time & dates

ART15.pdf

Devising futures for universities in a multilevel structure: A methodological experiment Attila Havas Institute of Economics, Hungarian Academy of Sciences, Budaörsi út 45.

H-1112, Budapest, Hungary Received 28 september 2006; received in revised form 19 december 2007; accepted 1 february 2008 Abstract Universities have traditionally been key players in producing

and validating new scientific knowledge, but other actors have also become major research performers. Meanwhile, the notion of research has been extended considerably,

and the environment of universities is also undergoing fundamental changes. Thus, it is timely to consider alternative futures for them,

A review of recent works on the future of higher education shows that the starting point in these exercises is either an existing or an abstract university.

Keywords: Alternative futures for the EU; The European research and Innovation Area (ERIA) and universities; Trends and drivers for changes;

Multilevel governance 1. Introduction The first universities emerged as responses to the need to harness the expanding intellectual forces of the era to the increasingly demanding knowledge requirements of the surrounding society

and economy in the 12th to 15th century in Europe as this major institutional innovation is described by P. A. David, masked in the language of our contemporary discussions of university research and training policies 1,

or have undergone a series of major changes in the last 700 800 years, there appears to be a strong consensus on the need for a new round of fundamental reforms from all corners:

e g. the harsh critique by a former British minister of Education and Skills published in The Guardian on 10 may 2003,

& Social Change 75 (2008) 558 582 starting point the‘unit of analysis'is either an existing or an abstract (hypothetical) university.

the starting point here is the EU, as the broadest socioeconomic context for universities, with its own science, technology and innovation (STI) policy tools,

The Spring European council meetings, assessing the progress towards the Lisbon strategy, using several indicators on HE performance,

& Social Change 75 (2008) 558 582 This is a sort of‘top-down'approach, and hence a number of‘micro-level'factors might be missing,

in Section 4 alternative visions are devised at three levels, with the time horizon of 2020 2025.

and its standing vis-à-vis the Triad regions are considered as major‘variables'of the alternative futures for the EU. At the second,

etc. 8 Several ERA visions have been devised by putting governance issues into the centre, see e g. 18 20 the ones developed in this paper follow a different logic. 9 The term‘universities'is used as shorthand for all sorts of higher education organisations. 10 The first attempt to do so can be found in a previous

& Social Change 75 (2008) 558 582 2. The role of universities in knowledge production 2. 1. The changing landscape of research systems Universities have traditionally been key players in

12 conducting various types of research. 13 Academies emerged in some countries as early as the end of the 16th century 1, pp. 5 6,

while further research actors became strong players in the 19th century, notably firms (often but not exclusively in the form of R&d units) and public labs 12.

For a more detailed discussion, see, e g. 31.562 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 As for the third aspect, the very fact that universities'research efforts lead to rather diverse outputs (outcomes

Indeed, for centuries universities had been elite education institutes for the elite in two respects:(i) only the elite of a given age cohort was offered higher education;

The last few decades, however, saw a major change: with 30 48%of the relevant age cohort attending tertiary education in most OECD countries, we cannot speak of the same‘higher'education (HE) system.

and the repercussions of the‘massification'of the‘third level'education. 17 Just to illustrate this with the example of the UK in the mid-1990s:

signalling in itself no great likelihood of later worldly success. 37.563 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 of universities,

The average share of universities in performing basic research was 54%across the OECD in 2003,

higher education and‘basic'science are not that closely interconnected nowadays as they used to be, partly because of the changing nature of higher education,

& Social Change 75 (2008) 558 582 The most important recent key trends concern the roles/responsibilities of universities.

(per cent, 2005). 22 A further recent key trend is triggered by the so-called Bologna Process.

At the Berlin Conference, held on 18 19 september 2003, however, the need to incorporate doctoral studies into the Bologna Process was mentioned specifically 41,

and that dimension is interconnected obviously closely with the research activities of universities, both in terms of the present research projects (in

and as the training of the future generation of researchers. 565 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 The most important driving forces can be derived by considering the increasingly intense global competition in research activities;

Second, new types of currently‘unthinkable'research players might also Fig. 2. The distribution of researchers by R&d performing sectors (FTE, most recent years.

566 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 evolve, 23 and that could change the‘ecology'quite radically, e g. in terms of more pronounced variety,

instead of returning to the EU. This proportion has risen notably over the past decade: from 44.5%at the beginning of the 1990s to 57.5%at the turn of the millennium (43, p. 57.

Competition for talents both intra-EU, and globally is likely not merely to continue, but intensify significantly. 2. Increasingly stronger international co-operation in research

That might lead to a much larger share of research classified by governments as military R&d. 23 A few decades ago no one would have thought of e g.

see, e g. 1, 22,42. 567 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 3. Stronger, better articulated needs for multi-(trans;

let alone among different types of them. 568 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 4. Futures for universities Vision-building requires an intense dialogue among stakeholders for two reasons:(

The second refers to lists of priorities and proposed actions (for different stakeholders, in this case e g. university rectors and deans, regional, national and EU policy-makers, businesses and local communities as partners of universities), inputs

Futures for universities can be devised by using various starting points. One possibility is to take the perspective of the sector,

These visions offer a description of future states in 2020 2025 rather than fully fledged or path scenarios

The modest aim is to sketch consistent and coherent descriptions of alternative hypothetical futures that reflect different perspectives on past, present,

569 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 Futures developed in genuine foresight processes can be direct (or positive) inputs for policy preparation or strategy building processes:

in July 2005 the European commission published a draft document on Cohesion Policy in Support of Growth and Jobs:

Community Strategic Guidelines, 2007 2013 44. One of the specific guidelines is to improve the knowledge and innovation for growth.

facilitate innovation and promote entrepreneurship. 570 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 vis-à-vis competitiveness;

and along the time dimension, too, i e. short-,medium-and long-term policies also need to be harmonised 48.

but a flexible interpretation of the Triad regions can easily include any relevant countries. 571 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 Table 2 Features of the ERIA in two

& Social Change 75 (2008) 558 582 be made concerning the probability of these visions. In other words, we do not have any sound

strong academia industry co-operation, mutually beneficial, intense links among large firms and SMES in a large number of regions (gradually increasing over time) Intense communication among businesses, academia,

and/or leading to waste of public resources. 573 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 all five of them are equally relevant from a policy strategy) point of view.

Moreover, devising 10 15 visions for the ERIA (2 3 ERIA visions times 5 EU visions) would introduce an unmanageable complexity into this exercise.

and thus devote more intellectual and financial resources to it. 574 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 other internal processes to the ever changing external environment, expressed by the needs of their‘clients':

largely unchanged universities would push hard to maintain their centuries-old monopoly to validate knowledge; yet, a number of other organisations e g. think tanks, private research organisations, private nonprofit research organisations, government laboratories, consultancy firms, patient organisations, various NGOS, trade associations and interest groups

are considered in 21.575 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 The methods, approaches,

as well as by offering these new types of insights for other actors 576 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 universities,

however, concerns the present, rather than the future: several commonly used notions and widely held beliefs are out of Table 4 Driving forces

not directly related to research activities of universities, are discussed in 21.577 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 touch with reality.

Further, as nowadays 30 40%of the relevant age group attend higher education courses, an ever larger number of higher education organisations offer mainly or only teaching.

The main advantage of taking existing or hypothetical universities as a starting point unit of analysis is that a wealth of micro-level factors can be considered.

or a starting point for actual policy preparation or strategy building exercises by considering different future states first for the EU and the European research and Innovation Area,

especially in terms of time needed for background analyses and then discussions among the participants. It has several advantages, too;

and on the‘mission'of the European 578 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 Innovation and Research area are made in a transparent and conscious way.

given the time needed for these processes, as well as the potential tensions occurring while discussing the actions and their consequences.

'What is striking in this respect is the sheer lack of alternative visions in the 2007 Green Paper on The European research area 3. A major benefit for policy-makers (at the EU,

The national 579 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 governments, international organisations and associations of universities can provide methodological and financial support for these initiatives.

References 1 P. A. David, Europe's Universities and Innovation Past, Present and Future, SIEPR Discussion paper No. 06-10,2006. 2 EC, The role of universities in the Europe

of knowledge, Communication from the Commission, COM (2003) 58 final, Brussels, 5 february 2003.3 EC, The European research area:

new perspectives, Green Paper, COM (2007) 161,4 April 2007.4 OECD, Four Futures scenarios for Higher education, OECD CERI, presented at the meeting of OECD Education Ministers, Athens

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Attila Havas (MA 1983, Phd 1997) is a Senior Research fellow at the Institute of Economics, Hungarian Academy of Sciences (e-mail:

havasatt@econ. core. hu. His academic interests are in economics of innovation theory and practice of innovation policy, and technology foresight.

In 1997 2000 he was Programme Director of TEP, the Hungarian technology foresight programme. He has participated in a number of international research projects on STI policies, innovation and transition,

He has advised national governments and international organisations on the above issues. 582 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582

ART16.pdf

Future-oriented technology analysis Impacts and implications for policy and decision making The unfolding acceleration of global innovation is expected to become the hallmark of the first half of the 21st century.

structures and capabilities that will take us forward to 2020,2035 and 2050? This special issue of TFSC presents a provocative alignment of papers designed to begin the probing of these fundamental questions about the future and future-oriented technology analysis (FTA.

Building on the success of the 2004 and 2006 events, the third edition of the Conference in October 2008

Out of the 166 abstracts that were submitted (50 more than in 2006), the Conference Scientific Committee selected 56 papers in order to build a comprehensive Conference program.

The papers and technical notes assembled from the 2008 FTA Conference were selected carefully and further nurtured to bring out three key themes:

10.1016/j. techfore. 2009.10.004 new power of IT and network analytical approaches, but it also directly aims its messages at policy makers responsible for designing more effective strategies for the deployment of public funds for R&d and those responsible for forecasting where and how to do this no small task indeed!

another Finnish team, bring this novel focus on tools further into the interface with policy approaches in their timely paper on the Role of Technology barometer in Assessing Past and Future development of National Innovation system.

storage and distribution in Australia. 1136 Technological forecasting & Social Change 76 (2009) 1135 1137 We conclude with the observation of Scott Cunningham

To conclude this special issue we welcome the column From My Perspective of the Founder and Editor-In-chief of this journal and one of the key participants of the FTA 2008 Seville Conference, Professor Harold A. Linstone.

He has written a fresh and thought-provoking analysis on Wall street A 21st century Crisis: Relearning Some Systems Lessons.

Impacts and implications for policy and decision making The 2008 FTA International Seville Conference. Online source:

http://forera. jrc. ec. europa. eu/fta 2008/intro. html (2009-07-30). 2 F. Scapolo, M. Rader, A Porter, Future-oriented technology analysis (FTA:

impact on policy and decision making The 2006 FTA INTERNATIONAL SEVILLE SEMINAR, Technol. Forecast. Soc. Change 75 (4)( 2006) 457 582.

Totti Könnölä is a research fellow at the Institute for Prospective Technological Studies of the Joint research Centre in the European commission.

She was the leader of the VTT Technology foresight and Technology assessment in 1999 2008 being also the Deputy Technology manager of the knowledge center since 2007.

She holds a Phd from the Helsinki Swedish School of economics and Business administration. Her Lic. Tech. and M. Sc. degrees are from Helsinki University of Technology.

and may not in any circumstances be regarded as stating an official position of the European commission. 1137 Technological forecasting & Social Change 76 (2009) 1135 1137

ART17.pdf

Received 17 november 2008 Accepted 15 july 2009 This paper examines a technique suitable for monitoring and analyzing systemic change in technology.

biotechnology and material sectors 1. The forces impelling convergence at the time are seen as radical, revolutionary,

the actual choices at the time appeared divergent and highly contested. One approach to the management of technological uncertainty has been to initiate the technological forecasting process only once a dominant design has emerged 3. Once a dominant design has been selected,

Thus, exploratory modeling is used to explore Technological forecasting & Social Change 76 (2009) 1138 1149 E-mail address:

In this section, the paper explicates the social and technological organizational structures which may permit a new era of open innovation.

and the edges are the component relationships that are present between the respective technologies. The challenge of the technology analyst is to usefully structure this information to anticipate change.

A structured representation of the data provides a principled account of where technological change is most likely to occur. 1139 S w. Cunningham/Technological forecasting & Social Change 76 (2009) 1138 1149 The article

then the designer will be free to spend more time at value-enhancing activities. The roadmapping activity achieves value by providing a single locus for coordinated research and development activity (see for instance 19.

& Social Change 76 (2009) 1138 1149 The hierarchical representation of the data grows more attractive as the network grows larger,

This network configuration should be expected to be observed 4. 3%of the time, or roughly one in 24 times.

In contrast, realization 2 is a possible but unlikely realization of the network since most of the high probability connections did not actually occur (Fig. 2). This network occurs 0. 8%of the time,

or roughly one in 125 realizations. These likelihoods may be calculated by an equation closely related to the binomial distribution.

We therefore need a way to structure the search to spend most of our time on the most likely network structures.

the total Fig. 1. Example hierarchical random graph. 1141 S w. Cunningham/Technological forecasting & Social Change 76 (2009) 1138 1149 number of links or edges

& Social Change 76 (2009) 1138 1149 4. Results In this results section we apply the methodology described in the previous section to a specific system of new technologies.

Finally, we eliminate all links to pages associated with dates and years as these pages are rarely directly relevant to the topic under consideration.

The second is the clustering coefficient, which is a measure of excess links between closely related nodes.

Fig. 4. Expanding network of hyperlinks in Wikipedia. 1143 S w. Cunningham/Technological forecasting & Social Change 76 (2009) 1138 1149 At least three other networks have been studied in the context

a million runs are completed in 54 s of processing time. In practice, the algorithm is let run as long as there are consistent improvements in model fitting and likelihood.

data. 1144 S w. Cunningham/Technological forecasting & Social Change 76 (2009) 1138 1149 The resultant hierarchical random graph usefully distinguishes between high-level concepts

& Social Change 76 (2009) 1138 1149 As seen in Table 3 the model anticipates a threefold combination of Internet explorer, rich Internet applications and the World wide web consortium (W3c), with a likelihood of 81%.

This new standard for rich Internet applications was incorporated in a recent beta version of Internet explorer 8. 5. Policy impacts These developments in ARIA are less than a year old at the time this paper was written the W3c posted a working draft

dated 4 february 2008 27. Wikipedia documentation of these developments is under 3 months old 28.

The current Wikipedia Accessible Rich Internet applications page is only a stub without the detailed hyperlinks typical of awikipedia page.

The history demonstrates that the first introduction of the page was 23 may 2008. The first release by Microsoft of a beta version of the browser incorporating the new standards was 5 March 2008.

In summary, the hierarchical random graph did seem to anticipate new technological changes in the area of new standards for accessible rich Internet applications.

The graph enabled recognition of changes roughly 200 days after W3c posted a new standard,

200 days after Microsoft released a new web browser beta, and 100 days after Wikipedia editors initiated new content.

It recognized these new changes without explicit linkages in the knowledge base of technologies. Thus, the hierarchical random graph approach may provide a new forecasting, analysis and design technique for architectural innovation.

high costs, high uncertainty, technological inexperience, business inexperience, lengthy time to market, and the general destruction of firm competence 17,29, 30.

%1146 S w. Cunningham/Technological forecasting & Social Change 76 (2009) 1138 1149 Techniques such as the link prediction algorithm described here might assist in radical innovation processes by providing rapid

and therefore already present within the system. The author suggests that the original conception of architectural change,

& Social Change 76 (2009) 1138 1149 If tacit knowledge has based a character upon the configuration of knowledge claims, then methods (such as the hierarchical graph)

Undocumented linkages may simply reflect out of date source materials. Nonetheless revealing undocumented links still provides a useful stimulus for technology monitoring efforts.

and Matlab scripts as provided by Aaron Clauset on his webpage (Clauset 2008). The author appreciates helpful discussion from Jan Kwakkel on the epistemology of knowledge networks.

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