Posts Tagged ‘trend’

EFP Brief No. 254: New Trends in Argentina’s Science, Technology and Innovation Policy

Thursday, February 14th, 2013

The brief describes the historical evolution of the national policy of science, technology and innovation (STI) in Argentina, identifying major turning points from the period of the import substitution model that lasted for 40 years to the current development pattern still in the making, with a sharp shift during the 1990s to a harsh market-led path. Domestic learning processes and emerging international trends led Argentina in the new millennium to adopt a new more proactive, flexible and participatory model of STI, which was further pushed by the creation of the Ministry of Science, Technology and Productive Innovation in 2007. The National Plan of STI 2012-2015 reflects on-going efforts to deepen the redesign of research, technology and innovation policies.

The Redesign of STI Policies and Institutions

Science, technology and innovation policies and institutions in Argentina constitute today an evolving system whose configuration is the result of a several-stage process involving discontinuity regarding priorities, approaches and intervention modalities.

STI policies over most of the 90s implied a significant shift with regards to the pattern prevailing during the four-decade model of import substitution industrialisation (ISI). In a nutshell, this shift involved a drastic move away from state support to the development of basic science and of human resources, as well as from direct public intervention in some sectors deemed strategic or at the technological frontier. The new pattern, framed within a economic policy stressing the opening and deregulation of the economy and the privatization of public assets, strongly emphasized the modernization of the private sector under a quasi-market rationale and made the first moves towards a greater articulation and coordination of STI public institutions. In line with this pattern, a demand-driven approach, under the assumption that firm knowledge requirements set research and development (R&D) lines, and sectoral neutrality (i.e., massive horizontal policies favouring stronger links of individual firms with the supply of advising and training services) set the tone of STI policies.

By the end of the 90s, a more complex set of policies was implemented in order to address the increasingly heterogeneous capacities of the private sector to generate and absorb scientific and technological knowledge, the different “stages of the innovation cycle” and the need to target support by sectors. This shift towards greater policy differentiation and directionality as well as deeper integration and coordination of the national system of STI was invigorated since 2003. Particularly, the creation of the Ministry of Science, Technology and Productive Innovation (MINCYT) in 2007 was a big push in that direction as it gave room to a process of increasing prestige and institutionalization of the STI; this process fuelled, in turn, an important redirection of the rationale for policy intervention.

Three main aspects distinguish this rationale shift: the greater emphasis granted to a systemic vision of support to innovation based on the construction of stronger links with the science and technology dimension; the deepening of the shift from horizontal to more focalized policies; and the gradual move from support targeting individual actors (firms or institutions) to support stressing different types of associative behaviour (value chains, consortia, networks, etc.). This reorientation of the rationality for policy intervention was grounded in the need of the MINCYT to adapt its strategic goals and policies to the particular traits of the context in which it operates, in particular the mounting relevance of technological change and innovation for international competitiveness and the need to upgrade the increasingly complex domestic production structure, the nature of the problems and opportunities calling for public intervention, and the need of a systemic approach in order the enhance the effectiveness of STI policies. This conceptual reorientation has been matched at the instrumental level by the “restyling” of the existing policy instruments as well as the design of new ones in the policy axes that shape today public interventions (see below).

The Conceptual and Empirical Drivers of Policy Changes

The current reorientation of the rationale for policy intervention is in line with STI policy trends in developed countries and in middle-income countries within the developing world. It also echoes academic debates and policy recommendations from technical cooperation agencies.

Limits of the Linear Model

The deepest motive of this reorientation, which comprises three main threads with different degree of progress and articulation, is the awareness of the limits of a static or lineal view of the relationship between science, technology and innovation. In fact, believing in a lineal view means that the new scientific and technological knowledge (usually created through R&D) is easily adopted by producers, without any significant participation or feedback about real needs of knowledge production.

Turning to Customized Production

Several traits of the present production situation reinforce the on-going redefinition of policy rationale. The first is the increasing heterogeneity of the production tissue, which cuts across sectorial and even sub-sectorial boundaries. Concretely, in the same sector and macroeconomic context, firm competitive strategies and practices differ along several dimensions, for instance the way they use technology and behave with regards to innovation. This heterogeneity turns horizontal and non-discriminatory policies, usually grounded on “broad range” market failures (complementary financing, imperfect information, coordination deficits and the like) largely ineffective to tackle down producers’ specific constrains to develop scientific and technological capabilities and to innovate. What it is rather required are policies geared to the provision of “customized” public goods (or “club” goods), in order to attend different needs at different levels of economic activity (firms, clusters, value chains, etc.).

In the same way, it is also important to foster a greater policy focalization through the identification of strategically significant areas as main targets of STI policies. Of course, this does not imply a return to old-fashioned practices of “picking winners” but instead the previous definition of activities and agents to be specifically targeted because of its relevance for upgrading and diversifying the production structure.

Endemic Uncertainty

The second relevant feature of the current production dynamics is not just the increasingly rapid pace of scientific and technological changes and, pari pasu, of the innovation process but the uncertainty of their direction that has led many people to talk about “endemic uncertainty”. Indeed, in an increasing number of production activities as well as in other areas of public interest (for instance, climate change, food safety or health care to mention just a few) it is more and more difficult to predict the next market demand, or, in the same vein, the next natural disaster, animal or plant disease, or virus variety, which will require to create and apply “new generation” scientific and technological knowledge that, in addition, can be rapidly turned into product and process innovations.

This giant uncertainty calls into question traditional notions of progress such as “technological frontier” or “technological catching up”. Knowledge production in these socio-economic and natural contexts calls instead for new policies and institutions that impulse the capacities of agents to search and detect new development opportunities by “de-codifying” them in response to the emerging needs, and to position themselves as providers of precompetitive knowledge for innovation.

The third driver of the reorientation of STI support policies or, to put it more accurately, of the intervention rationale is the fact that innovation – and much of the science and technology knowledge that nurtures it – is the work of inter-organizational networks including firms, public agencies, universities, research centres and other knowledge-producing organizations. Usually born spontaneously, although their emergence is more and more a public policy goal, the distinguishing trait of these public-private articulations is their role as instances of combination, coordination and synthesis of partial and complementary knowledge and resources coming from different disciplinary domains and fields of activity. These multidisciplinary networks tend to proliferate (though not exclusively) in high-technology activities in which it is highly unlikely that a sole agent have all the capacities and expertise to understand how those technologies work and how to apply them therein.

Specialisation of Argentina’s Production

Finally, on empirical grounds, Argentina is no alien to these trends towards increasing heterogeneity of production, acceleration of scientific-technological knowledge and network innovation, although the aggregate data on STI in the country does not properly reflect this. Indeed, in very distinct production activities (farm machinery, wine, technology-based agricultural inputs, nuclear research reactors, screening satellites, television scripts, sport boats, design-intensive clothing, software and boutique off-shore services, among the most relevant), firms or groups of firms have strongly grown, substantially upgraded production and achieved long-term competitive advantages in the domestic and foreign markets over the past decade on the basis of product and process innovation. These experiences share several features that link them to the above trends. Firstly, all involve the development of collaborative forms of production articulating public and private actors from different disciplines and institutional domains (final producers, part, input and service suppliers, science and technology agencies, universities and research centres in an relative reduced space (regions, counties, urban or semi-urban areas, etc.). Secondly, these networks share different but complementary resources (financial, human, etc.) and knowledge that allow them to identify the accelerating and changing innovation requirements and to generate the production responses to meet them. Finally, they include more or less institutionalized arrangements to coordinate knowledge creation, its application to production, the appropriation of the economic benefits accruing from its exploitation and financing that facilitate interest alignment among stakeholders.

Planning in STI Under the New Rationale for Intervention

STI planning in Argentina has shown a renewed vigour in the last decade and a particular concern at present to address the challenges posed by the emerging STI environment. In line with this purpose, the planning exercise for the 2012-2015 builds upon two main intervention strategies: the institutional development of the national system of STI and policy focalization.

Institutional development of the national STI system

The first strategy stresses transversal institutional development and changes required to achieve an effective intervention in the current STI conditions; it may be summarized with the productive innovation-institutional innovation formula under the understanding that the latter is a critical necessary condition of the former. This strategy involves the dimensions of capacity building, system linkage, process improvement and learning for network innovation. The assumption is that a system with strengthened endowments of resources and capabilities and, at the same time, better articulation allows to avoid the duplication of initiatives and actions (with the ensuing deficient resource allocation), to identify blind points, to contribute to align interests, to prioritize efforts and to generate synergies both within the public sector and between public institutions and productive and social actors, among other benefits.

Policy Focalization

As for the focalization strategy, the on-going planning effort has adopted a novel conceptualization cantered on the notion of strategic socio-productive nuclei (SSPN). This involves the identification of intervention opportunities in specific domains on the basis of the articulation of general-purpose technologies (GPT: biotechnology, nanotechnology, and ITC) with a bundle of sectors producing goods and services (agro-industry, energy, health, environment and sustainable development social development, and manufacturing). The rationale of this approach is to take advantage of the potential impact of GTP to generate qualitative improvements in terms of production competitiveness, people quality of life and the country’s standing with regards to emerging technologies and medium- and long-term foreseeable technological development. In other words, this approach seeks to go beyond the logic intervention driven only by the technological supply or demands requirements; looking forward to generate the conditions to adjust or adapt, if needed, transversal actions and policy instruments to the differentiated needs of selected SSPNs.

Both strategies comprise four operational work axes: coordination (inter-institutional, territorial, international), resources (human, infrastructure, information), processes (regulatory frameworks and monitoring & evaluation), and policy instruments and financing. The first three axes look at the new architecture, rules of the game, and agency/management capacities of the system of STI. The axe of instruments and financing concerns more horizontal tools to promote the expansion of the science and technology base, the search for selectivity and directionality in the public interventions to foster innovation as well as the impulse of the connectivity and coordination among STI actors and the mechanisms for funding support policies.

STI Policy Designed to Strengthen the Production Model

The history of STI in Argentina took several models of intervention or no intervention policy under different political rationalities. Nowadays, the development of STI has a greater potential because of the public planning strategies and concrete lines of action according to the production needs of the country. On the whole, this has meant a redesign of public policy institutions in order that science, technology and innovation strengthen the production model by generating greater social inclusion and improving the competitiveness of Argentina’s economy, becoming knowledge the backbone of national development.

Authors: Miguel Lengyel           mlengyel@flacso.org.ar

Maria Blanca Pesado bpesado@flacso.org.ar

Sponsors: n.a.
Type: national exercise
Organizer: Latin American School of Social Sciences
Duration: 2007 – 2010
Budget: n.a.
Time Horizon: 2015
Date of Brief: August 2011

Download EFP Brief No. 254_Argentina’s New STI Policy

Sources and References

Porta, F., P. Gutti and P. Moldovan, “Polìticas de ciencia, tecnología e  innovación en Argentina. Evolución reciente y balance”, Buenos Aires: Universidad de Quilmes y Centro Redes, febrero de 2010.

Sanchez, G., I. Buttler and Ricardo Rozmeberg, “Productive Development Policies in Argentina”, Washington DC: IADB, 2010.

Lengyel, Miguel, “Innovación productiva e innovación institucional: el vínculo virtuoso”, en D. García Delgado (comp.), Rol del estado y desarrollo productivo inclusivo, Buenos Aires: Ediciones Ciccus, 2010.

Programa de las Naciones Unidas para el Desarrollo (PNUD), “Innovación productiva en Argentina”, Buenos Aires: PNUD, 2009.

Sabel, C., “Self-discovery as a Coordination Problem”, forthcoming in C. Sabel, E. Fernández Arias, R. Hausmann, Andrés Rodríguez-Clare  and E. H. Stein (eds.), Self-discovery as a Coordination Problem. Lessons from a Study of New Exports in Latin America, Washington DC. IADB, 2011.

EFP Brief No. 245: Trend Database Design for Effectively Managing Foresight Knowledge

Tuesday, January 29th, 2013

In 2010, the German Federal Government launched one of its largest research initiatives in the area of logistics and supply chain management with the central aim to secure tomorrow’s individuality, in the sense of mobility and distribution, with 75% of today’s resources. One of the projects, the ‘Competitiveness Monitor’ (CoMo) develops an innovative, webbased foresight platform, which supports strategic decision-making and contingency planning as well as competitive and environmental intelligence.

Sophisticated Architecture to Support Foresight Processes

The development of an innovative Trend Database (TDB) is part of an extensive cluster initiative that was launched by the German Federal Ministry of Education and Research in June 2010. The ‘Effizienz­Cluster LogistikRuhr’, synonym for leading-edge cluster in logistics and mobility in the German Ruhr area, aims to boost innovation and economic growth in Germany by bridging the gap between science and industry (BMBF 2010). The cluster involves 130 companies and research institutes that cooperate in a strategic partnership in order to shape a sustainable future for the region and beyond. The determined challenges of future logistics (e.g., urban supply) are currently being addressed in more than 30 joint research projects. In this way, the cluster contributes to finding new ways to growth and employment that gear not only Germany’s but the European Union’s economy towards greater sustainability (see, e.g., Schütte 2010).

One of the joint research projects is developing an innovative foresight tool, the Competitiveness Monitor (CoMo), which will contribute to the validity and robustness of foresight activities by digitally combining quantitative and qualitative forecasting methods. The CoMo aims to enhance cooperation in multi-stakeholder environments through a fully integrated web-based software solution that utilises existing knowledge and users’ conceptions. The tool links several applications for forward-looking activities as well as the development, processing and storage of foresight knowledge. The goal is to provide decision-makers from business, academia and government institutions with a valid knowledge base for future-robust decision-making.

 

The CoMo consists of three innovative foresight tools – Trend Database, Prediction Market app and a Future Workshop (“Zukunftswerkstatt”) app – which are implemented in an IT-based Futures Platform (Figure 1). The Futures Platform will serve as login portal in form of a dashboard and can be adapted by each user according to his or her individual interest. Within the TDB, future-oriented numbers, data, and facts on specific logistics-related topics or technologies can be stored or collaboratively developed by its users. Furthermore, the TDB shall not only include trend-related data but also handle weak signals, wildcards and disruptive events. The high practicability of the Trend Database is planned to ensure filtering of the query results through an intelligent algorithm.
245_bild1
Figure 1: Conceptual framework of the Competitiveness Monitor

Development of Trend Database Requirements

In the beginning of the TDB development process, we analysed and evaluated eight relevant TDBs in order to identify the state of the art. After that, we conducted several creative workshops and interviews with more than 40 interdisciplinary cluster partners and futures researchers to identify further requirements.

First of all, we compiled an extensive list of requirements and constraints in several participatory workshop sessions, which are considered relevant to our TDB. After conducting a requirement analysis according to the ‘Volere Requirements Specification Template’ (Robertson and Robertson 2006), we derived four categories and adapted them to the CoMo project concerns: (1) functional requirements, (2) non-functional requirements, (3) design requirements and (4) constraints. Whereas functional requirements describe the fundamental functions and processing actions a product needs to have, non-functional requirements are the properties that they must have, such as performance and usability. We clustered the final long list of 160 collected requirements in 9 categories as presented in the following:
245_bild2

In the next step of the TDB development process, we conducted a stakeholder analysis in order to generate possible use cases. Different use cases were defined according to the specific needs and organisational structures of the CoMo project partners and members of the EffizienzCluster involved. In doing so, we were able to conceptually test and complement the identi-fied requirements and constraints.
Finally, we revised the results of the trend database analysis and specification analysis and summarised our research results in a specification sheet, which now provides a clear and structured collection of TDB features for the programming process of a prototype.

Challenges and Differentiators

For the identification of the key challenges, we evaluated best practices and innovative features of existing TDB concepts regarding their applicability and efficiency. For this purpose, we focused on the surrounding conditions and primary objectives of the presented TDB, determined by its purpose within the CoMo and the cross-project objectives of the leading-edge cluster. We identified four main challenges of utilising a TDB, which we will discuss in the following: (1) extent and quality of trend information, (2) cooperation within the TDB community, (3) linking mechanisms and (4) creating incentives for users.

Extensiveness and Quality of Trend Information

Most of the TDBs analysed provide an extensive set of opportunities to describe and evaluate a certain trend or future signal. Since it is hardly possible to decide without further knowledge about the user’s purpose or what the right amount of information is, we continued to compare the ways in which future knowledge is contributed to the TDB. We see two main strategies within the examined sample of TDBs: (1) input from experts and futures researchers or (2) active participation of the user community. In the latter strategy, information is revised and complemented by the community, which more accurately meets the CoMo objectives of realising cluster potentials. However, in case of low interest in a certain trend, the information may remain fragmentary and lack reliability.

The combination of both strategies seems to be promising since it ensures certain quality standards as the information provided is subject to scrutiny from two sides: an expert review process, on the one hand, and user participation, on the other. Against the background of all our analyses, we propose that providing a certain amount of trend specifications (e.g., short description, key words, time horizon etc.) should be obligatory when entering a trend into the TDB. In addition, the CoMo TDB is planned to offer a regulator for the ‘level of aggregation’, which will enable users to constrain the trend search results regarding time, geography, economic scale and further aspects.

Cooperation within the TDB community

The so-called “wisdom of the crowds” is based on the logic that many people (a “crowd”) know more than single individuals (Surowiecki 2004). Consequently, the sharing of knowledge can improve the knowledge basis of different stakeholders as well. Therefore, it is useful – particularly in dealing with future-relevant knowledge – to motivate users to co-operate and to develop their knowledge further.

Regarding our TDB architecture, users shall therefore evaluate trends in terms of impact or likelihood, participate in surveys or add further evidence or aspects to existing future-oriented knowledge (Kane and Fichman 2009). Especially the stakeholders of the leading-edge cluster, who are aiming to improve their competitive situation through collaboration, are interested in sustaining topicality, validity and relevance of future-relevant knowledge in the trend database. Our TDB is expected to contribute to an improved quality of data and provide a more accurate basis for decision-making processes.

Linking Mechanisms

The CoMo TDB will be linked in three dimensions. First, the trends within the TDB will be linked among each other. This supports users by providing a more comprehensive causal picture of the future and allows decision-makers to identify early warnings and weak signals. Second, the trend database is linked to two other CoMo apps: the Prediction Market and the Future Workshop. Both apps require raw data from the TDB for purposes of evaluation (i.e. prediction markets) or analysis (i.e. future workshops). Furthermore, they define data sources by providing new or evolved future-oriented knowledge, which needs to be re-imported into the TDB. Third, the trend database will be linked to external data pools. Facilitating the idea of linked data, relevant external information can be included, increasing the basis to be drawn on in making future-relevant decisions (Auer and Lehmann 2010). Thereby, we aim to link our dataset intelligently by attaching metadata using the Semantic Web approach. This not only facilitates the process of finding relevant and recent data but also enables identifying related topics.

Motivation of Users

In contrast to the traditional World Wide Web, the application of a Semantic Web offers information that can be sorted by relevance, topicality and quality (Berners-Lee, Hendler et al. 2001). However, the Semantic Web requires the linkage of datasets first. Therefore, users have to be encouraged to tag, for instance, the trend information as good as possible, and the community needs to be motivated to edit and complete the tagging process.

In the process of developing the CoMo TDB, we discussed several concepts and ideas to address the challenges involved in motivating users. One concept that is planned to be applied in the CoMo is the lead users approach (Leimeister, Huber et al. 2009) in which users are incentivized by an awareness of the measurability of their contributions. Considering that most of the existing trend databases use an expert-based concept instead, we infer that this was thought to be the only efficient way of providing and processing future-oriented knowledge so far. However, current tendencies, such as the disclosure of previously protected data (i.e. open source/innovation) or the increasing activity in social networks, suggest that existing concepts need to be adapted to the new requirements forward-looking activities must meet.

Metadata Approach Using the Semantic Web

Future-oriented knowledge as a basis for decision-making is always critical due to its inherent uncertainty. Therefore, innovative concepts and tools need to be developed in order to provide users with the most valid, relevant and up-to-date information possible. With our new TDB concept, we try to acknowledge current challenges such as motivation and collaboration of users, usability of information and modern linkage methods. To meet these challenges, we aim to link our dataset intelligently by attaching metadata using the Semantic Web approach. This not only facilitates finding relevant and recent data but also enables identifying related topics. However, the linkage of the data has to be conducted manually. Thus, motivating users to share their knowledge within the community is essential to provide an accurate and comprehensive picture of the future reflecting the wisdom of the crowd. Finally, we will design our TDB to present future-oriented knowledge in a sufficiently comprehensive and detailed manner with an emphasis on clarity and thereby aim to contribute significantly to the robustness and quality of future decisions.

Authors: Christoph Markmann                christoph.markmann@ebs.edu

Stefanie Mauksch                     stefanie.mauksch@ebs.edu

Philipp Ecken                           philipp.ecken@ebs.edu

Dr. Heiko von der Gracht          heiko.vondergracht@ebs.edu

Gianluca De Lorenzis                G.DeLorenzis@dilotec.de

Eckard Foltin                           eckard.foltin@bayer.com

Michael Münnich                       M.Muennich@brainnet.com

Dr. Christopher Stillings                        christopher.stillings@bayer.com

Sponsors: German Federal Ministry of Education and Research
Type: National foresight project
Organizer: EBS Business School / Center for Futures Studies and Knowledge Management (CEFU)
Duration: 2010 – 2013
Budget: € 2,300,000
Time Horizon: Long-term
Date of Brief: October 2011

Download EFP Brief No. 245_Foresight Trend Database Design

Sources and References

Auer, S. and J. Lehmann (2010). “Creating Knowledge out of Interlinked Data.” Semantic Web Journal 1.

Berners-Lee, T., J. Hendler, et al. (2001). “The Semantic Web.” Scientific American 284(5): 34-43.

BMBF (2010). Germany’s Leading-Edge Clusters. Division for New Innovation Support Instruments and Programmes. Berlin, Bonn, Bundesministerium für Bildung und Forschung / Federal Ministry of Education and Research (BMBF).

Kane, G. and R. Fichman (2009). “The Shoemaker’s Children: Using Wikis for Information Systems Teaching, Research, and Publication.” Management Information Systems Quarterly 33(1): 1-22.

Leimeister, J. M., M. J. Huber, et al. (2009). “Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition.” Journal of Management Information Systems 26(10): 187-224.

Robertson, S. and J. Robertson (2006). Mastering the Requirements Process, second edition. Amsterdam, Addison-Wesley Professional

Schütte, G. (2010). Speech by. Germany’s Leading-Edge Cluster Competition – A contribution to raising Europe’s profile as a prime location for innovation. State Secretary at the Federal Ministry of Education and Research framework of the European Cluster Conference. European Cluster Conference. Brussels.

Surowiecki, J. (2004). The Wisdom of Crowds, Random House.

Note: 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). Joint research project partners are Bayer MaterialScience, BrainNet, dilotec, EBS Business School. Responsibility for the content is with the author(s).