developing a backcasting approach for systemic transformations

4th International Seville Conference on Future-Oriented Technology Analysis (FTA)FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations

SEVILLE, 12-13 MAY 2011

DEVELOPING A BACKCASTING APPROACH FOR SYSTEMIC TRANSFORMATIONS TOWARDS SUSTAINABLE MOBILITY

– THE CASE OF THE AUTOMOTIVE INDUSTRY IN GERMANY

Martin Zimmermann, Johannes Warth, Heiko von der Gracht, Inga-Lena Darkow

Centre for Futures Studies and Knowledge Management

EBS Business School, Soehnleinstraße 8F,

65201 Wiesbaden, Germany

[email protected]

Keywords: Backcasting, real-time Delphi, automotive industry

SummaryRadical systemic changes to current systems of mobility, especially in industrialized countries, are required to achieve sustainable development (Nykvist & Whitmarsh, 2008). These changes on a system level are referred to as systemic industrial transformations towards sustainability. Such transformations require combinations of technological, cultural, societal, institutional, and organisational changes, while affecting many stakeholders when diffusing into society and involving complex processes of societal change on the long term (Quist & Vergragt, 2006). Transformations towards sustainability in logistics and mobility are very complex phenomena: inherent uncertainty of technological and infrastructural developments and the inherent ambiguity of stakeholders having different value sets and mental frameworks. Within the field of FTA an approach for achieving transitions towards a sustainable future is backcasting (Dreborg, 1996). We demonstrate how an innovative backcasting process can be applied to analyze possible transformations to sustainable logistics and mobility and how such a process can facilitate future-oriented decision making as well as option planning and roadmap development.1

Backcasting incorporates developing a desirable future scenario and looking back at how this can be achieved, before defining appropriate strategies (Quist & Vergragt, 2006; Robinson, 1988). We designed an innovative backcasting process by implementing four steps including a Delphi survey. The first step was the problem definition and description of the current situation in a qualitative way by executing workshops with dedicated experts from major stakeholder groups. Second, a sample of 140 experts from 15 different stakeholder groups provided estimations on 20 projections concerning probability, desirability, and impact in a web-based Delphi survey. Resulting from this survey, scenarios were developed and influencing factors were identified: Delphi participants’ answers were statistically analyzed in order to identify a desirable future scenario. Furthermore, by systematically coding the participants’ arguments the major influencing factors for the transition paths to the desirable scenario were identified. Thirdly, backcasting interviews with 43 experts from various stakeholder groups were conducted in order to discuss measures on the transition to the

1 The content of this publication is based on the joint research project “Competitiveness Monitor”, funded by the German Federal Ministry of Education and Research (project reference number: 01IC10L18 A). Project duration: 06/2010 – 05/2013. Responsibility for the content is with the author(s).

THEME: Premises and practices in combining quantitative and qualitative FTA methods

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desirable scenario. The fourth step was the dissemination of results and recommendations in workshops with the relevant stakeholders.

We combined a structured method with a semi-structured and qualitatively oriented method. Based on the Delphi statistics and the coding of arguments of the semi-structured expert interviews we conducted a backcasting exercise, thus combining the advantages of both methods. As a result, we were able to assess the way towards sustainable mobility scenarios in Germany for the year 2030 in a more structured way than it was known before. We developed transition paths towards a sustainable mobility based on a multi-method approach, combining both established as well as innovative quantitative and qualitative FTA methods. This innovative and structured backcasting approach can be a new path to integrated FTA endeavors.

1 IntroductionRadical systemic changes to current systems of mobility, especially in industrialized countries, are necessary to achieve sustainable development (Nykvist & Whitmarsh, 2008). An example for such systemic changes is the plan of the German government to establish up to 1 million electric vehicles on the streets until 2020 and over 5 million vehicles until 2030 (Federal Ministry of Transport, Building and Urban Development, 2009). This can be seen as the start of a systemic change towards electric mobility in Germany. These changes on a system level are referred to as systemic industrial transformations towards sustainability. Such transformations require combinations of technological, cultural, societal, institutional, and organizational changes, while affecting many stakeholders when diffusing into society and involving complex processes of societal change on the long term (Quist & Vergragt, 2006). Transformations towards sustainability in logistics and mobility are very complex phenomena: inherent uncertainty of technological and infrastructural developments and the inherent ambiguity of stakeholders having different value sets and mental frameworks. Within the field of FTA an approach for achieving transitions towards a sustainable future is backcasting (Dreborg, 1996). We demonstrate how an innovative backcasting process can be applied to analyze possible transformations to sustainable logistics and mobility.

In order to gain a better understanding of the essence of backcasting, an in-depth look at the work of Robinson (1990) provides valuable insights. Accordingly, the main characteristic of backcasting is its inherent focus on how desirable futures can be attained. The whole process is explicitly normative which includes working backwards from a particular desired future end-point to the present. Furthermore, Robinson recommends the definition of endpoints that are quite far in the future in order to give space for futures that are completely different to what is common in the present situation.

Dreborg (1996) defines a set of application criteria for the backcasting approach. Thus, backcasting is particularly useful when

complex and persistent problems are in focus,

dominant trends are part of the problem,

external factors are present,

the need for major change exists,

the time frame and thematic focus allow for radical changes.

In our case of sustainable mobility, all these criteria were fulfilled. A further indication that backcasting is an advantageous approach compared to common forecasting methods is a study of Ebert et al. (2009), concluding that “backcasting is likely to be the superior method


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when the future period evokes strong feelings” (p. 364). As the hype around electric cars or the discussion about biofuels in Germany shows, sustainable mobility is a very emotionally discussed topic (Peard, 2011; Reed & Soble, 2010).

The overall objective of the paper is to demonstrate how an innovative backcasting process can be applied to analyze possible transformations to sustainable mobility and to facilitate future-oriented decision making. In our case, we combined a web-based, real-time Delphi approach with semi-structured interviews. The advantages of combining these two approaches will be highlighted and critically reflected.

2 Overview of backcasting

2.1 Historical evolvement and current stateIn the past, the backcasting approach was used for different research purposes. According to Quist and Vergragt (2006), three different phases of backcasting studies can be identified: 1) Backcasting in energy studies, 2) Backcasting for sustainability, and 3) Participatory backcasting.

The method was first used in the field of energy policy planning in the 1970s: Back then, Lovins (1976) used the term ‘backwards-looking analysis’ for a method that is looking backwards from a point in the future and proposed it as an alternative planning technique for electricity supply and demand. Robinson (1982) introduced the term ‘energy backcasting’. After being established for soft energy paths, backcasting became popular for other issues such as sustainability issues. Especially in Sweden, a number of backcasting studies dealing with sustainability topics were executed, such as sustainable transport or sustainability for companies (Höjer & Mattsson, 2000; Holmberg & Robèrt, 2000).

In the early 1990s, backcasting was employed in several projects in the Netherlands with a more participative approach. Contrary to the first backcasting project in the energy industry, the aim was to include as many different stakeholders as possible. Examples for the participatory backcasting approach are the governmental programme for Sustainable Technology Development (STD) and its spin-off, a research project called “Sus House” (Green & Vergragt, 2002; Vergragt & van Grootveld, 1994).2

Several research projects were designed on the fundaments of participatory backcasting: Svenfelt et al. (2011) employ a four-step backcasting approach that refers to some initial thoughts of Höjer and Mattson (2000) using focus group workshops for elaborating on possible solutions for the Swedish national plan to decrease energy usage in buildings by 50% by 2050. Within the model, the first step encompasses both problem definition and target setting. The second step comprises the analyses of current trends and forecasts. Within the third step, future images are developed with input from the reference group and from the respective workshops. According to Svenfelt et al. (2011), the future images functioned mainly as inspiration and stimulation device for the stakeholder group participants. The main factors affecting the achievement of the 2050 targets were identified in workshops as well and subsequently clustered into two main clusters (1. technology & building, 2. behavioural change). As a last step, the stakeholders discussed how the images could possibly be realised. As a result, a list of about 250 specific measures was identified, including a list of stakeholders who are responsible for the respective measures.

When reflecting on both project and methodology, Svenfelt et al. (2011) identified some shortcomings: First, the initial aim of identifying actual policy instruments and detailed

2 For a detailed overview of the different phases in the development of backcasting please refer to Quist and Vergragt (2006).


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pathways could not be realised. They state that it would be beneficial to use a more heterogeneous stakeholder sample in order to be able to discuss more concrete policy measures. On the other hand, they state that the development of too detailed paths towards the future images might be counterproductive and lead to lock-ins. This is also underlined by the contribution of Akerman and Höjer (2006).

Due to specific research purposes and stakeholder involvements, different backcasting approaches feature a strong degree of heterogeneity. Therefore, describing backcasting in general terms is essentially difficult. Höjer et al. (2011) for instance, employ a target-oriented backcasting approach that puts much emphasis on targets and descriptions of target fulfilment. In contrast, Giurco et al. (2011) combine backcasting with the principles of industrial ecology. Eames and Egmose (2011) provide another interpretation of backcasting. They developed an innovative backcasting approach that comprises a bottom-up community foresight process for research on urban sustainability. Moving away from “traditional” backcasting exercises, they employ innovative tools for involving local communities into the backcasting process. In their five-stage backcasting model, they are using not only diverse stakeholder workshops including the community’s residents, but also film projects in order to visualize what the environment and sustainability actually means to the residents.

2.2 4-Step backcasting approachDrawing from the different backcasting approaches and experiences presented above, we developed an innovative participatory backcasting approach. Concerning its different steps, it can be best compared with the approach used by Svenfelt et al. (2011) described above, although the methodology behind the single steps differs especially concerning the development of future images and influential factors as well as the discussion of the realisation of the future images. In the optimal case, all relevant interest groups are included in order to develop the desirable scenario and examine pathways to realise this scenario.

The backcasting approach used at hand consists of 4 subsequent steps:

1. Strategic problem orientation

2. Development of future images and influencing factors

3. Development of measures

4. Continuation

In the following paragraphs, we will elaborate on each step in detail.

1. Strategic problem orientationIn the first step of our backcasting approach, we identified the fundamental research issues to be addressed. For this purpose, several stakeholder workshops were held. In addition, the findings of the workshop were enriched by comprehensive desk research activities.

For the underlying study, we focused on the German government’s plans to reduce CO2

emissions by supporting new mobility technologies: The German federal government announced to reduce carbon dioxide emissions until 2020 by 40%, compared to 1990 (Federal Ministry of Economics and Technology, 2007). A key pillar is strengthening e-mobility: The goal is to account for one million electric vehicles in usage until 2020 and over five million vehicles until 2030 (Federal Ministry of Transport, Building and Urban Development, 2009). The automotive industry faces a radical technological change and systemic innovations that shake the entire industry (Zapata & Nieuwenhuis, 2010). Not only the German automotive industry landscape seeks to establish e-mobility, but also other


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nations such as Japan, China, or the US are trying to dominate the future growth market of e-mobility. Therefore, it is of utmost importance for the German automotive industry to be prepared for the challenges ahead.

For the development of the future projections, we followed an iterative process based on a PEST-analysis (political, economical, social and technological factors): Firstly, relevant and challenging drivers regarding the future of sustainable mobility where emblazed by conducting intensive desk and data base research. After a wide-ranging research, we finally concentrated on in-depth analysis of a sample of academic studies and governmental-related reports. Secondly, we invited 11 business and six academic experts to take part in a future workshop where the already identified drivers were presented while further driving factors were elaborated. Overall, we identified driving factors which built the fundament of the projection development process. The experts were asked to merge and aggregate the drivers. Consequently, we formulated 20 projections in short, descriptive and provoking propositions. In a final step, we tested the projections for ambiguity, consistence and face validity and revised them against the background of our research purpose.

2. Development of future images and influencing factorsFor the development of scenarios for sustainable logistics and mobility, we conducted a real-time-Delphi study with focus on 2030.3 According to Armstrong (2001), Delphi is a “method for obtaining independent forecasts from an expert panel over two or more rounds with summaries of the anonymous forecasts (and perhaps reasons for them) provided after each round” (p. 776).

In general, the Delphi technique can be described by four characteristics (Rowe, Wright, & Bolger, 1991): (1) anonymity, (2) iteration, (3) controlled feedback, (4) statistical group response.

1. As a first characteristic, Delphi surveys are conducted in an anonymous manner. Among other advantages, this leads to a reduction of the effect of dominant individuals influencing the statements of other participants. Therefore, it is possible for participants to express opinions that are less biased.

2. A further characteristic of Delphi is that such surveys are executed in a series of rounds. This means that participants receive a questionnaire, provide their first set of answers, send back the questionnaire to the study’s facilitator, receive feedback, and then have to reconsider their answers given the answers of other panellists as a last step.

3. A controlled feedback takes place between rounds. This feedback gives insights about the participants’ answers in comparison to each other, e.g. via a simple statistical summary of the group response. This way, the experts are able to reconsider their initial assessment in the light of new information.

4. As a last characteristic, statistical group responses can be presented either in a numerical or graphical way and often comprise measures of central tendency (median, mean), dispersion (interquartile range, standard deviation), and frequency distribution (histograms and frequency polygons) (Dunn, 2004).

In the past, the Delphi method has been criticised because of the inefficiency of the survey process. The often long time periods to complete or analyse a survey are said to increase drop-out rates of experts (Gordon & Pease, 2006; Landeta, Matey, Ruíz, & Galter, 2008; Tapio, 2002). Further mentioned shortcomings include expert panel biases or time scale disadvantages (Scapolo & Cahill, 2004). By applying an innovative real-time Delphi variant 3 For a detailed description of the real-time methodology see Gordon and Pease (2006) and von der Gracht et al. (2011).


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we were able to address this major shortcoming of the Delphi survey and to streamline the process. First of all, experts could access our real-time Delphi tool via Internet which enabled them to participate without any regional restrictions. Moreover, the easy usability of the tool was expected to decrease the likelihood of drop-out rates. Furthermore, applications such as a graphical real-time feedback, an ‘ease of use facilitator portal’, and a ‘consensus portal’ increased the experts’ efficiency to participate and reduced the survey moderator’s efforts to manage the survey.

Our backcasting approach can insofar be called participatory, since Delphi can be defined as “a group process which utilizes written responses as opposed to bringing individuals together . . . . it means for aggregating the judgments of a number of individuals in order to improve the quality of decision making” (Delbecq, Van de Ven, & Gustafson, 1986, p. 83). This means that a Delphi process helps to execute a more structured group discussion than it would be possible with focus groups – the method that is usually used in backcasting exercises.

The usage of our real-time Delphi tool resulted in a participation of 140 experts, equalling a response rate of 31% (of 441 invitations). The expert panel consists of a diverse sample of experts from 15 different groups (Figure 1):

Figure 1: Delphi Participants by Stakeholder Group

From the quantitative and qualitative expert data, we were able to develop a desirable and a probable scenario in a valid and reliable way. The creation of scenarios based on Delphi data has been executed before, e.g. by von der Gracht and Darkow (2010). For this purpose, we took a closer look at the experts’ ratings of the projections expected to be most likely and the projections perceived to be most desirable. The expected scenario mainly served as a starting point for later interviews in order to have a clear differentiation of what will probably be the reality and what should become reality in 2030 instead.

Expected Scenario: Electric Mobility - Still far away from Breakthrough


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Alternative Ansichten

Automobilzulieferer

Hersteller Elektrofahrzeuge

Energieversorger

Flottenbetreiber

Handel

Infrastruktur

Institute / Universitäten

Journalisten

Marktforschung

OEM

Öffentliche Hand / Politk

Umweltorganisationen

Unternehmensberatungen

Verbände

Automotive suppliers 19%

Energy suppliers 4%

Fleet operators 2%

Dealers 4%

Infrastructure 2%

Institutes / Universities 16%

Journalists 4% Market research 2%

OEMs 17%

Politics 5%

Environmental organisations 4%

Consultancies 9%

Associations 5%

Alternative Views 4%



The expected scenario draws a quite pessimistic picture of the future development of technological innovations that foster sustainable mobility:

In 2030…

…crises in oil-exporting countries, environmental disasters and related fluctuations in oil prices have dramatically increased the pressure for action to enforce new drive concepts. Individual problems, such as the recycling of batteries have been resolved. Nevertheless, most attempts in recent years to realise market penetration with new drive concepts have not been very successful. Vehicle manufacturers are therefore still active both in passenger car and commercial vehicle segments with a broad technological product portfolio – and a corresponding high financial and organizational effort.

A further complication is that the acceptance of new propulsion technologies differentiates the international market: Technologically advanced conventional drive concepts are demanded – among others – in the United States and Europe. In the major urban centres of countries such as India and China simple new drive concepts from domestic production partly dominate the city centres. A nationwide market penetration of new propulsion concepts is not recorded here. In Western countries, a strong urban-rural gap is also observed. For example, a sufficient infrastructure of charging stations in rural areas is largely missing.

In addition, new technologies are supported on different levels: while in countries such as China, new drive concepts of the public authorities are strongly supported by the government, other countries engage less in this respect: Neither subsidies for the industry, nor sales incentives for customers are granted.

Table 1: Expected Scenario

Desirable Scenario: Electric Mobility’s Dominance

The desirable scenario is characterized by the following aspects:

In 2030…

…electric drives (especially battery-electric vehicles, range extender and plug-in hybrids) dominate the number of new registrations in Germany. Conventional power trains cannot keep up with electrical drives with regard to essential performance indicators, environmental friendliness, and total cost of ownership. The success of electric vehicles was supported by three key developments: (1) the increased willingness of customers to pay more for these drive systems, (2) the fact that a large part of energy obtained for new drive concepts originates from renewable sources, as well as (3) the comprehensive provision of efficient and cheap charging facilities for electric drives - even in rural areas.

In the segments of medium and light commercial vehicles partially or fully electric drives are standard, especially since in many urban areas, in recent years only vehicles were allowed to pass that are not causing any kind of local emissions. The market for new drive concepts is dominated by manufacturers from Europe and Asia alike: European manufacturers are positioned in the area of complex new drive systems, while Asian manufacturers dominate the market segment of the less sophisticated and affordable new drive concepts.

In this case the raw material supply does not constitute a bottleneck in the dissemination of new drive concepts: First, there is high global political stability. Second, not only different


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raw materials are used as substitutes, the industry has also found ways to recycle all materials and fluids in the sense of a closed loop. The customers mainly use an optimally coordinated network of multi-modal mobility services (public transport, car sharing, own car, etc.).

Table 2: Desirable Scenario

Apart from the plausible and consistent scenario stories, the large number of participants also resulted in more than 2,000 qualitative arguments. The arguments were coded to identify patterns in the participants’ argumentation. The main factors that were identified by coding the arguments of the Delphi panel participants are:

1. Energy mix for vehicle operation

2. Infrastructural conditions

3. Customer preferences

4. Raw material supply

5. Government intervention

6. Technological maturity

7. Changing market structure

8. Comodal mobility

9. Germany's competitiveness

3. Development of measuresIn order to analyse the way to the desirable scenario for 2030, 43 expert interviews were conducted.

The three main goals of the interviews were the following:

1. Identification of measures that need to be taken in order to realize the desirable scenario

2. Identification of actors that need to become active in order to make the change happen

3. Determination of the timeliness of the different measures

Selection of Participants

Concerning potential respondents, the aim was to invite experts from different interest groups in order to achieve a multi-faceted picture of the scenario paths. Thus, we were able to ensure that all relevant stakeholders are not only involved in the development of the scenarios and the identification of influencing factors, but also participate in the development of the measures that need to be taken to realise the desirable scenario. Furthermore, experts in the backcasting phase were recruited from the panel of Delphi survey participants. The major advantage of this approach was that experts that were previously involved with the issue are already familiar with the topic so that rich in-depth discussions could be achieved. In addition, we expected a targeted assessment of the scenarios and the way to the desirable one, since the scenarios and influencing factors were based on the Delphi assessments of the same experts. We experienced another advantage of this approach in the easier acquisition of potential interviewees.


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A total of 43 interviews were conducted with experts proceeding from 15 different interest groups similar to the Delphi survey. We aligned the interview sample’s distribution in order to achieve equilibrium among stakeholder representation. The distribution of interviewees is shown in Figure 2:

Figure 2: Interviewees by Stakeholder Group

Execution of the backcasting interviews

For the semi-structured interviews we specifically developed a graphical interview guide that was tailored to the needs of a backcasting exercise (see Figure 3). Every interview started with a briefing (Delphi results, probable and desirable scenario etc.). If agreed upon, interviews were recorded (only two participants did not agree with a recording).

The scenario narratives (see Table 1 and 2) were supported by visual representations of their content. For complexity reasons, the scenarios were in condensed form, but the full texts had been provided via email before.

Subsequently, we presented our identified nine factors by giving examples and explaining their status quo 2010.

We conducted the actual interview using the graphical interview guide with a timeline from 2010 to 2030. The goal here was to discuss the role of individual factors on the way towards the desirable scenario and to develop measures for the short-, medium-and long-term.

The time horizon from 2010 to 2030 was created according to the time horizon of the Delphi survey, the next 5 years should be seen as short-term time horizon, until 2020 as the medium–term horizon, and the stage to 2030 represented the long-term time perspective.


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Alter nati ve Ansi chten

Automobi lzuli ef er er

Her stel ler E l ektr of ahr zeuge

E ner giever sor ger

Flottenbetr eiber

Handel

Inf r astr uktur

Ins ti tute / Uni ver s itäten

J our nal isten

Marktf or schung

OE M

Öff entl iche Hand / P ol i tk

Umweltor gani sationen

Unter nehmensber atungen

Ver bände

Institutes / Universities 12%Journalists 5%

Market research 2%

OEMs 19%

Politics 2%

Associations 7%

Environmental organisations 7%

Consultancies 2%Alternative Views 9%

Automotive suppliers 7%

Manufacturers electric vehicles 5%

Energy suppliers 7%

Fleet operators 7%

Dealers 5%

Infrastructure 5%



Energy mix for vehicle operation

Customer preferences

Government intervention

Changing market structure

Germany's competitiveness

Infrastructural conditions

Raw material supply

Technological maturity

Multimodal mobility

When do you see which crucial events on the way to the desirable scenario?

2010Factors 2012 2014 2016 2018 2020 2025 2030

Main actors

Electric Mobility’s

Dom

inance

OEMs/Suppliers Politics Energy suppliers Customers

Figure 3: Graphical Interview Guide

The interviews were carried out personally or by telephone. We executed the interviews with two interviewers, with the first three interviews being conducted jointly in order to ensure maximum uniformity in the way they were carried out subsequently. Similar to Engels & Powell Kennedy (2007), all the interviews were transcribed verbatim. In order to properly analyse the content, we applied inductive coding using the Nvivo 9 software4 for qualitative data analysis. Using the nine main factors identified during the Delphi process as categories, we organized the interview data in subcategories of the main factors by using inductive coding in an iterative process. The meaningfulness of the subcategories and their organization were discussed with further researchers from the project team. After the recorded interviews were transcribed and coded, a list of measures was developed and the respective actors were identified.

4. ContinuationAccording to Quist & Vergragt (2006) follow-up of backcasting exercises is very important to actually use and implement the content that has been generated.

The results of our research have already been used at several occasions. Multiple workshops with stakeholders from the automotive industry were held. Here, the implications of the measures for the different stakeholder groups were further discussed. In addition, all results contribute to the joint research project “Competitiveness Monitor” (CoMo) within the EffizienzCluster LogistikRuhr of the German Federal Ministry of Education and Research.5

The CoMo will combine three foresight tools in a single IT-based Futures Platform. This platform integrates user specific information from (1) a Trend Database (TDB), (2) a

4 See www.qsrinternational.com

5 We present detailed insights on the Competitiveness Monitor (CoMo) in our 4th FTA 2011 conference paper “Competitiveness Monitor: An integrated foresight platform for the German leading-edge cluster in logistics”.


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collaborative Prediction Market app, and (3) an individual Future Workshop (“Zukunftswerkstatt”) (Jungk & Muellert, 1987) app. Backcasting promises high procedural value for the latter (Future Workshop) and it is intended to integrate developed elements into the concept of the Future Workshop in order to establish a valid and reliable action-oriented foresight process.

3 Methodological discussionThe method that we used in this study combined an innovative Delphi survey with individual semi-structured expert interviews. Our approach enables to both include a large number of different stakeholders and still having a structured process of how the results are analysed and further used. The importance of involving different stakeholders is also emphasized by Cuhls (2002). Moreover, the anonymity of the Delphi and the comforting atmosphere of personal interviews give interviewees the opportunity to comment on issues that they would otherwise not have commented on. This leads to a broad data pool of differing opinions and a multi-faceted view.

The Delphi method has much potential to be enlarged by further methods. Engels and Powell Kennedy (2007) discuss ways of how to enhance Delphi findings by including further methods into a study. During their study on family-focused prevention, they employed the Delphi methodology after a literature review to develop a framework for their family-focused preventive intervention programme. This step was then followed by both focus group interviews and individual interviews to evaluate the framework developed during the Delphi study and to refine it.

A key difference to our case is that the follow-up interviews were used to rework the Delphi results instead of using them as a starting point for further analyses in a backcasting sense. Nevertheless, the study of Engels and Powell Kennedy (2007) is a good example for showing the usability of combining Delphi with interviews as is our presented case.

The combination of backcasting and Delphi has been executed by Höjer (1998) in a study concentrating on transport telematics in urban transport. The overall aim of the study was to formulate scenarios that are potentially leading towards sustainable development. Contrary to our approach of combining Delphi and backcasting, however, Höjer (1998) employed scenarios that had been developed within the research project were exposed to repeated and structured critique by experts in a Delphi-like way. The experts were asked to evaluate certain scenarios and give hints for potential changes or new content of the scenarios. Höjer’s study focused on probability and impact of the Delphi scenarios rather than focusing on the desirability. Furthermore, the actual backcasting step of drawing pathways was left out. However, a similarity to the approach used at hand is the fact that experts were asked to rate the scenarios’ desirability, probability, and economic consequences of implementing these scenarios.

By using the web-based real-time Delphi approach we were able to invite a broad number of participants to the study which enabled us to reach experts from 15 different user groups. The idea to recruit the interviewees for the individual interviews from the Delphi panel clearly was an advantage, since all interviewees immediately knew what the discussions were all about.

So far, participatory backcasting approaches have been executed mostly with the help of workshops and focus group discussions in order to discuss the paths towards desirable scenarios with the relevant stakeholders (Carlsson-Kanyama, Dreborg, Moll, & Padovan, 2008; Kok, Patel, Rothman, & Quaranta, 2006; Svenfelt, et al., 2010). In this respect, a focus group is a form of qualitative research, in which a group of people is asked about its


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perceptions, opinions, beliefs, and attitudes towards a product, service, concept, advertisement, idea, or packaging (Henderson, 2009).

The aim of our backcasting exercise was to include a diverse group of stakeholders not only in the development of the scenarios, but also in the backcasting step itself. Therefore, we employed a methodology of semi-structured, in-depth interviews with selected interviewees using a tailored interview guide for the backcasting interviews.

4 Limitations and conclusionFirst of all, the broad range of opinions in both the Delphi and the interview step did not allow for a consensus in all cases e.g. when developing the scenarios or elaborating on the measures that need to be taken in order to reach the desirable scenario. An initial aim of the study was to develop measures that are linked to a concrete point in time. Due to the fact that quite many different topics were included in the study and that a great variety of stakeholders was involved, the issues raised were covered in their breadth. This leads into the same direction of what Svenfelt et al. (2011) remark as a limitation in their study: According to them, it is often difficult to develop concrete pathways to the desirable scenario. Here, follow-up research could dig deeper concerning single details.

Backcasting has been applied using a number of different techniques. Mostly, however, focus groups were used to develop scenarios or measures on the pathway towards a desirable future. In our backcasting approach, we showed how (real-time) Delphis and guided interviews can be combined in order to tackle problems such as sustainable logistics and mobility in Germany in a very structured discussion.

Specifically, with the help of combining (real-time) Delphis and semi-structured in-depth interviews, shortcomings of focus groups such as the suppression of alternative opinions could be circumvented. Furthermore, by using an internet-based real-time Delphi survey and individual interviews via telephone it was possible to include a sample of 140 experts in the Delphi step and 43 participants in the interviews, coming from 15 different groups in each case.

New research endeavours could combine several Delphis were the first survey develops scenarios as we used it in the study at hand. A following (real-time) Delphi survey could, in turn, be employed to evaluate measures according to their timeliness or importance.


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developing a backcasting approach for systemic transformations

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