Foresight support systems to facilitate regional ...

TFS-17931; No of Pages 14 Technological Forecasting & Social Change xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Technological Forecasting & Social Change

Foresight support systems to facilitate regional innovations: A conceptualization case for a German logistics cluster Jonas Keller ⁎, Christoph Markmann, Heiko A. von der Gracht EBS Universitaet fuer Wirtschaft und Recht, EBS Business School, Konrad-Adenauer-Ring 15, 65187 Wiesbaden, Germany

a r t i c l e

i n f o

Article history: Received 2 March 2013 Received in revised form 3 August 2013 Accepted 10 December 2013 Available online xxxx Keywords: Foresight support systems Regional innovation Information technology Cluster Trend database Prediction market

a b s t r a c t We present the conceptualization of a foresight support system (FSS), which is designed to implement a continuous and embedded foresight process among partners of a business cluster. We argue that engaging in foresight (1) enables clusters and organizations to face discontinuous change, (2) avoids lock-in of business clusters by networking and knowledge exploitation and that (3) collaborative foresight can support such networking. As systemic instruments that connect stakeholders and support continuous foresight processes, FSSs are especially suited to achieve these results. We present five basic premises to conceptualize an FSS and concretize them by a requirement analysis for an FSS funded by the German Federal Ministry of Education and Research in the course of Germany's Leading-Edge Cluster Logistics. The requirements lead to the conceptualization of a foresight database, a digital future workshop application, and a prediction market application integrated in a futures platform. All applications are interlinked to support users through a guided, web-based, multi-method foresight process. The implementation of the FSS in business clusters and insights acquired during the three-year project are discussed. Overall, we present insights on how regionally implemented foresight contributes to regional innovation systems and thereby contribute to the emerging research stream on FSS. © 2014 Published by Elsevier Inc.

1. Introduction In business clusters, networking and cooperation among companies and institutions facilitate organizational learning and enable even small and medium-sized enterprises (SMEs) to profit from economies of scale and scope while maintaining flexibility [1,2]. Clustering is a much-cited approach for resource-constrained enterprises to react to the constant pressure to innovate in light of increasing competition and market dynamics. However, business clusters – defined by Porter [1: p.78] as “geographic concentrations of interconnected companies and institutions in a particular field” – can suffer from lock-in effects. Rigid resource allocation then leaves companies and regions with inflexible product portfolios or

⁎ Corresponding author. Tel.: +49 611 7102 2113; fax: +49 611 7102 10 2113. E-mail address: [email protected] (J. Keller).

business models in light of discontinuous or long-term change [3]. One way to prevent lock-in effects is through an effective regional innovation system (RIS) that incorporates external and unorthodox knowledge into the region's and companies' learning processes [4,5]. Collaboration of the regions' companies, research institutions, and policy actors in a “networked” approach contributes to such a development. In this article, we argue that foresight methodology and particularly foresight support systems (FSS) [6] can play an important role in supporting the functionality of an RIS. By integrating various foresight instruments electronically to support a continuous foresight process, FSS support us in investigating challenges from different perspectives and thus improve upon the individual methods' results [cf. 7]. This support enables companies to prepare for discontinuous change and to acquire new perspectives that can provide new impulses. If all actors of a cluster, i.e. companies, research organizations and governmental institutions, work collaboratively to apply a foresight support

0040-1625/$ – see front matter © 2014 Published by Elsevier Inc. http://dx.doi.org/10.1016/j.techfore.2013.12.031

Please cite this article as: J. Keller, et al., Foresight support systems to facilitate regional innovations: A conceptualization case for a German logistics cluster, Technol. Forecast. Soc. Change (2014), http://dx.doi.org/10.1016/j.techfore.2013.12.031

2

J. Keller et al. / Technological Forecasting & Social Change xxx (2014) xxx–xxx

system, the ensuing embedded and continuous foresight processes can contribute to a successful preparation towards discontinuous change. From this argumentation, we derive five basic premises for a collaborative FSS to be applied in business clusters. We then develop an FSS concept for the regional logistics industry of western Germany, where research institutes and businesses are pooling their competencies to support innovation for the EffizienzCluster LogistikRuhr initiative funded by the German Federal Ministry of Education and Research (BMBF) [8]. The cluster's 120 companies and 11 research institutes are specialized in diverse areas and are committed to achieving long-term growth and prosperity. In addition, the logistics industry is largely dominated by SMEs that exploit current market opportunities. However, due to resource constraints, these companies have limited capacity to advance innovation and explore new market opportunities. Furthermore, global competition and rapid technological advancements have increased this traditionally slow-changing industry's exposure to change. These aspects made the Leading-Edge Cluster Initiative for Logistics an ideal environment to establish a foresight support system – the Competitiveness Monitor (CoMo) – to collaboratively exploit cluster potentials and activate innovation capabilities. The conceptualized FSS combines quantitative and qualitative data and foresight methods. A foresight database (FDB) serves as the knowledge pool of the FSS. This information is further used in a digital futures workshop application (FWA), which is derived from the concept of Jungk and Muellert [9], and brings together stakeholders (from the cluster) to work on their common, future-related challenges. The application guides the users in a collaborative manner through a complete foresight workshop, from problem identification and definition, to a multi-method foresight process, to the development of practical solutions. The third tool, a prediction market application (PMA), can be used to further quantify possible future developments relevant to the cluster by trading different options of future factors among Competitiveness Monitor's users. Prediction markets build on the wisdom of the crowd's assumption [10] and the efficient market-hypothesis [11]. While they are an established method in a variety of fields [e.g. 12], the tool's interlinkage within a foresight support system is novel. The three tools can be accessed from a futures platform (FP), which also allows for adapting the FSS to individual needs. While many foresight methods are individually applied [13], information about ways of reasonably combining them is limited and strongly differs according to specific requirements and objectives [14]. Furthermore, their application and combination within an action-oriented foresight process, which also includes an initial depiction of a potential future problem and a final development of a concrete plan of action, is currently hardly explored but starts to receive increasing attention. A more topic and task-oriented process extends the traditionally explorative foresight process and thereby raises its relevance and usability for practice [15]. Due to the variety of future challenges that could be examined, an FSS should offer a wide range of method combination opportunities but also guide the selection process according to the topic of investigation to keep the foresight process clear and focused. Furthermore, the linkage to external sources has only been investigated to a limited extent. Most input is provided

manually through short-term surveys, expert assessments, or different search operations. However, the interlinkages to other databases or corporate IT infrastructure can add value to users' foresight processes and provide an improved, updated and always accessible information base. Thereby, our research contributes to the emerging field of FSS in two ways. First, we demonstrate that FSS can lead to more embedded and continuous foresight processes and thereby facilitate regional innovation. Second, we present design considerations of an FSS and demonstrate how the interlinkage of methods, tools, and external sources can result in a more accessible and actionoriented foresight process. In the following section, we demonstrate how collaborative foresight processes in an FSS environment expand the scope of future awareness and peripheral vision of open foresight [16] and thus may support the organizations of business clusters in implementing RISs and dealing with discontinuous change. From this discussion, we derive basic premises for the establishment of such an FSS and subsequently specify them by single requirements. We then discuss how the different applications are integrated into an FSS and conclude with a summary of challenges and insights acquired from this case. Finally, limitations and opportunities for future research are presented. 2. Regional innovation systems and foresight SMEs often locate in business clusters in order to cope with resource constraints [17]. The geographic proximity as well as the linkages among cooperating and competing peers provides firms with cost advantages, access to otherwise less available resources, technological externalities, as well as more intense knowledge transfer [18,19]. Moreover, networking and location effects in clusters have been found to improve the innovativeness of respective firms [20]. However, research demonstrates that clusters usually only support SMEs in adopting and diffusing incremental innovation as opposed to discontinuous innovation [21]. Pouder and St. John even determine that after some time, firms in clusters are more vulnerable to discontinuous change [22]. This can be traced back to the path-dependency of clusters, i.e. past developments lead to present resource configurations, which determine future actions [3]. Path-dependency in business clusters comes with the danger of a lock-in effect, i.e. excessively focusing on one trajectory to react to more innovative competition [4,23]. Thus, in order to stay competitive, firms (or clusters) should incorporate links to external and non-local knowledge into their local network [24]. Toedtling and Trippl [5] discuss that clusters' successful adaptation to changing external conditions is closely related to the RIS which the cluster is part of. While Porter does not differentiate between business clusters and innovation systems [25], a more general view in academic literature views clusters and RIS as similar but not congruent [e.g. 24,26]. Autio [27] developed a widely cited RIS-framework which distinguishes among knowledge application and knowledge generation and sharing. He labels knowledge application as an “exploitation subsystem”. This subsystem contains the industrial companies and is usually put at the same level as the cluster [cf. 24]. The knowledge generation and sharing subsystem contains those organizations – such as research and



education institutions – that generate and diffuse knowledge and skills. Asheim and Coenen [4] argue that it is exactly the interaction with research institutes and universities in a “networked” RIS that can bring in the external knowledge referred to above. Toedtling and Trippl [24] add a regional policy dimension to Autio's framework because these actors play an important role in shaping innovation processes. Thus, a cluster embedded in a knowledge generation subsystem and supported by governmental institutions forms a functioning triple helix of industry, research and administration [28] at a regional level. In accordance to Ahlqvist et al. [29] as well as Renn and Thomas [30], we argue that a common and regional foresight process may facilitate this interaction. As a systematic, collaborative, and action-oriented process [31], foresight can facilitate an efficient exchange among actors in an RIS. Cuhls [32] also argued about foresight “bringing together different stakeholders of the innovation system […] seems to be as important as empirical results”. Following the phases “selecting search areas and information sources”, “collecting data”, “analyzing and interpreting data” and “evaluating and decision making” [33], a foresight process additionally has the potential to contribute unconventional knowledge and ideas to prepare for discontinuous change, and to foster discontinuous innovation [34]. It can contribute to a reduction of uncertainty in the perception of the environment and support the adjustment of organizations in light of uncertainty [35]. Bessant et al. [36] determine that traditional methods of innovation management often fail to work in light of discontinuous change. They state that applying foresight methodology can create a “tolerance of ambiguity” and thus contribute to alternative vision building, as an innovation practice for discontinuous change. Furthermore, foresight has the potential to strengthen the knowledge generation (exploration) side of the RIS. Andersen and Andersen [37] even coin the term “innovation-system foresight”, arguing that foresight can strengthen innovation systems. Regional foresight has long been promoted by the European Union for setting regional trajectories and involving stakeholders [30,31]. Regional foresight might help regions and companies to adapt to changing circumstances [15] and thus strengthen the RIS [38]. On a similar note, Könnölä et al. [39] present voluntary and collaborative foresight as a method to escape lock-in effects. The authors argue that the focus of “incumbent industries” on exploitation and related inertia in innovative and exploratory undertakings could be overcome by implementing foresight processes. While many regional foresight exercises implemented across Europe [e.g. 40–42] are highly useful for the regional development trajectory, they are not designed to facilitate a continuous foresight process for the regions or organizations involved. We determine that in order to connect the previously discussed sub-systems of knowledge application, knowledge generation and policy making, continuous and embedded foresight processes should be used in RISs. This view corresponds with Sarpong and Maclean [43], who criticize the notion that futures studies or scenario exercises is commonly described as a one-off process. Instead, the authors advocate an embedded process of adaptive scenario thinking in order to ameliorate companies' innovation capacities. In their concept of “emergent

3

foresight”, Salmenkaita and Salo [44], transfer a collaborative, continuous, and action-oriented concept of foresight to industrial clusters. They argue that “stakeholders with overlapping interests” can more effectively use iterative bottom-up processes than singular top-down foresight exercises (“explicit foresight”) in order to align companies' innovation agendas and transfer these into action.1 Since Cooke [2] observes that companies in successful clusters readily exchange ideas and have a free flow of information, we particularly emphasize the necessity for collaborative foresight. Similarly, Uotila and Harmaakorpi [46,47] argue that only foresight embedded with the region's stakeholders can overcome what they call the “black holes of regional strategy making”. We find the collaborative nature of foresight in clusters closely related to the systemic character of innovation [cf. e.g. 37]. Smits and Kuhlmann [48] document the increasingly systemic character of innovation and suggest systemic instruments, including systemic foresight instruments, as an appropriate approach. Saritas [49] also provides a systemic idea of foresight and – together with Nughuro [50] – describes the integration of several foresight tools as a veritable method for a systemic approach. Similarly, Heger and Rohrbeck [7] argue that method integration helps to take into account multiple perspectives and thus delivers a more holistic picture of the situation. In fact, approaches that combine or integrate foresight methods instead of applying them discretely are becoming more common [cf. e.g. 51–53]. When considering information and communication technology (ICT) tools, Skulimowski [54] underlines the importance of including both quantitative and qualitative data as well as including input from political, economic, social, and technological perspectives. We argue that such integrated approaches are appropriate for the idea of systemic instruments, especially if they are designed as what Bañuls and Salmeron [6] call foresight support systems (FSS). While Bañuls and Salmeron were not the first to use the term FSS – this was done by Walden et al. [55] – they pioneered the definition of the term. In accordance with our previous discussion, the authors specify that foresight support systems should support continuous foresight processes and connect decision makers with involved stakeholders on a permanent basis. In order to achieve adequate results, FSS should also be designed to include multiple problem-solving techniques, a database, as well as other instruments for data assessments. According to Smits and Kuhlmann [48], systemic instruments should additionally provide access to all relevant actors. As such, an FSS corresponds to a part of what Doloreux [56] calls the knowledge infrastructure of regional innovative systems, which thus could serve as the binding feature between the knowledge exploitation and generation of RIS sub-systems. Following the logic of this discussion, we developed five basic premises for the design of an FSS. The first premise stems from the notion that employing foresight can be used

1 Salmenkaita and Salo [44,45] differentiate “emergent foresight” from their concept of “embedded foresight” which refers to stakeholder-driven processes that are part of larger research and technology development programs. However, from our point of view the collaborative, broad, and “bottom-up” nature of the “emergent foresight” concepts corresponds better to our understanding of continuous and embedded foresight.


4


as effective preparation for discontinuous change by both individual organizations and cluster: (1) The FSS should support in creating, linking, and processing information about future relevant developments in government, economics, society, and technology. Relevant knowledge for the future is generated and the introduction of unorthodox knowledge to the cluster may help to avoid lock-in for the region. (short description of premise: information platform) We derive two further premises from the idea that in order to effectively tackle discontinuous in addition to incremental changes, clusters need to be embedded in an RIS. Foresight potentially contributes to linking the knowledge exploitation and generation sides of the RIS: (2) The FSS should stimulate collaboration among cluster stakeholders in order to activate the cluster's innovative and competitive potential. The FSS needs to become a driver of the knowledge infrastructure between the knowledge exploitation and generation sides of the RIS. (collaboration) (3) The FSS should motivate stakeholders and provide them with the tools to systematically deal with their future and strategic options as well as to foster innovation. We argue that iterative bottom-up processes are much more effective than singular top-down exercises. Therefore, only continuous and embedded foresight processes can support the cluster and its partners to develop their own foresight capabilities and deal with both incremental and discontinuous change. (incentivization) As systemic and embedded instruments, collaborative FSS are especially suited to support integrated, continuous and embedded foresight processes of RIS. Hence: (4) The FSS should integrate different electronic foresight applications into a “true” FSS for the cluster. The integration of different instruments facilitates in tackling foresight problems more effectively from multiple angles. The consecutive application of interconnected tools can additionally structure the process. Accordingly, different tools should be interlinked by using each other's input and output. Furthermore, a balanced mix of quantitative and qualitative applications [cf. 54] should be achieved in order to fully exploit the benefits of both worlds. Finally, the integration of the FSS into the existent IT-landscape of an organization links the system to additional resources and thus enhances success factors of such a foresight method tool-kit. (systemic FSS) Finally, the resource constraints of some cluster-constituting SMEs lead to the fifth premise: (5) The FSS should provide educative information on futures studies and teach future skills in order to overcome the resource constraints of SMEs. The FSS is designed to strengthen SME foresight and innovation capability at the network level. However, the relative rareness of multi-perspective foresight exercises

and the unfamiliarity of many SMEs with these methods require support in explanation and application of foresight processes. (support)

3. Research framework and development of a conceptual FSS architecture From the literature review, we derived the aforementioned five basic premises for the development of an innovative FSS. These basic premises were further defined during the requirement analysis process of the FSS Competitiveness Monitor (CoMo). It was designed as part of a joint research project of the leading-edge cluster for Logistics (“EffizienzCluster LogistikRuhr”) in the Rhine-Ruhr metropolitan region of Germany. As the third largest industry in Germany by market volume (2011: €223 billion) and employees (2011: 2.82 million), the logistics industry will play a major role in Germany's high-tech strategy over the next decades [57]. The industry is confronted with enormous challenges such as globalization, technological advancements, and competition. Foresight methodology has been particularly underused in the logistics industry in the past. While von der Gracht and Darkow [58] stated that the application of the scenario technique in logistics has increased over the last decade, they also determined that there are still few applications, of which most focus on operational problems, such as warehousing or transportation. Such approaches only provide marginal support in dealing with the larger uncertainties of the globalizing world. In 2005, Flint et al. summarized that “logistics research has largely ignored the concept of innovation” [59: p.113]. The lack of innovation as well as lack of opportunities for development has been recognized by the German Federal Ministry of Education and Research, who has granted €100 million between 2010 and 2015 to the Leading-Edge Cluster. More than 130 companies, research institutions, and local governmental institutions, i.e. actors from all parts of the triple helix [28], are collaborating in 35 joint research projects. This mix of diverse actors provides the basis for an RIS. The clusters' purpose to bridge the gap between research and industry [60] is, in effect, the basic idea behind an innovation system. In the following sections, the results of an extensive requirement analysis from national and international workshops and questionnaires of this research initiative are briefly presented. The analysis is used to develop a novel FSS for later implementation in organizations of the aforementioned cluster. 3.1. Requirement analysis The aforementioned basic premises were refined during a systematic requirement analysis based on the Volere Requirements Specification Template [61,62]. We conducted semi-structured interviews and participatory workshops on a continuous basis with joint project partners of different backgrounds, competencies, and interests. This mix ensured a high level of methodological diversity, interdisciplinary usability, and practical relevance. Through this process, we determined an extensive list of requirements for a proposed FSS, which was subsequently rated and assessed by all project



partners along the dimensions of importance, innovativeness, and feasibility. In order to ensure state-of-the-art technology and to avoid biased group-think, the requirement process was supported by additional empirical research. More than 400 experts, including scientists, practitioners, and graduate students from the fields of futures research, information technology, education, and logistics were involved via interviews, workshops, and Delphi surveys during the three-year project in order to generate knowledge for requirement analysis, concept development, and content development [e.g. 63–65]. The empirical research included, among others, (1) 21 personal interviews with cluster partners regarding their conceptions of FSS, focusing on the importance of individualization, collaboration, interfaces, and incentivization, (2) a real-time Delphi study on the future role of ICT for foresight among 177 strategists and futurists worldwide [66], (3) a real-time Delphi study on the future of learning environments among 48 MSc students within a strategic foresight course (see Appendix 1), and (4) a real-time Delphi study among 142 futurists on the foresight profession and standards in futures teaching [67]. Several external speakers were invited to Competitiveness Monitor development sessions and interviewed. Related or contributing research included cognitive biases in foresight [68], backcasting [69], and open innovation [70], among others. Furthermore, (interim) results were presented and discussed within the research and practitioner community at 30 international conferences.2 A benchmark analysis of leading trend databases [63] and other ICT-based foresight tools (e.g. prediction markets, cross-impact analysis, wildcard analysis) ensured the innovativeness of the FSS under development. Overall, the aggregation of the different analyses and reflections revealed a long list of different requirements. These requirements were categorized and attributed to the five basic premises from the literature review (see Table 1). The premises and main requirement categories were considered in the development of a concept for an FSS. We defined a framework for a general FSS following the design considerations of Bañuls and Salmeron [6], including modules for the generation, storage and analysis of futures data, with all modules contributing to the fulfillment of the basic premises in several ways. Four major modules, as shown in Fig. 1, were used to define the FSS: This includes a (1) foresight database for data storage and retrieval, (2) a prediction market application for further generating and assessing foresight data based on crowdsourcing processes, and (3) a future workshop application for facilitating a systematic, solution-oriented foresight and analysis process. These tools are integrated in a (4) futures platform that allows an adaption of the FSS according to individual needs, facilitates a modular extension, and enables the interlinkage to external sources and corporate IT structures. The different FSS modules and their contribution to the basic premises, as presented in Table 1, are further defined in Section 4. 2 Conferences included 2011 Seville Conference on Future-Oriented Technology Analysis (FTA), 13th International Conference: Finland Futures Research Centre 2011, YIRCoF'11 Yeditepe International Research Conference on Foresight, WorldFuture 2011, 9th International ISCRAM Conference 2012 (Foresight Track), XXIII ISPIM Conference 2012, BAM 2012 Conference (Foresight Track), International Conference on Innovative Methods for Innovation Management and Policy (IM2012), 5th ISPIM Innovation Symposium, among others.

5

This structure provided a framework to integrate the first set of specific foresight methods. In particular, we considered Popper's futures diamond [71] as a suitable overview of the most frequently applied foresight methods (see Fig. 2). We defined an initial set of complementary foresight methods that cover key areas of foresight processes, such as identification, thematic exploration, analysis, and the derivation of fields and opportunities of action [72]. All of the methods were applied in foresight exercises separately and have recently been investigated on a larger scale in combination [14]. However, a broader and more flexible framework about which methods could be combined and how is still missing and will be a major point of interest in future FSS. Up to now, information about opportunities and challenges in combining them to create an individually adaptable, multi-perspective foresight process, especially electronically in a web-based FSS, are hardly available and need to be explored. Thus, the FSS needs to be developed incrementally and extended, improved, and adapted continuously according to practical and theoretical advancements. In this way, the FSS can also be adapted to specific regional characteristics [73]. Furthermore, methodological interfaces, data formats, and transfer protocols first need to be defined for the IT architecture of the FSS, before they can be tested and adjusted. Based on the basic premises from research and our requirement analysis, we selected an initial set of foresight methods, reasonably compatible without major difficulties, and capable of supporting a foresight process along its key stages [13,72]. These methods were also chosen to expand the array and connectivity of already existing electronic instruments and thereby to develop the field of FSS in new directions, such as stimulating innovations in business clusters. This ruled out the initial implementation of a scenario process, as several respective software packages already exist. However, the scenario technique and further foresight methods can and should be incrementally integrated later on in order to improve and extend the foresight process of the FSS. Fig. 2 also indicates the foresight methods that are partially or fully implemented in the Competitiveness Monitor and the methods which are planned to be considered in the subsequent advancements of this FSS. 4. Integrated modules and foresight methods of the FSS “Competitiveness Monitor” 4.1. Futures platform Users can obtain access to the various foresight applications through the futures platform, which serves as a single point of entry. Each user has a personal account for a maximum of individualization. The FSS can be personalized, individually extended, and linked to other systems, such as enterprise databases or trend databases. Personal information, trend favorites, and foresight work can be saved or shared with other FSS users. This generates a transparent working environment and facilitates the collaboration among cluster partners. Thereby, the FSS can serve as a platform for confidential corporate foresight, integrated in the corporate ICT infrastructure but also for open foresight exercises [74,75]. An integrated foresight curriculum supports the user with learning material about the application spectrum of the Competitiveness Monitor, different foresight methods, and best practices in futures


6


Table 1 Basic premises and requirements for FSS. Basic premises 1 Information platform

2

3

4

5

Main requirement categories

Meaning/implication (extract of individual requirements)

Quality standards

Quality check by administrator or lead user; limited/controlled access and manipulation by user Structure of information Homogenous information structure; individual extension according to defined schemes; informational hierarchy (i.e. flight-level); categorization along established frameworks (e.g. PESTE, Value-Chain, sector index etc.) Amount of information Mandatory fields of information; extensive opportunities to add information; attachment of picture, graph, pdf, etc.; web-link; evaluation of information Collaboration Collection of information Online brainstorming, tag-clouds; display of trend favorites of “contacts”; “most seen/liked”-display; advanced search function; conduction of survey Communication platform Private and group messages; chats; forums Co-Creation Interactive development of information; open and closed evaluation features; Security Use of confidential information; “closed” user groups Incentivization Individualization Adaptable design; individual profile and user rights; display of preferred topics, trends etc.; individual newsletter; notifications; corporate branding Practicality Action-oriented outcomes, feasible processes Play money Fictive currency/credits; competitive elements; reward program; quick-wins; distinctions; exclusive features Flexibility Mobile application; offline usage; real-time workshops Design Futuristic design features; clear, intuitive structure and user interface; simplicity of information; inventive graphs System integrity Tool-kit Innovative combination of methods; holistic approach; guided processes; transferability of input and output data Interlinkage Interlinkage of tools and methods; tagging; keywords; interlinkage of futures data; Compatibility Compatibility to corporate IT; standard interfaces; browser-independency; link to external sources Standardized formats Export and import of standard formats (e.g. doc, pdf); low-entry barriers for usage Support Methodological support Guidelines for tools and methods; process charts; support for setting up workshops or uploading data; IT-support; tutorials Educative information Information on futures research;; literature; best practices, method overview; frequently asked questions; (interactive) teaching videos Partial automation Industry-/company-/country-specific search and analysis function; SME standard analysis; automated analysis recommendations; advanced filter function (flight-level specification for search and analysis)

Tool implementation FDB; PMA FDB, FWA, PMA

FDB, PMA FDB; FWA; PMA FP FDB; FWA; PMA FP All applications FWA PMA; FDB All applications All applications FDB, FWA, PMA All applications FP FDB; FWA FP FP FDB; FWA

FP= futures platform; FDB = foresight database; FWA = future workshop application; PMA = prediction market application.

research. This curriculum reduces the inhibition threshold of companies to deal with their future in a structured and continuous manner by integrating FSS in their business activities. The foresight curriculum includes, for instance, self-study modules about history, fundamentals and logic of futures studies, description and application instruction of foresight methods and guidelines for foresight transfer. In addition, case studies concerning open foresight and corporate foresight enrich the curriculum. The futures platform also has special significance with respect to interlinkage. It connects the different FSS applications and foresight methods and also constitutes the interface for external linkage. This includes the linkage to external sources as well as the integration into existing companies' IT structure. Its modular design allows a flexible adaptation and extension of the methods and the uniform data structure. As described in the following section, this allows a smooth transfer of data. 4.2. Foresight database A core element of the FSS is the storage as well as the retrieval and provision of a variety of information. While there are many commercial trend databases already available, most of them feature one or more shortcomings to be integrated in a community-based FSS [63]. Since they are mostly segregate applications, they usually do not feature a

data structure, which is aligned for an automated transfer to other foresight applications. Furthermore, their data accuracy is usually ensured by professionals, which search, develop and maintain relevant information. In an RIS, such a trend database can be valuable, but must offer opportunities to integrate contributions by group members, which require an incentive for their information and opinion sharing. In the following discussion, we examine how a foresight database could overcome these and other shortcomings. First of all, a FSS requires a uniform and transferable data structure, which needs to be defined according to the specific characteristics of futures information. In our database, we differentiate among future factors and their development options and wildcards. Future factors are used as basic elements of the database. They are variables (e.g. natural gas price) with an impact on the users' future, depending on the direction in which the factor will develop. These directions are designed as development options (e.g. increase, decrease, remain constant), which can be derived from reports, empirical evidence, or human judgments and quantified by values of probability. However, since information about the future is merely based on assumptions, individual assessments, or extrapolation of historical data, the accuracy of foresight data is strongly dependent on the point of time. Current evidence as well as the users' assessments determine which of these options will be temporarily indicated as the trend of this future factor, i.e. the option regarded as the most probable one (see Fig. 3).



User/Company/Cluster

Futures Platform (FP)

External Sources

Internal Sources Foresight Database (FDB)

Results

Trend & Future factors database Wildcard database

Company Company figures Confidential information Assumptions & preferences Internal databases

Future Workshop (FWA) 1. Futures analysis 2. Deduction of emerging challanges and threats 3. Systematic development of recommendations

Other Sources Future databases Historical data External databases

7

Prediction Market (PMA)

Foresight Curriculum

Fig. 1. Conceptual architecture of the FSS Competitiveness Monitor.

change” [80]. They are sudden, improbable, short-term or long-term events with severe consequences [cf. 81,82]. Wildcards can thus cause sudden disruptions (e.g. in supply chains) or even a paradigm shift and thereby constitute the turning point in the evolution of a certain future factor [80]. Therefore, wildcard events are crucial to consider in futures analysis. Consequentially, the architecture of the Competitiveness Monitor contains separate databases for future factors and wildcards, which allows for discrete analyses as well as adequate

We design the required continuous communication and evaluation of future factor developments comparable to and following logics of current real-time Delphi approaches [76,77] as a dynamic Delphi voting system [78]. Thereby, evaluation dimensions, such as probability, impact, desirability, and feasibility, of a future factor development can be easily assessed and supported by arguments and evidence. Wildcards, also often referred to as black swans [79], are “one of the most unpredictable and damaging triggers of

CREATIVITY EXPLORATORY METHODS

SF WILD CARD* SCENARIO VIGNETTE

* GENIUS/EXPERT FORECAST BACKCASTING** ROLE PLAY/GAMING

* TEEPSE ANALYSIS* SWOT** BRAINSTORMING

EXPERTISE

RELEVANCE TREES

EXPERT PANEL * SYSTEM DYNAMICS/SIMULATION IMPACT ANALYSIS * DATA/TEXT MINING

STAKEHOLDER ANALYSIS **

* CROSS-IMPACT/STRUCTURAL ANALYSIS INTERVIEW

LOGIC CHART

INDICATOR/INDEX EXTRAPOLATION

LEGEND: QUALITATIVE

BENCHMARKING SEGMENTATION

REGRESSION ANALYSIS PATENT ANALYSIS

BIBLIOMETRICS

SCANNING*

LITERATURE REVIEW WEAK SIGNAL**

QUANTITATIVE SEMI-QUANTITATIVE SF

SURVEY

CONFERENCE/WORKSHOP*

MORPHOLOGICAL ANALYSIS** POLLING/VOTING *

KEY TECHNOLOGIES

SMIC

CITIZEN PANEL

MULTIPLE PERSPECTIVE ANALYSIS*

MULTI-CRITERIA ANALYSIS

INTERACTION

ADVISORY METHODS

PREDICTION MARKET * WEB-BASED CROWDSOURCING* RULE-BASED FORECAST

PARTICIPATORY METHODS

** ROADMAPPING** DELPHI* SCENARIO WORKSHOP

SNA

Science fiction

SNA Social network analysis SMIC Cross Impact Systems and Matrices

EVIDENCE

*

METHODS INTEGRATED IN THE COMPETITIVENESS MONITOR

** METHODS FOR SUBSEQUENT IMPLEMENTATION

EXPLANATORY METHODS

Fig. 2. The futures diamond according to Popper [71].


8


Wildcard event %) Option 1 (5

d

tren

3%)

(6 n1

io

Opt

Future factor

Option 2 (15%) Opt

ion

7%)

n 2 (8

Optio

trend

3 (2

2%)

Option 3 (22%)

Fig. 3. Relationship among future factor, trend and wildcard events.

evaluation, and extension of futures data. The collaborative reduction of information asymmetry as well as the joint verification of data validity and timeliness may incentivize by creating a win-win situation for each cluster member. Furthermore, Miemis et al. [75] particularly mention reward mechanisms for public recognition (e.g. virtual currency, participation points) and gaming techniques (e.g. mini games, challenges among participants) are relevant for effective incentivization. However, social and professional recognition of a lead user status within the cluster can also motivate. One of the most relevant incentives for participation, however, remains the clearly discernible value creation by and the resulting applicability of FSS [75]. 4.3. Future workshop application

compilation of different kinds of foresight data and the opportunity to link them meaningfully. The accuracy of a future factor's development strongly depends on its premises. Characteristic dimensions, such as geography (e.g. local, national, regional, global), time horizon (e.g. short-term, mid-term, long-term), and economic level (e.g. macro, micro), may yield a significant variation in the assessment of a future factor and therefore have to be clearly defined (e.g. long-term natural gas price development in Asia). The categorization of foresight data along specific dimensions additionally increases the utilization efficiency by enabling search algorithms to filter the query results according to a flexible combination of these trend descriptives (see Appendix 2). Categorization schemes also facilitate the logical interlinkage of foresight data. Thereby, all long-term wildcards and future factors related to Europe can be automatically interlinked. However, a future factor (e.g. natural gas price in Europe) is also directly dependent on its paramount (i.e. global natural gas price) and subsidiary (e.g. German natural gas price) basic factor. This partially automated linking process of foresight data, based on logical connections, is particularly valuable for the usability of the foresight database. The interlinkage of data in general is one of the most challenging, but at the same time most relevant functionalities of such databases. Therefore, the necessity to link future factors, which are not obviously related to each other, renders the commitment of members as a highly critical resource for ensuring efficiency and usability of a foresight database. Most of the current trend and foresight databases are operated by single organizations employing their own professional trend analysts, which assures quality, accuracy, and timeliness of data [63]. However, several providers of databases currently tend towards open platforms of shared knowledge, involving larger numbers of people. This concept of “open foresight” [74] was inspired by the concept of “open innovation” [83], in which external ideas and actors, especially lead users [84], are integrated in companies' innovation and value generation process. It draws on the “wisdom of the crowds” theorem, which is assumed to improve outcome quality [10,85]. The involvement of different stakeholders and thereby different perspectives can help to draw a more holistic picture of future developments and further reduces knowledge asymmetries. In addition to the collective and participatory structure of an open access, web-based foresight platform, the incentivization of users is challenging [75], even though common interests of an industrial cluster, for instance, may support continuous updating,

In applying the future workshop application, we follow the notion discussed in Section 2 to improve foresight quality by combining several foresight methods in a guided process instead of applying them discretely. A future workshop provides users the opportunity to elaborate on the data from the foresight database (Section 4.2) and diverse prediction markets (Section 4.4) according to their individual purpose and research question. The initial concept of a Future Workshop (“Zukunftswerkstatt”) was developed in the 1970s by Jungk and Muellert [9], who divided it into four phases: (1) preparation, (2) critique, (3) fantasy, and (4) implementation. The idea was to develop a structured process to develop recommendations for handling future questions and challenges. In 2004, Rueppel [86] depicted the new capabilities for foresight collaboration made possible by the Internet, which allows sharing perspectives, experiences, large amounts of data, and personal estimations almost instantaneously [75]. He extended the concept and was the first to introduce the idea of facilitating a future workshop online, suggesting the three phases: criticism, idea generation, and realization. Bañuls and Salmeron [6] describe these phases more pragmatically as future events, which have various effects that require appropriate recommendations for action. In the course of the Competitiveness Monitor project, we advanced these ideas and thoughts, developed new processes and routines, as well as availed to the latest technological standards including interlinkages to other foresight tools in an integrated environment. Based on the consideration of previous approaches, we designed a three-phase approach (Fig. 4). In the first phase of identification, relevant future factors and their potential future developments are identified and assessed regarding their relevance to the topic. In the second phase, the factors are analyzed and their potential effects anticipated and described as general or specific challenges for organizations and companies. In the third phase of implementation, creativity techniques support the elaboration of recommendations for action, their assessment, and finally their implementation in action plans. A single future workshop conducted via the future workshop application can be described as a specific project which is supervised by a project leader who defines its duration, set of methods, designated participants for each phase of the future workshop, as well as potentially relevant input and output variables. The content of the future workshops is determined by a problem which needs to be examined and a clear definition of



Identification ((event/situation))

Analysis (effect)

9

Implementation (recommendations)

Identification of relevant future factors, wildcards and other factors Assessment and annotation of factors Categorization of factors Selection of factors

Cross-impact analysis Derivation of future challenges

Development of practical recommendations using creativity and problem solving techniques (e.g. TRIZ analysis) Aggregation to an action plan

Intended extensions: Systematic identification of weak signals

Intended extensions: Consistency analysis Morphological boxes Scenario development

Intended extensions: SWOT, roadmapping and backcasting approach

Fig. 4. Workflow chart in Competitiveness Monitor's future workshop application.

the goal of the foresight process. Once the future workshop has started, each participant may add additional future factors or wildcards and other company-specific or external data to the future workshop. All data is assessed by every workshop participant of this futures workshop phase with respect to its relevance to the workshop topic and goal. The resulting prioritized list of factors, events and other information is reworked by the future workshop initiator, who makes a final decision about which data is transferred to the next phase. Prioritization and selection after each of the phases in the future workshop supports in keeping the foresight process efficient by narrowing down the data before the new phase allows an extended scope for analyzing the transferred data again [86]. In the analysis phase, the cross-impacts of factors may be examined to analyze correlations and dependencies. This phase offers many opportunities for enrichment and further development, for example by combining several factors and events into different scenarios (e.g. desirable, probable, extreme scenarios), using morphological boxes, consistency analyses or scenario-axis techniques [87]. Every method benefits from active participation of each workshop member, including assessments, qualitative feedback, and own proposals. After the analysis, possible effects of the most relevant future factors on the different areas of the cluster partners are determined. Subsequently, the potential future challenges are assessed and commented on by each workshop participant. The aim of describing concrete challenges and problems is to enable the later development of solution approaches for these specific challenges and thereby to develop an extensive plan of action for the initial challenge of question of the future workshop. This demanding exercise of finding appropriate solution approaches can be supported by problemsolving techniques, such as business war-gaming, 6-3-5 method, TRIZ, or a SWOT analysis. The creativity technique TRIZ [88,89], which offers a framework to foster innovation along 40 basic principles of problem-solving, was implemented in the Competitiveness Monitor. This method has proven to be highly valuable in a technical context over the last decades and thus been adapted also to business contexts [cf. e.g. 90–92]. Thereby, the challenges derived from the selected future situations and events can be solved in a structured way and appropriate recommendations for action can be suggested (see Appendix 3). A further evaluation and revision of the recommendations for action by each workshop participant finally results in an action plan, which is tailored to the specific future challenges.

4.4. Prediction market application (PMA) The PMA is the platform to create, monitor, and trade single prediction markets. Prediction markets are used to aggregate participants' individual information about unknown future events [12]. Events are traded similar to stocks: successful forecasters earn money and poor forecasters lose money. Prediction markets were first used in psephology. Their results have proven to be more accurate than election polling, regardless of when the comparison occurred [93] and the method has consequently been transferred to many other disciplines. Problems with prediction accuracy occur mainly under conditions of low liquidity or speculative bubbles [12]. In accordance with the ideas of Bañuls and Salmeron [6], the Competitiveness Monitor's prediction market application can be used to generate new foresight data but also to validate results. Prediction market results are used in the database to quantify expectations of futures factors and their options. On the other hand, factors and options from the database can be directly transferred to the prediction market application for further validation. Similarly, the outcomes derived from a futures workshop may be applied in the prediction market application to be tested. Trading the idea among cluster partners can thus determine the idea's viability. Markets can also be created directly in the application. The PMA also contributes to the incentivization premise. Traders in the PMA are more likely to regularly log-on to the Competitiveness Monitor because of “peer appreciation”: the respect of other traders due to successful trading has a motivating effect [cf. e.g. 94]. The PMA thus motivates cluster partners to participate in the common FSS and thereby better integrate it in daily business practice. For the Competitiveness Monitor's PMA, we use a market design according to Hanson's [95] market scoring rule. This design avoids problems of low participation and thus low liquidity by using an automatic market maker. Furthermore, the Competitiveness Monitor prediction market uses play money. While Rosenbloom and Noth [96] demonstrate that real-money markets are more accurate than play-money markets, they also admit that play-money markets produce accurate results as well. Servan-Schreiber et al. [97] determine that both market types often work equally well because the low barriers of play money attract more participants and thus more information to the PMA.


10


5. Discussion and conclusion FSS research has only emerged over the last few years and many different research projects are currently paving the way for this new research field. Undoubtedly, the role of ICT for foresight is increasing and its application will facilitate future virtual collaboration as it has already happened in other domains. Especially in the field of emergency preparedness, the application of different foresight methods with a strong emphasis on web-based communication and collaboration to better prepare for and act in challenging future situations has recently been promoted and recommended for further investigations [98–100]. However, other potential or currently emerging environmental changes, such as new laws, disruptive technologies, emerging business models, or social and political riots, also require a versatile IT-system for analysis and assessment. Consequently, in addition to methods for general foresight processes, FSS should provide a broad and adaptable platform for communication, collaboration, information gathering and sharing, combined analyses and evaluations, and allow a general but also specific solution-oriented approach to analyze long-term as well as short-term future situations. With our research, we contribute to the field by presenting the efforts and developments made during a three-year joint research project for a national cluster of multiple organizations. The FSS design described offers a structured and transparent foresight process, the relevance of which has been emphasized by multiple authors [e.g. 7,101,102]. Thereby, it fosters webbased, real-time communication and collaboration, and does not narrow or limit the foresight process to specific methods, input or output variables. The process can be further adapted or enriched with other methods. In addition, external sources or systems can and should be connected and integrated in the foresight process. Previous trend and scenario studies can be used as the basis for developing an action plan, or as the final result of a future factor analysis. This structure of a foresight process offers flexibility and provides various opportunities for individualization. Moreover, it was developed based on the multiplicity of stakeholders' characteristics, simulations, perspectives, and preferences. Thereby, it fulfills the main purpose of an FSS, which is to support and not to dictate a foresight process. Our conceptualization aims to fulfill the five basic premises for the design of an effective cluster-based FSS derived from the literature in Section 2. Premise 1 (information platform) is addressed by all three applications, where the prediction market application contributes to creation, the foresight database to creation, storage and linkage, and the future workshop application to linkage and processing of futures data. While in the Competitiveness Monitor case, the foresight database was filled with initial content for prototyping and facilitation of usage by reducing entry barriers, the fulfillment of premise 1 is reliant on the users' further contribution. The quality of information will increase with broader and more diverse user input. Premise 2 (collaboration) is addressed by the overall architecture of the FSS. Its approach supports open collaborative as well as closed corporate foresight. Foresight data can be freely shared, assessed, and revised without jeopardizing or losing the competitive advantage of one's own business model, service, or product. This collaboration is a

pivotal factor for the success of an FSS. Only a wellfunctioning cluster with a high degree of collaboration can provide the kind of co-creation needed for successfully preparing for discontinuous change. If this is the case, a more transparent as well as richer and broader knowledge base can enhance the individual decision process. Premise 3 (incentivization) is provided by all applications of the designed FSS. Competitive elements from the PMA as well as personalized information and interfaces from FDB and FP incentivize on an individual level. However, the primary incentivization of the overall FSS stems from the practical implications for implementing organizations. These are the outcome of the future workshop application and its guided, action-oriented process. As all tools are designed to benefit from collaboration and broad participation, incentivization in a collaborative FSS should ultimately be self-reinforcing. The more participants the system attracts, the more attractive it becomes for other users and stakeholders. Premise 4 (systemic FSS) is addressed by designing a processoriented workflow in which data can be interchanged among all applications. This is facilitated by an overarching data structure. Quantitative input, primarily from the prediction market application and foresight database, can thus be combined with qualitative procedures and input in futures workshops and the foresight database. The foresight database is designed as the central source of information in the FSS, providing continuous access and retrieval of foresight knowledge. The future workshop application serves as the central application for a concrete transfer of foresight knowledge into solutions for specific challenges. Importantly, users are guided through a foresight process. This process is enabled by both the interlinkage of data for a more systemic look at and by a multi-perspective approach on foresight challenges. As the linkage of data is only semi-automatic, continuous dedication to data linkage is required. Linkage interfaces, used to continuously add and integrate new methods to the methodpool of the FWA, are a central requirement to the IT architecture in order to advance the foresight process and to further enhance the multi-perspective approach. As both tasks are challenging and time-consuming, constant investments as well as user dedication are required to make the FSS work at the highest level. Premise 5 (support) is mainly addressed by two applications. The foresight curriculum integrated in the futures platform aims to provide users with both an understanding of the applications and basic knowledge on the foresight discipline for self-study. The guided, semi-automatic processes of the futures workshop application aim to support inexperienced users with practical support for foresight process implementation. However, such support can only provide a framework and requires commitment to learning and using foresight methodology in order to better prepare for future situations. This discussion of the basic premises indicates that a collaborative FSS only works with adequate user participation and dedication. Therefore, it is important that the target group of the FSS shares a common identity and overlapping interests. Only then is it realistic to expect meaningful collaboration among FSS users that are also willing to contribute to the shared information basis. The regional and industry sector



accordance as well as the existing network of partners thus rationalize implementation in a business cluster. Successful implementation, with reinforcing incentivization, may then help to improve collaboration among cluster partners, and generate a much higher awareness for discontinuous change. An important issue however, is the inclusion of research institutions as well as policy makers. Resource-constrained SMEs benefit in particular from such partners. The collaborative nature enables companies to integrate external and unorthodox knowledge from research and governmental organizations in their planning and innovation processes. In the case of the Competitiveness Monitor, a large research institution (a Fraunhofer institute) is a main player in the cluster. On the other hand, such institutes have access to the entrepreneurial thinking of business enterprises and can test their research under practical and competitive conditions. The cooperation also provides marketing potential as further impetus for research. Van Lente [41] demonstrates how collaborative foresight may help a research center to market its research among cluster partners. Moreover, the multi-perspective foresight process provides all actors with a realistic overview of how the region is evolving. This supports keeping the focus on path-dependency while avoiding a lock-in effect. Policy makers are provided with ample input for local policy initiatives. By bridging these gaps among business, research institutions, and policy makers, the FSS can facilitate the construction of an effective RIS. Together, foresight capability and an effective RIS potentially provide the cluster partners with strong capabilities to face not only incremental but also discontinuous change. In management research, the ability to manage incremental as well as discontinuous change is discussed as organizational ambidexterity [103]. The ambidextrous “hands” used by organizations are often referred to as exploitation and exploration [104] and thus mirror the sub-systems of RISs. While usually a single organization is the unit of analysis for discussing organizational ambidexterity, networking has been increasingly identified as an enabler [cf. 105–107]. Consequently, the goal should be to achieve a degree of organizational ambidexterity for both single organizations and clusters when implementing a collaborative FSS. The development of an FSS, such as the Competitiveness Monitor, over several years has taught us that the constellation of this joint research project, including partners from the IT sector, production industry, consultancy, and academia, was successful in addressing the required variety of competencies, perspectives, requirements, and reflections. Joint research projects may be a key success factor in FSS development. In addition, both empirical studies and conferences were valuable for research and development. Their contribution should not be underestimated during an FSS development process. The progress of international research has to be continuously reviewed and integrated. Moreover, our target user group, the cluster partners, consists of a heterogeneous group, ranging from research and governmental organizations to companies with different industry foci, varying company sizes, etc. Such a multi-stakeholder environment places further demands on the design of the FSS architecture. Therefore, in addition to an intuitive, individually adaptable and user-friendly architecture, we integrated a foresight curriculum in order to reduce the barriers of entry for usage. Furthermore, we experienced many

11

feedback loops with programming staff during prototyping phases in order to ensure system stability and interlinkage of the applications — in our understanding the key characteristic of a foresight support system. The entire development of FSS should be process-oriented and solution-oriented since ICT can only be an enabler of collaborative work.

6. Limitations and future research We presented the development of an innovative FSS, which was systematically developed within a regional cluster initiative but is not yet disseminated as a finished product. Testing will reveal the operational capability of the FSS as well as indicate areas which require further improvement and extension necessities. This incremental development process is a systematic approach to embed foresight in companies' and industrial clusters and ensures the practical usability of the system. All partners in the joint research project have specific ideas of how to include the Competitiveness Monitor in their projects and realization plans. Another challenge will be the integration of the FSS in companies' IT structures and the automated linkage to other sources and databases. Research has demonstrated that many different software applications are used within companies for strategic decision support. There will be situations where additional programming is needed to integrate an FSS into existing systems. Furthermore, the Competitiveness Monitor project had a strong focus on innovation. Therefore, the project disregarded a standard scenario planning application since several software packages are already available on the market. In future research, such an application could be added to the architecture. Similarly, further tools may be added to the foresight process, such as road-mapping, modeling, or war-gaming. Future research might go beyond cluster borders and extend the solution across multiple industries and contexts. Concerning the generalizability of this research, we can conclude three aspects. First, although the project is embedded in a logistics cluster, the cluster members include manufacturing companies, trading companies, FMCG manufacturers, retailers, and service providers. Thus, the project rather focused on generating general instruments for decision makers that have a particular but not exclusive value for a logistics environment. Second, the logistics content in the trend database can easily be complemented by any industry-specific content. Third, the platform includes a training curriculum, which players from other domains can access in order to understand the rationales and adapt the content to their specific needs. Thus, we may conclude that our results can be generalized for other industry settings. Finally, the premises and requirements identified for our specific FSS as well as the presented discussions support the development of the emerging field of FSS in general. FSS mark a new field of research where only few articles have been published so far. Future progress and developments can also be expected from the field of emergency preparedness (although following a different notion), where valuable results have recently been presented in a special issue [98]. We expect these two research streams to stimulate each other in the future.


12


Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.techfore.2013.12.031. Acknowledgments 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) in the course of its Leading-Edge Cluster initiative. Bayer MaterialScience, BrainNet/KPMG, dilotec, and EBS Universitaet fuer Wirtschaft und Recht collaborated in this research. We would like to thank all contributors for their input, especially Dr. Christopher Stillings, Eckard Foltin, Dr. Christos Lecou, Michael Muennich, Gianluca de Lorenzis, and Dr. Rixa Kroehl. We would like to express our gratitude to Daniel Pulko, Dr. Philipp Ecken, and Dr. Stefanie Mauksch for their valuable support and work in earlier phases of the Competitiveness Monitor project. In addition, we would like to thank Janice Magel for proofreading and her valuable support in the final stages of the manuscript. References [1] M.E. Porter, Clusters and the new economics of competition, Harv. Bus. Rev. 76 (6) (1998) 77–90. [2] P. Cooke, Knowledge economics: clusters, learning and co-operative advantage, Routledge, 2001. [3] W.B. Arthur, Increasing Returns and Path Dependence in the Economy, University of Michigan Press, 1994. [4] B.T. Asheim, L. Coenen, Knowledge bases and regional innovation systems: comparing Nordic clusters, Res. Policy 34 (8) (2005) 1173–1190. [5] F. Todtling, M. Trippl, Like phoenix from the ashes? The renewal of clusters in old industrial areas, Urban Stud. 41 (5–6) (2004) 1175–1195. [6] V.A. Bañuls, J.L. Salmeron, Scope and design issues in foresight support systems, Int. J. Foresight Innov. Policy 7 (4) (2011) 338–351. [7] T. Heger, R. Rohrbeck, Strategic foresight for collaborative exploration of new business fields, Technol. Forecast. Soc. Chang. 79 (5) (2012) 819–831. [8] ECM, Presenting the leading edge cluster, management summary, retrieved from http://www.effizienzcluster.de/files/7/788/525_ management_summary_eng.pdf(on 18.02.2013). [9] R. Jungk, N. Muellert, Future Workshops: How to Create Desirable Futures, Institute for Social Inventions, London, 1988. [10] J. Surowiecki, The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, Doubleday, 2004. [11] F.A. Hayek, The use of knowledge in society, Am. Econ. Rev. 35 (4) (1945) 519–530. [12] J. Wolfers, E. Zitzewitz, Prediction markets, J. Econ. Perspect. 18 (2) (2004) 107–126. [13] R. Popper, How are foresight methods selected? Foresight 10 (6) (2008) 62–89. [14] K. Cuhls, Methoden der Technikvorausschau — eine internationale Übersicht, Fraunhofer IRB Verlag, Stuttgart, 2008. [15] K. Cuhls, H. Kolz, C.M. Hadnagy, A regional foresight process to cope with demographic change: future radar 2030, Int. J. Foresight Innov. Policy 8 (4) (2012) 311–334. [16] G.S. Day, P.J. Schoemaker, Peripheral Vision: Detecting the Weak Signals That Will Make or Break Your Company, Harvard Business Press, 2006. [17] H. Schmitz, K. Nadvi, Clustering and industrialization: introduction, World Dev. 27 (9) (1999) 1503–1514. [18] M. Fujita, J.-F. Thisse, Economics of Agglomeration: Cities, Industrial Location, and Regional Growth, Cambridge University Press, 2002. [19] A. Karaev, S.L. Koh, L.T. Szamosi, The cluster approach and SME competitiveness: a review, J. Manuf. Technol. Manag. 18 (7) (2007) 818–835. [20] G.G. Bell, Clusters, networks, and firm innovativeness, Strateg. Manag. J. 26 (3) (2005) 287–295. [21] B.T. Asheim, Industrial districts as ‘learning regions’: a condition for prosperity, Eur. Plan. Stud. 4 (4) (1996) 379–400.

[22] R. Pouder, C.H. St. John, Hot spots and blind spots: geographical clusters of firms and innovation, Acad. Manag. Rev. (1996) 1192–1225. [23] W.B. Arthur, Competing technologies, increasing returns, and lock-in by historical events, Econ. J. 99 (394) (1989) 116–131. [24] F. Tödtling, M. Trippl, One size fits all?: towards a differentiated regional innovation policy approach, Res. Policy 34 (8) (2005) 1203–1219. [25] M.E. Porter, Locations, Clusters, and Company Strategy, The Oxford Handbook of Economic Geography, 2000. 253–274. [26] B. Asheim, L. Coenen, J. Moodysson, J. Vang, Regional innovation system policy: a knowledge-based approach, CIRCLE (Centre for Innovation, Research and Competence in the Learning Economy) Working Paper, 2005, pp. 2005–2013. [27] E. Autio, Evaluation of RTD in regional systems of innovation, Eur. Plan. Stud. 6 (2) (1998) 131–140. [28] H. Etzkowitz, L. Leydesdorff, The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of university– industry–government relations, Res. Policy 29 (2) (2000) 109–123. [29] T. Ahlqvist, V. Valovirta, T. Loikkanen, Innovation policy roadmapping as a systemic instrument for forward-looking policy design, Sci. Public Policy 39 (2) (2012) 178–190. [30] O. Renn, M. Thomas, The Potential of Regional Foresight, European Commission, Brussels, 2002. [31] J.P. Gavigan, F. Scapolo, A Practical Guide to Regional Foresight, FOREN, 2001. [32] K. Cuhls, From forecasting to foresight processes — new participative foresight activities in Germany, J. Forecast. 22 (2–3) (2003) 93–111. [33] G. Reger, Technology foresight in companies: from an indicator to a network and process perspective, Tech. Anal. Strat. Manag. 13 (4) (2001) 533–553. [34] R. Vecchiato, C. Roveda, Strategic foresight in corporate organizations: handling the effect and response uncertainty of technology and social drivers of change, Technol. Forecast. Soc. Chang. 77 (9) (2010) 1527–1539. [35] R. Rohrbeck, J.O. Schwarz, The value contribution of strategic foresight: insights from an empirical study of large European companies, Technol. Forecast. Soc. Chang. 40 (8) (2013) 1593–1606. [36] J. Bessant, B. Von Stamm, K.M. Moeslein, A.K. Neyer, Backing outsiders: selection strategies for discontinuous innovation, R&d Manag. 40 (4) (2010) 345–356. [37] A.D. Andersen, P.D. Andersen, Innovation-system foresight: explicating and systemizing the innovation-system foundations of foresight and exploring its implications, DTU Working Paper, 2012. [38] M.S. Gertler, D.A. Wolfe, Local social knowledge management: community actors, institutions and multilevel governance in regional foresight exercises, Futures 36 (1) (2004) 45–65. [39] T. Könnölä, G.C. Unruh, J. Carrillo-Hermosilla, Prospective voluntary agreements for escaping techno-institutional lock-in, Ecol. Econ. 57 (2) (2006) 239–252. [40] C. Roveda, R. Vecchiato, R. Verganti, P. Landoni, A new methodology for regional foresight, Int. J. Foresight Innov. Policy 3 (2) (2007) 218–234. [41] H. Van Lente, J. Van Til, A combined roadmapping-cluster approach for emerging technologies, Int. J. Foresight Innov. Policy 3 (2) (2007) 121–138. [42] T. Uotila, T. Ahlqvist, Linking technology foresight and regional innovation activities: network facilitating innovation policy in Lahti region, Finland, Eur. Plan. Stud. 16 (10) (2008) 1423–1443. [43] D. Sarpong, M. Maclean, Unpacking scenario thinking in product innovation teams: a practice approach, 2011. [44] J.P. Salmenkaita, A. Salo, Emergent foresight processes: industrial activities in wireless communications, Technol. Forecast. Soc. Chang. 71 (9) (2004) 897–912. [45] A. Salo, J.P. Salmenkaita, Embedded foresight in RTD programs, Int. J. Technol. Policy Manag. 2 (2) (2002) 167–193. [46] V. Harmaakorpi, T. Uotila, Building regional visionary capability. Futures research in resource-based regional development, Technol. Forecast. Soc. Chang. 73 (7) (2006) 778–792. [47] T. Uotila, H. Melkas, V. Harmaakorpi, Incorporating futures research into regional knowledge creation and management, Futures 37 (8) (2005) 849–866. [48] R. Smits, S. Kuhlmann, The rise of systemic instruments in innovation policy, Int. J. Foresight Innov. Policy 1 (1) (2004) 4–32. [49] O. Saritas, Systems thinking for foresight, PhD Thesis for PREST The University of Manchester, 2006. [50] O. Saritas, Y. Nugroho, Mapping issues and envisaging futures: an evolutionary scenario approach, Technol. Forecast. Soc. Chang. 79 (3) (2012) 509–529. [51] V.A. Bañuls, M. Turoff, Scenario construction via Delphi and cross-impact analysis, Technol. Forecast. Soc. Chang. 78 (9) (2011) 1579–1602.


J. Keller et al. / Technological Forecasting & Social Change xxx (2014) xxx–xxx [52] F.-M. Tseng, A.-C. Cheng, Y.-N. Peng, Assessing market penetration combining scenario analysis, Delphi, and the technological substitution model: the case of the OLED TV market, Technol. Forecast. Soc. Chang. 76 (7) (2009) 897–909. [53] H.A. Linstone, A.J. Meltsner, Multiple Perspectives for Decision Making: Bridging the Gap Between Analysis and Action, North Holland, New York, 1984. [54] A.J. Skulimowski, Discovering complex system dynamics with intelligent data retrieval tools, in: Y. Zhang, Z.-H. Zhou, C. Zhang, Y. Li (Eds.), Intelligent Science and Intelligent Data Engineering, Springer Berlin Heidelberg, 2012, pp. 614–626. [55] P. Walden, C. Carlsson, S. Liu, Industry foresight with intelligent agents, Hum. Syst. Manag. 19 (3) (2000) 169–180. [56] D. Doloreux, What we should know about regional systems of innovation, Technol. Soc. 24 (3) (2002) 243–263. [57] Federal Ministry of Education and Research (BMBF), Ideas. Innovation. Prosperity. High-Tech Strategy 2020 for Germany, BMBF, Berlin, 2010. [58] H.A. von der Gracht, I.-L. Darkow, Scenarios for the logistics services industry: a Delphi-based analysis for 2025, Int. J. Prod. Econ. 127 (1) (2010) 46–59. [59] D.J. Flint, E. Larsson, B. Gammelgaard, J.T. Mentzer, Logistics innovation: a customer value-oriented social process, J. Bus. Logist. 26 (1) (2005) 113–147. [60] BMBF, Germany's leading-edge clusters, Division for New Innovation Support Instruments and Programmes, Bundesministerium für Bildung und Forschung/Federal Ministry of Education and Research (BMBF), Berlin, Bonn, 2010. [61] S. Robertson, J. Robertson, Mastering the Requirement Process, Addison-Wesley Professional, Amsterdam, 2006. [62] S. Robertson, J. Robertson, Volere requirements specification template, retrieved from http://www.volere.co.uk/template.htm(on 17.2.2011). [63] C. Markmann, P. Ecken, H.A. von der Gracht, I.-L. Darkow, G. de Lorenzis, E. Foltin, D. Hartmann, N. Helfenbein, M. Münnich, C. Stillings, Trend database design for effectively managing foresight knowledge — a sophisticated FTA content base architecture to enable foresight processes, 4th International Seville Conference on Future-Oriented Technology Analysis (FTA), Seville, Spain, 2011, pp. 161–163. [64] C. Markmann, J. Keller, H. Gracht, R. Kroehl, S. Mauksch, A. Spickermann, G. Lorenzis, V. Kaffe, M. Münnich, C. Stillings, E. Foltin, M. Baciu-Gotter, The competitiveness monitor as an innovative foresight support system for mobility, logistics and beyond, in: U. Clausen, M. Hompel, M. Klumpp (Eds.), Efficiency and Logistics, Springer Berlin Heidelberg, 2013, pp. 31–41. [65] J. Keller, H.A. Von der Gracht, R. Kroehl, C. Markmann, Improving quality and efficiency in scanning and decision-making: future developments of ICT-based foresight tools, International Conference on Innovative Methods for Innovation Management and Policy (IM2012), Beijing, 2012. [66] J. Keller, H.A. von der Gracht, The influence of information and communication technology (ICT) on future foresight processes — results from a Delphi survey, Technol. Forecast. Soc. Chang. (2013), http://dx.doi.org/10.1016/j.techfore.2013.07.010(Article in press). [67] J.E. Gary, H.A. von der Gracht, The Future of the Foresight Profession: Results from a Delphi Study, Futures — The Journal of Policy, Planning and Futures Studies, submitted manuscript, 2013. [68] P. Ecken, H.A. Von der Gracht, Fatal mix: managers and wishful thinking, World Futur. Rev. 4 (2) (2012) 10–17. [69] M. Zimmermann, I.-L. Darkow, H.A. von der Gracht, Integrating Delphi and participatory backcasting in pursuit of trustworthiness — the case of electric mobility in Germany, Technol. Forecast. Soc. Chang. 79 (9) (2012) 1605–1621. [70] H.A. von der Gracht, C. Stillings, An innovation-focused scenario process — a case from the materials producing industry, Technol. Forecast. Soc. Chang. 80 (4) (2013) 599–610. [71] R. Popper, T. Teichler, 1st EFP mapping report: practical guide to mapping forward-looking activities (FLA) practices, Players and Outcomes, 2011. [72] K. Cuhls, W. Ganz, P. Warnke, Foresight-Prozess im Auftrag des BMBF, ISI, 2009. [73] M. Keenan, R. Popper, Comparing foresight “style” in six world regions, Foresight 10 (6) (2008) 16–38. [74] C. Daheim, G. Uerz, Corporate Foresight in Europe: Ready for the Next Step? Second International Seville Seminar on Future-Oriented Technology Analysis, Seville, 2006. [75] V. Miemis, J. Smart, A. Brigis, Open foresight, J. Futur. Stud. 17 (1) (2012) 91–97. [76] T. Gordon, A. Pease, RT Delphi: an efficient, “round-less” almost real time Delphi method, Technol. Forecast. Soc. Chang. 73 (4) (2006) 321–333.

13

[77] T. Gnatzy, J. Warth, H. von der Gracht, I.-L. Darkow, Validating an innovative real-time Delphi approach — a methodological comparison between real-time and conventional Delphi studies, Technol. Forecast. Soc. Chang. 78 (9) (2011) 1681–1694. [78] M. Turoff, S.R. Hiltz, C. White, L. Plotnick, A. Hendela, X. Yoa, The past as the future of emergency preparedness and management, Int. J. Inf. Syst. Crisis Response Manag. (IJISCRAM) 1 (1) (2009) 12–28. [79] N.N. Taleb, The Black Swan: the Impact of the Highly Improbable, Random House Trade Paperbacks, 2010. [80] S. Mendonça, M. Pina e Cunha, J. Kaivo-oja, F. Ruff, Wild cards, weak signals and organisational improvisation, Futures 36 (2) (2004) 201–218. [81] J.D. Rockfellow, Wild cards — preparing for “the big one”, Futurist 28 (1) (1994) 14. [82] E. Cornish, The WILD cards in our future, Futurist 37 (4) (2003) 18. [83] H.W. Chesbrough, Open Innovation: The New Imperative for Creating and Profiting From Technology, Harvard Business School Press, Boston, 2003. [84] E. Von Hippel, Lead users: a source of novel product concepts, Manag. Sci. 32 (7) (1986) 791–805. [85] S. Dalal, D. Khodyakov, R. Srinivasan, S. Straus, J. Adams, ExpertLens: a system for eliciting opinions from a large pool of non-collocated experts with diverse knowledge, Technol. Forecast. Soc. Chang. 78 (8) (2011) 1426–1444. [86] H. Rüppel, Zukunftswerkstatt-online-Wege zu einer telekooperativen Lernkultur?, Explorative Erkundung zur Theorie und Praxis einer telekooperativen Lernumgebung: Dissertation, Universität Kassel, 2004. [87] S.A. van't Klooster, M. van Asselt, Practising the scenario-axes technique, Futures 38 (1) (2006) 15–30. [88] G. Altshuller, S. Rodman, The innovation algorithm: TRIZ, systematic innovation and technical creativity, Technical Innovation Ctr, 1999. [89] G.S. Altshuller, A. Williams, Creativity as an exact science: the theory of the solution of inventive problems, Gordon and Breach Science Publishers Luxembourg, 1984. [90] V. Souchkov, TRIZ and systematic business model innovation, 10 th ETRIA world TRIZ future conference, bergamo university press, Italy, 2010. [91] B. Ruchti, P. Livotov, TRIZ-based innovation principles and a process for problem solving in business and management, TRIZ J. (2001) 677–687. [92] D. Nikoll, TRIZ für Betriebswirte: Systematische Problemlösung in Unternehmen, Vdm Verlag Dr. Müller, Düsseldorf, 2007. [93] J.E. Berg, F.D. Nelson, T.A. Rietz, Prediction market accuracy in the long run, Int. J. Forecast. 24 (2) (2008) 285–300. [94] J.M. Leimeister, M. Huber, U. Breitschneider, H. Krcmar, Leveraging crowdsourcing: activation-supporting components for IT-based ideas competition, J. Manag. Inf. Syst. 26 (10) (2009) 187–224. [95] R. Hanson, Combinatory information market design, Inf. Syst. Front. 5 (1) (2003) 107–119. [96] E. Rosenbloom, W. Notz, Statistical tests of real-money versus play-money prediction markets, Electron. Mark. 16 (1) (2006) 63–69. [97] E. Servan-Schreiber, J. Wolfers, D.M. Pennock, B. Galebach, Prediction markets: does money matter? Electron. Mark. 14 (3) (2004) 243–251. [98] M. Turoff, S.R. Hiltz, V.A. Bañuls, G.V. Den Eede, Special Issue on “Planning and foresight methodologies in emergency preparedness and management”, Technol. Forecast. Soc. Chang. 79 (1) (2012) 2. [99] V.A. Bañuls, M. Turoff, S.R. Hiltz, Supporting collaborative scenario analysis through cross-impact, 9th International ISCRAM Conference, Vancouver, Canada, 2012. [100] M. Turoff, S.R. Hiltz, C. White, L. Plotnick, A. Hendela, X. Yao, The past as the future of emergency preparedness and management, Int. J. Inf. Syst. Crisis Response Manag. v1 (i1) (2011) 12–28. [101] E. Amanatidou, K. Guy, Interpreting foresight process impacts: steps towards the development of a framework conceptualising the dynamics of ‘foresight systems’, Technol. Forecast. Soc. Chang. 75 (4) (2008) 539–557. [102] C. Dong, G. Schoups, N. van de Giesen, Scenario development for water resource planning and management: a review, Technol. Forecast. Soc. Chang. 80 (4) (2013) 749–761. [103] M. Tushman, C. O'Reilly III, Ambidextrous organizations: managing evolutionary and revolutionary change, Calif. Manag. Rev. 38 (4) (1996) 8–30.


14


[104] J.G. March, Exploration and exploitation in organizational learning, Organ. Sci. 2 (1) (1991) 71–87. [105] M.P. Koza, A.Y. Lewin, The co-evolution of strategic alliances, Organ. Sci. 9 (3) (1998) 255–264. [106] F.T. Rothaermel, D.L. Deeds, Exploration and exploitation alliances in biotechnology: a system of new product development, Strateg. Manag. J. 25 (3) (2004) 201–221. [107] A. Russo, C. Vurro, Cross-boundary ambidexterity: balancing exploration and exploitation in the fuel cell industry, Eur. Manag. Rev. 7 (1) (2010) 30–45. Jonas Keller is a doctoral student at EBS Business School in Wiesbaden, Germany. He holds diplomas in Economics and Chinese Studies from the University of Cologne, Germany. His research interests include foresight support systems, corporate foresight and foresight networks.

Christoph Markmann is a doctoral student at EBS Business School in Wiesbaden, Germany. He obtained his engineering diploma in management of technology from the University of Stuttgart. His main research interests include the development of foresight methodologies (especially the advancement of Delphi studies), technological forecasting and risk analysis. Dr. Heiko A. von der Gracht is an external Post-doctoral Researcher at EBS Business School in Wiesbaden, Germany, and is also Head of the Think Tank for Futures Management at the Institute of Corporate Education e.V. (incore), which is supported and sponsored by KPMG in Germany. His research interests include corporate foresight, Delphi and scenario techniques, decision support, and foresight support systems. His works have been published in several books and in peer-reviewed journals, among them Technological Forecasting & Social Change, Futures, European Journal of Futures Research, and Business & Society.
