Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”

FORPOL-01185; No of Pages 8 Forest Policy and Economics xxx (2014) xxx–xxx

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Managing information in forest policy networks: Distinguishing the influential actors from the “postmen” Nikolaos D. Hasanagas ⁎ Aristotle University of Thessaloniki, University Forest Administration, Pertouli, Trikala GR 42032, Greece

a r t i c l e

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Article history: Received 3 March 2014 Received in revised form 4 September 2014 Accepted 7 September 2014 Available online xxxx Keywords: Network structure Salience Internal resources Expertise

a b s t r a c t What differentiates the influential actors imposing information as salient from a “postman” who redistributes it is the main question of this research. Complete network analysis has been conducted in 8 European countries including 421 actors. Determinants differentiating influential actors from “postmen” in general information arena are: a) to be capable of achieving single strategy (by not having to be confronted with strong internal individual potentials, namely members with considerable strengths), b) to be free of dogmatism, c) to have powerful cooperators, and d) to employ appropriate expertise, characterized by objectivity- and specialty-based legitimacy. Determinants differentiating influential actors from “postmen” in scientific information arena are: a) to gain the trust, b) to intend cross-sectoral cooperation, c) to be a scientific institution, d) (or) to be an interest group, and e) to employ appropriate expertise characterized by credibility. Scientific institutions (universities and research units) are not so distinct in scientific information compared to non scientific participants. Internal resources related to member strengths or qualifications, or external consulting do not make significant difference on the salience or “postman” role. Extensive expertise does not favor the salience or “postman” role in scientific information. Salience of appears to be power-dependent. © 2014 Published by Elsevier B.V.

1. Introduction In order to clarify the basic question of this paper, it is appropriate to begin with an example of everyday life: If someone wants to have an application for a loan approved by the bank, ideally he should intend to be directly informed from the director of the bank about the progress of his application so as to try to influence him before he makes the final decision and not to be informed from the clerical assistant of the bank who is going to bring the answer as a “postman”. In case of the bank, it is clear who the decision maker and who the “postman” is. In case of a policy network (formal and informal structures of power and other relations focusing on particular issue), it is often unclear which actor (meaning not individual physical persons but any kind of organization such as public agency, semi-state body, association or enterprise) is the one which imposes information as salient and thereby influences policy and politics and which a “postman” is that merely transfers this information to other participants of the network and the most it can do is only control the flow of the information. In lobbying it is important to invest time in trying to contact the actor which can select and impose information (and arguments) as relevant to the others (Krott, 2012) and, subsequently, is able to set agenda, norms and impose values and ideologies (e.g. what is "environmentally dangerous", what are the “appropriate solutions” for the “\main problems” of a ⁎ Tel.: +30 24340 91206; fax: +30 24340 91109. E-mail address: [email protected].

national forestry etc.) and not to lose time dealing with the one which plays the role of a “postman” of this information. However, many participants in a policy network cannot clearly distinguish the really influential actor from the “postman”. The basic question of this research is to propose features letting influential actors emerging and differentiating them from the “postmen” in general and in scientific information arena. The influential actors by definition attract numerous other actors which desire to be directly informed by them, as they regard their information as salient, namely relevant for "solving problems". What makes an actor “salient” for the others? The awareness of such determinants can be useful for an actor who desires to be influential or to a new participant in a network who intends to evaluate more accurately who the influential actor is so as not to lose time trying to change the mind of a “postman”. The influential actor possesses strong persuasion potential and can thus influence decisions, attitudes, policy contents or even politics. A “postman” can mainly destroy the flow of information by not redistributing it but it cannot intentionally promote (or hinder) a meticulously designed and intended change. In other words, distinguishing influential actors from “postmen” is a basis of effective lobbying. The main hypotheses and questions which are going to be examined are the following: - The power (in terms of trust, financial incentives and coercion based on irreplaceablity) strengthens the ability of an actor to impose selected information as salient

http://dx.doi.org/10.1016/j.forpol.2014.09.007 1389-9341/© 2014 Published by Elsevier B.V.

Please cite this article as: Hasanagas, N.D., Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”, Forest Policy and Economics (2014), http://dx.doi.org/10.1016/j.forpol.2014.09.007

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- Do science-related attributes and resources of an actor, such as its possible scientific identity (university or research unit), its state or private character, its external consulting or internal expertise, affect its salience in scientific or general information? And which kind of expertise does matter? - Dogmatism, though it is a characteristic of powerful actor, leads up to negative impact on its salience - Which are the main characteristics that differentiate salient actors from the “postmen”? Are they immanent characteristics and particular resources of an actor (non-network characteristics) or structural external conditions (network-related characteristics)? The correlation of certain network-related characteristics (s. vertical column of Table 1) and non-network-related characteristics (s. vertical column of Tables 2 and 3) with salience and “postman” property will be examined. - Not only the characteristics of the actor or its own resources (such us expertise) but also of its network environment, such as crosssectorality, may create the need to use scientific information as a tool of imposition and integration 2. Literature review The role of dominant information (Krott et al., 2013) has been emphasized as a core power-related element. According to Cash et al.'s (2002) model, three factors can be identified that shape the importance of information; salience, credibility and legitimacy. The salience, namely the relevance that information seems to have for the choices, is of direct significance for influencing while credibility and legitimacy can be examined as determinants influencing salience. Either an actor (scientific institutions) is influential or not should be critically examined (Pielke, 2007) in terms of salience. In extension of this logic, it will also be examined whether expertise does matter. The role of basic dimensions of power, namely gaining trust, offering incentives and exerting pressure by being irreplaceable, of cooperators (allies) power, of the dogmatic attitude of an actor to the others involved in the network and other network- and organization-related

characteristics (Hasanagas, 2004) as well as basic network characteristics such as cross-sectorality (Krott and Hasanagas, 2006; Giessen and Krott, 2009; Giessen, 2010) will also be examined. Janse (2006, 2008) has provided an insightful approach, proposing information types (policy process, legal instruments), personal-related sources etc. as well as a discussion on science/policy interface. He focused on questions about information supply and accessibility and he provided interesting results on the difference between policy-makers and scientists perceptions about what the relevance of information determines. The role of stakeholder participation and of the uncertainty articulation in the flow of scientific information (Joyce, 2003) as well as of possible conflicting scientific information in policy making (Ellefson, 2000) has also been discussed implying issues of reliability, and, subsequently, of credibility and legitimacy. However, further insights in the role and properties of particular expertise areas in policy arena would be desired. Böcher and Krott (2010) have pointed out the meaning of trust in forest and environmental policy making and have elaborated models of interaction between scientific and politico-administrative arena (RIU-models). Stevanov et al. (2013) have further applied RIU models in science-based policy advice trying to support them with detailed case studies. Further suggestions of letting these models function would be useful. 3. Method In this research, social network analysis has been applied in 27 forest policy-related networks of 8 European countries which have been entered in a single data bank from 2002 until 2011. The first actor of each network was randomly selected from a list of forestry actors and it was asked about a forest policy issue in which it was “successful” according to its self-assessment and about other contacts it had concerning this issue. Snowball sampling was continuously conducted in each network until new actors (“nodes”) involved in the particular issue cease to appear. Such a social network analysis is usually called “complete” though certain limitations can be posed, i.e. one may claim

Table 1 Network-related determinants of salience and “postman” role. Simple salience ingeneral information (CC %) .702(a) .000 Concentration of dependence gained by offering incentive (in-degree %) .228(a) .000 Concentration of dependence based on irreplaceability (in-degree %) .515(a) .000 Self-assessment of power of each actor (from 1 to 3) .154 .063 Radicalism ascribed by other actors in each network (from 1 to 3) −.033 .610 Dogmatism ascribed by other actors in each network (from 1 to 3) −.086 .191 Power of cooperators (total trust, incentive- and .422(a) irreplaceability-based dependence %) .000 Cross-sectorality of each network (from 1 to 11) sectors) −.109(b) .038 Unambiguity of program content of each network as perceived −.131(b) by each actor (from 1 to 3) .046 General information concentrated by each actor (in-degree %) .122 .062 Scientific information concentrated by each actor (in-degree %) .218(a) .001 Concentration of trust relation gained (in-degree %)

“Postman” of general information (BC%)

Simple salience in scientific information (CC %)

“Postman” of scientific information (BC %)

Tendency to pure salience in general information (difference: CC-BC)

Tendency to pure salience in scientific information (difference:CC-BC)

.348(a) .000 .243(a) .000 .384(a) .000 .128 .123 −.075 .254 −.011 .867 −.012

.485(a) .000 .306(a) .000 .382(a) .000 .091 .277 −.160(b) .015 −.084 .199 .102

.280(a) .000 .255(a) .000 .311(a) .000 .057 .496 −.149(b) .022 −.128 .051 .054

.063 .340 −.153(b) .019 −.050 .445 −.056 .504 −.023 .731 −.148(b) .023 .163(b)

.141(b) .031 −.076 .245 .017 .795 −.002 .980 −.051 .439 −.009 .889 .071

.881 .026 .626 −.069

.214 .095 .072 −.119

.510 −.186(a) .000 −.137(b)

.047 −.003 .955 .006

.389 .159(a) .003 −.017

.292 .710(a) .000 .536(a) .000

.068 .064 .333 .249(a) .000

.036 .377(a) .000 .457(a) .000

.924 −.579(a) .000 −.367(a) .000

.801 −.353(a) .000 −.305(a) .000

Spearman test. a Correlation is significant at the 0.01 level (2-tailed). b Correlation is significant at the 0.05 level (2-tailed).

Please cite this article as: Hasanagas, N.D., Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”, Forest Policy and Economics (2014), http://dx.doi.org/10.1016/j.forpol.2014.09.007

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Table 2 Non network-related determinants of salience and “postman” role.

Non-scientific institution = 0, scientific institution = 1 State vs. private character of an actor (state actors = 1, para-state actors = 1.5, private actors = 2) Non interest group = 0, interest group = 1 Non enterprise = 0, enterprise = 1 Heterogeneity of activity sectors of an interest group Member percentage with strength (economic, social, political) Member percentage with Higher Education degree

Simple salience in general information (CC %)

“Postman” of general information (BC %)

Simple salience in scientific information (CC %)

“Postman” of scientific information (BC %)

Tendency to pure salience in general information (difference: CC-BC)

Tendency to pure salience in scientific information (difference: CC-BC)

.019 .700 .025

.020 .685 .053

.126(a) .010 −.074

−.024 .622 −.137(a)

.021 .672 .032

.102(b) .037 .064

.619 .067 .176 −.093 .057 .085 .400 −.076 .474 −.175

.283 .081 .100 −.077 .114 −.109 .277 .190 .071 −.095

.131 −.005 .911 −.138(a) .005 .029 .770 .021 .842 .006

.005 −.186(a) .000 .080 .102 .044 .665 .097 .363 .083

.522 .075 .127 −.017 .727 .055 .588 −.259(b) .013 −.033

.191 .121(b) .014 −.117(b) .017 −.102 .310 −.082 .437 −.122

Spearman test. a Correlation is significant at the 0.01 level (2-tailed). b Correlation is significant at the 0.05 level (2-tailed).

that some answers can be not so disclosing or not so sincere and not all actors or relations will be disclosed but only those who are perceived as important by the interviewees. On the other hand, many actors have common partners or adversaries (namely other actors involved in the network as well). Thereby, the existence (or inexistence) of each actor which is possibly involved in the network is multiple-verified and its role (power properties etc.) is cross-assessed by several interviewees (actors that already have been detected) and not only by one. Thus, one may also claim that the answers are to certain extent verified and it is difficult to have an involved actor fully undisclosed at the end of the snowball sampling. Moreover, the standardized questions may be

formulated in such a way that they clearly asking about “all” or only i.e. “up to the three most important actors” that each actor has contacted concerning the particular issue. To certain extent, the correctness of a network research can also be assessed on the basis of how reasonable, repeatable and realistic the results are using also qualitative empirical material. The vectors of the networks were from the beginning measured as directed. Naturally, several of the relations proved also to be mutual during the snowball sampling. For example, actor A declared that it was providing information to actor B, but when B was also asked it was possible to declare that it also had offered information to actor A.

Table 3 The role of organized expertise in salience and “postman” role.

Use of external consulting Total internal expertise (number of disciplines employed by an organization) Specific disciplines of internal expertise: Architects Agriculture scientists Biologists Economists Civil engineers Forest scientists Geologists Informatics experts Lawyers Media scientists Political scientists

Simple salience in general information (CC %)

“Postman” of general information (BC %)

Simple salience in scientific information (CC %)

“Postman” of scientific information (BC %)

Tendency to pure salience in general information (difference: CC-BC)

Tendency to pure salience in scientific information (difference: CC-BC)

.176 .098 .168(a)

−.038 .722 .146(a)

.074 .490 .087

.065 .543 −.102(b)

.200 .059 .051

.080 .452 .079

.001 .001 .977 .185(a) .000 .067 .172 .162(a) .001 .126(b) .010 .086 .080 .063 .200 .104(b) .034 .132(a) .007 .026 .600 .035 .479

.003 −.097(b) .047 .022 .647 .110(b) .025 .172(a) .000 −.021 .667 .141(a) .004 −.073 .136 −.063 .198 .187(a) .000 −.043 .380 .100(b) .041

.076 −.016 .747 .148(a) .003 .044 .367 .094 .056 .099(b) .043 .047 .334 .035 .473 .139(a) .004 .110(b) .024 .070 .156 .024 .622

.037 .027 .576 −.040 .412 −.035 .480 −.102(b) .037 −.039 .428 −.100(b) .042 −.065 .182 −.057 .249 −.023 .638 −.073 .135 −.006 .905

.297 .069 .159 .110(b) .025 −.022 .648 .030 .545 .046 .353 .021 .676 .100(b) .042 .126(b) .010 .010 .836 .041 .401 −.031 .527

.108 −.094 .055 .104(b) .033 .004 .929 .110(b) .025 .043 .384 .092 .061 .075 .128 .139(a) .005 .059 .226 .104(b) .033 −.014 .771

Spearman test. a Correlation is significant at the 0.01 level (2-tailed). b Correlation is significant at the 0.05 level (2-tailed).

Please cite this article as: Hasanagas, N.D., Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”, Forest Policy and Economics (2014), http://dx.doi.org/10.1016/j.forpol.2014.09.007

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This is a method producing multifarious empirical results in policy analysis. Selected examples are the following: Malinick et al. (2013) have used network analysis for exploring involvement of environmental activists in news-communication networks. Primmer (2011) has applied a network approach in exploring learning mechanisms and information flow in forest policy. Chasioti (2010) and Papadopoulou et al. (2011) have applied it in rural development issues. Hasanagas et al. (2009) have analyzed power structures in hunting policy networks. How the network (“snowball”) sampling was carried out and how the various variables measured in this research were operationalized is in details described in accessible publications by Krott and Hasanagas (2006) and by Hasanagas (2004). Especially the salience grade of general and scientific information was operationalized by the algorithm of Closeness Centrality (CC) while Betweenness Centrality (BC) was used for measuring the property of “postman” (Hasanagas, 2012). This operationalization was followed, as CC expresses how close each actor of a network wants to be to each other actor. An actor has high CC, if each other intends to receive information from this actor directly (through one path) and not through other actors. In this sense, one expects that high CC characterizes an actor which is considered as important enough so that the others try to receive as directly as possible information from him. An actor which sends directly information to all other actors of the network has CC = 100%. In simplified words, BC expresses how many path chains of information is an actor involved in. An actor involved in numerous chains is automatically a go-between in information. In other words, it is a “postman” who can control the information flow in the network. If actor A receives information from actor B and sends it to actor C, then actor A has BC = 100%. The algorithms of CC and BC will not be presented here, as they are described in Hasanagas (2004, 2012) as well as in numerous websites. To detect more clearly the tendency of an actor to be salient in general or scientific information arena, or to become a “postman”, the following variable will also be examined: “tendency to pure salience” = CC-BC. In order to distinguish it from CC, the CC will be called “simple salience”. Determinants have been detected by Spearman test. This bivariate test has been used in order to avoid effect of outliers.

4. Results and discussion Although descriptive statistics does not pertain to the questions of this research, the following basic information about the 27 networks is provided: The whole empirical material included consisted of 6 networks from region Imathia, Greece concerning LEADER planning, implementation and evaluation in mountainous area, 2 networks from region Imathia, Greece, and 2 in region Etoloakarnania, Greece, concerning rural development program including issues of forest land use, 5 networks also in Greek regions Kozani, Veria, Kastoria, Thessaloniki and Drama, concerning afforestation, forest fire protection, wildlife management, rural animal breeding systems, wildlife habitat improvement, respectively, 1 network in Greece concerning revision of forest-related articles of the Constitution, 2 networks in Spain concerning forest genetics research on Castanea sativa and forest certification, 1 network in Ireland regarding provisional marketing services in forestry, 2 networks in Scotland about forestry strategy and Loch Lomond and the Trossachs National Park, 2 networks in Bavaria about eco-account and forest biotope mapping, 1 network in Finland about forest certification, 2 networks in Sweden concerning forest strategy and key biotopes, and 1 network in Denmark concerning forest certification. The networks included in total 421 actors (public actors, interest groups and enterprises). 216 of them were private and 205 public actors. The private actors included 147 interest groups, 55 enterprises and 14 scientific institutions (universities and research units). The public actors included 197 agencies of 11 sectors (forestry, rural development, tourism, nature conservation, angling, water management, culture, hunting,

taxation, spatial planning, community administration) and 8 scientific institutions. 4.1. Network-related determinants At first it should be clarified that the network-related determinants mentioned in the Table 1 are actually in part power components, namely dependence relations based on trust, incentive or irreplaceability. These can be based on internal organizational resources or resources received and transmitted through the network (third party actors). However, the question of this analysis is not what the basis of these power components is but whether and to what extent these tend to determine the salience and “postman” property of actors involved in the network. That also applies to the rest parameters of Table 1 (namely radicalism and dogmatism ascribed, self-assessment of power position, perceived unambiguity of policy network program, information and crosssectorality) which are also network-related, as they depend on the interactions among the actors involved in a network and on the relative position of each actor within it. All parameters of Table 1 have namely a systemic rather than individual character. They are operationalized and measured as in previous studies (Hasanagas, 2004, 2011; Krott and Hasanagas, 2006). In Table 1, the salience of an actor both in general (0.702) and in scientific (0.485) information is indeed fostered by trust, as it provides protection against criticism. The trust, however, seems to enhance also the role of “postman” in both types of information (0.348, 0.280). This means that trust does not only strengthen the persuasion potential of an actor about which information is relevant for a certain issue (either for “problem solving” or for “hindering solution”) depending on the interests of each actor (cf. Krott, 2012), but also strengthens the persuasion potential about which actor is apt or reliable enough for managing and controlling communication in a network. Using trust acquired, an actor can concentrate the information flow channels excluding other actors from them. The trustable actor can not only impose selected part of information as salient but also receive and possibly redistribute domestic information of the network, constituting other actors passive recipients. Of course, the trust, as any other dimension of power, is not unlimited or invulnerable. The trustable actor should not exaggerate and use the trust and manage the information reasonably in order to remain plausible and trustable. It is noticeable that trust seems to be specifically favorable for the strengthening of the pure salience in scientific information (0.141). This shows that trust (not critical thinking) tends to play a strong role in characterizing scientific information as important. The other two components of politico-administrative power, namely offering financial incentives and being irreplaceable in a process are also significant determinants of salience and “postman” role. Public or private actors offering incentives (i.e. funding of wood research, wildlife protection projects, improvement of hunting habitats) define thereby to high extent which general (0.228) or scientific (0.306) information is salient. Such actors are also strong in playing the role of a redistributive “postman” of domestic information of general (0.243) or of scientific nature (0.255). However, they tend to be “postmen” rather than salient agenda-setters at the level of general argumentation (−0.153). Thus, their power practically consists of controlling who will be involved in communication and cooperation process rather than of imposing arguments and issues. On the other hand, the interpretation of these correlations of offering incentives and control of information is also possible vice versa: Actors who have convinced that they possess “relevant” information, can attract more funding which enable them to offer more incentives. Last but not least, formal or informal institutional pressure derived from irreplaceability (Hasanagas, 2004) appears to be of significance similar to this of the incentive and trust concerning simple salience in general (0.515) and scientific (0.382) information as well as regarding “postman” role (0.384, 0.311). However, this coercive form of power

Please cite this article as: Hasanagas, N.D., Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”, Forest Policy and Economics (2014), http://dx.doi.org/10.1016/j.forpol.2014.09.007

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can apparently only exclude undesired sources of information without really improving the pure salience of an actor. The self-assessment of power of an actor within a network does not seem to be in clear accordance with its position in the hierarchy of salience or “postman” role (0.154, 0.128, 0.091, 0.057, −0.056, −0.002 insignificant). This is an evidence of the unawareness of the position of the actors in the whole network structure. This unawareness of their power position is a result of the unawareness of the whole network. Each actor usually knows only the restricted part of the network actors which it directly contacts. It has no idea of the whole pyramid of power and information structure. Radicalism seems to have a negative impact on the simple salience (− 0.160) and on the flow control (− 0.149) of scientific information. Evidently, a basic characteristic of scientific information is, even ostensibly, the dialog on the basis of analytical theory logic and empirical proofs, and surely not radical attitudes or behaviors. Dogmatism is the adoption of a belief independent from empirical data. Thus, dogma can be regarded as a “luxury” of powerful actors (“the powerful actor does not need to learn”). For this reason, dogmatism does not present any serious impact on information salience or control. However, it presents a slight negative impact (− 0.148) on the acquiring of pure salience in general information. That is, even powerful actors with the “luxury” of being dogmatic seem to lose in persuasion potential in general information. Another parameter which seems to enhance the simple (0.422) as well as the pure (0.163) salience of an actor is the power of its cooperators within a network. As this concerns only the general and not the scientific information, this is an evidence that normally these cooperators may tend to be external and not internal allies of science (cf. Krott, 2012). Such cooperators help an actor impose information which it has selected according to its values or interests, constructing a supportive sub-network around it. Interestingly, while salience and the subsequent argumentativeness seems to be enhanced by a sub-network of cooperators being a simple “postman” does not need any cooperator (or none is willing to help someone become a “postman”) (insignificant coefficients). In any case, persuasion seem to need—and they may have—allies while controlling information tend to be a lonely and individualized process. The impact of cross-sectorality appears to be negative both on simple salience (−0.109) in general information as well as on the control (−0.186) of scientific information. Normally, the leading actors in a policy sector do not desire any involvement of other sectors as they are afraid of subversive restructuring of the network and of losing their dominance (Krott and Hasanagas, 2006). Possible organizational, cognitive and communicative differences respectively suggested by Social Systems Theory, Advocacy Coalition Theory and Communicative Action Theory, as obstacles hindering cross-sectoral integration (Shannon and Schmidt, 2002, p.24) may be valid statements of analytical theory in certain cases while sometimes they may be only excuses of powerful actors for avoiding cross-sectorality. Numerous factors such as sectoral bias, existence of powerful sectoral institutions and maintaining autonomous sectoral funding instruments, lack of trust, of communication skills and capacity as well as uncertainty in policy outputs can also be serious obstacles to cross-sectoral integration and forestry sector seems to play a minor role being often characterized by negative attitude toward such integration (Giessen, 2010). The sectoral isolationism (i.e. the minor role of forestry in integrated rural development programs) can also be attributed to unwillingness to cooperate, to lack of opportunities, ability or institutional preconditions (Giessen and Krott, 2009). The afore-mentioned negative impact on the salience of general information of an actor involved in a cross-sectoral network can be understood as a consequence of lack of common beliefs according to Advocacy Coalition Theory (i.e. timber production supported by foresters, forest recreation in undisturbed forest supported by conservationists). Similarly, the negative impact of cross-sectorality on the role

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of “postman” controlling scientific information flow may also be attributed to communication difficulty due to special terms or models of analytical theory or just to unwillingness of actors to learn or even examine new approaches. However, appropriate use of information seems to be a possible basis of over-bridging sectors. The need of developing crosssectorality makes science emerge as a substantial possible basis for communication and legitimate argumentation among actors, breaking belief, communicative or organizational-institutional barriers (Krott and Hasanagas, 2006). Thus, there is a tendency (and pressure) to develop pure scientific salience (0.159) in order to break barriers and develop cross-sectoral cooperation. Namely, appropriate use of scientific information and subsequently trust and communication skills and capacity seem to be a crucial factor in developing cross-sectorality according to the results of this research which are in accordance with the qualitative findings of Giessen (2010) especially concerning the role of trust and communication which favor the adoption of innovative ideas and practices and the development of new partnerships from different sectors. However, using scientific argumentation for breaking barriers among different policy sectors does not necessarily mean breaking barriers among different scientific disciplines. These results concern only politico-administrative cross-sectorality and not cross-sectorality of scientific disciplines. Whether politico-administrative cross-sectorality also leads to integration of different scientific disciplines (e.g. building common theories or methods) or, in contrast, to a stronger bordering and distinction of them remains an open question. Unambiguous policy content (goals, means, standards, opportunities and restrictions) in a network can hardly allow a flexible selection of arguments which can present general information as salient (− 0.131). Under conditions of unambiguity about what and how should be done in the framework of a program, every actor is aware of its obligations, rights and ways of possible action. There are no many alternative choices about what it needs to know or not. For similar reasons, the possibility for an actor to play the “postman” who selectively intermediates and shares scientific information which is supposed to be interesting for the other actors is also restricted in a network with a clear political program (−0.137). Finally, a comparison between general and scientific information received by each actor may also provide insightful results: The receiving of affluent general information by an actor, as expected, strongly fosters (0.710) its “postman” role in general information, as one should receive in order to redistribute. However, it is noticeable that the receiving of general information also foster the role of “postman” in scientific information (0.377). Why? This can be attributed: a) to the fact that an affluent amount of general information may include or be directly connected with a part of more specific and insightful information which may be further presented as scientific, after a slight or no process at all, b) to the fact that general information is helpful for better understanding the dominant values, ideology and interests of the other actors of the network. Thereby, the “postman” can more easily find out and select scientific information which can be interesting for other actors. Naturally, achieving pure salience (−0.579, −0.353) is not the priority of an intensive recipient of general information. Instead, the recipient invests time and attention in redistributing it and not in imposing it as salient. The concentration of affluent scientific information does not favor the pure salience neither in general (− 0.367) nor in scientific (− 0.305) argumentation. However, it is conducive in simple salience and “postman” role both in scientific (0.249, 0.457) and in general (0.218, 0.536) communication. The last result about the favorable effect of scientific information “deposit” on the salience and control of general information can be attributed to the legitimacy assured by the objectivity of science, which leads up to influence on the agenda-setting and persuasion potential in the arena of general information too.

Please cite this article as: Hasanagas, N.D., Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”, Forest Policy and Economics (2014), http://dx.doi.org/10.1016/j.forpol.2014.09.007

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4.2. Non network-related determinants In Table 2, various non network-related determinants of salience and control are presented. At first place, it is noticeable that the scientific institutions (namely universities and research institutes) do not necessarily possess a distinct position in salient scientific information. The coefficients in simple as well as in pure scientific salience are far less than 1 (0.126 and 0.102, respectively). This is not expectable for institutions that are dedicated to science development. Moreover, these institutions present much weaker control potential in scientific information and not high enough relevance, rhetoric or legitimacy in order to hold any eminent position in salience or control of general information (insignificant coefficients), though they are supposed to have “scientifically” critical thinking to select and redistribute valid information. Thus, the scientific institutions are not free or able enough to be incorporated in the scientific arena of policy making, as one could expect. These coefficients of salience in scientific information are quite short for institutions which have a scientific mission and show that they are also not so strongly and unconditionally recognized as real science-holders. This can be attributed to the fact that the activity developed by universities and research units are either not adequately relevant to policy making, e.g. too theoretical or thematically inconvenient (cf. Janse, 2008), or too critical to be compatible with it. Concerning legal status, the more an actor approaches the status of a private organization, the weaker it becomes in the control of scientific information (−0.137). Inversely, state actors seem to be “stronger scientists” in policy making in terms of controlling information which is characterized as scientific. Why? On the one hand, the state sector needs science in order to assure legitimacy in quick and concentrative policy making. On the other hand, it has the luxury of investing time and resources in meticulous selection and reconstruction of scientific information. Examining the private sector in details, we can distinguish the different relation of interest groups and enterprises with scientific information; the interest groups tend to be weak scientific “postmen” (−0.186). However, it seems that they pursuit an ambitious influential position in the arena of scientific communication at a level comparable to that of scientific institutions (0.121). Namely, the universities and the research units do not monopolize any more what can be characterized as salient scientific information. This is understandable from the everyday experience: what is nowadays considered to be “scientific” knowledge is accessible by everyone by internet. Many interest groups can employ (partly or fully) clerical assistants who may also often be Higher Education graduates and efficiently conduct internet research, develop and actualize libraries and archives. Thereby, an interest group can be as competitive as a scientific institution in scientific information arena. Often they try in this way to be integrated in corporatist systems intending to influence state decision making by providing legitimate scientific information (e.g. governmental institutions often adopt protection biotope lists submitted by environmental groups). Particularly, such interest groups could become valuable mediators between scientific and politico-administrative arena and thereby promote the integration of research in policy making (RIU) as described by Böcher and Krott (2010). Interest groups could play this role especially in RIU models with durable cooperative interaction between research and integration leading to output utilized at practical or scientific level. Such cases are the so-called “preliminary research” and “research oriented toward scientific and practical utilization” (Stevanov et al., 2013). In RIU cases where multidisciplinary and cross-sectoral cooperation is needed, the mediation of interest groups (especially outside forestry sector, i.e. environmental or cultural associations) could accelerate over-bridging of sectors. In contrast to interest groups, enterprises seem to remain weak in simple (− 0.138) or pure (− 0.117) salience in scientific information. The enterprises prefer to seek clients and economic profit following specific paths of communication and cooperation in a policy network rather

than to massively persuade using scientific information. Acquiring salience or control potential in general information seems also not to pertain to their strategy and practices (insignificant coefficients). It is discussable, however, whether investing time and resources for improving their scientific or general persuasion or control potential would be worthwhile in order to favor their entrepreneurial or economic interests. Concerning heterogeneity of activity, whether an interest group is dealing with more or less activity sectors (i.e. forestry, multifunctional rural development, nature protection, angling, hunting etc.), this seems to be irrelevant to salience or control of information (insignificant coefficients), though dealing with numerous activity sectors implies the employing of numerous relevant experts. As will be presented in Table 3, interest groups employing experts of numerous disciplines do not achieve any strengthening of salience or “postman” role in scientific information (only in general information). Simultaneously, the heterogeneity of activity itself appears to be independent from any potential of persuasion or control of information, either general or scientific. In other words, it neither enhances nor harms the communication efficiency in politico-administrative arena. This lack of focus (specialization) does not seem to negatively affect the reputation of an interest group in the arena of scientific or general argumentation. This is understandable as the disadvantage of lack of focus is in part outweighed by employing experts who are going to deal with the different activity sectors (even if they develop general and not strong scientific argumentation, as discussed below). It is noticeable that the member strength does not strengthen the salience or control potential of an interest group in any type of information. Far from it, it seems to restrict the tendency for acquiring pure salience in general argumentation (−0.259). This is understandable as associations with strong members normally are confronted with difficulty in achieving coordination of the individual potentials of their members so as to formulate a single strategy of arguing (cf. Krott and Traxler, 1992). A result subversive to the dominant expectations is that the percentage of qualified members (Higher Education degree holders) of an organization does not seem to improve the salience or the control potential of any information type (insignificant coefficients). Thus, resource dependence model seems not to be verified in the case of argumentation potential, as the member qualification is one of the most obvious internal resources of an organization. Once again, the deficit in coordination of internal member-related potential seems to have external effect (weakness or non strength to utilize the member qualifications in the argumentation arena).

4.3. Does the expertise matter? As analyzed in Table 2, the unorganized expertise (member qualification), though it is supposed to be an internal resource of an organization, cannot be effectively coordinated so as to achieve any considerable favorable external effect in the scientific or general argumentation arena (insignificant coefficients). The same stands also for the case of outsourced organized expertise (Table 3). External consulting seems to take place for finding out specific practical solutions concerning the organization itself rather than for arguing outside in the policy arena at scientific or general level. Alternatively, an organization may use external consulting sometimes also for developing general or scientific argumentation. However, this mainly seems to satisfy a feeling of wishful thinking without really enhancing the salience or the control potential of the organization in scientific or general communication arena. If, i.e., an environmental group uses external consultants for arguing against timber production in a forest, then the opposing association of timber producers should also ask consultants, but for understanding the seriousness or not of the threat rather than for really constructing argumentation.

Please cite this article as: Hasanagas, N.D., Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”, Forest Policy and Economics (2014), http://dx.doi.org/10.1016/j.forpol.2014.09.007

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The total expertise which is permanently employed in the internal structure of an organization seems to make it a salient discussant or agenda-setter who selectively impose arguments (0.168) as well as a dynamic “postman” who controls (0.146) the information flow. However, this strengthening surprisingly occurs in general and not in scientific information. On the contrary, organizations which employ wide range of expertise present a rather restricted role of “postman” in scientific information (−0.102). This is understandable as a wide range of expertise is used for persuading in general political discussion that a “problem” exists rather than for offering specific suggestions for “problem-solving” (Hasanagas et al., 2007). Apart from that, an organization with internal expertise may use this in order to achieve its own political, economic or social success and not in order to invest time systematically in collecting external scientific information and redistributing it to others. Examining the employed disciplines more specifically, it is noticeable that not all of them are equally favorable for achieving salience or “postman” role in general or in scientific information. As nowadays in the light of multi- and interdisciplinarity, it is risky to categorize disciplines according to structural or morphological criteria (e.g. natural– technical disciplines may adopt sociological problematization, every discipline may present to certain extent “basic” character and export knowledge or function as an “applied” science adopting exogenous models and knowledge), the disciplines employed are ordered alphabetically in Table 3. It is remarkable that most disciplines are effective on enhancing the simple salience or the control achieved by an organization in general information. Disciplines which tend to enhance salience of general argumentation are Agriculture science (0.185), Economics (0.162), Civil engineering (0.126), Informatics (0.104) and Law (0.132). This can be attributed to the objectivity- and specialty-based legitimacy which characterizes them. On the basis of this legitimacy derived from “scientific laws” related to agriculture, economy, urbanization, technology and also from state laws, organizations employing relevant disciplines can select appropriate parts of knowledge and arguments in order to persuade about the values and general policy contents they adopt and to influence the agenda-setting, defining what is important to be discussed. Specific disciplines make an organization not only influential in general information but also “postman” who redistributes it. Such ones are also Economics (0.172) and Law (0.187), as these are bridges to various sectors of activity (e.g. Economics is a bridge to Environmental Economics, Accounting, Political Economy, a lawyers deals with Environmental Law, legislation issues of agricultural or forest associations, of technology etc.). Additionally, Biology (0.110) and Forest science (0.141) are two realms with multidisciplinary practical involvement (from general ecology to molecular biology issues and from wood technology to tropical forestry, respectively). This allows concentrating and redistributing general information about technical and political issues of a wide variety of policy sectors. Political science (0.100) is also a realm suing case studies and paradigms of several sectors (CAP, forest policy, wood industry etc.). Simple salience in scientific information seems to be fostered by using also Agriculture science (0.148), Civil engineering (0.099), Informatics (0.139), Law (0.110) and not significantly by Forest science (0.047 insignificant). This can be attributed to the fact that the first four disciplines elaborate arguments which are not only or necessarily based on multifaceted data or smart models but they are, at least ostensibly, characterized by directly recognizable credibility. For example, a new agricultural hybrid, a suggested plan for constructing a national road through a natural area, a new software for environmental education or a law suit concerning a forest issue can directly be evaluated as “successful” or “feasible” (or not), in contrast to a forest management plan the effects of which are going to be observable after several years or decades. No expertise realm enhances the role of “postman” in scientific information (− 0.102, − 0.100 or insignificant coefficients) for

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reasons similar to those which have been analyzed above in the case of total expertise (s. −0.102). Finally, pure salience in general information tends to be achieved by using Agriculture science (0.110), Geology (0.100) and Informatics (0.126), due to their strong legitimacy and credibility. Especially, Geology adopts quite specific models far away from the everyday political, social and environmental experience. This enhances its legitimacy, leading up to increased salience in case that geological issues are posed (e.g. to develop mining or not in a forest area). The highly legitimate knowledge of these realms function as a filer distinguishing the “important” from “unimportant” issues and general arguments, depending of course on the interests, values and ideology of each actor. Pure salience in scientific information is more susceptible to be developed not only by organizations employing agriculture (0.104) and informatics experts (0.139) due to the credibility of these realms, as explained above, but also by those which employ economists (0.110) and media scientists (0.104). The latter two disciplines may not be characterized by high credibility but they are based on other mechanisms of imposing information as “important”. The best “ally” of the economists is the fear of economic threats (cf. Pielke, 2007) and the hope of development opportunities. For this reason, they usually find acceptance or at least audience willing to hear about economic models and determinants (e.g. concerning wood market, energy pricing, development of mountainous areas, tourism). Media scientists may not have specific arguments to offer which can be used for “problem-solving” but they advise more accurately which arguments should be selected out of a large material of scientific data.

5. Conclusions: what makes an actor influential? In general, one can remark that most of the statistically significant coefficients are weak. This is normally the case in socio-political phenomena (strong coefficients usually appear when a socio-political correlation is so trivial and obvious that no research is needed). However, even weak coefficients can be useful as they can indicate slight tendencies and latent structures. Very weak coefficients may also be supportive for specific hypotheses like these regarding the weak role of scientific institutions (0.126 and 0.102) in Table 2. In this way, even very weak coefficients may contribute to the deconstruction of stereotypes and to a more critical view of social reality. In any case, social research has as a rule indicative and not accurately calculative function, in contrast to many analyses i.e. in natural or to certain extent also in economic disciplines (Hasanagas, 2010). It is supported that influential actor achieving to impose selected information and, subsequently, arguments as “important” and a “postman” who mainly redistributes and possibly control existing information are two properties which often are fostered by the same network-related determinants or immanent characteristics of an actor. However, there are also certain determinants which tend to differentiate an influential actor from a “postman” (namely maximizing the difference CC-BC). The most distinct of them in general information arena are the following: a) to be capable of achieving single strategy (by not having to be confronted with strong internal individual potentials, namely members with considerable strengths), b) to be free of dogmatism, c) to have powerful cooperators, and d) to employ appropriate expertise, characterized by objectivity- and specialty-based legitimacy. Determinants b, c and d have very weak coefficients while determinant a seems to be more crucial. The most basic determinants which differentiate an influential actor from a “postman” in scientific information arena are: a) to gain the trust of other participants in the network, b) to be motivated to develop cross-sectoral cooperation, breaking structural or cognitive barriers, c) to be a scientific institution, d) (or) to be an interest group (not a single enterprise), and e) to employ appropriate expertise which should be

Please cite this article as: Hasanagas, N.D., Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”, Forest Policy and Economics (2014), http://dx.doi.org/10.1016/j.forpol.2014.09.007

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characterized by credibility (or, at least, impression of credibility). All of them are characterized by very weak coefficients. It is noticeable that the scientific institutions only slightly are differentiated from “postmen” and that their influence is comparable to this of interest groups only in pure salience. Thus, they are not as competitive in scientific information as one could expect. Academic-scientific title is thus not enough for persuading. Massive resources such as member qualification look to be too difficult to be coordinated so as to be externally effective. External consulting cannot be proven to make a difference on the salience or “postman” role and the extensive internal expertise does not favor the salience or “postman” role in scientific information. Only employing appropriate disciplines seems to make effect. While salience of scientific information appears to be secondarily or not so strongly dependent on qualifications, titles and resources, it seems to be strongly power-dependent, particularly trust-dependent (salience of general information depends also strongly on trust).The fact that not affluent but selected expertise seems to be appropriate tool for achieving pure scientific salience implies that actors which search for scientific information have adopted established patterns of information which they regard as “scientific” or are critically thinking when searching for solutions. Thus, "useful science” seems to be a contingency-dependent cognitive structure (only specific disciplines appear to create persuasion potential). Thereby, there seems to be no wide scope of innovative and interdisciplinary action in policy making. How the notion of “science” is perceived by the actors involved in policy networks? How legitimacy, credibility and salience interact? To what extent the disciplines employed in forest-environmental and forest-related rural development policy networks are characterized by legitimacy effect or credibility? How and to what extent a “postman” can become (or be perceived) by the other actors of the network as salient, i.e. regarding financial means even if it has no any own financial resources but it only distributes information about funding resources? These are certain questions for future research which may further improve our understanding of science implications in policy and politics. References Böcher, M., Krott, M., 2010. Umsetzung des Konzepts einer modernen Ressortforschung im Geschäftsbereich des BMU. Umweltbundesamt, Dessau-Roßlau, Deutschland. Cash, D., Clark, W., Alcock, F., Dickson, N., Eckley, N., Jäger, J., 2002. Salience, credibility, legitimacy and boundaries: linking research, assessment and decision making. John F. Kennedy School of Government, Harvard University Faculty Research Working Papers Series. Chasioti, St, 2010. Applying socio-informatics in rural development policy. Case study: Etoloakarnania, Greece. Aristotle University of Thessaloniki. Department of Rural Economics, (Master thesis supervised by assist. prof. Eleni Papadopoulou). Ellefson, P.V., 2000. Integrating science and policy development: case of the national research council and US national policy focused on non-federal forests. Forest Policy Econ. 1 (1), 81–94.

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Please cite this article as: Hasanagas, N.D., Managing information in forest policy networks: Distinguishing the influential actors from the “postmen”, Forest Policy and Economics (2014), http://dx.doi.org/10.1016/j.forpol.2014.09.007