We subdivided the following section into two parts to generate hypotheses regarding our two main research questions. First, we asked how the linking of CIB-based context scenarios and energy modeling could be structured and which roles the method CIB could play within those designs. Second, we wanted to know what effects the linking design, the CIB method itself and the interplay of different methods have on interdisciplinary knowledge integration.
Linking designs in CIB based hybrid scenario construction and roles of CIB
To answer our first research question, we compared the processes of the three case studies. We described similarities and differences concerning their methodological design and presented different design options for linking CIB-based context scenarios with quantitative energy models. In addition, we wanted to build a bridge between the purposes of the use of context scenario approach in the respective case studies and the potential roles CIB could play with regard to the linking.
Differences and similarities in the methodologies of the three case studies
Generally, integrating societal contexts requires the integration of perspectives of experts of multidisciplinary scientific domains. Thus, in addition to CIB as systematic qualitative scenario technique and a quantitative model, further qualitative and/or quantitative methods need to be applied to integrate this knowledge and to perform the different process steps described previously. Depending on the model or set of models, more or less complex contexts must be considered. For instance, the application of a set of models makes it more complicated to find joint descriptors which fit or are linkable to all models. Other models might be more or less flexible with regard to required changes due to the linking of qualitative scenarios with quantitative models. Furthermore, there might be less data or knowledge available for some contexts, so that knowledge integration is somewhat “restricted” to limited sources. We show the effects of the design on knowledge integration and that the applications can be manifold and need to be decided on the basis of specific project requirements. Table 3 shows the detailed methodologies of the three case studies.
The table shows that there are many similar methods applied in specific process steps (in more than one project). For instance, in step 1, the methods desk research, workshops and interviews were each applied by two case studies. To implement step 3, all case studies chose the method literature review. However, literature review was carried out in different intensities; it ranged from reviewing only their own prior work (C1) to reviewing a whole scientific discourse (C2). Step 9 is also supported by similar methods in all three case studies, namely by discussions, literature review and the parameterizing of the linking factors.
The main differences between the case studies in the use of qualitative and quantitative methods can be found, for example, in step 6. Complex model contexts require integrating the perspectives of multidisciplinary scientific domain experts. The possibilities of integrating the perspectives of different domain experts and handling dissent between them can be realized in very different ways. For example, regarding impact assessments, C1 intended to reach consent between experts (to define a joint impact judgement) and reached this within a workshop. C2, in most cases, only reached an approximation concerning the impact assessments with the applied method (Delphi-style written “discussions”, see footnote 14). Most non-consensual impact assessments were resolved by averaging the assessments statistically. Important differences can also be identified during step 7. Discussions between energy modelers and CIB experts are an integral part of this step in all cases. But further qualitative methods such as interviews as well as further quantitative methods like correspondence analysis were applied by the different case studies. Furthermore, methodical enhancements of CIB were reached in this step like the linking of three matrices of different scales (cf. Vögele et al. 2017). Steps 2 and 4 also revealed evident differences in the application of qualitative and quantitative methods between the cases. Despite these differences, all individual combinations of methods and techniques used in Steps 2-8 were effective in creating context scenarios. And the methods (modeling approaches) in step 9 led to model runs and the effective calculation of energy scenarios (step 10)[1]. What effects the application of the individual methodologies had on knowledge integration is discussed later in this paper.
Different designs linking context scenarios and energy modeling and roles of CIB
In order to answer research question one, we identified different designs of linking CIB with energy modeling, and defined the roles of CIB within this relationship.
Overall, two designs of linking context scenarios based on CIB with energy models emerged from our empirical study. These are shown in Figure 2: CIB as provider (basic design) and CIB as equal partner (extended design).
[1] In C3 calculations with the optimization model have been carried out and showed that the hybrid scenario approach worked. Unfortunately, no official source is available to show this evidence.
CIB as provider means that energy models use consistent context scenarios that have been derived by a CIB analysis. The results of both the CIB and the energy modeling are interpreted separately; the CIB provides a service for the energy model(s) and the final products are energy scenarios with (consistent) socio-technical context assumptions. This linking design was performed in all three case studies. The extended design, CIB as equal partner, is an optional extension: CIB-based context scenarios and energy model(s) are of equal value for a joint final product, the socio-technical energy scenarios. For a specific energy scenario, the results are reflected and explained on the basis of its underlying societal implications. This procedure leads to a more balanced integration of techno-economic and societal aspects in the hybrid scenario construction. C2 is the only case study which realized this extended type of linking.
Within design alternative CIB asprovider, we furthermore found two alternatives concerning the position a) of the context scenarios within the approach (Design I) and b) of quantification of descriptors definitions within the process (Design II) respectively. The position of context scenarios can be distinguished as Design Ia ‘Energy model first’ (C1 and C2) and Design Ib ‘Context scenarios first’ (C3). In Design Ia the choice of energy model(s) enables the definition of input requirements for the subsequent context scenario construction. The context is designed depending on the model scope and can serve specific model needs. In Design Ib – if the decision which energy model(s) are to be chosen is still open – the context scenarios are constructed with CIB in a first step and then serve to adapt the final model set (i.e., later and accordingly). The context scenarios are intended to define the context under consideration first (to make mental models and context assumptions explicit) and the models then serve to examine the consequences of the explicit context assumptions. Context scenarios are instead constructed independently of model requirement as consistent framework scenarios for various (potential) models. One or the other design alternative can then be combined with Design II. In Design IIa ‘Quantification as part of context scenario construction’ (C2 and C3), the descriptors and variants are already defined qualitatively and quantitatively within the process of context scenario construction. In Design IIb ‘Quantification as part of energy modeling’ (C1), on the other hand, the quantification takes place immediately before energy modeling which means that the expert judgements of the impact assessments within the context scenario construction process are based on relative classifications, e.g., high/medium/low share instead of quantitative descriptions like 80%/50%/20%.
In sum, linking context scenarios and energy models can be realized in two designs: the basic design, CIB as provider (C1, C2 and C3); and the extended design, CIB as equal partner (C2). The latter needs the preliminaries of the basic linking. Within the basic linking design, the main differences in design between the case studies are the position of CIB in the process (Design Ia – C1 and C2 vs. Design Ib – C3), as well as the position of the quantification (Design IIa – C2 and C3 vs. Design IIb – C1).
In addition to these two general roles of CIB in the linking design, CIB can resume more specific roles within the relationship of context scenarios and models. These roles were not appointed as such by the project members, but are derived through interpreting project objectives, experiences and results, which are partly collected through the questionnaire and partly through personal information.
Firstly, “knowledge representation requires a language to represent the knowledge in” (Hinkel 2008:15) so CIB can effectively function as such a ‘meta-language provider for interdisciplinary groups’. The language expresses itself through descriptors and variants describing system elements as well as their reciprocal promoting or hindering impacts to discuss their interactions. All types of knowledge can be linked by applying this meta-language. Explicit ‘linking factors’ can be integrated into the context scenarios, and context scenarios and energy models thus have, due to this direct interface, the ability to mutually inform each other during the process. Furthermore, CIB can function as an ‘input-data provider’, meaning that the context scenarios provide combinations of input factors to function as (scenario) frameworks for the energy model(s). Additionally, the inclusion of qualitative (context) factors and the explicit consideration of the socio-technical system results in enhanced system understanding and in a substantiation of the choice of input assumptions made in the context scenarios. The role of an input-data provider in combination with more than one model (it is not restricted to energy models) can moreover result into the role of a ‘manager of context assumptions for a multi-model exercise’, meaning that the scenarios reflect the context of different models and therefore can be applied by all of them (see Weimer-Jehle/Wassermann/Kosow 2011; Trutnevyte et al. 2014, Kosow 2016).
Because of the natural effect of CIB providing various consistent scenarios and enabling the calculation of energy scenarios in the light of different contexts, reflecting alternative future possibilities – future openness – in a changing society, CIB can also function as a ‘context-uncertainty dealer’. Another role of CIB in combination with energy models is the ‘conceptual modeling of the social system or of the socio-technical system’. CIB can enable the adaptation of scenario premises, the linking of further qualitative factors (if only slightly) with the energy model through plausible arguments (cf. Weimer-Jehle et al. 2016, Pregger et al. 2019). As a model is never static, we assume that the role of conceptual modeling can also result in (deep) structural adaptations of the energy models in the form, that, for example, the model can be further developed depending on the requirements[1] resulting from the linking with the context scenarios.
Lastly, CIB can also play the role of a “knowledge container” (Weimer-Jehle et al. 2020), a provider (and storage) of argumentations for socio-technical pathways: within CIB, additional explicit information on contexts and their (assumed) internal structure are stored so that it can be accessed if necessary.
Effects on interdisciplinary knowledge integration
To answer our second research question we first analyze which form of knowledge integration could be assigned to the different process steps conducted by the case studies and then developed hypotheses on how similarities and differences in knowledge integration could be explained by different methodological factors.
Interdisciplinary knowledge integration in the different designs of the case studies
We conceptualized the forms of knowledge integration before the analysis, deriving them from the current literature. As can be seen in the following section, the analysis then showed that differences between the cases in some steps could not be depicted, although the methodological design was different. We found that, for example no literature review against profound literature review cannot be depicted in the form of knowledge integration other than showing that there also was compiling in the latter beforehand. This is how we dealt with such differences in this paper. Furthermore, we are not able to show all the results of the case studies within the limits of a paper, but ‘Examples of interdisciplinary knowledge integration visible in process and results [see Supplementary Table 2]’ (ST2) are given.
Knowledge integration starts in step 1 with the definition of the design of the context scenario approach. The linking of the context scenarios and energy modeling is prepared and planned and the roles CIB should play as well as the ambition of integrating methods are decided upon. This step requires bringing knowledge (from different perspectives) together and the research partners need to develop strategies of how to link and to integrate knowledge with CIB and energy modeling. Thus, the knowledge integration form combining is reached through this step (for an overview see Table 4).
Then the process of constructing the context scenarios begin. We assessed all processes leading to products and important interim products separately in regard to their forms of knowledge integration.
[1] In C2, for example, the descriptor “Individual energy consumer behavior” could not be coupled with the model because the model uses the aggregated indicator “per capital consumption”. If “consumption” and “device efficiency” would be separated, the descriptor “individual energy consumer behavior” could have been directly considered, too. This separation could have been realized through a model extension which was not possible within the scope of the project.
In the following section, the steps ‘definition of context, descriptors and variants’ (Step 2 and 3) are split into two processes each, namely identification and definition, as each process results in its own forms of knowledge integration. While the identification of descriptors and variants simply serves as a collector of ideas of different perspectives (compiling interdisciplinary knowledge, see example A, ST2), during the definition, the descriptors and variants are described in-depth and need to be mutually understood and supported by experts of the same domain or by domain experts and energy modelers. The experts need to define descriptors and variants in a way that they can be used by scientific domain experts and energy modelers (combining interdisciplinary knowledge, see example B and C, ST2).
During the quantification of descriptor definitions (step 4), C1 compiled quantifications of their own prior research, while C2 compiled knowledge from various energy scenario studies to identify the range of quantifications discussed in the literature (see example D, ST2). The latter approach increased the diversity of knowledge sources that were integrated in the following and thus, the level of interdisciplinarity. C3 integrated new knowledge by calculating population data for a specific region and applied data from existing sources. As the quantification took place early in C2 and C3, jointly usable content was produced, which means translated, by finding quantitative equivalents to the qualitative descriptor definitions. Thus, in this step the integration form combining was achieved, as joint sense-making took place and was then applicable by experts constructing the context scenarios as well as later by the experts running the energy models (see example E, ST2). C1 has quantified much later in the process, just before energy modeling. Thus, the knowledge was translated into data. As no joint sense-making took place between the CIB expert and the modeler, and the quantified knowledge is no longer usable for the construction of the context scenarios, it could therefore be assigned to the knowledge integration form compiling. This design had the advantage that the scenarios “could be interpreted, in principle, as frameworks” (Vögele et al. 2017:942) which allowed C1 to analyze the consistency of other scenarios, which was one of the initial aims of the project (see example F, ST2).
Another difference regarding the forms of knowledge integration between the cases could be found in step 5, the assessment of descriptor interdependencies and step 6, the handling of dissent of assessments of interdependencies between descriptors. C1 and C3 performed group discussions to assess interdependencies between descriptors and decided on one assessment per interaction between descriptor developments. They handled dissent directly during a workshop by discussing and finding one solution. The result was joint assessments, where dissent was not visible (any more) (type combining). C2 carried out several interviews per descriptor assessment and thus compiled assessments on interactions between descriptor developments (step 5). Dissent could not be dealt with (step 6) during the survey. Thus, in the aftermath, dissent was dealt with in a written Delphi-style process, as an offer to agree and change one´s own arguments, to disagree and to stay with one’s own assessment or to approximate one’s prior assessments to others. The aim was to get a “cross-checked” CIB assessment for each interaction, to validate assessments by asking more than one expert and also to represent the legitimate dissent within the scientific discourse. Performing step 6 with this multiple interview and Delphi-style technique showed some aspects of combining knowledge, but not fully achieves it, no matter whether this was the aim or not. Thus, knowledge was mostly compiled. Compiling diverging assessments allowed them to maintain the ambivalence of equally valid, but different, arguments (vs. forcing everything into one matrix)[1]. If the arguments for the diverging assessments are traceable, which is the case if CIB is applied, a wider spectrum of diverging scenarios can be interpreted later (see example G, ST2). However, the CIB-specific impact scale to decide on assessments was the joint language used by all experts. Thus, to further process data, joint content was possible to be brought about methodically by averaging the impact assessments (type combining).
During the analysis of the interdependencies (step 7), knowledge was synthesized by applying the CIB with its balance algorithm as a bridging method. Single assessments were balanced against each other and produced new content, namely raw scenarios (see example H, ST2). But the following understanding of those raw scenarios and their interpretation require more (knowledge) than applying CIB and had to be implemented in a subsequent step (step 8).
Further differences in the three case studies as to forms of knowledge integration can be found in step 8, scenario selection. C3 combined different contents: researchers used one of the consistent national scenarios of C1 reflecting a positive development of the energy transition and matched it qualitatively (verbally) with one of their own regional scenarios. They mirrored the national scenario of C1 and discussed which of the regional scenarios would best fit it. Also, C1 selected the final scenarios regarding their regional compatibility, albeit in a much more complex approach: they constructed individual context scenarios on the sectoral, national and global scale and developed their own approach to tie the individual CIB matrices to each other. They selected scenarios out of the spectrum of scenarios that were mutually consistent regarding main drivers (tie-descriptors) on the three levels (Vögele et al. 2017) and thus, further integrated knowledge to a joint product (type synthesizing, see example B, ST2). In contrast, researchers in C2 applied various additional methods and further processed their results together with interim and final products of the CIB analysis (e.g. by using the CIB coding of the scenario constellations as input for statistical analysis) and created new content to assess the CIB output (type synthesizing). For example, knowledge was condensed by entering the datasets of all consistent context scenarios into correspondence analysis, which is able to identify latent structures in the datasets. By doing so, a new understanding of the scenario space was reached and it was possible to visualize scenario landscapes (see example I, ST2). By also linking it with the simplified energy model of C2, energy-related knowledge can be assigned to the different scenarios (see example J, ST2).
During the energy scenario construction, a set of framework assumptions needed to be derived from the selected consistent scenarios (step 9). This was realized by directly linking either the already quantified descriptors in the specific variant constellation as the basis for model parameter translation (directly integrated or with only minor adaptations, see example E, ST2) (C2 and C3), or by parameterizing the quantifiable qualitative descriptors in this step (see example F, ST2) (C1). Additionally, some qualitative descriptors were used as model parameter by integrating them as plausibility arguments (defined as soft linking). This therefore resulted in the knowledge integration form combining in all three case studies.
The prepared sets of framework assumptions are the basis for the model calculations (step 10). This is where a direct link between context scenarios and energy models takes place. Applying different context scenarios implies that the resulting energy scenarios reflect future uncertainty (which in turn is reflected in different sets of framework assumptions). In this way, the energy scenarios reflect the spirit of the different selected context scenarios. Not only is this an application of the context scenarios as framework assumptions for energy modeling, but new knowledge can be derived from the explorative approach of confronting energy technologies with uncertain societal conditions. Therefore, knowledge integration in this step can be characterized as synthesizing in C1 and C2 (see example K, L and M, ST2). No such exploration occurred in C3, as it only applied one of the consistent scenarios which fulfilled a normative aim. In this approach no new knowledge could be derived regarding context uncertainty and therefore knowledge was only combined.
Finally, step 11, an integration of context and energy scenarios as socio-technical energy scenarios was implemented by C2 only. CIB and an energy model were combined as equal methodical partners and created a new product, a socio-technical energy scenario (an example is given in Pregger et al. 2020, SM-(7)). In this step, a predefined energy scenario from the preceding analysis was chosen and examined for its hidden societal implications and reveales new knowledge. The result was a joint product, a socio-technical energy scenario, containing not only quantitative model results, but also “qualitative impact diagrams show[ing] how societal drivers act on the techno-economic factors in the sector, explaining the connection between societal and technical dynamics” (Pregger et al. 2019, SM-(7):22). Therefore, we classified the knowledge integration that occurred at this moment as synthezising (see example N, ST2).
In sum, the forms of knowledge integration found during the same process steps (steps 1-10) in the three different case studies were either partly equal or very similar (steps 1-3, 7 and 9) and partly differ (steps 4-6, 8, and 10). As step 11 was only applied by C2 no comparison was possible.
Effects of CIB on interdisciplinary knowledge integration
CIB as a knowledge integration method which, in different roles, shapes and, at times even predefines, the forms of knowledge integration that are achieved through certain steps. This holds true mainly for steps 2, 3, 5 and 7.
The definition of context, descriptors and variants (step 2 and 3) is strongly shaped by the requirements of CIB, as ‘meta-language provider’ to codify knowledge from different (disciplinary) perspectives in the joint form of distinct descriptors and their variants (see Weimer-Jehle 2006). The CIB method thus supports and, at the same time, requires that knowledge is combined into descriptors and variants to be further processable by CIB. Regarding this step, the integration form of combining knowledge is predefined from the outset, when the CIB method is applied and when at the same time multidisciplinary experts are participating (C1, C2 and C3). The same applies for CIB as ‘manager of context assumptions for a multi-model exercise’, if context assumptions need to be defined for various models which reflect the same problem from a different perspective (C3).
The assessment of mutual impacts between descriptors (steps 5) is also strongly shaped by the requirements of CIB to use a joint meta-language to communicate about interdependencies. This is namely a joint (and semi formalized) impact scale that is coding interrelations as hindering and fostering impacts. The method CIB at this moment leads to combining knowledge.
Analyzing interdependencies in process step 7 turns CIB into an ‘input-data provider’ and reveals a synthesizing effect of CIB: the CIB balance algorithm creates new, joint knowledge that integrates the individual (pairwise) impact assessments into an impact network and identifies internally consistent constellations of this network.
Despite those roles of CIB predetermining the form of knowledge integration from the outset, there are other roles showing trade-offs with regard to the form of knowledge integration. This applies to the assessment of mutual impacts between descriptors (step 5). On the on hand, this step is also strongly shaped by the requirements of CIB to use a joint meta-language to communicate interdependencies. This is namely a joint (and semi formalized) impact scale that evaluates interrelations as hindering and fostering impacts. The CIB method at this moment leads to combining knowledge. On the other hand, we found that this CIB-specific effect can be outweighed in this step by the interplay of CIB with certain qualitative methods and in doing so lead to compiling knowledge (see Section after next).
A trade-off can also be identified to the role of CIB as ‘knowledge container’. If a project aims for socio-technical scenarios, accessible knowledge (in form of the impact network stored) is a precondition for step 11 being able to further synthesize knowledge by systematically linking model calculations to their specific societal implications in socio-technical energy scenarios. On the other hand, as described in the former section, dissent between experts concerning the impact assessment can be solved (by deciding on one joint assessment) or kept (by storing the different assessments). This has content-specific implications, but also has effects on the form of knowledge integration as knowledge can be compiled (if dissent is kept) or combined (if dissent is solved).
Choosing CIB as ‘context uncertainty dealer’ implies that distinct context scenarios are selected to be linked with the energy model. Thus, new (synthesized) knowledge develops through dealing with different future perspectives. However, if CIB is not applied to deal with uncertainty, but to provide ONE consistent scenario only (no matter for which reason), knowledge is not synthesized, but combined.
Lastly, applying CIB as ‘conceptual modeling for the social system or socio-technical system’ also shows a trade-off with regard to the form of knowledge integration. Depending on how the context scenarios are reflected in energy modeling, the form of knowledge integration can change. Combined knowledge integration can be found if the context scenarios are linked through exogenous parameters with models which directly (linking descriptors), softly (plausibility arguments) or indirectly (through impact network) affect the model. If the context scenarios also include endogenous model parameter (which was not the case in the three case studies analyzed), in addition to the direct, soft and indirect links, this could lead to an adaption of model structures, if the impact network required it, and would show synthesized knowledge integration. The same holds true if the model is extended with specifically developed sub-models.
Effects of the linking design on interdisciplinary knowledge integration
In our three case studies, depending on time and financial resources, the researchers defined the purpose of the endeavor and agreed on method(s) and their interplay to fulfill these purposes. Additional competencies in the form of other researchers and/or methods were integrated, if they were judged necessary to reach the project goals and if financial and time resources allowed for them.
CIB is the joint method used by all case studies. We have shown that, in the approach of linking context scenarios based on CIB with energy models, the context scenario approach can follow two design alternatives (see Figure 2) depending on the interplay of CIB and the model (CIB as provider for the energy model(s) vs. CIB as equal partner). Both designs can lead to a high level of knowledge integration (synthesizing). Within the first design, which is a prerequisite for the second, we can, furthermore, distinguish two different design options depending on the position of CIB and the timing of the quantification within the process. We found out that the order of application (CIB first or model first) does not matter with regard to knowledge integration in the three case studies. C1 and C2 applied the energy model first option (Design Ia), which means that an energy model already existed to be used for the energy scenario construction and context scenarios were constructed in the aftermath. The advantage of this option was that the context scenarios could be constructed in a more target-oriented way, considering specific model premises and model input needs. C3 applied the CIB first option (Design Ib). In all three case studies the overall form of knowledge integration was not affected by the chosen approach.
The timing of the quantification of qualitative descriptors had an effect on the form of knowledge integration and entailed an important trade-off. If the quantification took place early in the process (quantify during context scenario construction Design IIa as in C2) before the impact judgements were assessed (step 7), the quantifications made were thus the basis for the impact judgements as well as for the energy modeling and therefore were the bridge and joint content between them (combining). Furthermore, the impact judgments made on the basis of qualitative and quantitative definitions could be more consistent. On the other hand, this led to a kind of restriction of the modeling, because model results were more or less fixed to the specified values of the quantified descriptor variants. If, on the other hand, the quantification took place later in the process, i.e., after the impact assessment (quantify later during energy modeling, Design IIb as in C1 and C3), the impact judgements were based only on relative classifications, which might be interpreted differently by each expert and therefore might lead to unclear impact assessments. The quantifiable descriptors in this case had a more indicative character. The advantage is that the quantifications can be adapted more easily to modeling requirements. With regard to knowledge integration of the design option IIb (quantify later) reveals the form of compiling as the quantification is made without joint sense-making.
Effects of the interplay of different methods on interdisciplinary knowledge integration
CIB is a method which can be applied for the integration of knowledge. Furthermore, its specific storage function in form of discrete assessments within a matrix, enables final and interim products of the CIB process to be further processed by linking them with additional (disciplinary) methods. Also, additional methods can build a bridge between context scenario products and energy models. An example of this is the application of correspondence analysis (C2): firstly, to further condense results by considering the whole context scenario set in the future uncertainty space and thus revealing new knowledge about the scenario set (type synthesizing) and secondly, to integrate the context and energy system knowledge (type synthesizing).
The three case studies differ little in their overall methodological approach. But when we go further into the detail of their individual methodologies, rather important differences become visible. C2 made the most innovative choices by trying out new/additional methods and C1 also developed methodical enhancements. The reasons for this might be that knowledge integration and the application of CIB were part of the project agendas and adequate resources were planned for this. C3 did include the application of context scenarios only in the aftermath and, in addition, without having planned adequate resources.
In sum, we found out that the individual (qualitative) methods used to operationalize different process steps mattered only slightly for the forms of knowledge integration that were reached. The only major differences could be found in step 5 (assessment of interdependencies) and step 6 (handling of dissent). C2 has decided to conduct the assessments with individual interviews and has obtained several expert opinions for each. Even if these experts used the same “language” to assess the interdependencies, it is not possible to reach consensus in interviews conducted separately within the same step. To deal with dissent C2 implemented a written Delphi-style process. A Delphi process is generally designed to generate consensus or to establish consent on dissent (Martino 1993) on a specific topic among a group of experts. The written Delphi applied in C2 via expert interviews did not strive for consensus on impact assessments at all costs, but rather were used to clarify where justified dissent was present. This decision thus had an effect on the form of knowledge integration in addition to the choice of the interview method. The methodological choice to carry out interviews to assess the impact assessments (step 5) with more than one expert from one domain (C2) leads to less integration (type compiled), at the very moment when interviews are carried out. But, integration could be reached methodically. In terms of content, this approach has led to the fact that the richer background information could be synthesized in a later process step which also reflects the ambivalence and dissent of the scientific discourse. However, in our conceptualization of forms of interdisciplinary knowledge integration this cannot be made visible. Other methods which do matter (directly) concerning the form of knowledge integration are additional (disciplinary and quantitative) methods as well as CIB method enhancements which have the potential to produce new knowledge such as correspondence analysis (C2) or Multi-level-CIB (C1).
[1] Generally, this is independent from the method (e.g. interview vs. workshop).