestoring Puget Sound ecosystem health by 2020. Scientific research plays an essential rolein this effort, and the Biennial Science Work Plan outlines strategies to identify and supportscientific research that is most needed. Among these strategic actions, the Partnership is

Kristin Hoelting and Beth Moore share equal authorship.Address correspondence to Kristin Hoelting, University of Washington, School of Marine and

Environmental Affairs, 3707 Brooklyn Ave NE, Seattle, WA 98105-6715, USA. E-mail: [email protected]

Color versions of one or more of the figures in the article can be found online atwww.tandfonline.com/ucmg.

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Puget Sound Marine and Nearshore Collaboration Network 333

interested in generating a description of the “existing functional networks of scientistsworking on Puget Sound issues” (Puget Sound Partnership 2012, 6). The Partnership’sSocial Science Working Group noted network analysis as a key area where social sciencecan contribute to assessment of the current management framework in Puget Sound (PugetSound Partnership 2011).

In addition, the Partnership seeks ways to “build capacity for better coordinationbetween science disciplines, institutions, non-governmental organizations, and the tribes”(Puget Sound Partnership 2012, 16). In the field of natural resource management, there isincreasing recognition of the importance of collaborative, interdisciplinary research, whichhas been found to generate more complete, balanced, and useful results (Berkes 2009;Christie 2011; Omenn 2006; Pressey and Bottrill 2009). In addition to collaboration withinthe scientific community, collaboration between researchers, managers, and policymakerscan increase the impact of scientific findings (Weber 1998; Weber, Leschine, and Brock2010).

Studies have shown a wide range of incentives that may motivate research collabora-tions. These include logistical considerations (e.g., access to expertise; access to equipmentor other resources; or improved access to funding sources), and personal and professionalmotivations (e.g., desire to learn a new technique or to educate others; potential for higherproductivity; to work across disciplinary boundaries; to gain prestige; or for pleasure)(Bozeman and Corley 2004; Katz and Martin 1997). Barriers to collaboration have beenfound to include differences in the culture of institutions or disciplines, as well as thepersonality of individual researchers who may differ in approach, methods, or perspectiveon what constitutes high quality research (Birnholtz 2007).

Limited research has been conducted regarding the network of researchers workingon Puget Sound issues (Puget Sound Partnership 2011). The primary goal of this studywas to generate a broad description of the collaborative network, and to identify incentivesand barriers to collaboration present in the context of Puget Sound. It was funded bythe Partnership and conducted between September 2012 and January 2013. The studydocuments and assesses patterns of collaboration between individuals working on onepriority science area of the Partnership’s Biennial Science Work Plan: marine and nearshoreecosystems (Puget Sound Partnership 2012).

In addition to providing a general description of this network of practitioners, the articleexplores the utility of network analysis as an applied tool to enhance strategic management.Various network structures play important roles in the facilitation of information flow andcollaborative ties within a network. Among these key structures, cohesive sub-groups aretheorized to facilitate information flow and trust relationships. Actors that bridge betweendisconnected sub-groups or who connect a large number of other actors (high “between-ness”) may broker the exchange of new ideas, information, and perspectives. Actors with ahigh number of connections (high “degree”) may have the potential to act as opinion lead-ers (Adler and Kwon 2002; Bodin, Crona, and Ernstson 2005; Burt 2001; Prell, Hubacek,and Reed 2009; Provan, Fish, and Sydow 2007; Rogers 1995; Vance-Borland and Holley2011).

“Network interventions” can be used to make strategic alterations to improve thequantity and quality of relationships in a network, to achieve desired network function(Valente 2012). Such interventions have been utilized in fields such as public health,counterterrorism, business, and natural resource management. Examples of interventionsin collaborative networks include efforts to increase linkages between individuals withdiffering skill sets (Cross, Borgatti, and Parker 2002) or to foster new collaborationsbetween diverse stakeholders and managers (Vance-Borland and Holley 2011).

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334 K. Hoelting et al.

The article concludes by offering policy recommendations and suggested next steps inPuget Sound social network research and application of findings. Policy recommendationsare based on comparing qualitative results to a framework of “assurance mechanisms” thatlead to successful collaborations in an environmental and political setting (Weber 1998).These recommendations, along with key findings from network analysis, are intendedto serve the Puget Sound Partnership in determining next steps in the development andapplication of network analysis tools to enhance the existing collaborative network andstrategic coordination of research.

Methods

This study draws from multiple sources of information and methods: focus groups, inter-views, and an on-line survey. Using data from these distinct methods of inquiry allowsfor the triangulation of recurring and prominent themes from study informants (Miles andHuberman 1994; Patton 2001). The primary sampling target was researchers focused onone priority science area of the Partnership’s Biennial Science Work Plan: marine andnearshore ecosystems. Sampling also included managers and policymakers identified assignificant contributors working on marine and nearshore issues.

In an effort to sample widely within the network, the collaborative relationships ofinterest were broadly defined to be inclusive of diverse interactions. These interactions couldinclude: (1) direct collaboration on shared research projects, (2) consulting with researchersto discuss ideas and get feedback, (3) sharing research findings, or (4) participation in thesame panel, committee, volunteer program, or other group or activity.

Focus Groups

Science and policy leaders were selected for participation in one of two focus groups(Olympia and Seattle) based on their employment, publication record, and participation inrecognized Puget Sound science and policy organizations. Individuals were contacted byphone and e-mail, and were provided with human subjects-approved “request for consentto participate” and confidentiality statements. In total, 16 individuals participated. Focusgroups lasted two hours. The first hour involved a discussion of incentives and barriers tocollaboration. Each participant was asked to briefly note his/her name, type of Puget Soundresearch, and to describe a collaborative research project that was particularly successful.Three of these project descriptions were selected for more in-depth discussion during thesecond hour.

Focus groups were carried out prior to initiation of key-informant interviews andsurvey data collection. Themes that emerged during focus group conversations helpedframe this study by providing initial lines of inquiry and shaping questions for survey andkey-informant interview guides.

On-Line Survey

Following focus groups, an on-line survey was developed and field tested in two phases: (1)cognitive interviews with three University of Washington (UW) researchers and (2) a betatest of the draft on-line survey with 10 individuals from National Oceanic and AtmosphericAdministration (NOAA) Fisheries, the Partnership, and the UW. The survey was revisedbased on feedback received.

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In order to elicit the greatest number of responses, a pre-survey recruitment e-mail wassent to 388 potential respondents, describing the need for the study and including a humansubjects-approved “request for consent to participate” and confidentiality statement. Withinthe following week, a unique link to the on-line survey was e-mailed to each individualon the list. Individuals who did not respond within two weeks were sent a remindere-mail. Those who had not responded within two weeks of the reminder e-mail were thencontacted by phone to request their participation. The survey included sections on (1) thesurvey respondent’s personal background and research, (2) nomination of collaborators,and (3) open-ended questions regarding incentives and barriers to collaboration.

Respondents were directed to nominate the first 5–10 individuals who came to mindwith whom they collaborate most frequently. At the conclusion of data collection, a totalof 222 complete responses were received (57.2% response rate), along with 31 partialresponses (for a total response rate of 65.2%). Data from partial responses were included inanalysis. Of the 388 potential respondents, 117 did not return the survey. When contactedby phone, a small number of individuals reported reasons for their lack of response, whichincluded: (1) the survey was not relevant to them, (2) they were too busy to fill out a survey,(3) they felt uncomfortable providing names, or (4) they collaborate with too many peopleto choose only 5–10 names.

The large, unbounded nature of the desired sample, along with a limited project time-line and funding, precluded sampling all collaborators nominated by survey respondents.Therefore, the resulting network does not represent a census of individuals working onthese issues. Potential sampling bias exists due to both survey non-response and lack ofsaturation in the invited sample. A more directed, in-depth sample is needed to increase theconfidence in assessment of network structures; however, the large network sample derivedfrom the survey allows for identification of general trends in collaboration within the PugetSound marine and nearshore research community.

Key-Informant Interviews

In total, 20 key-informants were interviewed through the course of this study. Poten-tial key-informants were contacted by phone and e-mail, and were provided with humansubjects-approved “request for consent to participate” and confidentiality statements. De-tailed insights from key-informant interviews were used to expand the data collected inthe survey, offering more context about topics such as incentives that lead respondents tocollaborate, barriers to collaborative efforts, and characteristics of research that seem tohave “high-impact” regarding Puget Sound Recovery. For the purposes of this analysis,high-impact research is defined as “research with outcomes that directly catalyze or in-form measurable recovery, restoration, or policy changes affecting Puget Sound marine andnearshore environments.”

Sample Generation

The community of Puget Sound marine and nearshore researchers is a large, unboundedpopulation. Because the target sample lacked a pre-defined sampling frame, a snowballsampling approach was utilized to generate a list of potential study participants (Patton2001). Focus group participants provided initial seed names. Additional names were col-lected during key-informant interviews and from sources including regional conferenceproceedings, institutional and personal websites, and publications.

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In order to achieve a balance of research interests, disciplinary training, and employertype, nominated individuals in underrepresented affiliations or disciplines were prioritizedfor inclusion in the study. In the interest of ensuring that social science efforts were ade-quately represented in our sample, focus group and interview respondents were specificallyasked whether they knew of social science researchers working on Puget Sound marine andnearshore issues.

Qualitative Analysis

One member of the research team used the qualitative data analysis software, ATLAS.tiv.7 (Scientific Software Development 2012), for coding and organizing data from focusgroup and key-informant interviews. Several open-ended questions in the survey providedadditional qualitative information. Qualitative data were coded into 28 themes based ontopics defined a priori as well as major themes that emerged repeatedly during focusgroups, interviews, and in open-ended survey responses (Miles and Hubermann 1994;Patton 2001). Theoretical memos were written that explored the meanings of themes, linksbetween themes, and links to survey data. Potential new lines of inquiry were highlightedand pursued in subsequent key-informant interviews.

Results were related to relevant frameworks describing high-impact, successful col-laborative research (Cummings and Kiesler 2005; Jakobsen, Hels, and McLaughlin 2004;Maglaughlin and Sonnenwald 2005; Weber 1998; Weber, Leschine, and Brock 2010). In ad-dition, relevant qualitative findings were used to discuss trends and potential opportunitiesin communication patterns identified through network analysis.

Quantitative Analysis

The on-line survey generated information about ties between actors in the network, as wellas characteristics of individual actors. These included their disciplinary training, employer,and the topic(s) on which their work is focused. The social network analysis softwareUcinet (Borgatti, Everett, and Freeman 2002) was used to generate descriptive statistics ofboth the overall network sample as well as sub-networks of researchers working on specifictopical focus areas. The network analyses presented in this article are focused on patternsin interdisciplinary collaboration, as well as a comparative look at the degree of cohesionand connection within sub-networks of researchers working on specific research topics.

The relational contingency analysis tool was used to measure rates of collaborationwithin and between disciplinary categories. Disciplines were binned by category into naturalscience, social science, interdisciplinary (training in both natural and social science dis-ciplines), or “other discipline” (e.g., education, law, management, mediation/facilitation).Table 1 lists the individual disciplines that were included in each category. The relationalcontingency analysis tool uses multiple permutations to calculate the “expected” numberof ties within and between categories that would be present in a random network. Theexpected numbers of ties are then compared to observed numbers. Results are reported asobserved/expected values for frequency of ties both within each category (internal) andbetween categories (external). If the result of the contingency analysis is found to be sig-nificant (p ≤ .05), values less than 1 can be considered less frequent than expected, andvalues greater than 1 can be considered more frequent than expected (Belaire et al. 2011;Hanneman and Riddle 2005).

In addition to discipline, survey respondents were asked to associate each actor inthe network with one or more topics on which the individual’s work is focused. A set of

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Table 1Breakdown of Disciplinary Categories

DisciplinaryCategory Individual Disciplines in this Category

Natural Science Ecology; Biology; Chemistry; Geology; Oceanography; FisheriesScience; Physical Geography; Engineering; Genetics; Hydrology;Toxicology

Social Science Anthropology; Archaeology; Sociology; Human Geography;Economics; Psychology; Political Science; Policy Analysis

Interdisciplinary Combination of training in at least one natural and one social sciencediscipline

Other Discipline e.g. Education; Mediation/Facilitation; Natural ResourceManagement; Conservation

cohesion measures was used to describe and compare the connections within sub-networksof actors working on specific topics. These measures included average actor degree (numberof ties to other nodes), link density, number of weak components, the percentage of nodesthat were isolates, fragmentation, and closure (Borgatti, Everett, and Freeman 2002; Prell2012; Scott 2000; Wasserman and Faust 1994). Actor degree was also calculated for eachnode in the network sample to identify actors with the highest degree centrality.

Collaborative relationships in the network of Puget Sound marine and nearshore re-searchers were assumed to be reciprocal (i.e., to lack directionality). In a non-directionalnetwork, no distinction is made between in-degree and out-degree ties. This affects result-ing measures of density and centrality, because whether or not both actors in a pair of nodeshave nominated each other, the relationship is assumed to flow in both directions.

Results and Discussion

Results and Discussion are divided into five sections: (1) Descriptive statistics of the networksample and topical sub-networks, (2) Analysis of collaborative ties within and betweendisciplinary categories, (3) Comparison of fragmentation and other cohesion measuresacross topical sub-networks, (4) Incentives and barriers to collaboration, and (5) Fosteringhigh-impact research.

Descriptive Statistics of the Network Sample and Topical Sub-Networks

Responses to the on-line survey resulted in a matrix of ties representing collaboration be-tween actors in the Puget Sound marine and nearshore science network. Data were collectedabout a total of 522 individuals, including the 253 survey respondents, and an additional269 individuals who were nominated as collaborators but did not personally fill out thesurvey. Although a broad definition of collaboration was included in the survey instrument,a majority (92%) of ties reported by survey respondents represented the highest intensitycollaborative relationships: joint research or consultation relationships. The remaining tiesrepresented information-sharing or other close working relationships.

A total of 1892 symmetric ties were present, representing 946 collaborative relation-ships. The overall link density was quite low (0.007), indicating a sparsely connectednetwork. Of the 522 nodes (actors) in the network, only 11 (2%) were not connected to

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the main component (the largest group of connected nodes), and the network containedno “isolates” (i.e., nodes lacking any ties to other nodes in the network). These featureswere reflected in the low fragmentation of the network sample (0.042). The relatively lowdensity of ties was reflected in low scores for both closure (0.129) and average actor degree(3.625).

The low density was in part an artifact of sampling: survey respondents were instructedto nominate the individuals with whom they most frequently collaborate. In addition,due to timeline and funding constraints, a follow-up survey was not sent to nominatedcollaborators. This resulted in a large number of “pendants” (i.e., nodes that are onlyconnected to the nominating individual).

The Puget Sound marine and nearshore science community consists primarily ofnatural scientists (Figure 1). The size of nodes in Figure 1 reflects the degree centralityof each actor, highlighting the individuals with the greatest number of connections in thenetwork. The distribution of disciplinary training among the 50 actors with the highestdegree centrality closely matches the distribution of disciplines throughout the sample:80% of actors in the overall network were reported to have disciplinary training in oneor more natural science disciplines, and 80% of the 50 most central actors were naturalscientists; social scientists accounted for 6.5% of the entire sample and 8% of the 50 mostcentral actors; interdisciplinary scientists made up 11.5% of the overall sample and 12% ofthe 50 most central; 2% of actors were reported to have training in some ‘other discipline’,and none of these were included in the 50 most central nodes.

Figure 1. Map of entire network sample, sized according to the degree centrality of each actor(total number of ties). Nodes are arranged by disciplinary training. Shapes pertain to the disciplinarycategories specified in the legend. (Number of nodes = 522, Density = 0.017, Average Degree =3.625, Fragmentation = 0.042, Closure = 0.129)

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Figure 2. A) Percentage of entire network (522 total nodes) reported to have training in eachdiscipline. B) Percentage of entire network (522 total nodes) reported to focus on each topical focusarea. It is important to note that focus area is not necessarily an indication of the individual’s training.

Disciplinary training with the greatest representation in the sample included ecology(60% of nodes reported training), biology (48%), and fisheries science (38%). Humandimensions disciplines with the greatest representation of training included policy analysis(12% of all nodes), sociology (4%), and economics (4%) (Figure 2A). Informants reportedtheir topical focus, with biological, ecological, or physical processes as the most common.The greatest number of actors in the network was reported to study habitat (49%), fol-lowed by habitat restoration (36%), water quality (35%), salmon (32%), and food webs(32%). Social science or policy-related focus areas studied by the greatest number includedpolicy implementation and effectiveness (22%), governance processes (22%), and envi-ronmental perception and public awareness (19.3%). Topics such as algae and seagrass,

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climate, disease, sediments, shellfish, aquaculture, groundfish, tourism and recreation,human health, and stormwater were reported as “other topics” not included on the originallist (Figure 2B).

In addition to discipline and topical focus area, survey respondents were asked to notetheir employer as well as the employers of their collaborators. The greatest percentage ofnodes in the network were employed by academic institutions (34%), while 23% workedfor U.S. federal agencies and 16% worked for Washington State agencies. Employees oflocal agencies (city/county) made up 5% of the actors in the network, as did consultants.Employees of tribal entities and employees of non-profits each made up an additional 6% ofactors. Additional employer categories that made up small percentages of the sample wereCanadian agencies, including federal, provincial, and local level (1.1%), industry (1%),small businesses (0.4%), public outreach and education (0.4%), interagency partnerships(0.4%), and funders (0.2%).

Analysis of Collaborative Ties within and between Disciplinary Categories

The relational contingency analysis tool in Ucinet was used to examine the probability thatwithin- and between-group interactions were more or less frequent than expected on thebasis of chance alone. This test was applied to all nodes in the network, partitioned bydisciplinary category (Table 1). The frequency of ties within and between categories wassignificantly different from a random network (χ2 = 161.074, p = .0001). Two disciplinarycategories were shown to have the highest rates of within-group collaboration: socialscientists (observed/expected = 5.80) and interdisciplinary scientists (observed/expected =2.20). Natural scientists were shown to collaborate with other natural scientists at about theexpected rate (observed/expected = 1.06).

Between-group ties were lowest in the case of natural scientist–social scientist collab-orations (observed/expected = 0.42). However, the rate of collaboration between naturalscientists and interdisciplinary scientists (i.e., those with training in both natural and socialscience disciplines) was shown to be at about the expected rate (observed/expected = 1.02).This result suggests that natural scientists more readily collaborate with social scientiststhat also possess natural science training.

Differences in collaborative behavior between scientists of different disciplines havebeen shown to result from differences in the nature of the work they are engaged in (Birnholtz2007); it is not reasonable to expect that the rate of collaboration between these disciplinarygroups be equal. Information about rates of collaboration across disciplines may be mostuseful when assessing changes and trends over time (Hossain and Fazio 2009). However,based on the current results, further integration between natural scientists and individualswith social science or other disciplinary training appears to be an area of opportunity.

Comparison of Fragmentation and Other Cohesion Measures across TopicalSub-Networks

Densely connected sub-groups are one form of network structure hypothesized to con-tribute to information flow and productive working relationships (Burt 2001; Rogers 1995).Identification of less cohesive sub-networks within the Puget Sound marine and nearshorescience community could present opportunities for managers to foster and enhance existingcollaborative relationships through network interventions.

Useful cohesion metrics include fragmentation, closure, and link density. Sub-networksthat score highest in fragmentation have the most nodes that are not connected to one another.

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Figure 3. A) Map of the "Environmental Perception and Awareness" topical focus sub-network. Thissub-network recieved the highest fragmentation score of all topical focus groups. (Number of nodes= 101, Density = 0.014, Average Degree = 1.386, Fragmentation = 0.887, Closure = 0.090). B)Map of the "Salmon" topical focus sub-network. This sub-network received the lowest fragmentationscore of all topical focus groups. (Number of nodes = 166, Density = 0.017, Average Degree = 2.855,Fragmentation = 0.289, Closure = 0.165). Shapes pertain to the disciplinary categories specified inthe legend.

Sub-networks that have thelowest closure scores have the lowest number of connectedtriples (i.e., three individuals that are all connected to one other). Closure is theorized tofacilitate both trust and flow of information (Burt 2001; Prell 2012). Link density is theratio of existing ties over the number of possible ties. It is important to note that density isnot expected to hold constant when comparing networks of different sizes, and is primarilyuseful as a baseline for identifying changes within the same network over time, or tocompare against networks of similar size and relationship type (Scott 2000).

Table 2 presents cohesion metrics for each topical focus sub-network, excluding the“other topics” category. Comparison of these metrics reveals that human dimensions–relatedtopical sub-networks tend to have the lowest cohesion. The three topical focus networksthat scored highest in fragmentation and lowest in closure—both indications of lowcohesion—are Environmental Perception and Awareness, Culture and Human Well-being,and Governance Processes. The three natural science-related topical focus sub-networksthat scored highest in fragmentation (low cohesion) were Birds, Marine Mammals, andShoreline Processes, while the lowest closure scores were reported for Ecosystem Services,Marine Mammals, and Water Quality. The lowest fragmentation scores (highest cohesion)were reported for the Salmon, Water Quality, and Habitat sub-networks.

Figure 3 presents illustrations of the most fragmented (Environmental Perception andAwareness) and least fragmented (Salmon) topical focus sub-networks. In the case of thesalmon network, 84% of the nodes are included in the main component, whereas only 27%of the nodes reported to study environmental perception and awareness were included inthe main component. In addition, the salmon network had a lower percentage of isolates(11%), compared to 21% in the environmental perception and awareness network.

One individual often studies multiple, sometimes related, topics; actors identified towork on salmon issues may also work on environmental perception and awareness along

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with a variety of other topics. A majority of topical focus groups contained individuals fromdiverse disciplinary categories. Natural scientists were present in all topical sub-networks.Social scientists were present to some degree in all but three sub-networks. Social sciencetraining is also represented by actors coded as “interdisciplinary,” which were found in allsub-networks. Individuals with training strictly in “other disciplines” such as education,law, management, and mediation/facilitation were present in 10 sub-networks.

Individuals with training only in social science disciplines were present at the highestrates in the four human dimensions related sub-networks: Culture and Human Well-being(34% of nodes were social scientists), Governance Process (20%), Policy Implementationand Effectiveness (18%), and Environmental Perception and Awareness (14%). In addition,social scientists made up a relatively large percentage of actors that were reported to studyEcosystem Services (11%). Individuals with training strictly in “other disciplines” werealso most represented in the human dimensions–related topics, making up between 2% and4% of these sub-networks. In addition, 2% of actors in the Water Quality sub-network werecoded as “other disciplines.”

Incentives and Barriers to Collaboration

Incentives toward Collaboration. The first of the open-ended survey questions pertainedto respondents that answered “yes” to the survey question: “Are there incentives for youto collaborate with researchers?” The open-ended question was then: “If yes, what are theincentives?” The following were the most frequently stated responses in order of frequency.Many of the 178 survey respondents who answered this question provided more than oneresponse, for a total of 226 distinct responses:

1. Incentives regarding the need for information or other resources that are not readilyavailable (76 respondents)◦ Unfamiliar methods made available by consulting another researcher◦ Access to complementary data◦ Access to additional areas of expertise◦ Additional field sampling access through other researchers’ activities◦ Increased diversity of ideas and other ways of thinking about problems

2. Incentives regarding funding (69 respondents)◦ Increased likelihood of receiving grants◦ Increased access to funded projects◦ More efficient use of existing funding

3. Incentives regarding the increased quality or impact of project outputs (46 respon-dents)◦ Higher quality science◦ Increased effectiveness of scientific results◦ Better research proposals◦ Can accomplish more projects

4. Incentives regarding personal rewards or other intrinsic benefits (35 respondents)◦ Collaborative work is more interesting◦ Increased motivation and inspiration◦ Increased creativity◦ Increased excitement◦ Gaining new perspectives/learning

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As a complement to the incentives identified in the survey, focus group, and key-informantinterview participants were asked to identify factors that help facilitate collaboration. Manyfocus group responses reflected similar themes of cost savings, personal motivations, anda need for information or expertise that was not readily available without collaborativeefforts. Key-informants reiterated a number of the incentives raised in focus groups andopen-ended survey questions, such as the motivation of increased funding opportunities orcost savings. In addition, key-informants also identified personal incentives to be importantdrivers motivating them to collaborate. At times, the incentive was identified as a personaldesire to do the best job possible and to make a difference in their field, even if their jobdoes not necessarily require that level of dedication.

Incentives [for collaboration] are few and far between. For those who justtruly want to make a difference or influence policy and management, then theincentives are more personal and not often rewarded in a departmental sense.(Independent, interdisciplinary researcher)

Necessity is not really the case. You don’t need to reach out to do your job, butif you want to solve problems in real places it is a necessity. (Funds managerfor Puget Sound restoration)

The above quotes demonstrate that diverse informants found utility and fulfillment inpursuing collaborative relationships, especially when considering how they meet theirpersonal and professional goals.

Barriers to Collaboration. The second open-ended survey question states: “Whatare challenges or barriers to collaboration with researchers?” The most frequently statedresponses to this question follow. Some of the 148 survey respondents who answered thisquestion provided more than one response, for a total of 167 distinct responses:

1. Challenges associated with time constraints (75 respondents)◦ Time spent maintaining collaborative relationships◦ Respondents are generally overworked◦ Time spent researching potential collaborative projects◦ Time spent learning about others’ areas of expertise◦ Difficulty in coordinating schedules

2. Challenges with funding (68 respondents)◦ Funding is generally lacking for long-term collaborative projects◦ Institutional funding structure does not lend itself to collaboration◦ Costs associated with travel for meetings with collaborators◦ Lack of funds to support the effort of seeking collaborations

3. Challenges with varied institutional cultures or vision (24 respondents)◦ Varied agency mandates and expectations can lead to poorly focused studies◦ Researchers and policymakers have different purposes for their work◦ Generally incompatible institutional policies or “red tape”◦ Competing long-term and short-term goals

When focus group participants were asked to identify challenges and barriers to collab-oration, their responses strongly corroborated survey data describing barriers associatedwith time constraints, lack of funding, and variable priorities. Funding was a particularlycomplex subject, as it was reported to act both as an incentive and a barrier to collaboration.

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In cases where collaborative funding was available, it incentivized researchers to collabo-rate. However, the general lack of these types of funds, or the lack of funding structuresthat permit creative collaborations, also seemed to prevent the collaborative process fromoccurring freely.

Key-informant interview participants were not specifically asked to comment on theirperceived barriers to collaboration; however, a number of respondents voluntarily identifiedbarriers to collaboration through the course of conversation. Many of these barriers pertainto information already learned from the survey and focus groups, such as time and fundingchallenges, but a few interesting points differed, such as challenges caused by the inherenttransaction costs associated with starting new interdisciplinary collaborations.

. . . it’s risky due to the opportunity and transaction costs that come with collab-oration when you’re working with new disciplines, and there’s always a longstart-up speaking the same language, due to jargon in some new collaborations.(Independent, interdisciplinary researcher)

The lingo, literally the language and terms, are not well understood betweendisciplines. (Federal agency policymaker and science supervisor)

Indeed, transaction costs, particularly costs involving time constraints, are an importantpoint that has been highlighted as a defining feature of collaborative projects involvingresearchers and policymakers and interdisciplinary research teams (Cummings and Kiesler2005; Jakobsen, Hels, and McLaughlin 2004; Weber 1998). Ideally, collaborative arrange-ments in which various skills, knowledge, or resources are combined should decrease theoverall transaction costs of conducting effective research, otherwise there would be little tono incentive to participate.

Transaction costs are not the only factors that present barriers to collaborative pro-cesses. As Weber (1998) noted, reduction of costs alone may not be sufficient incentiveto participate in successful collaborations. Informants also remarked on frustrations expe-rienced due to varied institutional cultures and lack of recognition. Additionally, as hasalso been noted by Jakobsen, Hels, and McLaughlin (2004) and Cummings and Kiesler(2005), respondents mentioned problems of generalized institutional failures in establishingcollaborative relationships and commitments.

Having people be self-aware enough to place themselves in a collaboration issomething we don’t teach, and it doesn’t get recognized. (Academic interdis-ciplinary researcher and science supervisor)

You didn’t get the feeling that the Partnership was really looking for partnersamong scientists. (Academic natural science researcher)

. . . there were a lot of internal staff management problems [at the PSP] . . . .therewas no feedback like there was in the Shared Strategy [for salmon recovery]where we would give science, then they would implement it, then they wouldcome back with another follow-up question and we would analyze that. (Inde-pendent, interdisciplinary researcher)

Fostering sustained collaborations is complex and difficult. The creation of incentives,norms, and mechanisms to foster collaboration is essential. While there is clearly

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considerable communication and collaboration taking place in the realm of PugetSound marine and nearshore science, various informants were concerned that mecha-nisms to sustain collaborations are lacking. Researchers and policymakers frequentlyfelt challenged to meet the demands of their appointed positions, as well as PugetSound recovery. Sustained collaborations suffer as personnel are stretched thin in theworkplace.

Researcher-Specific Incentives and Barriers to Collaboration. During the analysis ofcollaborative incentives and barriers, it became clear that active researchers have specificcollaborative circumstances that differ from non-researcher constituents of the Puget Soundnearshore science community. This is particularly true for researchers working within theacademic community, where historical differences in the culture and values of academicresearch and applied research became readily apparent (Stokes 1997). Some respondentsfelt that the incentives to collaborate outside normal institutional boundaries were presentand growing.

I think NOAA science benefits from having a connection to academic sciencebecause I think it keeps the science more up to date. Then academics stronglybenefit from being able to work on applied research because it makes ourresearch more relevant. (Academic natural science researcher)

As a scientist, [it is becoming] clearer and clearer that human activities aredriving changes, and it is good for me to work with people understandingthe drivers of human dynamics. The applied ecology literature is increasinglymoving toward interdisciplinary science, so the bar for getting papers publishedis getting higher and higher for actually doing interdisciplinary work. (Federalagency natural science researcher)

However, many respondents also felt that these institutional challenges remained problem-atic in promoting cross-institutional, interdisciplinary collaborations. There are barriers oflanguage, norms, and prestige, particularly in an academic setting.

. . . if you want to collaborate and write a paper, you’re an economist, andyou want to collaborate with an ecologist or behavioral scientist, then maybeyour department will look at a paper that you publish in a weird journal thatthey don’t recognize and not value it as much. (Independent interdisciplinaryresearcher)

. . . [academics] have a different rewards system since they are generally look-ing for peer-reviewed pubs, which are slightly less valued in the governmentsystem. (State agency natural science researcher)

There are also challenges of meeting financial and professional goals through interdis-ciplinary collaborations that involve institutions with distinct expectations and cultures.Funding structures within research disciplines often do not accommodate novel, or innova-tive collaborative project designs.

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[Many] academics are living on soft money, so it’s harder to engage them ina substantive way unless there is a project or funding source. (State agencynatural science researcher)

I think the scientists there like having external collaborators but the fundingstructure doesn’t allow it very well. (speaking of agency scientists) (Academicnatural science researcher)

In summary, while many academic scientists are interested in collaborative scientificrelationships, incentives, funding, and institutional structures within the academy, andamong affiliated academic scientists, do not always foster collaboration and may notbe keeping pace with the demand for collaborative scientific pursuits beyond academicboundaries.

Fostering High-Impact Research

Interview informants were asked to reflect on the kinds of research projects they felthad the most traction and momentum toward making significant impact on Puget Soundrecovery. It is not surprising that many “applied” research projects fell into the high-impact category for respondents, as these projects are often driven by management needs.However, in addition to traditionally management-driven projects, research programs thatengage the public and directly include policymakers and resource managers were fre-quently identified by key-informants as high-impact. Through the semi-structured inter-views, it became clear that collaborative arrangements in Puget Sound spanned a farmore diverse network of people than just those involved with, or connected to, primaryresearch.

When asked what qualities had contributed to the increased impact of their chosenprojects, a number of respondents spoke of collaborations within institutions customarilyviewed as separate and distinct: academics, citizens groups, stakeholders, and county orhigher level government representatives. Additionally, a number of respondents commentedthat projects producing particularly useful outputs were successful as a result of effectiveinterdisciplinary collaborations or cross-discipline communication that often included thesekinds of groups.

. . . has a lot of traction because her research combines so many aspects ofphysical drivers dealing with oceanography, chemical drivers, and biologicaldrivers that actually impact people with toxic shellfish. (Federal agency naturalscience researcher)

In some cases, these collaborations were actually a defining feature of the project’s initialdesign. They were conceptualized in such a way that they would ultimately be useful to abroader audience than simply the scientific community.

Our approach is to mindfully work with the end user from the beginning.[We] try to talk to people who use the information and they help constrainthe question. Sometimes it’s a NGO or government entity and then [we] make

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sure the academic information can be used. (Independent, interdisciplinaryresearcher)

. . . [projects that] produce results that are usable, very applied, and veryapplicable. . . . They were modeling studies done in consultation with thepeople who were going to use the results. (Tribal researcher and sciencesupervisor)

It was a high-level political leadership call for good science, and that broughtall the scientists. It really motivated everybody. . .these political leaders saidthey were going to take this seriously and adjust their decisions based on theanalysis, and they did. (Independent, interdisciplinary researcher)

It’s not necessarily the best research that has an impact, but it is designed sothat it will work for people. (Tribal researcher and science supervisor)

In other cases, respondents described participatory collaborative processes that en-hanced the nature and effectiveness of projects.

We met with agencies every six months and they provided input and asked ques-tions. . . . They were really good about listening to what we were finding . . .

(Independent, interdisciplinary researcher)

Hood Canal Coordinating Council’s Regional Watershed Plan has the potentialfor a lot of traction because a lot of stakeholders are at the table and they havehad them there for a while . . . also a good team of scientists to inform that plan.(Non-profit natural science researcher)

There was that circle-back that brought back the problems of Puget Sound tothe public eye, a social political management dynamic. (Federal natural scienceresearcher)

These projects also often spanned traditional organizational and institutional boundaries.Respondents were also asked to identify obstacles or barriers to impactful research in

Puget Sound. Not surprisingly, lack of funding was the most frequently identified barrier toimpactful research, but many other collaborative barriers, particularly between the researchand policy communities, were also noted. Often these barriers were caused by a lack ofcommunication between researchers or individuals immersed in diverse disciplines.

The technical basis for this stuff is complex, and we who understand it somewhatare not good communicators . . . and there’s some resistance from politicianstoo in getting to the essence of the problem. (Tribal researcher and sciencesupervisor)

Although the role is not always promoted or recognized, the importance of skilled “scienceliaisons” (Weber, Leschine, and Brock 2010), “translators” (Maglaughlin and Sonnenwald2005), and/or “scale-crossing brokers” (Cohen, Evans, and Mills 2012) has been identifiedin the literature as a means to lower the transaction costs of difficult communication. Theseindividuals provide a networking role that helps to bridge gaps, encourage synthesis of

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information, and communicate across disciplinary and institutional boundaries. A numberof respondents spoke of the need to better communicate information between disciplines,institutions, and particularly, policymakers.

. . . the impact comes from people who can tell stories. . . . Barriers to un-derstanding Puget Sound are getting things digested and used to the pointthat they impact policy. . .. (Academic interdisciplinary researcher and sciencesupervisor)

. . . it takes a constant drumbeat [from a] science leadership person who knowswhat’s going on [with] the Puget Sound policy side to sort of be that trans-lator and just talk to people and network. . . (Independent, interdisciplinaryresearcher)

[The work is] very solid fundamentally, but it’s translated, through [the re-searcher’s] efforts, into products that the policy community understands. Thatwork presented at fisheries meetings is state of the art and can also be com-municated successfully to policy folks. . . . What’s missing is the people whoare good at translating research, and good researchers are not compensated fortheir efforts in that area. (Academic interdisciplinary researcher and sciencesupervisor)

Several respondents mentioned leadership roles as highly important in promoting impactfulresearch. The role of leadership in successful collaborations was also noted by Weber(1998), Tress, Tress, and Fry (2005), and Christie (2011). Interview respondents notedthat this was especially true of projects that involved volunteered time or resources fromcollaborators who might have few other incentives to participate.

A lot has to do with leadership, I think, and a couple of people who are justtireless in their efforts. (Academic natural science researcher)

In a lot of cases, it takes a champion, someone who feels strongly that it needsto happen. They have to be diligent. Everyone is short on resources and time,so they have to make it worth people’s time. There have to be incentives.(Independent social science researcher)

. . . a lot of work gets done and a lot of things actually get accomplished becausethere are a few either very intense people that really kind of keep pressure oneverybody else to keep moving, or really charismatic leaders where everybodywants to follow. . . (State agency natural science researcher)

Based on these data, informants in this study agreed that cross-boundary collaborationshave a potential to deliver high-impact outcomes. These partnerships can also be enhancedby increased leadership, as well as enhanced communication among players within acollaborative effort.

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Conclusions and Recommendations

Numerous studies have noted that cross-boundary and interdisciplinary collaborations areincreasingly important across a number of different scientific communities, though chal-lenges remain difficult to overcome (Birkholtz 2007; Christie 2011, Cummings and Kiesler2005; Ruckelshaus and McClure 2007; Weber 1998; Weber, Leschine, and Brock 2010).As illustrated in Figure 1, the Puget Sound marine and nearshore scientific network con-sists mainly of natural scientists, perhaps a manifestation of how natural resource andenvironmental problems have been framed historically in the United States (Christie 2011).

Relational contingency analysis highlighted stronger than expected collaborativeties within the social science disciplines and between individuals with interdisciplinarytraining. This result suggests strong potential for information sharing within those groups.In contrast, results showed lower than expected collaborative ties between natural scien-tists and social scientists. Qualitative results also highlight barriers in interdisciplinary,cross-boundary collaboration that stem from differing cultures and values of institutions,as well as disciplinary language, norms, and prestige. Funding structures were also notedas a common barrier to non-traditional, collaborative research. However, some interviewand focus group respondents felt that incentives to collaborate outside normal institutionalboundaries are present and growing.

There is a growing interest in high-impact research that is explicitly designed toimprove policy. Researchers, science communicators, and policymakers are motivated tocollaborate because they feel it is effective to address complex policy concerns that havesocial and ecological dimensions. Successful interdisciplinary collaborations in the PugetSound marine and nearshore science community are clearly recognized as valuable compo-nents of high-impact, meaningful research projects. However, successful interdisciplinarycollaborations also require tangible, participatory incentives such as access to increasedfunding and adequate professional recognition for collaborative pursuits.

Analysis of topical focus group cohesion also helps identify opportunities to supportand enhance collaboration and information sharing within the Puget Sound marine andnearshore research community. Cohesion statistics reported in Table 2 showed relatively lowlevels of fragmentation (high cohesion) within salmon, water quality, habitat, restoration,fisheries, and forage fish topical sub-networks. The highest levels of fragmentation (lowcohesion) were reported for human dimensions-related topical sub-networks, as well assome of the natural science-related topics with the smallest number of nodes in theirnetworks (birds and marine mammals). The present findings demonstrate the potential ofcohesion metrics to identify fragmented sub-networks. However, further investigation isneeded to clarify the specific dynamics related to the fragmentation of each of these topicalsub-networks.

Specific recommendations are presented in two sections. First, recommendations areoffered for next steps in social network analysis research and potential network interven-tions that could be undertaken to foster collaboration within the Puget Sound marine andnearshore science community. Second, recommended policy actions are identified throughapplication of Weber, Leschine, and Brock’s framework of ‘assurance mechanisms’ (2010)to qualitative results generated in this study.

Suggested Next Steps in Social Network Analysis Research

Vance-Borland and Holley (2011) present network analysis methods and describe a sub-sequent network intervention based on the findings of their network analysis. Following

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analysis of key structures in their network, including cohesion within sub-groups, collabo-ration patterns within and between organizations and ecosystem groups, and bridging andbonding structures, the research team held workshops to present the findings to individualswithin the network. They discussed network maps and metrics, and encouraged workshopparticipants to think of ways they could develop projects with individuals they hadn’tworked with in the past. The process represented “strategic identification of specific otherswith whom a new relationship might be mutually beneficial” (Vance-Borland and Holley2011, 286).

Network analysis tools have the potential to assist the Puget Sound Partnership inidentifying strategic network interventions as well. A primary recommendation to facilitateuseful network analysis moving forward is identification of well-defined sub-groups withinthe Puget Sound marine and nearshore community of that are of interest for in-depthinvestigation. Bounding of the sample should be geared toward functional categories thatconsist of a manageable number of researchers. Such categories could be based on researchtopic (e.g., “juvenile salmon” or “eelgrass”), or on specific geographic areas (e.g., HoodCanal, or a watershed level).

Time and funding should be invested to achieve a more thorough sampling protocol thatgenerates more representative network samples. There is a need for baseline network datathat can be compared to across time (Hossain and Fazio 2009) and to researcher networksin other regions. Examination of cohesion metrics similar to the analysis presented in thisarticle could be useful in identification of network interventions to improve the efficiencyof the network structure (Cross, Borgatti, and Parker 2002; Valente 2012), and to buildcapacity for better coordination between science disciplines, institutions, nongovernmentalorganizations, and tribes. In addition, a more targeted, in-depth sample would possesscharacteristics allowing for exploration of other structures theorized to be important in thecontext of information sharing, collaboration, and sustainable natural resource management(Adler and Kwon, 2002; Bodin, Crona, and Ernstson 2005; Burt 2001; Prell, Hubacek, andReed 2009; Provan, Fish, and Sydow 2007; Rogers 1995; Vance-Borland and Holley 2011).

Policy Recommendations

Weber (1998) studied the nature of collaborative research in policy efforts and providesa framework of “assurance mechanisms” that lead to successful collaborations in an en-vironmental and political setting. Three of his five assurance mechanisms are particularlypertinent to this discussion and may be adapted for the Puget Sound arena. The followingpolicy recommendations are intended to apply to the majority of collaborative relation-ships identified throughout this study. Recommendations are general and do not distinguishbetween unique collaborative arrangements and their specific challenges.

1. Transaction-specific conditions must be met. Weber (1998) discussed transactioncosts in a context of policy implementation. However, the impact of a diverse rangeof transaction costs can be identified for collaborative research in the Puget Soundscience arena. The transaction costs (time and resources) of developing, implement-ing, and communicating research to a broad audience for higher impact are high.As has been shown, cross-boundary, collaborative processes often notably increasethe overall impact of research outcomes. But the transaction costs of maintainingthese collaborations are prohibitive at times. Significant savings could be achievedby employing collaborative methods, if there was incentive for the creation andmaintenance of long-term collaborations. This seems to have the potential to lead to

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higher-impact project outcomes. Consideration of the time and logistics required toestablish and maintain effective collaborations in funding allocations would greatlyincentivize this type of work, making collaborative projects more attractive.

In the current Puget Sound research arena, transaction costs without clearfiscal support mechanisms for these kinds of long-term, sustained, and strategiccollaborations may be prohibitive. Those who are capable of communicating acrossdisciplinary divides are often under-recognized for their efforts and successes, andfunding structures do not accommodate time and transportation needs that wouldpromote effective cross-boundary collaborations (Cummings and Kiesler 2005).Noted by respondents in this study, as well as by Weber, Leschine, and Brock(2010), is the value of skilled “science liaisons,” who could be employed to filla networking and communication gap. Again, the role of these individuals wouldbe one that ultimately reduces the transaction costs of maintaining lively, effectivecollaborations. Science liaisons help lessen the time and communication constraintsthat inherently exist between scientific disciplines, policy and management, andperhaps even citizens and stakeholders. Ideally, these individuals would be valuedand accounted for in the fiscal planning processes during the design phase ofcollaborative research projects.

2. Credible commitment to collaboration by “entrepreneurial” political leaders mustbe present. As was noted by respondents, political leadership is essential for suc-cessful collaborations that span across science and policy. When applicable, thiskind of leadership helps keep participants motivated and feeling that their work isdirected toward a real outcome or impact. Although in Weber’s (1998) study, hespecifically noted the need for these kinds of “entrepreneurial political leaders,” thepresence of committed and charismatic leaders even outside the political scope, hasbeen shown to greatly enhance collaborative processes. Efforts such as the SharedStrategy for Puget Sound were highly valued by informants in part due to the high-level political leadership that was committed to maintaining ongoing dialogue withthe technical side of the project (Weber, Leschine, and Brock 2010).

If there isn’t a call from people who can actually influence decisions,it’s not really going to go anywhere. (Independent, interdisciplinaryresearcher)

Identifying and supporting leaders who will foster and shepherd collaborative pro-grams is clearly a useful investment.

3. A reputation for commitment to the collaborative processes by governing agencies.This study demonstrates that increasing the support of innovative and collaborativeresearch projects is likely to lead to an increase in the overall impact of fundedprojects. This appears to be particularly true of collaborations that include endusers, researchers, and policymakers in design and implementation activities. Acommitment to collaborative efforts from a governing organization would alsoencourage public support, as it proves the agency is flexible and willing to considercooperative arrangements. In the context of Puget Sound, a demonstration of thiscommitment to collaboration could manifest itself as increased funding specificallyfor collaborative, interdisciplinary projects. These might include National Centerfor Ecological Analysis and Synthesis (NCEAS)–type working groups, employment

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of science liaisons who work among a number of funded projects, or other creativecollaborative arrangements.

Acknowledgments

The authors thank Diana Pietri, Amber Himes-Cornell, and the two anonymous reviewersfor their insightful comments during the development of this article. Thanks also to all whotook the time to participate in the study.

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