The “Netweave-Approach”—A Platform Combining Sociology, Resource Management and Psychology for Networking Conservation Stakeholders

SNA is a sociological approach that examines relationships and structures within networks, focusing on how nodes (e.g., individuals, organizations, or species) interact to understand the structure and dynamics of these interactions (Wasserman and Faust 1994). In conservation and resource management, SNA is used to map and analyze complex stakeholder relationships. By highlighting key actors and their connections, SNA helps identify influential entities that can drive regional conservation efforts (Cohen et al. 2012; Ernstson et al. 2010). Structural analyses of social networks allow for interpreting properties within stakeholder networks, such as network density or mutually separated subgroups, in the context of effective regional conservation (Bodin et al. 2017; Friedman et al. 2020; Mbaru and Barnes 2017; Morgans et al. 2017).

The Netweave Approach uses SNA to map the current relationships among regional stakeholders across the dimensions of “acquaintance” (both stakeholders know each other), “collaboration” (both have worked together on a project in the last 3 years; includes “acquaintance”), and “conflict” (both were involved in a conflict in the last 3 years; includes “acquaintance” and can overlap with “collaboration”). These data are valuable for individual stakeholder networking: If contact or even a previous collaboration already exists, a brief information to both stakeholders is sufficient to enable collaboration. If the two stakeholders do not know each other or are in conflict with each other, more complex interventions are necessary to initiate collaboration.

Additionally, using programs such as Gephi (Bastian et al. 2009), network data can be extracted from the Netweave Platform and visualized in a network graph, providing a “map” of the network that stakeholders can use to see their position within the regional network, while regional managers gain a graphic overview of current stakeholder interactions (Fig. 1). Observing a network map can reveal phenomena such as central actors with numerous connections or subgroups that are separated from the main network. Further analysis, if appropriate, can yield quantitative statistical data on actors with various functional centralities (Cohen et al. 2012; Ernstson et al. 2010; Przesdzink et al. 2021). For instance, Degree Centrality identifies actors with the most direct connections, indicating which stakeholders serve as key points of contact within the network. Betweenness Centrality, on the other hand, highlights actors positioned on the shortest paths between others, marking them as essential bridges for connecting different groups and facilitating information flow. Additionally, network analysis can highlight communities of actors poorly connected to the rest of the network (Girvan and Newman 2002; Newman and Girvan 2004; Przesdzink et al. 2021).

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However, a sole focus on the structural aspects of a stakeholder network overlooks the qualitative significance of connections within it. Given the limited time resources of all stakeholders, it is essential to consider whether connecting every isolated group to the broader network and creating networks as dense as possible truly adds value. Due to the transaction costs associated with maintaining contacts (Boschet and Rambonilaza 2018; Gallemore et al. 2015), a maximally dense network may not necessarily be a “good” network from the stakeholders’ perspective. To identify which (potential) interactions genuinely add value for stakeholders, it is necessary to assess their available and required resources, which can be achieved through Stakeholder Analysis (SA).

SA is a holistic approach for gathering information about stakeholders to understand their behavior, intentions and relations as well as to assess their influences and resources (Reed et al. 2009; Varvasovszky and Brugha 2000). The approach originates from political economy and management science (Grimble and Wellard 1997) and ensures that groups and interests of all stakeholders are fairly represented (Prell et al. 2009). It is particularly useful in complex situations in which the compatibility of interests of the involved is hampered and, thus, is frequently applied in the field of natural resource management (Grimble and Wellard 1997; Vogler et al. 2017). In this context, SA has been used primarily as an instrument for achieving specific goals, such as including stakeholders in a particular decision-making process (Reed et al. 2009) or as a decision-support tool (Bendtsen et al. 2021). Overall, the concept of SA is very flexible and can be adapted to the respective research or project context. It can, but does not necessarily have to, include all of the aspects mentioned above.

The Netweave Approach collects data on the availability of and the requirement for resources of the individual stakeholders. In our case, “resources” include, among other things, knowledge (typically specialized biological expertise in various categories, such as peatlands or birds), bureaucratic skills, financial resources, equipment, manpower, or project areas. A complete list can be found in the Supplementary Material “List of Resource Categories”. These resource categories reflect our pilot platform’s focus on conservation stakeholders in our pilot region. In other regions or thematic segments (e.g., climate protection), different categories would need to be selected independently (see Transferability). Using this data, it is possible to compare whether two stakeholders in the network possess a resource that each other requires—in this case, collaboration would be mutually beneficial. Data from the SNA can be used complementary to compare the current relationship between the two stakeholders and establish contact accordingly.

However, the members of stakeholder organizations are human beings whose actions are influenced by various factors, including psychological aspects such as the organizational culture they work in or their environmental worldview. Numerous studies emphasize the importance of focusing on these “human” aspects (Bennett et al. 2017; Niemiec et al. 2021; Sanborn and Jung 2021), including trust (Young et al. 2016), values (Biggs et al. 2011; Crow and Baysha 2013), and attitudes (Ansong and Røskaft 2011; Ray and Bhattacharya 2013). The influence of such psychological factors on interactions among stakeholder representatives was noted in our data acquisition interviews and a previous study in our pilot region (Przesdzink et al. 2024–1), using colloquial terms such as “harmonizing” or “being on the same wavelength.” The Netweave Approach employs organizational and environmental psychology frameworks to replace these colloquial expressions with specific scientific constructs.

In this paper, stakeholders refer not to individuals but to organizations. Organizations are characterized by core values, leadership styles, working methods, success criteria, and behavioral norms (Cameron and Quinn 2006). These cultural traits are captured as “Organizational Cultures,” shared among members (Glisson and James 2002), shaping management style (Sulich et al. 2021), decision-making, and member attitudes and behaviors (Smircich 1983), and consisting of enduring beliefs, values, and assumptions (Cameron and Quinn 2006; Schein 2010). The concept of Organizational Culture is supported by the “Competing Values Framework” (Quinn and Rohrbaugh 1983), which organizes organizational values within an integrated, competitive model. This framework is structured along two main dimensions: “flexibility versus stability” and “internal versus external focus.” The intersection of these dimensions forms a matrix with four quadrants representing distinct organizational values (Fig. 2a, b), classifying four cultures: (1) Clan (internally focused and flexible, emphasizing collaboration and unity), (2) Adhocracy (externally focused and flexible, prioritizing innovation and leadership), (3) Market (externally focused and stable, focused on competition and achievement), and (4) Hierarchy (internally focused and stable, emphasizing efficiency and structure) (van Eijnatten et al. 2015). The key characteristics of these four cultures are shown in Fig. 2c.

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The concept of Organizational Culture originates from management consulting, where it is used to compare a company’s existing culture with a target culture and to develop strategies for improvement. The Netweave Approach creates Organizational Culture profiles of stakeholders (as shown in Fig. 2a) using the Organizational Culture Assessment Instrument (OCAI). These profiles, along with stakeholder scores on the OCAI subscales (Dominant Characteristics, Organizational Leadership, Management of Employees, Organization Glue, Strategic Emphases, Criteria of Success), are then compared. This combination of culture profiles and subscale scores provides detailed insights into each organization’s working methods, enabling well-founded assumptions about how two stakeholder organizations may interact in collaboration. For example, both the Clan culture of the hunting association in Fig. 2a and the Hierarchy culture of the public authority in Fig. 2b are focused on internal processes and integration, as seen on the x-axis. However, on the y-axis, the Clan culture emphasizes flexibility and discretion, whereas the Hierarchy culture prioritizes stability and control. This divergence could lead to conflicts over the perceived necessity of bureaucratic processes such as project documentation or clear role distributions. As illustrated in Fig. 2c, the Clan culture places significant value on teamwork, trust, and commitment, while the Hierarchy culture emphasizes structure, formality, reliability, and efficiency. These differences could also result in conflicts related to varying project goals or interpersonal aspects of collaboration.

Significant differences in organizational cultures between two collaborating organizations can have a complementary effect or, conversely, result in “working at cross-purposes” (Przesdzink et al. 2024–2). The Netweave Approach uses these insights to anticipate potential areas of conflict and complementarity in collaborative projects. The findings are communicated to stakeholders to foster mutual understanding and ensure that differences are considered in cooperative planning.

When organizations with an ideological stance for or against nature conservation collaborate with others—or with organizations neutral on the topic—conflicts can arise, regardless of the rational benefits of collaboration (Freese and Rüffer 2005). Therefore, assessing stakeholders’ environmental worldviews may be beneficial to preemptively address such conflicts with tailored communication strategies. The Netweave Approach uses the “New Environmental Paradigm” scale (NEP; Dunlap and Van Liere 1978; German translation by Byrka et al. 2010) to measure these views. The corresponding questionnaire is available in Supplementary Material “Online Questionnaire (OCAI, NEP, MoN)”. The revised NEP encompasses five dimensions: (1) “balance of nature,” (2) “limits to growth,” (3) “anti-anthropocentrism,” (4) “anti-exemptionalism” (regarding human separation from nature), and (5) “possibility of an eco-crisis.” Collectively, high agreement with these dimensions indicates a “pro-ecological worldview” (Dunlap et al. 2000). The NEP’s validity is supported by numerous studies (Davis and Stroink 2016; Dunlap et al. 2000; Hawcroft and Milfont 2010; Pienaar et al. 2015). Communicating differences in NEP scores has been recommended for resolving land use and conservation conflicts (Edgell and Nowell 1989; Kaltenborn et al. 1998; Przesdzink et al. 2024–2).

The “Cultural Theory” proposes that individuals align with one of four cultural biases, or “myths,” which shape views on human and environmental nature (Schwarz and Thompson 1990). These are the “hierarchical” myth (humans as flawed but governable, nature as resilient within limits), the “egalitarian” myth (humans as cooperative, nature as fragile), the “fatalistic” myth (humans as unreliable, nature as capricious), and the “individualistic” myth (humans as self-centered, nature as benign) (Grendstad and Selle 2000; Schwarz and Thompson 1990). These myths reflect broader worldviews and influence preferences for managing human and environmental systems (van Asselt et al. 1995) and environmental resources specifically (Halik et al. 2022; Van der Wal et al. 2014). Conflicting management preferences often lead to stakeholder disputes (Halik et al. 2018). Since human and environmental biases are not directly correlated, they can be considered independently (Grendstad and Selle 2000). The Netweave Approach focuses solely on the myths of physical nature (MoN), which are more relevant in environmental discussions. The four MoN are illustrated in Fig. 3.

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The Netweave Approach compares stakeholders’ NEP scores, NEP subscales, and MoN to evaluate the potential for ideological conflict. If results are similar, ideologically harmonious collaboration is likely. For widely divergent results, differences are communicated to stakeholders beforehand to foster understanding and establish an objective communication basis before joint projects begin.

Our pilot region comprises the medium-sized city of Osnabrück, with a population of 171,994 and an area of 120 km² (Melderegister Stadt Osnabrück 2023), where many stakeholders operate in close spatial proximity, and the rural district of Osnabrück, with a population of 366,221 and an area of 2,100 km² (Niedersächsisches Landesamt für Statistik 2021), where stakeholders are more dispersed, and their influence is often limited to their local communities. Figure 4 provides a detailed map of the pilot region.

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As of November 2024, our regional database includes 191 stakeholder organizations. These comprise public authorities and academics focused on environmental conservation and resource use, as well as associations from agriculture, water management, fishing, hunting, forestry, apiculture, cultural heritage, environmental education, environmental planning, and conservation. The database expands continuously through a snowball system, where new stakeholders are identified during interviews with existing participants. Therefore, it is inherently dynamic and cannot be considered “complete.”

The data required as part of the Netweave Approach is preferably collected through individual interviews with one, and in some cases two, representatives from each stakeholder organization. Stakeholder organizations in our pilot region were initially identified through an online search for regional organizations in the sectors of environmental research, environmental governance, environmental protection, and the use of regional environmental resources. Additionally, during the data collection interviews, the snowball sampling method was employed to identify further actors relevant to the conservation and sustainable use of regional environmental resources. For a detailed methodology, see Przesdzink et al. (2021). The organizations mentioned during the interviews were subsequently included in the platform as well. The person who is most responsible for communicating with other stakeholders on regional nature conservation issues or who acts as the contact person for such issues, as chosen by the organization, is interviewed in each case. In the case of associations, for example, this could be the chairman, in the case of research groups the group leader or in the case of municipalities the environmental officer.

Data acquisition interviews are conducted in person, online, or via telephone. Online questionnaires on SNA, OCAI, NEP, and MoN (see Supplementary Material “Online Questionnaire (OCAI, NEP, Mon)”), integrated into the platform, are completed either on a tablet provided during in-person interviews or sent to participants via email link. In our case, the highest data quality was achieved during in-person interviews with a tablet, as these facilitated more relaxed communication, particularly with the typically older participants (average age: 54 years, SD = 13.5), who were often less familiar with digital tools. Additionally, the interviewer could assist participants in real time if technical issues arose while completing the questionnaires. For establishing a network consultation system, in-person interviews are recommended to allow stakeholders to personally meet “their” network consultant, demonstrate interest in their concerns, and build trust. However, digital formats are significantly more time-efficient for the consultant, as they eliminate travel time to and from stakeholder locations. In these cases, assisted completion of the questionnaire remains feasible: the interviewer can send the questionnaire link before or during the interview and stay connected with the participant to provide support during the process.

To ensure compliance with data protection regulations, explicit informed consent was obtained from all stakeholders prior to data collection. For the online questionnaires, a consent form was integrated into the beginning of each questionnaire, requiring acceptance before proceeding. For interviews, printed or digital consent forms were provided, which stakeholders signed before the interview began. These consent forms explicitly authorized the storage of stakeholder data, including personal data, in the database and its use by network consultants solely for networking purposes. Access to the data by other stakeholders was not permitted and therefore was not included in the consent process. Similarly, the publication of personal data was neither planned nor requested and is not part of this paper.

During the interview, which typically lasts between 20 and 40 min, questions are asked about the organization’s available and required resources, spatial and thematic limitations of their work, criteria for or against collaborations with other organizations, as well as typical allies and adversaries. The detailed interview guide used is provided in the Supplementary Material “Interview Guideline”. In our case, interviews were digitally recorded and auto-transcribed using Amberscript (Amberscript Global B.V., 2024) or ChatGPT 4.0 (OpenAI, 2024). The collected data, such as information on available resources, were manually entered into the platform based on the transcript. If the network consultation team has fewer resources for data collection, it is also possible to take brief, handwritten notes during the interview and later input these into the platform, or to directly enter the data into the platform during the interview. However, the latter approach may disrupt the natural flow of the conversation.

As the list of stakeholders continuously grows through snowball sampling, it is necessary to have the SNA questionnaire completed at regular intervals to maintain an up-to-date and comprehensive picture of the social network of all stakeholders registered in the platform. This is also advisable for monitoring the development of stakeholder interactions over time. Based on our project experience, resource availability and requirements are dynamic and frequently change. In contrast, an organization’s Organizational Culture, Environmental Worldview, and Environmental Risk Perception tend to remain relatively stable. However, significant changes in these aspects can occur following a management change or internal restructuring. Generally, it is recommended to repeat the entire interview annually, if time allows, to ensure all data remains current.

While the front end of the Netweave platform is presented in detail in the supplementary material “Stakeholder Platform Frontend,” we will focus here on a description of the back end: The backend of the stakeholder database platform was developed using Laravel, a PHP framework, in version 8.12. It operates on PHP 8.0 or higher and uses Blade templates as its server-side templating engine. Blade compiles templates into plain PHP and caches the output for efficiency, avoiding reliance on a separate API layer for front-end interactions. This setup ensures dynamic server-side rendering of the platform’s web interface. The platform utilizes MariaDB as its database management system. The Database scheme (see Fig. 5) is designed to handle stakeholder and resource data efficiently.

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Key tables in the Netweave database include the stakeholders table, which contains information such as names, contact details, affiliations (via “category”), survey participation status, and additional metadata fields related to collaboration criteria and notes. The resources table stores details about resources owned or required by stakeholders, with unique identifiers and descriptions. Relationships in the database are represented in different ways, depending on their structure. For 1:1 or 1:n relationships, foreign keys in the related tables are sufficient. For instance, the “category” affiliation of a stakeholder is directly stored in the stakeholders table using a foreign key. In contrast, n:m relationships or those requiring metadata are represented using pivot tables. Examples include the stakeholders_have_resources and stakeholders_need_resources tables, which document stakeholder-to-resource relationships, as well as stakeholder_conflicts_with_stakeholder, stakeholder_knows_stakeholder, stakeholder_cooperates_with_stakeholder, and stakeholder_exchanges_information_with_stakeholder tables, which handle stakeholder-to-stakeholder interactions. These pivot tables may also store additional information about the relationships, such as a description of how a stakeholder utilizes a specific resource. The tables questions and question_stakeholder store custom survey questions and stakeholder-specific responses, respectively, facilitating network surveys. Finally, the users table contains information on authorized network consultants, including encrypted authentication credentials for accessing the platform.

The backend provides several essential functionalities for the platform’s operation. (1) Data Management supports the creation, retrieval, updating, and deletion of stakeholder and resource data, implemented through Laravel controllers. (2) Survey Integration allows for the configuration and management of questions related to SNA and evaluations via the database, with stakeholder responses recorded and linked for further analysis. (3) Recommendation System functionality identifies stakeholders who can fulfill the resource requirements of others, based on database relationships, offering targeted recommendations.

The platform is hosted on a shared server with specifications including 200 GB of redundant storage, 9 GB of RAM, and a single virtual CPU. It operates under the primary domain netweave.de, with each region served by an independent subdomain (e.g., os.netweave.de for Osnabrueck). These instances are deployed separately but share code managed via a Git repository (https://github.com/Netweave-Managed-Networking). Access to the platform is secured through OAuth2, implemented via Laravel Sanctum. Stakeholder data is encrypted using OpenSSL with AES-256 and AES-128 algorithms, ensuring data confidentiality. Only network consultants have direct access to the system, with sensitive information appropriately restricted.

The platform’s architecture is designed for scalability, allowing the straightforward addition of new regions by duplicating backend instances. Each instance operates independently to maintain data separation between regions.

The networking consultation involves five primary use cases. (1) Addressing Resource Requirements: The first use case focuses on fulfilling resource requirements already listed in the platform. This process considers the automatic, resource-based networking recommendations within a stakeholder’s profile (Supplementary Material “Stakeholder Platform Frontend”; Fig. 3b) and involves manually compiling a list of stakeholders who can meet these requirements. The manual process evaluates organizational culture, NEP scores, MoN preferences, spatial and thematic limitations, and personal demands regarding collaboration quality. Additionally, the profile owner’s social network is reviewed to analyze their current interactions with potential partners. Existing collaboration is optimal, prior acquaintance is advantageous as no introduction is needed, and conflict is problematic due to prior negative experiences. Based on these factors, an informed recommendation is made regarding the best-fit stakeholder organization. This step requires practical experience with the platform. In the long term, the goal is to integrate all data into the automatic recommendations to simplify the process.

(2) Adding a New Stakeholder Organization to the Platform: The second use case involves onboarding a new stakeholder organization. On the stakeholder list (Supplementary Material “Stakeholder Platform Frontend”; Fig. 5), accessible via the button in the top left corner of the main menu (Supplementary Material “Stakeholder Platform Frontend”; Fig. 2), a new profile is created and named. Contact information can already be manually entered. The organization’s representative is sent a link to the landing page (Supplementary Material “Stakeholder Platform Frontend”; Fig. 1), where their organization is automatically listed. By selecting their organization, the representative can complete all active online questionnaires for their organization. Additional data collected during interviews are manually entered into the profile by the network consultant.

(3) Responding to New Resource Requests: The third use case occurs when stakeholders personally report new resource requirements or when someone without a profile in the platform submits a quick request for a resource. In this scenario, the search function (Supplementary Material “Stakeholder Platform Frontend”; Fig. 4) is used to compile a list of stakeholders who can meet the requirement. If the requesting stakeholder is already in the database, the manual process from the first use case is followed. If the stakeholder organization is not in the database, in our case they must first get a profile by following the second use case. Afterward, the process again follows the first use case. Whether to handle external requests or require stakeholders to create a profile before being advised should be determined during the initial platform setup. Since stakeholders are often motivated to offer resources to others in anticipation of receiving resources later, a closed system requiring stakeholders to have a profile and provide resources might be preferable. This approach could prevent “free-riders” who only make requests without contributing resources themselves.

(5) Viewing the Social Network of All Stakeholder Organizations: Using the button in the top right corner of the main menu (Supplementary Material “Stakeholder Platform Frontend”; Fig. 4), cross-tables of interactions among stakeholder organizations across the recorded dimensions—Acquaintance, Collaboration, and Conflict—can be downloaded. These interactions can be visualized in a network graph (see Fig. 1) using freeware tools such as Gephi. Such a graph is particularly useful for providing an overview of the overall network structure or identifying distinct, separated groups. Additionally, it allows stakeholders to see their own position within the network. If desired, the cross-tables can also be utilized for more advanced network analyses.

The concept of networking recommendations is illustrated below using a real-life example of Use Case 1: Addressing Resource Requirements. The stakeholder in this case was a public authority seeking biological expertise for the creation of a meadow orchard. A database analysis identified three stakeholders capable of meeting this need. Further examination revealed that the public authority’s available resources could provide reciprocal value to all three stakeholders, creating potential win-win scenarios. Table 1 summarizes the following process of manually assessing additional platform criteria to determine the best fit for collaboration.

To assess compatibility, we analyzed differences in Organizational Cultures, Environmental Worldviews, and MoN between the public authority and the three stakeholders. Stakeholder B showed the largest divergence in organizational culture but the smallest ideological differences. Stakeholders A and B had NEP scores closely aligned with the public authority, while Stakeholder C showed significant deviation. Regarding MoN, Stakeholders B and C shared the public authority’s belief, while Stakeholder A did not.

The social network review showed that Stakeholders A and B had the best relationships with the public authority. Spatial and thematic constraints were then evaluated. Stakeholder A was geographically distant, while B and C were located nearby. However, Stakeholder C was only marginally involved in meadow orchard projects, making them thematically the least suitable. Lastly, personal collaboration criteria were reviewed. Stakeholder C expressed skepticism towards bureaucracy, making them incompatible with the hierarchical public authority. Overall, Stakeholder B, a nature conservation association, was determined to be the best fit. The public authority could reciprocate by offering financial support.

After confirming compatibility, we contacted the public authority to obtain consent for networking advice. This was followed by a call with the nature conservation association to confirm their agreement and resource availability. With positive responses from both sides, we sent a joint email to initiate collaboration. To address cultural differences, the proposal highlighted key considerations: The public authority’s hierarchical culture emphasizes regulated processes, which the association was advised to accommodate when providing orchard advice or writing funding applications. The public authority was encouraged to be flexible if the association’s approach did not fully align with its expectations. Simultaneously, the association’s clan culture, prioritizing personal and collegial communication, was acknowledged, and the public authority was asked to engage accordingly. Both parties were advised to show mutual understanding during the collaboration. This tailored approach facilitated the successful initiation of collaboration between the stakeholders.

Based on the Netweave platform’s theoretical constructs we developed an integrative framework to quantify the compatibility of stakeholder organizations and provide systematic recommendations for collaboration. This framework responds to the need for a cohesive model that brings together the diverse dimensions assessed in the Netweave Approach. It is designed to support manual consultancy using the current version of the Netweave platform and, in future platform-versions, automated stakeholder matching.

The logic model (Fig. 6) calculates a Matching Score ranging from 14 to 1400 points, providing a numeric indicator of collaboration potential. The weighting of the seven dimensions in the Matching Score reflects their relative operational relevance for predicting successful collaboration, as derived from empirical findings in our pilot region and from theoretical considerations:

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1. Resource Fit is treated as a strict prerequisite: Only stakeholder pairs for which at least one actor provides a resource that the other requires are considered for collaboration. Ideally, both organizations benefit through reciprocal resource exchange. This restriction reflects findings from interviews and empirical studies in our pilot region, where time constraints led many stakeholders to reject networking offers without clear, direct benefit (Przesdzink et al. 2021; Przesdzink et al. 2024–2).

3. Geographical Distance is weighted strongly (weight 4) for location-dependent resources (e.g., land, equipment, or on-site services). It replaces the measure spatial fit used in our pilot platform, which was a rather vague free-text field, with a more finely structured and measurable variable. These distances are considered relevant because spatial proximity was a frequent criterion for collaboration preference in our pilot region. In contrast, for location-independent resources (e.g., data analysis, policy advice), distance does not affect the score. The specific thresholds (e.g., <10 km, 10–40 km) were derived from our observations in a mid-sized German district and may need to be adjusted for more local or national contexts. In the current framework, we propose using Euclidean distance due to its computational simplicity and ease of integration into existing systems. However, in future versions of the platform, more precise isochrone-based measures—which take into account travel time and infrastructure—may offer a more realistic assessment of accessibility and collaboration feasibility (Neutens et al. 2010).

5. Organizational Culture Fit is assessed by calculating the sum of differences across the six OCAI dimensions. Lower divergence implies better compatibility in work styles and communication habits (Przesdzink et al. 2024–1). This dimension is weighted with a factor of 2, as substantial differences in working styles, communication habits, and decision-making norms can cause friction even when practical incentives for collaboration exist. Nevertheless, culture is only one of many potential compatibility factors and does not carry the same integrative, outcome-based evidence as social interaction history.

6. Ideological Alignment is reflected in two separate but related constructs: the difference in NEP scores and agreement in preferred Myths of Nature (MoN). Both reflect the organization’s general stance toward environmental protection and sustainability. Given their conceptual similarity, both were included with weight 1 each to combined match the weight of organizational culture fit. While ideological alignment can support trust-building and reduce conflict risk, misalignment does not necessarily prevent collaboration if other drivers—such as resource interdependence or political necessity—are strong.

This weighting scheme ensures that the most decisive, empirically grounded predictor—interaction history—has the largest influence, while psychological and ideological variables are integrated in a way that balances their theoretical importance with their more limited predictive scope. Spatial and thematic constraints, where applicable, act as strong contextual filters, and resource fit remains the fundamental condition for considering any match. While the resulting score is continuous, preliminary thresholds can provide practical guidance for network consultants. Based on the theoretical range of 14–1400 points and our pilot experience, the following intervals may serve as a tentative orientation: scores above 1000 indicate very high compatibility and can be acted upon with minimal moderation needs; scores between 700 and 1000 suggest high compatibility, with only minor preparatory communication recommended; scores in the range of 400–700 represent medium compatibility, where targeted moderation or pre-collaboration dialog is advisable; scores between 200 and 400 indicate low compatibility, and collaboration should be initiated only if there is strong external or political necessity, accompanied by careful facilitation; and scores below 200 imply very low compatibility, where active matchmaking is generally not recommended. These intervals are intended as starting points for operational decision-making and should be refined over time using empirical data from specific network contexts.

In cases where major differences in environmental worldview or organizational culture are detected, these aspects are addressed selectively and with care during the consultancy process. A brief explanation of the divergence and the other party’s perspective is provided to support mutual understanding. This situational communication offers clear benefits: it can prevent misunderstandings, prepare stakeholders for differing expectations, and help build trust. However, it also bears risks—particularly if such information is perceived as judgmental or overly theoretical. For this reason, we limit such exchanges to cases where ideological or cultural distance is likely to affect collaboration, and frame them as neutral context information rather than evaluation.

Importantly, we deliberately chose not to calibrate the threshold values of organizational culture differences and NEP-score differences (e.g., for “high” or “low” NEP divergence) to our own network’s internal distribution, in order to maintain absolute compatibility thresholds. While in our pilot region the cultural and ideological diversity among stakeholders was limited, normalizing based on local variance would have overstated the importance of small differences in relatively homogeneous systems. Instead, the current scaling ensures that in such homogeneous networks, ideological or cultural mismatches have minor impact on the score, which is consistent with stakeholder feedback. It also simplifies the case-independent use of the framework.

While the Netweave platform already integrates stakeholder interaction data across various relational dimensions—such as acquaintance, collaboration, and conflict—classical social network metrics like degree centrality, betweenness centrality, or modularity have not been systematically applied within the operational consultancy process. In earlier stages of the project, however, such metrics were used to identify scale-crossing brokers, structural communities, and network hubs in the Osnabrück region (Przesdzink et al. 2021). Although these analyses provided valuable scientific insights, their practical relevance for everyday network consultancy proved limited.

During interviews and UX workshops, stakeholder organizations consistently emphasized severe time constraints as a central barrier to networking engagement. As a result, we prioritized direct resource-based matchmaking over more abstract structural considerations. Moreover, network metrics such as centrality or modularity, though well-established in sociological research, were often perceived as too complex or abstract by regional network managers without a background in SNA. Consequently, these metrics were not included in the platform’s operational layer.

Nonetheless, integrating structural network measures remains promising—especially in the context of broader consensus-building processes or future research applications. Metrics such as degree centrality can help identify influential actors, betweenness centrality highlights bridge-builders, and modularity can detect stakeholder clusters requiring targeted inclusion strategies (Bodin and Crona 2009; Bodin et al. 2017; Prell et al. 2009). While not directly relevant to everyday platform use, these analyses can be done using the Adjacency Matrices provided by the platform and may support more strategic interventions or contribute to academic understanding of stakeholder systems.

In addition, spatial proximity has so far only been considered as a qualitative element (spatial fit) based on stakeholder self-reports. The integrative framework presented in this paper now proposes a more systematic use of geographical distance—e.g., through threshold values for location-dependent resources—as part of the matching algorithm. Further enhancements such as spatial network visualizations or GIS-based proximity measures (e.g., Isochrones) could be explored in future developments of the platform.

Biosphere reserves and Nature Parks represent optimal application examples for the Netweave Approach. Biosphere reserves, by definition, fulfill three core functions: (1) biodiversity conservation, (2) promotion of sustainable social and economic development, and (3) research, monitoring, and education (UNESCO 1996). Biosphere reserves explicitly include anthropogenic use of their areas and ecosystems (Weber and Weber 2014). Consequently, they must balance the interests of various stakeholders with environmental conservation, ensuring that both people and nature thrive without one disadvantaging the other (Van Cuong et al. 2017). This necessitates collecting stakeholder data and highlighting the importance of collaboration among stakeholders from diverse backgrounds, often with conflicting objectives. The literature identifies several requirements for successful biosphere reserves, including effective management, adequate finances and resources, clear communication, and stakeholder collaboration and participation (Bouamrane et al. 2016; Van Cuong et al. 2017; Schliep and Stoll-Kleemann 2010; Schultz et al. 2011). Among these, stakeholder collaboration and participation are deemed the most critical factors (Van Cuong et al. 2017). Given their dual focus on nature and people, biosphere reserves present a compelling use case for the Netweave Approach, which provides insights into stakeholders’ characteristics, resources, and relationships.

The tasks of German “Nature Parks” include nature conservation, tourism, environmental education, and landscape maintenance (Weber and Weber 2014). Additionally, they are legally obligated to promote sustainable regional development. Weber and Weber (2014) emphasize that Nature Parks should evolve into regional managers tasked with coordination and networking, engaging local actors, supporting projects, and resolving disputes among stakeholders. The Netweave Approach can serve as a tool to facilitate this transformation, supporting a bottom-up process of sustainable regional development, as advocated by Weber and Weber (2014).

A stakeholder database can also be valuable beyond the specific context of large protected areas. Since effective environmental management should extend beyond protected areas to the broader landscape—for example, to enhance habitat connectivity (Noss 1991; Tambosi et al. 2014)—establishing stakeholder networks can contribute significantly to the sustainable development of regions, particularly those with a low proportion of protected areas. However, a central institution is required to host the network consultancy, as this role is not inherently provided—unlike in large protected areas where management is typically well-established. This role could be fulfilled by regional authorities or larger associations. As outlined in detail in the Supplementary Material “Implementation Analysis”, it is crucial that the institution chosen is regionally recognized and perceived as competent, trustworthy, and impartial. Furthermore, they must be able to ensure the long-term maintenance and support of the platform to guarantee its effectiveness.

Since a Netweave platform is essentially a tool for data-driven and efficient networking of organizations, it has the potential to be applied in entirely different contexts. Use cases that have already emerged from presentations of the Netweave Approach at conferences include regional climate protection networks, national networks for advancing the energy transition in heating systems, and networking booths at a major environmental trade fair. With adjustments to data structure and stakeholder types, the approach may be applicable in contexts beyond nature conservation.

For organizations or network managers interested in exploring the Netweave Approach in their own context, we provide a freely accessible technical guide and sample configuration materials at: https://github.com/Netweave-Managed-Networking (open access). These materials outline the platform’s technical setup and database structure and are intended to support the deployment of independent Netweave instances. While the conceptual foundations are detailed in this paper, the practical application of the approach in new contexts generally requires case-specific adaptation—for instance, identifying relevant stakeholder groups, selecting or modifying assessed constructs, or aligning processes to the institutional environment. We anticipate some of these adjustments below and discuss which variables may require contextualization. We welcome interest in applying the Netweave Approach to new network settings and are happy to support implementation efforts where possible—either through advisory input or collaborative development. Interested actors are encouraged to get in touch via the contact information provided in the repository.

The set of stakeholders used in our use case reflects the administrative and voluntary structures in northern Germany. Transferability to other regions in Western Europe should be relatively seamless, provided similar administrative structures are present. Nonetheless, it is essential to develop an adapted stakeholder set in collaboration with regional key players at the beginning of any new implementation. This stakeholder set can then be expanded during data collection via a snowball sampling system to include all regionally relevant stakeholders over time. In regions with culturally or administratively distinct contexts, such as those on other continents, stakeholder sets may differ significantly from ours.

Furthermore, the psychological constructs used in the Netweave Approach—Organizational Culture, Environmental Worldview, and Environmental Risk Perception—have primarily been validated in Western societies. Their applicability outside this context is uncertain (Abi-Raad 2019; Corral-Verdugo and Armendariz 2000; Ogunbode 2013; Przesdzink et al. 2024–2). When applying the platform in culturally or administratively distinct regions, both the stakeholder composition and the constructs being assessed must be carefully reviewed and, if necessary, adapted. This adaptation is technically straightforward, as the platform’s questionnaires are fully editable. For instance, if constructs more relevant to indigenous communities in South America are identified, the questionnaires can be adjusted accordingly while still enabling automated data collection. In remote regions without internet access, data collection via paper-and-pencil questionnaires during stakeholder interviews is a viable alternative. These responses can subsequently be manually entered into the platform.

In other use cases, it is crucial to handle data protection with the same level of care, adhering to relevant guidelines, such as the GDPR (General Data Protection Regulation) in Germany. All types of data collected and their intended uses should be explicitly included in the consent process. This includes potential future uses, as retroactively adding new purposes would require obtaining new consent from all stakeholders—a time-consuming and cumbersome process for all involved. A sample GDPR-compliant privacy statement is included in the Supplementary Material “Consent Form for Data Collection and Use” as a reference.

To implement the Netweave Approach in a new use case, network consultants must be trained in its methodology. These individuals should possess strong communication skills, be fluent in the regional language and dialect, and ideally come from the region to build trust and rapport with stakeholders—prerequisites for effective stakeholder management (Gorris and Koch 2024; Torabi et al. 2016). In contexts involving indigenous communities, such as those in Central and South America, building trust often requires significant time and effort. For example, researchers may need to live with the community for a month or more to establish relationships before conducting interviews and gathering information (O’Brien et al. 2022; Riley and Kaneakua 2024). This process should be explicitly planned and integrated into project timelines to ensure meaningful engagement. At the same time, network consultants must maintain neutrality, avoiding excessive alignment with specific stakeholders. An interdisciplinary background, such as a degree in biology and agricultural sciences, can further enhance their effectiveness by providing relevant expertise for stakeholder consultations.

It is important to emphasize that the stakeholder database serves as a foundational tool for interventions, but the success of these interventions depends on the skills of the network consultants in the region. If these individuals can implement the recommendations of the networking consultation and foster co-creation processes (Wielinga and Robijn 2020) among stakeholders, this approach has the potential to reduce conflicts and enhance both the efficiency and public acceptance of nature conservation and sustainable development efforts.