In a recent report on risk and innovation 1 the UK Government scientific adviser, Mark Walport, states that debates about risk are also debates about values, ethics and choices and fairness,
Work by scientists such as Walport 1 as well as by thinkers such as Schön 12 Galimberti 8, 1 http://gamesforchange. org/2 http://www. ushahidi. com/and Feenberg 5 help to understand,
References 1. Annual Report of the Government Chief Scientific Adviser (2014) Innovation: Managing Risk, Not Avoiding It The Government office for Science, London. 2. Ayres, C. J. 2012.
& Whittle, J. 2014) Imaginative labour and relationships of care: Co-designing prototypes with vulnerable communities.
lack of resources, lack of skilled employees, lack of easy to use technology adapted to SMES, and also lack of awareness of the potential benefits for them.
and e-business adoption at the EU level. 2 1 This major survey covered SMES with 10-249 employees,
local or regional organisations that work with SMES to facilitate the change process. A recent assessment report4 launched by the European commission, Directorate General information Society,
Shortage of knowledge, skill, entrepreneurship The lack of suitable technical and managerial staff with sufficient knowledge and expertise is a major barrier.
Some Member States have taken action to attract ICT experts from third countries. Bringing in outside expertise is costly,
and use consultants to help prepare for the organisational changes required by e-business. Complexity of regulations Although today's regulatory environment seems to accommodate ebusiness satisfactorily at national level,
Unlike larger companies, with their teams of lawyers and consultants, SMES tend to avoid the legal risks of engaging in cross-border commerce.
8 SMES10=enterprises with between 10 and 249 employees 9 large enterprises are considered by Eurosta the enterprises with more than 249 employees e-mail webpresence phases FN, September 2002 Digital
systems for ecommerce, e-procurement, Supply Chain Management, Customer relationship management, Enterprise resource planning, logistics, planning, knowledge management, business intelligence, e training.
Elements, like the employees resistance to the change, the non-support from the 10 20.2.02 Eurostat Statistics In focus newsletter ISSN 1561-4840 KS-NP-02-012-EN
Transformations bring additional challenges involving organization, staff training, and includes outsourcing non-core operations, changes in processes and systems,
and share knowledge and experiences When groups of organisations adopt networked methods of cooperative work,
while the other is comprised of work teams inside departments inside divisions inside businesses inside industries. 16 The new paradigm requires thinking in terms of whole systems that is,
The companies within them coevolve capabilities around innovation and work cooperatively and competitively to support new products,
Therefore, it also includes instruments for knowledge sharing, for knowledge basis setup, for community building, e-learning tools, support for e-learning and e training (in technology and in e-business),
Creation of local competence centers on e-business and on the local sectors of activities (e g. for improving quality) building virtual learning communities sharing e-learning and e training modules knowledge basis including models
use and promotion of standards sharing common solutions implementation of digital business ecosystems Investment/Costs Actions:
knowledge base of norms and laws alternative methods of conflict resolution e training and e-learning modules Shortage of Capital Actions:
are committed fully work together forming a community a critical mass of enterprises (including the small organizations) use the ecosystem as business tool.
and solutions creates more technically qualified employment and a framework of competence which stimulates the market,
encompassing elements of precursor works by Lowe (1982) and Sábato and Mackenzi (1982), interprets the shift from a dominating industry-government dyad in the Industrial Society to a growing triadic relationship between university
and job creation (see, for example Startx, Stanford's student start-up accelerator, which in less than a year 6 trained 90 founders and 27 companies4,
Thirdly, universities'capacity to generate technology has changed their position, from a traditional source of human resources and knowledge to a new source of technology generation and transfer,
and specific contributions to a complex division of labour in the production and use of knowledge for innovation (see the analysis of MIT in the 1930s in Etzkowitz, 2002).
is characterized by high specialization and work centralization, limited mobility of workers, rigid and inertial institutional boundaries, low interaction with entities of another institutional sphere,
and increase collaboration to improve work effectiveness. Subsequently, boundaries between the job categories involved in these hybrid structures become looser
manifested through increasing communication and interconnectivity between people and institutions, mobility of people and financial capital, delocalisation and globalisation of production sites, labour and social relationships, etc.
Elements like generation and internalization of new skills and abilities required for integration into dynamic work environments, access to both information and knowledge,
and conflict of interest into convergence and confluence of interest, compared to dyadic relationships, which are more subject to collapse into oppositional modes (Simmel, 1922 1955).
and conflict of interest into converging interests around common objectives and win-win situations is all the more important as the very nature of conflicts
and Mark Makula, the experienced semiconductor executive, who gave the original duo credibility with suppliers
and financers, were elided (Freiberger and Swaine 2000). 14 changing in the Knowledge Society, in line with the changing nature of work, workplace and organizations (Heerwagen,
and moving towards a vision of work that is defined as a lifelong process of education and cognitive development rather than a company career (Spittle, 2010).
for example when vocational training institutions take the lead over universities in engaging into joint initiatives with local firms (especially with low-tech,
low/non-R&d small firms) that prefer the more practical, shorter-term oriented opportunities of the vocational training institutions to the more complex, long-term programmes of the university (Ranga et al. 2008).
in order to generate a particular division of labour among the participants (David, Foray and Steinmueller, 1999). Networking reflects the growing non-linearity and interactivity of innovation processes (Kaufmann and Tödtling,
industry and government institutional spheres to work more closely together to promote innovation. In the early 1990's a group of foundations were created to fill gaps in the country's innovation funding system.
the industries it was intended to support had moved largely from the region to be close to raw materials, lines of distribution and access to inexpensive labour.
which invented the contemporary format for the venture capital firm, building upon family investment firms with a professional staff.
which put together resources to develop a strategy for the renewal of a region that had been in economic decline from the early 20th century due to departure of industries and firms to regions with raw materials and cheap labour.
p=1546&t=h401&l=en. 27 6. RELEVANCE OF TRIPLE HELIX SYSTEMS FOR KNOWLEDGE-BASED REGIONAL INNOVATION STRATEGIES Regional innovation policies have focused traditionally on the promotion of localized learning processes
and if a human resources attraction strategy is lacking15. Endogenous knowledge-based regional development strategies recognise that local factors,
such as strong knowledge base, skilled labour services and proximity to sources of knowledge and expertise, are much more important than cost reductions, especially for high-tech firms.
and better work conditions to attract distinguished researchers rather than develop young researchers. 16 The Brazilian popular cooperative incubator model was invented bottom-up by a university incubator
Therefore, the promotion of measures that support the formation and consolidation of the spaces is essential in designing Triple Helix-based regional innovation strategies (see Section 8 for a discussion of such measures).
especially research, education, labour market and development policies. Secondly, we also need to understand more about the growth of the spaces over time,
which importation of organisational innovations work and when do they impede development? What methodology should be developed for such an analysis,
The policy implications arising from the adoption of a Triple Helix systems approach to innovation focus particularly on the promotion of measures that support the formation and consolidation of the Knowledge
Policy initiatives may also be directed at developing human resources for R&d in sciences and arts at national/regional level, improving the labour market for researchers,
promoting better policies for employment, education and training, immigration to attract world-class researchers, making research careers more available for various categories of the local population,
especially women and minorities, reducing brain drain and improving brain gain. Similar directions of action are important in developing the Innovation Space:(
The Changing Nature of Organizations, Work and Workplace (downloaded on 8 april from http://www. wbdg. org/resources/chngorgwork. php.
‘The changing nature of work'(downloaded on 9 april from http://andrewspittle. net/2010/02/18/the-changing nature-of-work/)Steinmueller, W. E. 1994.
The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*
Here, alongside with the capability to provide workers with adequate training, also the firm's ability to attract highly qualified labor force will become one of its core competencies (Bougrain and Haudeville, 2002).
The entrepreneur's assessment of the importance of different internal factors of the firm (know-how, educational events, initiatives from employees, etc.
for innovation Know-how of the firm Educational events for employees Initiatives from employees Organization of work (
teamwork, job rotation, etc. Organized communication in the firm Spontaneous communication in the firm Social events and shared free time activities Five-point Likert-scale (1 Insignificant to 5 Very important) NETWORKA
Sum-variable measuring the importance of different network relations for innovation Customers Suppliers and subcontractors Competitors Sales and delivery organizations Business service firms and consultants Accounting companies
Local association of entrepreneurs City office of business and industry Local business development organizations Business incubators Employment and Economic Development Center (regional office) Five
about 62 percent with fewer than ten employees (i e. micro-firms), and strongly dependent on the work contribution of the entrepreneur and that of his or her family.
This kind of a starting point was of great importance for the study since, as we aspired,
Only 6 percent of the entrepreneurs had no vocational training at all. 3. 2 Variables and measures 3. 2. 1 Dependent variable.
are, in fact, as much experts in innovation and technological development as they are potential sources of finance and funding.
entrepreneurs do not consider the different internal factors in their firms (competencies and know-how of the entrepreneur and his staff, personnel initiatives, personnel training, organized and spontaneous communication between units and individuals in the firm, etc.)
making network organizations work, in Nohria, N. and Eccles, R. G. Eds), Networks and Organizations:
14 Executive Summary...17 1 Reviewing current rankings...23 1. 1 Introduction 23 1. 2 User-driven rankings as an epistemic necessity 23 1. 3 Transparency, quality and accountability in higher education 24
. 2. 2 Patent databases 82 4. 2. 3 Data availability according to EUMIDA 83 4. 2. 4 Expert view on data availability in non
Availability of U multirank data elements in countries'national databases according to experts in 6 countries (Argentina/AR, Australia/AU, Canada/CA, Saudi arabia/SA, South africa/ZA
Self-reported time needed to deliver data (fte staff days...106 Table 5-3: Self-reported time needed to deliver data (fte staff days:
European vs. non-European institutions...106 Table 6-1: Focused institutional ranking indicators: Teaching & Learning...
transparent and comparable information would make it easier for students and teaching staff, but also parents and other stakeholders, to make informed choices between different higher education institutions and their programmes.
Education and Culture but other experts drawn from student organisations, employer organisations, the OECD, the Bologna Follow-up Group and a number of Associations of Universities.
An international expert panel composed of six international experts in the field of mapping, ranking and transparency instruments in higher education and research.
Stakeholder workshops were held four times during the project with an average attendance of 35 representatives drawn from a wide range of organisations including student bodies, employer organisations, rectors'conferences, national university associations and national representatives.
The consortium members benefitted from a strong network of national higher education experts in over 50 countries who were invaluable in suggesting a diverse group of institutions from their countries to be invited to participate in the pilot study.
The web-site also includes a 30 page Overview of the major outcomes of the project. 17 Executive Summary Executive Summary Executive Summary Executive Summary Executive Summaryexecutive Summary Executive
or field based rankings. student staff ratiograduation ratequalification of academic staffresearch publication outputexternal research incomecitation index%income third party fundingcpd courses offeredstartup firmsinternational academic
staff%international studentsjoint international publ. graduates working in the regionstudent internships in regional co-publicationinstitution 4institution 8--Institution 3-Institution 5-Institution 1--Institution
Targeted recruitment of relevant peer institutions from outside Europe should be continued in the next phase of the development of U multirank.
and underlying database to produce authoritative expert institutional and field based rankings for particular groups of comparable institutions on dimensions particularly relevant to their activity profiles.
In the scientific debate, this statement is accepted at least since Popper's work (Popper, 1980: he has shown abundantly that theories are‘searchlights'that cannot encompass all of reality,
Recent reports on rankings such as the report of the Assessment of University-Based Research Expert Group (AUBR Expert Group, 2009) which defined a number of principles for sustainable collection of research data,
P*CPP/FCSM Citations-per-publication indicator (CPP) Quality of education Alumni of an institution winning Nobel prizes and Fields Medals (10%)Phds awarded per staff (6%)Undergraduates admitted per staff
(4. 5%)Income per staff (2. 25%)Ratio Phd awards/bachelor awards (2. 25%)Faculty student ratio (20%)31 HEEACT 2010
ARWU 2010 THE 2010 QS 2011 Leiden Rankings 2010 Quality of staff Staff winning Nobel prizes
%)Academic reputation survey (40%)Employer reputation survey (10%)General Sum of all indicators, divided by staff number (10%)Ratio international mix,
staff and students (5%)Industry income per staff (2. 5%)International faculty (5%)International students (5%)Website http://ranking. heeact. edu. tw
/en-us/2010/Page/Indicators http://www. arwu. org/ARWUMETHODOLOGY2010. jsp http://www. timeshighereducation. co. uk/world-university rankings/2010-2011/analysis
Surveys among stakeholders such as staff members, students, alumni or employers. Surveys are strong methods to elicit opinions such as reputation or satisfaction,
rankings and indicator experts, field experts (for the field-based rankings) and regional/national experts.
students, potential students, their families, academic staff and professional organizations. These stakeholders are interested mainly in information about a particular field.
if we know that doctoral students are counted as academic staff in some countries and as students in others,
we need to ask for the number of doctoral students counted as academic staff in order to harmonise data on academic staff (excluding doctoral students).
Feasibility The objective of U multirank is to design a multidimensional global ranking tool that is feasible in practice.
and can it be applied with a favourable relation between benefits and costs in terms of financial and human resources?
The design choices made here are in accordance with both the Berlin Principles and the recommendations by the Expert Group on the Assessment of University-based Research.
The AUBR Expert Group5 (a o.)underlines the importance of stakeholders'needs and involvement, as well as the principles of purposefulness, contextuality,
Based on our design context, in the following chapters we report on the construction of U multirank. 5 Expert Group on Assessment of University-Based Research (2010),
Various categories of stakeholders (student organizations, employer organizations, associations and consortia of higher education institutions, government representatives, international organizations) have been involved in an iterative process of consultation to come to a stakeholder-based assessment of the relevance
This first list was exposed for feedback to stakeholders as well as to groups of specialist experts. Stakeholders were asked to give their views on the relative relevance of various indicators
we invited feedback from international experts in higher education and research and from the Advisory board of the U multirank project.
To facilitate the consultation process we showed an expert view on the 50 indicators (making use of the feedback from the expert group consultation) in
Literature review Review of existing rankings Review of existing databases First selection Stakeholder consultation Expert advice Second selection Pre-test Revision Selection
Based on the various stakeholders'and experts'assessments of the indicators as well as on our analyses using the four additional criteria,
Data collection and availability problematic. 4 Relative rate of graduate (un) employment The rate of unemployment of graduates 18 months after graduation as a percentage of the national rate of unemployment
Field-based Ranking Definition Comments 6 Student-staff ratio The number of students per fte academic staff Fairly generally available.
Sensitive to definitions of‘staff'and to discipline mix in institution. 7 Graduation rate The percentage of a cohort that graduated after x years after entering the program (x is stipulated the normal')time expected for completing all requirements for the degree times
International comparisons difficult. 9 Qualification of academic staff The number of academic staff with Phd as a percentage of total number of academic staff (headcount) Proxy for teaching staff quality.
and definitions of‘staff'10 Relative rate of graduate (un) employment The rate of unemployment of graduates 18 months after graduation as a percentage of the national rate of unemployment of graduates 18
existence of external advisory board (including employers) Problems with regard to availability of data. 56 13 Inclusion of work experience into the program Rating based on duration (weeks/credits) and modality
Promotion of employability (inclusion of work experience) Index of several items: Students assess the support during their internships, the organization, preparation and evaluation of internships, the links with the theoretical phases 22 Student satisfaction:
Range of courses offered, coherence of modules/courses, didactic competencies of staff, stimulation by teaching quality of learning materials, quality of laboratory courses (engineering) 23 Student satisfaction:
Although the indicator may reflect the extent to which employers value the institution's graduates,
11 Research and experimental development (R&d) comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society,
The Expert Group on Assessment of University Based Research12 defines research output as referring to individual journal articles, conference publications, book chapters, artistic performances, films, etc.
One may mention audio visual recordings, computer software and databases, technical drawings, designs or working models, major works in production or exhibition and/or award-winning
design, patents or plant breeding rights, major art works, policy documents or briefs, research or technical reports, legal cases, maps, translations or editing of major
works within academic standards. 12 See: http://www. kowi. de/Portaldata/2/Resources/fp/assessing-europe-university-based-research. pdf 61 Table 3-2:
and experts) against the criteria discussed in the first section of this chapter. The indicators in the table are used in the pilot test (chapters 5 and 6). The majority of the indicators are normalized by taking into account measures of an institution's (or a department's) size that is:
referring to total staff (in fte or headcounts), total revenues or other volume measures. 62 Table 3-3:
However, focus on peer reviewed journal articles is too narrow for some disciplines. 4 Post-doc positions (share) Number of post-doc positions/fte academic staff Success in attracting post-docs indicates quality of research.
or ranking. 8 Number of art related outputs Count of all relevant research-based tangible outputs in creative arts/fte academic staff Recognizes outputs other than publications
and prizes won for research work Prizes, medals, awards and scholarships won by employees for research work
and in (international cultural competitions, including awards granted by academies of science. Indicator of peer esteem.
staff) Frequently used indicator. However, research findings are published not just in journals. 12 Doctorate productivity Number of completed Phds per number of Professors (head count)* 100 (three-year average) Indicates aspects of the quantity and quality of a unit's research.
but dropped is‘Presence of research related promotion schemes for academic staff'.'A performance-based appraisal/incentive system (e g. tenure track system) may increase the attractiveness of an institution to strong researchers,
The means by which members of professional associations maintain, improve and broaden their knowledge and skills and develop the personal qualities required in their professional lives,
These works of art, including artistic performances, films and exhibition catalogues have been included in the scholarly outputs covered in the Research dimension of U multirank.
together with in the right hand column some of the pros and cons of the indicators expressed by experts and stakeholders during the indicator selection process.
An important reference is published the report in 2009 by the Expert Group on Knowledge Transfer Metrics (EGKTM) set up by DG Research of the European commission. 17 Table 3-4:
relative to fte academic staff Indicates appreciation of research by industry. Reflects successful partnerships. Less relevant for HEIS oriented to humanities, social sciences.
which the university acts as an applicant related to number of academic staff Widely used in KT surveys.
Data are available from secondary (identical) data sources. 5 Size of Technology Transfer Office Number of employees (FTE) at Technology Transfer Office related to the number of FTE
academic staff Reflects priority for KT. Input indicator, could also show inefficiency. Data are mostly directly available.
Not regarded as core indicator by EGKTM. 6 CPD courses offered Number of CPD courses offered per academic staff (fte) Captures outreach to professions Relatively new indicator.
8 Number of Spin-offs The number of spin-offs created over the last three years per academic staff (fte) EGKTM regards Spin-offs as core indicator.
Field-based Ranking Definition Comments 9 Academic staff with work experience outside higher education Percentage of academic staff with work experience outside higher education within the last 10
years Signals that HEI's staff is placed well to bring work experience into their academic work.
staff Indicator of (applied) R&d activities. Indicator only refers to the size of projects, not their impact in terms of KT. 13 Number of license agreements The number of licence agreements as a percentage of the number of patents Licensing reflects exploiting of IP.
Number of licences more robust than licensing income. 14 Patents awarded The number of patents awarded to the university related to number of academic staff Widely used KT indicator.
relative to fte academic staff See above institutional ranking. Differences in relevance by fields. Cultural awards and prizes won in (inter) national cultural competitions would be an additional indicator that goes beyond the traditional technology-oriented indicators.
and promote international mobility of students and staff, Activities to develop and enhance international cooperation,
Sensitive to relative‘size'of national language. 2 International academic staff Foreign academic staff members (headcount) as percentage of total number of academic staff members (headcount.
Foreign academic staff is academic staff with a foreign Considered to be relevant by stakeholders.
Data available. 7 International graduate employment rate The number of graduates employed abroad or in an international organization as a percentage of the total number of graduates employed Indicates the student preparedness on the international labor market.
No clear international standards for measuring. 8 International academic staff Percentage of international academic staff in total number of (regular) academic staff See above institutional ranking 9 International
11 Joint international publications Relative number of research publications that list one or more author affiliate addresses in another country relative to academic staff See above institutional ranking,
The indicator‘international graduate employment rate'was dropped from the list for focused institutional rankings because a large majority of stakeholders judged this to be insufficiently relevant.
and promotion schemes of the institution 18 See: http://classifications. carnegiefoundation. org/details/community engagement. php 75 acknowledge regional engagement activities?
How much does the institution draw on regional resources (students, staff, funding) and how much does the region draw on the resources provided by the higher education and research institution (graduates and facilities)?
relative to fte academic staff Reflects‘local'research cooperation. Data available (Web of Science), but professional (laymen's) publications not covered. 4 Research contracts with regional business The number of research projects with regional firms,
Our analysis on data availability was completed with a brief online consultation with the group of international experts connected to U multirank (see section 4. 2. 5). The international experts were asked to give their assessment of the 21 The U multirank project was granted access to the preliminary
their coverage in national databases Dimension EUMIDA and U multirank data element European countries where data element is available in national databases Teaching & Learning relative rate of graduate unemployment
) Expert view on data availability in non-European countries 4. 2. 5the Expert Board of the U multirank project was consulted to assess for their six countries all from outside Europe the availability of data
Availability of U multirank data elements in countries'national databases according to experts in 6 countries (Argentina/AR, Australia/AU, Canada/CA, Saudi arabia/SA, South africa/ZA
graduation rate AR, CA, US, ZA AR, AU, SA, ZA relative rate of graduate unemployment AU, CA
staff ZA, US AR, AU, CA, SA, US, ZA joint degree programmes AR AR, AU, CA, US international doctorate graduation rate
According to the experts consulted, more data can probably be found in institutional databases. However, if that is the case, there is always a risk that different institutions may use different definitions
or other), our experts stressed that it is not always easy to obtain that information (for instance in case of data relating to the dimension Regional Engagement).
staff data: fte and headcount; international staff; income: total income; income by type of activity;
by source of income; expenditure: total expenditure; by cost centre; use of full cost accounting; research & knowledge exchange:
graduate employment; staff: fte and headcount; international staff; technology transfer office staff; income: total; income from teaching;
income from research; income from other activities; expenditure: total expenditure; by cost centre; coverage; research & knowledge transfer:
publications; patents; concerts and exhibitions; start-ups. As the institutional questionnaire and the U-Map questionnaire partly share the same data elements,
staff & Phd: academic staff; number of professors; international visiting/guest professors; professors offering lectures abroad;
professors with work experience abroad; number Phds; number post docs; funding: external research funds; license agreements/income;
joint R&d projects with local enterprises; students: total number (female/international degree and exchange students;
periods of work experience integrated in programme; international orientation; joint study programme; credits earned for achievements abroad;
Problems emerge however with some output-related 92 data elements such as graduate employment, where often data is collected not at the institutional level.
Information on international students and staff, as well as on programmes in a foreign language was largely available.
Problems with regard to the availability of data were reported mainly on issues of academic staff (e g. fte data, international staff), links to business (in education/internships and research) and the use of credits (ECTS.
The definition of the categories of academic staff(‘professors'‘other academic staff')clearly depends on national legislation
and include employers and other clients of higher education and research institutions, but that would make the task even bigger.
and making use of the advice of external experts and national correspondents in the testing and further execution of the survey is yet another part of the provision that needs to be part of the data collection strategy. 5 Testing UTESTING U Testing U
Self-reported time needed to deliver data (fte staff days) Data collection tool N Minimum Maximum Mean Institutional questionnaire 26 1. 0 30
Self-reported time needed to deliver data (fte staff days: European vs. non-European institutions Data collection tool Europe Non-Europe Mean N Mean N Institutional questionnaire 6. 2 15
Comments show that this criticism refers mainly to issues concerning staff data (e g. the concept of full-time equivalents)
which are classified in turn by Thomson Reuters experts into one or more Journal Categories. The Journal Categories, sometimes referred to as Subject Categories,
the control they exercise over their academic staff, and the legal norms on the assignment of intellectual property rights (IPR) over academic research results.
and/or experts have expressed some doubts regarding one or two selection criteria. The‘relevance'criterion has been the major reason to keep these indicators on the list for the pilot study.
Feasibility score Data availability Conceptual clarity Data consistency Recommendation Graduation Rate A b Time to Degree B b Relative Rate of Graduate (Un) employment
Most comments were regarding graduate employment. The fact that in many countries/institutions different measurement periods (other than 18 months after graduation) are used seriously hampers the interpretation of the results on this indicator.
rating Feasibility score Data availability Conceptual clarity Data consistency Recommendation Student/staff ratio A a Graduation rate A b Qualification of academic staff
B A Percentage graduating within norm period B b Relative rate of graduate unemployment B c In Interdisciplinarity of programmes B b Inclusion of work experience B A-B Gender balance
A number of institutions did not have information on graduate employment/unemployment at the field level.
use different time periods in measuring employment status (e g. six, 12 or 18 months after graduation).
As normally the rate of employment is increasing continuously over time, particularly during the first year after graduation,
The indicator‘inclusion of work experience'is a composite indicator using a number of data elements (e g. internships, teachers'professional experience outside HE) on employability issues;
rating Feasibility score Data availability Conceptual clarity Data consistency Recommendation Organization of programme A a Inclusion of work experience A a Evaluation of teaching A a
academic staff A b Percentage research income from competitive sources B b Art-related outputs per fte academic staff B c In Total publication output B b International awards
In general, the data delivered by faculties/departments revealed some problems in clarity of definition of staff data.
transfer A a Patents awarded**A b University-industry joint research publications*A a CPD courses offered per fte academic staff B b Start-ups per fte academic staff
B b Technology transfer office staff per fte academic staff B b Co-patenting**B A*Data source:
staff with work experience outside HE A b Joint research contracts with private enterprise A b Patents awarded**C C Out Co-patenting**B c Out Annual income from licensing B c
publications*A a Percentage of international staff B A Percentage of students in international joint degree programs A b International doctorate graduation rate B A Percentage foreign degree
A a-B Opportunities to study abroad (student satisfaction) A b International orientation of programs A b International academic staff B A-B International joint research publications
In order to test alternatives means of measuring percentages of international staff, we used different definitions in the institutional
The 131 institutional questionnaire referred to the nationality of staff; the level of staff with foreign nationality was easy to identify for most institutions.
In the field questionnaires, the definition‘international'referred to staff hired from abroad. This excludes foreign staff who were hired from another institution in the same country rather than from abroad.
Some universities had difficulties to identify their international staff based on this definition. Regional engagement 6. 2. 5up to now the regional engagement role of universities has not been included in rankings.
There are a number of studies on the regional economic impact of higher education and research institutions,
Here the problems concerning the availability of comparable information on graduate employment in general and the problems with the definition/delineation of`region'add up.
At the same time they are a link to potential future employees and in many non-metropolitan regions they play an important role in the recruitment of higher education graduates. 6. 3 Feasibility of data collection As explained in section 5. 3 data collection during the pilot
study was carried out via self-reporting from the institutions and analysis of international bibliometric and patent databases.
institutional and field-based questionnaires were implemented with different features (e g. definition of international staff). This procedure helped us to judge the relative feasibility of concepts and procedures.
And we believe that there are opportunities for the targeted recruitment of groups of institutions from outside Europe of particular interest to European higher education.
Beyond these two factors a diverse range of particular institutional issues came into play including competing claims on the time of the staff concerned
and changes in these key staff. Nevertheless for a pilot study a completion rate of 109 of 159 (69%)is more than respectable.
sorted by indicator‘research publication output student staff ratio graduation rate qualification of academic staff research publication output external research income citation index
%income third party funding CPD courses offered startup firms international academic staff%international students joint international publ. graduates working in the region student internships in local enterprise
& Learning Research Knowledge transfer international orientation Regional engagement student staff ratio graduation rate qualification of academic staff research publication output external
research income citation index%income third party funding CPD courses offered startup firms international academic staff%international students joint international publ. graduates working in the region
low student satisfaction scores regarding the support by teaching staff in a specific university or program is relevant information,
'International Ranking Expert Group 2006; principle 15. U multirank, as any ranking, will have to find a balance between the need to reduce the complexity of information on the one hand and, at the same time,
In addition access to and navigation through the web tool will be made highly user-driven by specific‘entrances'for different groups of users (e g. students, researchers/academic staff, institutional administrators, employers) offering specific information
recruitment could be reinforced, at which point the inclusion of these important peer institutions will hopefully motivate more institutions to join U multirank.
such as staff data (the proper and unified definition of full-time equivalents and the specification of staff categories such as‘professor'is an important issue for the comparability of data),
EUMIDA could contribute to improve the data situation regarding employment-oriented outcome indicators. An open question is how far EUMIDA is able to go into field-specific data;
The work packages for the next phase of implementation should be: Finalisation of the various U multirank instruments 1. Full development of the database and web tool.
user-driven ranking could work; now the real system has to be created, populated with data and tested,
The testing of national data systems for their pre-filling potential and the development of suggestions for the promotion of pre-filling are important steps to lower the costs of the system for the institutions.
If we take into account the response rate of institutions in the pilot phase the inclusion of 700 institutions in the institutional and 500 in each field-based ranking appears realistic. 6. Targeted recruitment of higher education institutions outside Europe.
Communication and recruitment drive. The features of and opportunities offered by U multirank need to be communicated continuously.
Since the success of U multirank requires institutions'voluntary participation a comprehensive promotion and recruitment strategy will be needed,
requiring the involvement of many key players 160 (governments, European commission, higher education associations, employer organizations, student organizations).
11. User-friendliness of the instrument. A crucial issue related to communication is the user-friendliness of U multirank.
Bringing the 11 work packages and the resulting products together in a feasible schedule leads to the following project structure (assuming the next project phase starts 01/2012 and ends 12/2013):
Elements of a new project phase Work package Products Deadline Database and web tool Functioning database Functioning web tool prototype 06/2012 Standards
-filling opportunities (including EUMIDA cooperation) Pre-filled questionnaires Coordination with national rankings 06/2012 12/2012 12/2012 Roll out Invitation Targeted non-European recruitment
business plan, user-friendliness and communication Advisory boards work Consortium, formal organization and business plan (including funding structure) established Development of marketable products 06/2012 12/2013
The ranking must be run by a professional organization with expertise in large-scale data analysis and in transparency tools.
We believe that it is not reasonable in the initial phase of implementing U multirank to establish a new professional organization for running the system.
therefore that rankings would be operated (initially) on a project basis by existing professional organizations with a strong involvement of both stakeholder and expert advisory bodies.
Stakeholder and expert advisory councils should be installed in a form that could continue to operate after the two years'project Phase in order to support the development of a viable business plan a partnership with professional
The professional organizations responsible for the first phase could establish the ranking unit as a joint venture with the stakeholder
and expert advisory structure remaining in place. This structure also allows the commercial unit to operate as a joint venture with for-profit partners.
Fixed and variable cost factors STEP FIXED COST FACTOR VARIABLE COST FACTOR Methodological developments and updates Staff demand Cycle of revision/update of concepts
Intensity of stakeholder involvement Communication activities Staff demand Number of countries and institutions covered Intensiveness of communication (written only, electronic, workshops etc) Implementation of (technical) infrastructure Basic
IT Indicators/databases used (e g. license costs) Development of a database Staff Basic IT costs Provision of tools for data collection Staff Basic IT costs (incl. online survey systems
and databases Data analysis Staff Number of countries and institutions covered Range of indicators and databases License fees of databases (e g. bibliometric) Publication Staff Basic IT costs Features of web tool
to present results Information services for users Staff Basic IT costs Number of countries and institutions covered Range of indicators and databases Scope of information services Internal organization
the design and development of information packages on ranking and the dissemination of the outcomes as well as the staff time needed to do this.
Staff time is the key cost. Meeting costs for the Board and the Councils: honoraria and travel and subsistence costs.
b) Funding/sponsorship from other national and international partners interested in the system. c) Charges from the ranking users (students, employers etc..
but there is a possibility of some cross-subsidization from selling more sophisticated products such as data support to institutional benchmarking processes, special information services for employers, etc.
Methods for Regionalisation, Sector Allocation and Name Harmonisation',Methodologies & Working papers, Publications Office of the European union, Luxembourg, 2011, ISBN 978-92-79-20237-7. Expert
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