ABSTRACT. Several studies agree to point out that scientific and technological (S&T) infrastructure is a key aspect for the economic development. As part of the recognition of the importance acquired by S&T infrastructure for innovation systems and in order to obtain updated information for the management and planning of future investments, since the 2000s, several countries started a measurement process for public S&T infrastructure. However, a comprehensive analysis that allows the establishment of consensus on the subject of study and forms of measurement has not been identified. Apart from that, it is widely recognized that in Latin America one of the weaknesses of the innovation systems lays in the fragility of S&T infrastructure. In this context, the general purpose of this work is to analyze the configuration of the Argentinean innovation system from the study of scientific and technological infrastructure. The specific objectives are to analyze the conceptualization of scientific and technological infrastructure, its role in the processes of knowledge dissemination, the measurement methods and the strategies implemented for its development. 

The work methodology is based on a qualitative analysis of the theoretical contributions and empirical experiences of Argentina, Brazil, European Union, Australia and India on programs of policy and planning tools concerning scientific and technological infrastructure in order to systematize, analyze and compare their general characteristics. The study is conducted basing on secondary data and primary sources of information are official documents, consulting works, articles published in international journals and reports available on the official sites of the public institutions involved. The work methodology is primarily qualitative although quantitative data are also used to illustrate the Argentinean case. 

Throughout the work, the scientific and technological infrastructure of Argentina is analyzed in depth, presenting the state of affairs, the policies implemented in terms of investments and the previous records in the field of measurement. As a result of this analysis, outstanding progresses regarding investments destined to reverse the chronic deficits in scientific and technological infrastructure have been observed. Nevertheless, shortcomings associated with a poor institutionalization and planning of the implemented actions and a lack of gathering and processing of information on the subject has been detected and, consequently, its low contribution to the processes of diffusion of innovations of the user community. On the side of international empirical experiences, the practices that assume specific features regarding the history and nature of measurement works, types of planning tools and the evolution of the proposed actions were surveyed, as well as the importance given to the issue by the political actions and their contribution to knowledge dissemination. 

Based on the Argentinean panorama on infrastructure developments for science and technology and according to the analysis of international experiences of measurement infrastructure, analytical findings regarding the experience of Argentina are presented, as well as possible policy lessons oriented to strengthening the infrastructure of the innovation system. In this line, we highlight the relevance of the retrospective measurement works through the mapping of infrastructure and inventories of broad geographic and disciplinary reach as effective tools aimed at the spread of technologies and investment assessment; the creation of institutional systems with long-term planning practices that guide short-term actions; as well as the creation of spaces oriented to the discussion and decision-making through coordinated and participatory practices and under prospective work methods.

ABSTRACT. 1.Introduction. In this paper we analyze the impacts of the Higher Education Reforms conducted in the 2000nds on the optimal size of universities in Germany. While the a large share of the existing literature has provided evidence of increasing returns to scale, i.e. size advantages, the generalizability of the results are limited because these analysis rely on cross-section analyses that implicitly take the institutional framework as given. In this paper we argue that the higher education reforms in the 2000nds (in particular the Bologna reforms and the vast array of NPM reforms) led to a decrease of the optimal size of universities. This is because universities as loosely coupled systems find it hard to adapt to uniform and global institutional change in the policy framework. Such change is can be more effectively catered for through hierarchical and centralized approaches to governance, which are more likely to exist in smaller universities. In order to derive our hypotheses we extend the concept of loose coupling by merging it with concepts from the returns to scale literature. Using non-parametric efficiency estimation we test our hypotheses based on a panel data set on all German universities from 2000-2011.

2.Theory outline. Loose coupling in a system means that different elements of a system have either few variables in common or that these variables are weak. Such systems are usually hard to steer by top down approaches because loose interdependence reduces the effectiveness of control that can be exerted by central decision makers through power, hierarchy, or the distribution of financial resources. Building on loose-coupling theory (Weick 1976, Orton and Weick 1990), we argue that loose coupling is not a dichotomous category but may differ in its level between universities. While it is recognized that loose coupling can be effective in supporting adaptation to local changes in the environment, strategic and global environmental change is usually hampered because of the weakness of central decision makers. We hypothesize that smaller universities were abler to adapt to the global change implied by higher education reforms because they are expected to have lower costs of adopting more management-oriented coordination processes. This effect should have implied a decline in optimal size of universities.

3.Methods and results. In order to test this hypothesis we use a longitudinal dataset on university inputs and outputs for German universities between the years 2000-2011. Based on this dataset we estimate efficiency models that allow us to determine the universities’ Most Productive Scale Size (MPSS), which can be viewed as the measure of an individual university’s optimal size of operation (Banker 1984, Banker and Thrall 1992, Cooper et al. 2007). We show that in the period between 2000 and 2011 there occurred an immense drop in the MPSS over the sample period. While in 2000 the optimal average scale of operations hovered around 30,000 in terms of students, which was more than the average number of students per university (approximately 17,500) this number declined to only about 3,500 in 2011. When running model variants that focus either on teaching or on publication output we are furthermore able to pinpoint that the drop almost exclusively related to teaching and not to research activities and that this drop took place in 2002. Because this is precisely the year of the implementation of the Bologna reforms, there is strong evidence that the impacts can be traced to these reforms. Finally, we analyze the transmission mechanisms and show that this drop has quite likely to do with the fact that larger universities had to increase their administrative staff much more than smaller universities during the sample period, implying that smaller universities were able to handle the new demands set by the new higher education policies at lower organizational costs.

4.Conclusion. We provided evidence that the Bologna reforms have not only changed the content and structure of teaching in higher education but have also changed optimal organizational structures. In particular, while the literature on returns to scale in science has emphasized the role of economies to scale arguing for larger universities, we provide evidence that there are also liabilities to size that could stem from the decreased ability to adapt to global change. The superiority of larger universities might therefore be quite contingent on the specific circumstances, suggesting that extrapolation to the mainly empirical results in the returns to scale literature to untested contexts might be problematic. In particular, in times where the institutional settings change fast, large scale universities might face severe adaptational disadvantages.

ABSTRACT. 1. Introduction
The German Excellence Initiative has had an impact on the national university and research system for nearly ten years. The Institute for Research Information and Quality Assurance (iFQ) has been conducting research on the German Excellence Initiative since 2007. The research is based on multi-methodological approaches and contains different online surveys, guided interviews and document analyses. This paper focuses on the findings of the current bibliometric analysis.

2. German Excellence Initiative
The German Excellence Initiative was launched in 2006 as a common funding program of the German federal government and 16 state governments (total funding 4.6 billion €). The main objective of the Excellence Initiative is to improve the international standing and competitiveness of German universities. However, the Excellence Initiative’s focus on competition concerns not only the global standing of the national university system. It also pertains to their standing within German, enforcing competition between German universities. It thus entails a fundamental change in German science policy: the shift from an egalitarian to a competitive science policy approach, based the assumption that this will have a positive effect on the German university system overall. Although the Excellence Initiative is dedicated to the university system the funding program also addresses additional collaborative aims.
Within the Excellence Initiative there are three funding lines: Graduate Schools, Clusters of Excellence and Universities’ Future Concepts. In our approach we focus on the Clusters of Excellence as the core research unit of the initiative which receives 60% of the total funding.

3. Methodology
For measuring the effects of the German Excellence Initiative we compare two time periods: A pre-funding period (2003-2006) and a funding period (2008-2011). As some funded projects started at the end of 2006, we assign 2006 to the pre-funding period. Since the last projects funded in 2007 did not begin their work not until the end of the year, this year is considered a transitional period and excluded from our analysis.
Funding Acknowledgements represent the most promising approach for evaluating third-party funding. First, we explore a sample of Cluster of Excellence publications to find out in which terms the authors address the excellence cluster funding. Based on these findings an elaborate search strategy for Cluster-specific funding acknowledgements has been developed. 215 search variants were used to identify the publications of the 31 Clusters of Excellence in engineering, natural and life sciences. A total of 6358 publications were found with this strategy. The search procedure’s precision is 96%, but, as all false-positives identified in the manual cleaning procedure are removed from the final corpus, we estimate their actual precision with 100% (6116 publications).

4. Results
- Do Clusters of Excellence produce a high rate of excellent publications?
Following the Initiative’s main premise and label, we operationalize the measurement of its outcome by using the bibliometric concept of highly cited publications. Citations are analyzed within a three-year citation window. The question is whether the clusters predominantly contribute to the top percentiles of the world’s highly cited papers. While in 2008-2011 only 14.3% of the publications from the German universities belong to the top 10% of the highly cited papers, the Cluster of Excellence produced 25.9% of the highly cited papers.
- Did the Excellence Initiative succeed in strengthening the collaboration between universities and the non-university sector?
Only 4.3% (2003-2006), respectively 5.2% (2008-2011) of the German university publications are written in collaboration with the Max-Planck-Society, while 21.0% of the Cluster of Excellence publications belong to the Max-Planck-Society, followed by the Helmholtz Association (8.5%). While the collaboration shares of the non-university research organizations in the Cluster of Excellence are well above the university average, the conclusion is that the Excellence Initiative has effects on the strength of the collaborations between both sectors.
- Does the Excellence Initiative have a positive effects for the German university system overall?
The starting point of this analysis is a cleaned dataset based on institutional addresses. The results show that the universities which received the most Excellence funding have the highest growth rate in degree of excellence and that nearly 75% of this growth can be attributed to publications of the Clusters of Excellence. Moreover, there is a measurable effect of the Cluster of Excellence for the whole university sector and the German science system overall, showing that more than 50% of the increase in highly cited papers can be attributed to the Clusters of Excellence.

ABSTRACT. The paper examines the capabilities of universities to rapidly build up and expand research capacities in new and emerging scientific fields following major scientific breakthroughs. Based on the Scanning Tunneling Microscope (STM), developed in 1982 (Nobel Prize in Physics, 1986), and Buckminsterfullerenes (BUF), discovered in 1985 (Nobel Prize in Chemistry, 1995), the paper investigates how fast scientists in German and US state universities built up follow-up research in response to these two breakthroughs. Most importantly, the paper explores to what extent the institutional framework in which universities are embedded supported such expansion and renewal.

The methodical basis of the study is the construction of a strictly comparable set of state universities. The paper analyses longitudinal quantitative data (1980-2010) of 84 German and 155 US state universities that award doctoral degrees. In addition, the paper provides case study evidence for state universities in Bavaria and California, two states in which both STM and BUF follow-up research has been strong.

Our bibliometric findings (dependent variable) demonstrate that scientists in US state universities were several years ahead of their colleagues at German universities in seizing on STM and BUF. Our institutional findings (explanatory variables) suggest that in the years following STM and BUF, i.e. the 1980s and 1990s, US universities provided better institutional conditions for scientific renewal than German universities. Below are our results in detail.

First, a high percentage of professors among scientific staff is conducive to building up and expanding research capacities in new and emerging fields. Two mechanisms are involved: A high percentage of professors raises the frequency by which new research opportunities are both detected and followed up by those who are expected to conduct independent research; in addition, a high percentage of professors raises the frequency by which new peers are hired, and new research topics and areas thus are imported in replacement of previous ones. A low percentage of professors, as in Germany, indicates that many more young scientists work in the academic system than can be possibly absorbed into professorial ranks. As a consequence, there is a bottleneck at the transition to professorial status, leading to prolonged periods of dependency and job insecurity in academic biographies. In the US, the transition to assistant professor, and thus scientific independence, takes place earlier in the biography, thus providing favorable conditions for seizing upon new and promising scientific opportunities.

Second, growth in the number of professors, growth in basic funding, and a high percentage of grant funding among all funding streams are key factors positively associated with building up and expanding research capacities in new and emerging fields. In fact, a declining or stagnating number of professors severely constrains the capability of universities and their departments to respond swiftly to new and emerging research fields by recruiting outstanding scientists, as demonstrated in our German case studies. Furthermore, as our US case studies show, if growth of basic funding is channeled into facilities and laboratories that are shared by professors both inside and across departments, supportive conditions for effective collaborations in new and emerging fields are created. Yet, as our US cases illustrate, too strong dependency of professors on grant funding and too high competitive pressure for external research resources may inadvertently end successful scientific collaborations before all fruits are harvested.

Third, our findings point to significant and increasing differences in the state university systems of Germany and the US with major implications for renewal in science. Although the percentage of professors decreased in both countries since the 1980s, this decrease took place at different scales. Furthermore, inflation adjusted basic funding for US state universities has grown by factor 2.0 since the 1980s with tuition fees providing the lion’s share in growth; in contrast, basic funding for state universities in Germany has grown by factor 1.5 only. Tuition fees, which had been introduced in the mid 2000s in some German Länder states, were abolished recently, thus reducing the level of basic funding. Based on our empirical findings, the conditions for renewal in science in German universities are worse today than they were in the 1980s and 1990s, in contrast to the US.

The paper closes with conclusions for research policy in Germany and the US. For Germany, the paper identifies as major policy problem the scientific staff structure which appears to severely impede intellectual renewal and growth of new research fields. For the US, the paper discusses the “contingent faculty” phenomenon which might compromise the strong position of US state universities in the future.

ABSTRACT. Exploring the Humanitarian Innovation Ecosystem

Professor Howard Rush

Centre for Research in Innovation Management

University of Brighton

In 2013, the most recent year in which we have accurate data, nearly 150 million people around the world were affected by natural disasters, wars and conflicts.  This is nearly double the number of people requiring assistance since the turn of the century.  The cost of meeting merely the basic needs of affected communities has risen by over 600% over this time.   Both financial costs and human misery are likely to continue to rise as displaced people are now spending long periods in ‘temporary’ refugee camps, many of which have now been in existence for over fifteen years.         

The humanitarian system simply cannot cope in its present form.  Without radical transformation the world is more than likely to witness the type of failures in coping with the scale of the disasters such as recently experienced in Rwanda, the Indian tsunami, and Haiti.  As a consequence of the inadequacies in response to these crisis, a growing number of those involved in humanitarian response acknowledge the need for change and have begun to recognise and embrace the role of innovation within the sector.

It is, however, only six years since the first studies of innovation within the humanitarian sector began to appear.  These include  Ramalingam et al (2009). Innovations in International Humanitarian Response; ALNAP (2012) The State of the Humanitarian System; UNOCHA (2013) Humanitarianism in the Networked Age, New York; Betts, A and Bloom, L (2014) Humanitarian Innovation: The State of the Art, UNOCHA Policy and Studies Series.  While such contributions have categorised the actors located within such a system, provides data on the scale of the system and offered important insights into the coverage, relevance, effectiveness and efficiency of the provision of humanitarian assistance, they  did not fully address the role of innovation within the system or the way in which the ‘ecosystem’ either facilitates or inhibited innovation.

This presentation summaries work conducted over the past year by CENTRIM, the Centre for Research in Innovation Management (University of Brighton), sponsored by the UK Department of International Development.  The aim of the research was to provide an in-depth analysis of the strengths and weaknesses of the ‘Humanitarian Innovation Ecosystem’.  In order to explore the complexities underpinning the Humanitarian Innovation Ecosystem, we established a project team that comprised of both innovation management scholars and humanitarian aid researchers and practitioners.  The work programme was built on three components, starting with an extensive review of the relevant literature in both innovation management and humanitarian innovation.[1]  This component drew upon the wealth of empirical studies on successes and failures in innovation management and identified the major opportunities and challenges from this literature which have relevance for the humanitarian sector.  These include the development of ‘capacities’ for innovation, the need for ‘ambidexterity’ to enable both radical and incremental innovation, the role of ‘entrepreneurship’, the potential for ‘user-led’ and ‘open innovation’, and the need to balance risk, reward and reliability across humanitarian innovation efforts.

In a second report,  the project team generated insights from an informed sample of fifty individuals who had expert knowledge of the role and nature of innovation and its management within the sector.  The interviews explored key themes derived from the literature review and offered insight into why the humanitarian innovation ecosystem currently operates as it does.  Through an analysis of the ‘six Rs’ – resources, roles, relationships, routines, rules, and results - it also led to the development of a dynamic systems approach to understand the important enablers and inhibitors influencing the humanitarian innovation ecosystem.  

Having reviewed the literature and gathered expert perspectives, the Humanitarian Innovation Ecosystem was explored further in a series of in-depth case studies.  Four case studies were conducted in the major humanitarian sub-sectors (i.e., Water, Sanitation, and Hygiene (WASH), Food, Health, and Shelter), while a fifth investigated the availability and models of humanitarian innovation financing.  In conducting the case studies, over 150 interviews were held and numerous documents reviewed. Each of the five case studies was subject to multiple extensive coding exercises related to the six ‘Rs’ innovation ecosystems drivers, in order to systematically identify common themes and issues.

This presentation presents the main findings from the research outlined above.  It describes how there are a number of structural issues and systemic problems facing the sector that serve to limit the effectiveness of the innovation ecosystem.

These issues include:

• Short-term and reactive financing for assistance
• Delivery focus of aid, with few investments between crises responses
• Organisational culture and mind-sets which give prominence to existing operating procedures ad widespread resistance to change
• Insular, individualistic and competitive nature of humanitarian responders
• Lack of engagement with actors seen to be outside of response, including a longstanding and unjustifiable lack of engagement with recipients of aid

The presentations concludes with a series of recommendations as to how to strengthen and facilitate the pathways of innovation into the humanitarian sector.   The project final report  (as well as the literature review, interview survey and case studies) are available for downloading via the project website (see outputs) at: https://www.brighton.ac.uk/centrim/research-projects/humanitarian-innovation-ecosystem.aspx

[1] Bessant J., Ramalingan, B., Rush, H., Marhsall, N., Hoffman, K., and Gray, B. (2014) Innovation Management, Innovation Ecoystems and Humanitarian Innovation. Literature Review for the Humanitarian Innovation Ecosystem Research Project, CENTRIM, University of Brighton. (http://r4d.dfid.gov.uk/Output/196762/)

ABSTRACT. Technological Innovation in Legacy Sectors

Charles Weiss and William B. Bonvillian

New approaches are needed to broaden the historic focus of science and technology policy on innovations leading to new industries like information technology and biotechnology. We have developed a new, unifying conceptual framework to address the problem of resistance to disruptive innovation in entrenched Legacy sectors of the U.S. economy, and have extended it to similar patterns of resistance in five other countries. A case study of the Legacy sector of U.S. manufacturing raises the problems of “jobless innovation” because of the shift abroad of manufacturing capability.

These Legacy problems inhibit innovation in more than half the U.S. economy. Both arise from an economic, political, legal and cultural innovation context in which incentives for innovators and producers in Legacy sectors run counter to job creation, technical efficiency, security, public health and safety, and environmental sustainability. This context is as important to the rate and direction of innovation as the better studied innovation system; the two together constitute the innovation environment. In Russia, China, India, France and Germany, the innovation environment poses obstacles to radical innovation throughout the economy; in China and Germany, it favors process innovation in manufacturing.

In our new book, Technological Innovation in Legacy Sectors (Oxford University Press, 2015), we apply our framework to identify obstacles to innovation that are common to energy, the electric grid, buildings, air and auto transport, manufacturing, higher education, health care delivery, and the military in the U.S., over and above the “valley of death” between research and late-stage development that is a traditional focus of U.S. innovation policy. Manufacturing is particularly important because it generates both high-productivity, high-wage jobs and important process innovations.

Innovations in these Legacy sectors must penetrate a well-established, well-defended technological, economic, political and social paradigm that favors existing technology, whereas “sustaining” innovations consistent with prevailing paradigms face no special problems beyond those confronting all innovations.

Legacy sectors share common features, including: perverse subsidies, prices and price structures that neglect externalities; institutions that favor existing technology and discourage new entrants; well-established, politically powerful vested interests that resist new technologies; financing support whose time horizon is unsuited to many new technologies; public expectations attuned to existing technology; aversion to innovation and consequent limited support to research and development; an established knowledge and human resources structure oriented to existing technology; and market imperfections like network economies, non-appropriability, and the need for collective action. To overcome these barriers, we set forth a new strategic systems “innovation organization” approach for developing strategy and policy.

Presenters:

Charles Weiss is retired Distinguished Professor and Chair of Science, Technology and International Affairs at the School of Foreign Service at Georgetown University. He was the first Science and Technology Adviser to the World Bank and has published papers on many topics of domestic and international science and technology policy. He holds a B.A., summa cum laude in chemistry and physics, and a Ph.D. in chemical physics and biochemistry, both from Harvard University.

William B. Bonvillian is Director of MIT’s Washington Office and is on the adjunct faculty at Georgetown and Johns Hopkins SAIS. He teaches and writes on innovation policy. He was previously a senior legislative advisor in the U.S. Senate. He has a B.A. from Columbia University with honors, an M.A.R. from Yale Divinity School, and a J.D. from Columbia Law School, where he was an Editor of the Law Review. Further bio information is available at http://bonvillian.wix.com/bonvillianwebsite.

ABSTRACT. Emerging technologies are perceived as new technologies with the potential to change the economy and society. For this reason, these technologies have been the subject of much debate in academic research and also a central topic in policy discussions. Evidence of the increasing attention being paid to the phenomenon of emerging technologies can be found in the growing number of publications dealing with the topic as well as in ad hoc governmental actions such as the “Foresight and Understanding from Scientific Exposition” research program funded by the US IARPA in 2011.

Nonetheless, no consensus has emerged as to what qualifies a technology to be emergent. Definitions proposed by a number of studies overlap, but also point to different characteristics. Certain definitions emphasise the potential impact emerging technologies are capable of exerting on society (e.g. Porter et al., 2002), while others give great importance to the uncertainty associated with emergence (e.g. Boon & Moors, 2008). This also extends to the wide variety of methodological approaches that have been developed, especially by the scientometric community, for the detection and analysis of technological emergence (e.g. Glänzel & Thijs, 2012; Small et al., 2014). These methods build on relatively loose definitions of emerging technologies or often no definition at all is provided. Approaches to the detection and analysis of emergence tend to greatly differ even with the use of the same or similar methods.

The present paper aims to address these shortcomings. To do so, we attempt first to integrate different conceptual contributions on the topic in a coherent definition of emerging technology. Second, we develop a framework for the operationalisation of emerging technologies.

The development of our definition of ‘emerging technology’ started from the definition of ‘emergence’ or ‘emergent’ that is “the process of coming into being, or of becoming important and prominent” (New Oxford American Dictionary). Emergent is a label for a process, the endpoint of which is variously described as visible, evident, important or prominent. We then enriched and contextualised the basic understanding of ‘emergence’ with a review of major contributions to innovation studies on the topic. Within an initial sample of 501 publications concerned to a different extent with emerging technologies, a core set of twelve studies elaborating definitions of emerging technologies was identified.

The analysis of these definitions revealed a number of features of emerging technologies. We summarised these in terms of five attributes of emergence: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. These attributes and the reviewed studies were then used to construct a coherent definition of emerging technologies. Specifically, we define an emerging technology as a radically novel and relatively fast growing technology characterised by a certain degree of coherence persisting over time and with the potential to exert a considerable impact on the socio-economic domain(s) which is observed in terms of the composition of actors, institutions and patterns of interactions among those, along with the associated knowledge production processes. Its most prominent impact, however, lies in the future and so in the emergence phase is still somewhat uncertain and ambiguous.

The identified attributes were used as guidelines to develop a framework for the operationalisation of emerging technologies. To do so, we reviewed key scientometric studies (55 studies) on the detection and analysis of emerging technologies. We then discussed on the extent to which the proposed techniques can operationalise the attributes of emergence.

This analysis revealed that the scientometric contribution to the operationalisation of the attributes of emergence is strongly dependent on time (techniques are intrinsically more effective for retrospective analyses), on the nature of the attribute (the uncertainty and ambiguity attribute is, for example, not easy to evaluate from a scientometric perspective), and on used data (publications and patents). The risk that the detected technological emergence is an artefact of the used models and choices (e.g. algorithms and parameters) adds to these limitations. To reduce the likelihood of detecting false positives or missing patterns, a combined scientometric-STS (Science and Technology Studies) approach is suggested.

References Day, G. S., & Schoemaker, P. J. H. (2000). Avoiding the pitfalls of emerging technologies. California Management Review, 42, 8–33. Glänzel, W., & Thijs, B. (2012). Using “core documents” for detecting and labelling new emerging topics. Scientometrics, 91, 399–416. Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43, 1450–1467.

ABSTRACT. The term “emerging technology” is used extensively to describe innovations that seem to appear suddenly in a community, and offer significant promise in the form of new capabilities or insight. A substantial number of researchers have addressed the challenge of identifying emerging technologies, focusing on understanding the criteria and unique features of such technologies. It seems natural to ask a more fundamental question: why do we care about emerging technologies? Assuming that one can distinguish between emerging and “non-emerging” technologies, what are the compelling reasons for paying special attention to emerging technologies? And, within that class of technologies, are there particular types of emerging technologies that merit additional scrutiny by governments, firms and academia?

In a previous paper (Alexander et al., 2012), we propose that at least two classes of emerging technology exist: those displaying “radical emergence” and those that undergo “incremental emergence.” We posit that those in the former class will manifest very clearly the attributes identified by Rotolo, Hicks and Martin (2015): radical novelty, rapid growth (or rapid evolution), coherence, prominent impact, and uncertainty. Importantly, we note that these attributes are seen in relation to the context of an emerging technology; i.e., these attributes must be measured in relation to other existing technologies and circumstances already present in the environment.

Policy organizations in particular are concerned with emerging technologies due to the special challenges that such technologies often present. Some of the challenges posed by an emerging technology may include:

• Challenging or undermining existing regulatory, legal and economic institutions • Requiring new competences and skills to understand the technology • Enabling capabilities that open up substantial new opportunities for exploitation • Creating new infrastructures and platforms for additional emerging technologies

Thus, it is the combination of the attributes cited by Rotolo and colleagues that contribute to the expectation that a particular emerging technology will have significant impact that will be especially difficult to forecast and manage. The difficulty stems from the fact that the emerging technology defies existing concepts about the nature and trajectory of the technology landscape—in other words, it breaks the context in which it exists.

An interesting implication of this observation is the strong similarity between the challenges posed by emerging technologies and the challenges posed by interdisciplinary research. In another paper (Alexander et al., 2013), we discuss the “special nature” of interdisciplinary research, and why there is a common expectation that interdisciplinarity is highly correlated with “breakthrough” innovations. This suggests some relationship between interdisciplinarity, emergence, and uncertainty in the provenance and implications of such innovations.

In this paper, we explore the conceptual foundations of interdisciplinary and emerging technologies in how they interact with their existing contexts. We examine a few cases of notable emerging technologies, such as synthetic biology, nanotechnology, and additive manufacturing (popularly called “3-D printing”). We show the importance in understanding the cognitive, technical, and social context of such technologies in assigning them particular importance among emerging technologies. This will produce a set of propositions intended to suggest further empirical work to characterize different categories of emerging technologies, and to improve approaches for prioritizing emerging technologies as topics of study for policymakers and researchers.

ABSTRACT. The notion of emergence has received an increasing amount of attention in recent years. This is not surprising in that agencies, companies, and researchers alike are all involved in looking for the next breakthrough or hot topic. Emergence involves features such as radical novelty, relatively fast growth, coherence, prominent impact, and uncertainty/ambiguity.

In this paper we build upon a life cycle model of the social structure of science. Indeed, most studies, whether conceptual or empirical, rely inherently on life cycle and/or S-curve characteristics which naturally reflect high rates of growth in a topic or technology as it progresses from ‘small’ (assumed to imply newness or novelty) to ‘large’ (assumed to imply importance or impact). We also note the important distinction between ‘little science’ and ‘big science’. An examination of the migration of authors into communities involved in graphene research suggests that the primary sources of researchers were big science areas that were underperforming.

The ability to more fully characterize the evolutionary structure of science is a recent development. This work benefits from recent advances in data availability, algorithms, and computing power to enable the identification of research communities within the context of the whole. A global model of science was created using a smart local moving algorithm, partitioning over 48M documents from Scopus into 91,726 communities using direct citation.

The use of a global model to characterize the evolutionary structure of science has three significant benefits when compared to small-scale studies. First, the distinction between small and large communities becomes clear within the context of the whole. Research communities follow the same power-law function as cities and towns. In this model, roughly 4k research communities are considered ‘big science’. The remaining 88k research communities are small. Second, one can detect and examine evolutionary patterns. Little science communities rarely become big, and the factors that predict growth (and corresponding instances of emergence) can be isolated. Third, a global model facilitates the exploration of the migration patterns that facilitate emergence. If one hundred scientists join a research community, where do they come from? The following example suggests that, for the case of recent graphene research, migration is coming from underperforming big science communities.

We focused on the migration patterns into four communities involved in graphene research. All four communities were considered ‘little science’ in 2006, while all four were considered ‘big science’ by 2012. We were most interested in the movement of elite authors. Thus, we identified 4,964 elite authors as those who published each year from 2000-2005, and who then subsequently (2007-2012) published in one of the graphene communities. Further, we identified the communities in which they published during the 2000-2005 time period. These are the communities from which the elite graphene authors migrated.

The four graphene communities and 10 research communities supplying the largest number of elite researchers to graphene research were characterized. The largest number of elite authors (summed fractional counts) migrating from any one community to the four graphene communities was only 31, or less than 1% of the total. Thus, migration is coming from a very large number of communities. Most of the donating communities lost authors from 2006 to 2012, while 6 of 10 had fewer hot papers (top 1% highly cited, age and field normalized) than expected. In addition, the four graphene communities were much more vital than the 10 donating communities. Vitality is defined as a mean reference age; the graphene communities are building on much more recent work (by two years on average) than the donor communities. These results correlate with the perception that authors will migrate to communities where breakthroughs are occurring due to their higher potential for future funding and impact.

Overall, we don’t question the fact that there are publication bursts in topics that qualify as examples of emergence. We do suggest, however, that emergence occurs primarily within an existing community structure (rather than being new communities) and is fueled by the migration patterns of full-time scientists. We note that the four graphene communities shown here did not appear suddenly, but had been in existence for many years before experiencing rapid growth. All four were existing ‘little science’ communities working on graphite, which is a close cousin to graphene. Additional details on the history of these research communities and migration network will be given in the presentation. We also note that this is a single example of migration into emerging areas of science, and note the need for further research to establish if the pattern shown here is unique to graphene or is pervasive in the sciences.

ABSTRACT. Recently, attention has been given to issues of responsible research and innovation in an array of emerging technologies. An underlying principle is that societal, ethical, economic and environmental effects and implications should be considered by the actors, stakeholders and institutions engaged in research and innovation processes. Stilgoe and colleagues (2013) suggest that the practice of science and technology raises a series of questions about impacts and risks (including ones related to distribution), decision making, responsibility, beneficiaries and the public interest, and alternative pathways. They argue that processes of responsible innovation should reflect dimensions of anticipation, reflectivity, inclusions, and responsiveness. Responsible research and innovation builds upon, and has connections to earlier concepts which continue to be relevant in science and technology policy, including technology assessment, technology foresight, road mapping, and real-time technology assessment. However, there is a sense in which responsible research and innovation is both broader and deeper. It is broad in that consideration should be given to the full-range of current and anticipated implications of scientific and technological development and also because deliberation should be undertaken not only by experts, government, and directly-connected interests but also more widely with broader publics and less powerful but still affected groups. It is deeper in that there is the expectation, at least among some proponents, that processes of responsible research and innovation should not only seek to mitigate potential risks but could lead to the pursuit of different scientific pathways and to technology redesign to better address societal and global challenges.

A series of projects in Europe, the US, and elsewhere are now exploring, in varied ways, responsible research and innovation. Some research sponsors require attention to responsible research and innovation in proposal development and review. Yet, there are significant challenges of implementation. There is ongoing debate about what exactly responsible research and innovation is and what it means. While this presents a series of important scholarly issues, we are particularly interested to explore the design, understanding and operationalization of the concept of responsibility in research and innovation within a community of researchers charged with reflecting and acting upon the concept. Our focus is within the research domain of synthetic biology. Proponents expect synthetic biology to radically restructure many existing industries and create significant new ones. At the same time, synthetic biology’s ground-breaking prospects are accompanied by concerns about societal implications. These include the ethics of engineering nature, potential environmental, health and safety risks associated with synthetically-engineered organisms, concerns about ownership and control, and effects on existing sectors and workforces. After considering this context, the paper explores responsible research and innovation within a major UK research center in synthetic biology. This center is designing and engineering biological parts, devices and systems for sustainable fine and speciality chemicals production, including new products and intermediates for drug development, agricultural chemicals and new materials for sustainable manufacturing. We are part of the center, comprising a responsible research and innovation group that is undertaking research and anticipatory analysis, and supporting collaboration and deliberation to prepare the centre as it addresses the implications of this emerging new technology in society. As an early component, we are undertaking a benchmark study that is exploring a series of key topics including: (1) Current research and perception/anticipation of applications of center projects within synthetic biology; (2) Perceived challenges, risks and benefits of synthetic biology; (3) Awareness and understandings of responsible research and innovation; (4) Practice and activities related to responsible research and innovation; and (5) Perspectives on how responsible research and innovation should evolve and what the center should do. The benchmark study draws primarily on interviews with center researchers, but also uses secondary data sources, group discussions, and engagement with external stakeholders. We draw comparisons with other relevant studies. Based on our analysis, we consider implications for the design and operationalization of responsible innovation at both center and larger levels, including reflecting on how our own research program. The paper will draw out broader implications for the practice of responsible research and innovation that will be relevant not only for synthetic biology but also for other emerging technologies.

ABSTRACT. With the implementation of the strategies of building an innovation-oriented country, the government has successively launched a series of talent-attracting programs, in particular the Thousand Youth Talents Program (TYTP) launched in 2011. Such effort has yet to get the very best of the Chinese academics to return from overseas fulltime (Cao, 2008; Zweig and Wang, 2013).

Several reasons explain this phenomenon. Besides low salaries, education for children, and jobs for spouses, more important are the institutional factors, including political liberalization and freedom in doing research (Cao, 2008), research culture (Cao, 2008; Shi and Rao, 2010), non-transparent decision making and a relatively stifling bureaucracy (Zweig and Wang, 2013).

However, Zeithammer and Kellogg (2013) report that the reason that Chinese doctoral graduates chose to remain in the U.S. is the large salary disparity between the two countries rather than other factors. And Wang et al. (2014) find that the role of economic incentives cannot be underestimated in the successful recruitment of overseas top talents, while the role of networking is more subtle.

Our study contributes to the existing debate by looking into whether there is a fit between overseas talent and the organization that attempts to recruit them and particularly analyzing the role of government’s talent-attracting programs in the fit process, which is critical to understand academics migration.

We use young returnees through TYTP to illustrate this. Young scholars have to apply to the program through domestic academic organizations, which indicates that they could adopt the domestic political, economic and academic environment and have a firm plan to return. Then, through a strict selection process, scholars are selected into TYTP, which means their performance meets the program’s requirement. Thirdly, most of the TYTP scholars do return on the fulltime basis. Therefore, the group of TYTP scholars shows not only “who” wanted to return but also “who” China has gained.

We base our analysis on the demographic information of the TYTP scholars and their academic achievement. Using survival analysis where returning of young academics is treated as a “survival” event, we attempt to examine the factors that could account for and impacts of the factors on the probability of their return. Specifically, we focus on two factors – returnees’ academic ability and personal relations. Academic ability includes a returnee’s academic ability proxied by his/her position before returning and his/her last employer’s academic reputation proxied by their academic ranking; personal relations include the returnee’s academic relationship proxied by whether he/she works for his/her alma mater, family relationship proxied whether he/she returns to his/her hometown. Control variables include age, gender, the country of Ph.D., the number of years between receiving their Ph.D. and selected into TYTP and so on.

Based on a small sample of 183 life scientists (the first four cohorts of TYTP returnees between 2001 and 2002), we have reached some preliminary conclusions. Scholars with stronger academic ability tended to stay overseas, and domestic academic organizations’ reputation has less influence on attracting returnees. Scholars’ academic relations and family relations are not significantly associated with their return. Age and gender are significantly, older scholars tended to stay overseas while male tended to return. Therefore, we tentatively conclude that the motivation for returnees is the pressure of overseas academic job market rather than the incentives offered by government’s programs. As scholars who lack competitive advantage tended to return and China has gained those scholars who have to return, TYTP and similar programs may function as “the icing on the cake.”

References Cao C.. China’s brain drain at the high end: why government policies have failed to attract first-rate academics to return. Asian Population Studies, 2008, 4 (3):332−345. Shi Y., Rao Y.. China’s Research Culture. Science, 2010, 329 (5996):1128. Wang Q., Tang L., Li H.. Return Migration of the Highly Skilled in Higher Education Institutions: a Chinese University Case. Population, Space and Place. DOI: 10.1002/psp.1855 Zeithammer R., Kellogg P.R.. The hesitant Hai Gui: Return-migration preferences of U.S.-educated Chinese scientists and engineers. Journal of Marketing Research, 2013, 50 (5):644−663. Zweig D., Wang H.. Can China bring back the best? The Communist Party organizes China’s search for talent. The China Quarterly, 2013, 215 (September):590−615.

ABSTRACT. This paper provides an historical perspective on China’s changing science and technology landscape, culminating with a discussion of the effects of Xi Jinping’s anti-corruption campaign and increasing emphasis on nationalist themes, including proposals for education reform along Chinese (as opposed to Western) lines. It draws on a decade of research in China, including a survey of Chinese academics and follow-up interviews at leading Chinese universities conducted during the spring of 2015.

In 2006, the Chinese government launched its National Medium- and Long-Term Plan for the Development of Science and Technology 2006-2020 (hereafter MLP), making “indigenous innovation” the top developmental priority. The broad goal of the MLP is to transform China into a technology-focused economy by 2020, and a global leader in R&D, science, and product innovation by 2050. Key thrusts of the MLP include significantly increased public investment in basic research as well as applied R&D, building science parks and research centers, funding focused venture capital funds, and recruiting prominent expatriate scientists and entrepreneurs from universities and businesses abroad through such initiatives as the Thousand Talents Program and the Thousand Young Talents Program. The MLP’s effort to foster indigenous innovation has been reinforced by China’s 11th and 12th Five Year Plans, which – in an effort to transition from “made in China” to “designed in China” – identify a number of “strategic emerging enterprises” such as biotech and new materials, and increasing the percentage of GDP that is invested in research. The intention is to wean China from its dependence on foreign technologies, enabling domestic advances in science and technology to drive product innovation - to move China from imitation to innovation.

Can such state-driven efforts succeed? At first glance, the results of China’s efforts are impressive. The MLP has resulted in the construction of numerous high-tech science parks, a dramatic increase in scientific publications by Chinese authors, and a substantial increase in the number of patents obtained by Chinese scientists and engineers. Yet China’s investment in research and development has yet to pay off in product innovation; as a study conducted by the present author concludes, paraphrasing an old Chinese saying, “research is high and the market is far away” (Cao, Appelbaum, and Parker, 2013). Moreover, reforms being initiated by the Xi government may prove to have a major effect on China’s high-tech ambitions.

In a much commented 2012 speech at the National Museum’s “Road to Rejuvenation” exhibition, soon-to-be President Xi set forth the path that is defining his decade as China’s leader: “I believe that realizing the great revival of the Chinese nation is the greatest dream of the Chinese nation in modern times.” One step in realizing this dream has been the weeding out of corruption, a widely popular move by the Xi government that, at least in the short run, may adversely impact China’s efforts to become a world technology leader. The “Eight Points” anti-corruption campaign has disciplined well over 100,000 party officials, ranging from local bureaucrats to top figures in the Chinese Communist Party. While this is intended to improve economic efficiency, it has also greatly amplified uncertainty in an economic system that historically has run on bribery and guanxi relations. As a result, many government officials are reportedly refusing to approve investment projects, either because in the absence of a bribe they lack the incentive to do so, or out of fear that they will be accused of corruption. One survey of local officials by Caixin, a Beijing-based media group, found that two-thirds of those surveyed “were reluctant to take decisions for fear of ‘doing something wrong’…. Whatever can be delayed will be delayed” (as reported in Fitzgerald, 2014). Moreover, the Xi government has recently sought to reign in “western thinking” in China’s universities, calling on the CCP to strengthen ideological control over what goes on in the classroom. In January 2015 the Party issued a document titled “Suggestions on further strengthening and improving ideological propaganda among tertiary institutions under new circumstances,” which called for “Turn[ing] our universities into a stronghold for learning Marxism” through “management of the use of western teaching materials” (Wang, 2015).

While it is clearly too soon to tell what effect these changes will have on indigenous innovation, it does not seem likely to encourage the kind of critical thinking, experimentation, and risk-taking that are necessary for indigenous innovation to succeed.

Works Cited

Cao, C. Appelbaum, R. and Parker, R. 2013. “Research is High and the Market is Far Away.” Technology in Society 35: 55-64

Wang, Ding. 2015. “A Mini Cultural Revolution is Storming Mainland Campuses,” eiinsight (February 6)

ABSTRACT. Despite its impressive achievements in science and technology (S&T), China’s S&T system has been criticized for its inefficiency in macro coordination, funding distribution, and performance evaluation. To fix the problems, numerous scholarly studies have been focused on the effectiveness of existing policies, yet much less effort has been devoted to the analysis of China’s S&T policymaking process.
This paper posits that, in searching for good policies, a thorough investigation of policy formulation and enactment is as important as studies of policy implementation and evaluation. Although studies of the western liberal democratic systems in policy process in general and S&T policymaking in specific have offered insights, they are not sufficient in mapping policymaking in China as China possesses a much different political system.  

This study aims to contribute to the literature by exploring patterns of S&T policymaking in China, with a focus on behaviors of major actors in the policy process. Under the Chinese system, the National People’s Congress is supposed to establish laws; its power, however, is stronger on paper than in practice. Rather, the State Council is the highest level of executive and has legislative power; it enacts most S&T policies. Ministry-level public agencies such as the Ministry of Science and Technology (MOST), Ministry of Education (MOE), and the Chinese Academy of Sciences (CAS) are often responsible for policy designs and implementations. Elite scientists such as the members of CAS occasionally propose policy ideas directly to the top leaders of the central government. Overall, the Communist Party dominates the state and society in every aspect with its power resting on all government institutional settings. 

Among various policy process models, Kindong’s policy window model seems to be the best-fit for this paper. The model encompasses three separate streams: (1) the problem stream resulting from assessment of relevant indicators or from focusing events; (2) the policy stream providing possible solutions to the problems; and (3) the political stream when key politicians are willing make policy changes. A window of opportunity is open when the three streams converge, possibly leading to formulation of new policies. Policy entrepreneurs may emerge from any of the three streams and act as advocates for policy proposals. They seize the policy window opportunity and push policy solutions onto the list of government decision agenda.  

This paper uses the case study method and employs the macro-meso-micro conceptual framework.  At the macro level, policies are contextual in essence, and serve as guidelines or roadmaps. Macro policies include guiding principles, medium and long term plans, and institutional settings. At the meso level are those transactional policies allocating resources through budgeting and research funding systems to implement macro policies, such as national research programs and master grants. The micro level policies deal with operational issues, such as personal assessment and research project evaluation.

Multiple policy cases are selected and analyzed with the emphasis on the dynamics of decision making and the role of policy entrepreneurs. Cases at the macro level include the development of the medium and long term science development plans and the formulation of the national guiding principles; at the meso level, the 211 project, the 985 project, and the Knowledge Innovation Project are used as cases of master grants, and the 973 Program, the 863 Program, and the new design of national research programs are to be analyzed as cases of research grants; at the micro level, two cases, the determination of social status of science workers and the One Thousand Talent Program are examined.  

As a result, several patterns are revealed. First, the Chinese policymaking process clearly follows the elite model, where participation from ground scientists, the public, and the media is very limited. Second, decisions are made largely by the central government core leaders and decision making relies heavily on the leaders’ personal vision and judgment.  Third, these core leaders sometimes act as policy entrepreneurs. Fourth, elite scientists and Ministry-level governments are also active as policy entrepreneurs. Fifth, as a late comer China draws heavily from international experience in seeking policy solutions to form the policy streams.  Sixth, availability of political streams is often the key to the successful policy making. Finally, problem streams result more from indicators than from focusing events.

ABSTRACT. In the emerging knowledge economy, science and technology has become the key factor of national competitiveness. Studies consistently report that as an unintended consequence of the accelerating R&D investment and expansion of scientific research activities, scientific misconduct fraud is becoming a pervasive global phenomenon. [1],[2] Given its deleterious consequences such as providing misinformation, endangering scientific integrity, and wasting public resources, scientific misconduct has become a rising concern of policymakers and drawn growing public attention. [3],[4] Indeed, there is a pressing need to inform public discussion and policy-making, both within the research communities and the society at large about the patterns, dynamics, identification and deterrence of scientific misconducts.

Notwithstanding the greater emphasis placed by governments our current understanding of scientific misconducts is limited. [5]There are few empirical studies that provide evidence-based policy advice on research integrity. [6] And this is particularly true for China, a fast rising scientific power, and a major country with an increasing number of paper retractions due to scientific misconduct. [7],[8],[9] To fill up some research gap this paper adopts a mixed research design approach, combining information obtained through bibliometric analyses, a large-scale survey, and in-depth interviews, to develop a comprehensive and nuanced understanding of the scientific misconduct phenomena. Specifically, the paper focuses on the following aspects.

The study begins with constructing a global dataset of fraudulent publications to explore the features, status quo, and dynamics of retractions due to scientific misconducts. Based on citation indicators and keywords turnover the study examines the impact of retraction on research visibility and research trajectory of primary authors of the retracted articles. Secondly, utilizing CV data of misconductors and their matching peers, this study profiles the characteristics of about 110 Chinese recidivists. Thirdly, using a large-scale survey and face-to-face interviews of scholars, research managers and journal editorials, the study attempts to understand the intertwined triggers of scientific fraud from individual, institutional, and cultural contexts such as the unawareness of research fraud, university promotion mechanism, the ignorance of intellectual property so on. . Policy implications on constructing research integrity practices in contemporary China will be proposed.

Selected References [1] Fang, F. C., Steen, R. G., & Casadevall, A. (2012). Misconduct accounts for the majority of retracted scientific publications. Proceedings of the National Academy of Sciences, 109(42), 17028-17033. [2] Steen, R. G., Casadevall, A., & Fang, F. C. (2013). Why has the number of scientific retractions increased? Plos One, 8(7), e68397. [3] Necker, S. (2014). Scientific misbehavior in economics. Research Policy, 43(10), 1747-1759. [4] Jeffrey L. Furman, Kyle Jensen, Fiona Murray. (2012). Governing knowledge in the scientific community: Exploring the role of retractions in biomedicine. Research Policy, 41(2), 276-290. [5] Servick, K. (2015). Targets of misconduct probe launch a legal counterattack. Science, 347(6217), 13. [6] Pozzi, A., & David, P. (2007). Empirical realities of scientific misconduct in publicly funded research: working paper, Stanford University. [7] Hvistendahl, M. (2013). China’s Publication Bazaar. Science, 342(29), 1035-1039. [8] Yang, W. (2013). Research Integrity in China. Science, 342(29), 1019. [9] Steen, R. G. (2011). Retractions in the scientific literature: do authors deliberately commit research fraud? Journal of Medical Ethics, 37(2), 113-117.

ABSTRACT. In the 1980s, the Chinese government started to invest heavily on infrastructure to facilitate transfer and dissemination of research and technological achievements. One of the most important measures was to establish National High-tech Industrial Development Zone (NHIZs) within given geographic boundaries. Since the early 1990s, the State Council has approved a total of 110 NHIZs nationwide. NHIZs are centrally governed by the Ministry of Science and Technology while also regulated by local governments and other ministries, manifesting the features of state-led innovation.

Changing development strategies and roles of STI in economic growth have made NHIZs science park, industrial zone, industrial cluster or smart city in different times. They have fostered high-tech industrial improvement and economic growth, strengthened the sharing and dissemination of knowledge within a local innovation system and formed a more coherent innovation infrastructure. However, the most important reason why NHIZs could survive is their taking advantages of policies in land usage, investment, taxes, finance, S&T, innovation, and talent, whose combined effects have made NHIZs superior to ordinary regions and districts. Each NHIZ operates under an administrative committee affiliated with local government, which gives the NHIZ strong implementation power.

Recently there is a shifting in the spatial geography of innovation worldwide. The domination of Silicon Valley-type suburban corridors with spatially isolated corporate campuses are being replaced by a new emerging urban model. In these geographic clusters, or “innovation districts,” leading-edge anchor institutions and companies connect with start-ups, business incubators, and accelerators, while offering mixed-use housing, offices, and retail outlets. Such innovation districts appeal to high-qualified talent to generate excellent science and to carry out entrepreneurial activities. China’s NHIZs could also be conceptualized as “innovation district” given their similarities to their counterparts in the US and Europe.

Against such global and domestic backgrounds, this paper will explore the evolution path and mechanism of NHIZs as well as their contributions to China’s high-tech industrialization, national S&T and innovation system as well as economic growth. Zhongguancun NHIZ in Beijing, Shenzhen NHIZ and Hangzhou NHIZ will be selected as sites for case studies taking into account not only their respective geographic locations but also their distinctive administrative regimes. A novel framework will be formulated to take into consideration of their development stages, bureaucratic level and structure of administrative committee, constituting enterprises, development of the dominant industry, main sources of STI capability and finance and innovation performance. Based on document analysis and in-depth interviews, the paper will provide a more comprehensive understanding of China’s innovation trajectory and dynamics through the angle of NHIZs.

Firstly, there is an urgent need to revisit the dominant innovation modes in the world, to which China’s innovation practice may have something new to offer.

Secondly, NHIZs once were overall regulated by a strong state. Now, some of them have become more autonomous with less administrative interventions. This paper will interpret the way and practices through which innovation agents produce novel interspaces under the given structural conditions.

Thirdly, the formulation of innovation policy needs to integrate the innovation chain from R&D, industrialization to marketing. NHIZs could provide evidence of how to integrate the collaborations among often contradicted stakeholders.

ABSTRACT. A central theme in science policy concerns how to direct public research resources for tackling emerging global challenges, such as climate change, antibiotic resistance or rapidly-spreading disease. Faced with public pressure to produce “quick” and “useful” results, public research must rapidly develop new programs or reallocate funds, while facing pressure to ensure that the investments represent “value for money”. But pathways to tackle these “grand challenges” are associated with high levels of uncertainty and disagreement among stakeholders (Ely, van Zwanenberg & Stirling, 2014). Avian influenza is an interesting case study in this respect, having been extensively studied as a public health issue, particularly in terms of how dominant national and international institutions have had an impact on control measures (Scoones, 2010). But very little is understood about the associated massive research undertaking in particular. Yet for science policy scholars and practitioners, there is value in taking stock of avian influenza research in order to understand how key stakeholders view the problem itself, the overall research effort to “tackle” it, and the institutions that dominate how new knowledge is produced and used.

When faced with a perceived serious risk of a global avian influenza pandemic beginning around 2003, substantial research funding from a variety of different organizations was made available to mitigate the risk of future global pandemic. Since then, avian influenza research has flourished, but has become a highly contentious issue in science policy, with extensive debates surrounding various types of research (the effectiveness of antivirals, the dangers of “gain-of-function” experiments, etc.). A myriad of national and international institutions – from scientific journals to public health authorities – have had a significant impact on how research priorities are set and, more importantly, carried out (Laudel & Gläser, 2014). Based on stakeholder interviews and scientometric analyses, our study sheds light on the gap between the health policy expectations and public research on avian influenza and on the role of different institutions in mediating or accentuating this gap.

First, we explore the divergences in narratives of “global risk” among stakeholders that determines how they perceive mitigation as expected outcomes of research. In addition, we reveal that the problem itself can be viewed in a myriad of ways: political, scientific, technological, etc. These narratives expose inherent ambiguity in how “avian influenza” is often defined as a single research problem. Second, we look at the often polarized worldviews of the research enterprise related to avian influenza. We find strong divergences in how the research enterprise is perceived: areas of influenza research (vaccine development, epidemiology, etc.) cannot be reduced to a series of agreed-upon options for possible resource allocation. In addition, stakeholders generally view the research landscape in narrow terms and distinct areas of research are associated with strong normative views (e.g., related to “rigor”). Overall, we show how it is problematic for stakeholders to link research to outcomes and to explicitly assess possible research priorities.

To understand these challenges, we turn our attention to the institutional conditions that dominate the undertaking of research in the area. In particular, we focus on the pressures related to scholarly publishing, public funding mechanisms, mandates of public research-based organizations and private-sector financing. Our findings point to systematic institutional biases embedded in the avian influenza “research enterprise”. We show how each of these pressures accentuates the gap between the expected outcomes and research (Nightingale & Scott, 2007). For instance, biomedical research seen as “excellent” or “innovative” is often favored over studies focused on local epidemics that receive less attention. Our work points to the need to revisit the governance of avian influenza research, by “unraveling” the problem itself and moving towards a pluralistic approach to risk mitigation. It also highlights problems related to coordination and dialogue among the most influential organizations associated with avian influenza.

References Ely, A., Van Zwanenberg, P., & Stirling, A. (2014). Broadening out and opening up technology assessment: Approaches to enhance international development, co-ordination and democratisation. Research Policy, 43(3), 505–518. Scoones, I. (Ed.). (2010). Avian Influenza: Science, Policy and Politics. London: Routledge. Laudel, G., & Gläser, J. (2014). Beyond breakthrough research: Epistemic properties of research and their consequences for research funding. Research Policy, 43(7), 1204–1216. Nightingale, P., & Scott, A. (2007). Peer review and the relevance gap: Ten suggestions for policy-makers. Science and Public Policy , 34(8), 543–553.

ABSTRACT. How do converging sciences and technologies − biology, the wider life sciences, and information technology, analytics and big data − intersect with current and future geo-strategic drivers in traditional security, including limiting proliferation of biological weapons and preventing bioterrorism? This paper will examine potentially disruptive advances at the intersection of big data and analytics within the life sciences. These advances are coming from large universities and major industrial firms, from agriculture to software and small start-ups - and are increasingly accessible to the public. What are the national and international security implications of the application of big data analytics in the life sciences to issues of bio-security and proliferation? What biological security risks, from traditional state-based programs through non-state actors to lone-wolf terrorists, might be associated with big data and analytics? What challenges and opportunities exist to prevent harmful uses of big data in the life sciences? How can the US and international communities respond to maximize beneficial outcomes and minimize malevolent uses? What national & international biological security risk prevention and mitigation approaches are needed to address the security risks posed? What capabilities could big data and analytics offer to national and international biological security?

For the last decade, the US and other nations have been grappling with limiting the threat of “Biotechnology in an Age of Terrorism,” (NAS National Research Council of the National Academy of Sciences, National Academies Press, 2004). Threat anticipation as biotechnology converges with information and communications technologies and cybersecurity through big data analytics is needed that differentiates among basic research, applied research, advanced development, and commercialization; incorporates innovation patterns; differentiates different types of technologies; balances ‘hope’ and ‘horror’ hype narratives that emerge from industry, the public sector, and other sources; integrates political, social, psychological, and economic factors; and is technically robust but not technologically deterministic.

In order to begin to understand the potential threat at the intersection of big data and biology, notional proliferation scenarios in which the use of big data analytics in combination with biotechnology or other facet of the life sciences are intended to be used to do harm will be considered. In order to present a fuller case and illustrate the dual use applications of helpful and non-harmful purposes, corresponding benefit scenarios will also be included.

To assess differential risk and threat from a capability like that potentially enabled by the convergence of biotechnology with big data analytics, the material and non-material characteristics need to be considered. One such framework is given in Scheme 1 below, in which risk can be defined as a product of probability and consequence. The magnitude of effects if a target is attacked is the consequence, whether in lives, economic costs, or other effects. Probability is a function of the threat and the likelihood of a specific attack being perpetrated against a target. In turn, threat is a measure of factors effecting actors in a position to use the technology; it is a function of capability, vulnerability and motivation.

Scheme 1. Threat = F [capability, vulnerability, motivation] Probability = F [threat, likelihood] Risk = Probability x Consequence

The three scenarios will present short vignettes of how the convergence of biology with big data may enable new means (capabilities) to cause harm. For each, there is requirement for significant access to materials and infrastructure, as well as a high level of both theoretical and tacit knowledge required. For each, the United States and allies have significant vulnerabilities. There certainly may be motivation by adversary states and non-state actors to use weapons of such nature. Factors to be evaluated will include how likely the each distinct threat is, which impacts the risk, is the crucial aspect, technical challenges and operational limitations, whether done using traditional biological means or leveraging the current capabilities of big data, and how the ability to use big data may decrease the time and effort required to achieve scenarios. Underlying it all is the important question of motivation as to why an adversary would choose to pursue such a route and when. Looking forward into the future, as big data and biological science breakthroughs occur, these limits on actors will change. Proper threat assessment walks the line between what is probable/likely now, as well as what is reasonably realistic in the coming 10-15 years.

ABSTRACT. Access to biological materials for research is a critical component of scientific research undertaken in government, universities and industry in the United States and many other countries. In the science policy literature, access to resources is recognized to be a key rationale for research collaboration (Katz and Martin, 1997; Melin, 2000; Thorsteinsdottir, 2000; Beaver, 2001; Heinze and Kuhlman 2008; Wagner, 2005). Bozeman and Corley (2004) show that scientists use collaboration strategies to enhance their ability to obtain resources and van Rijnsoever et al. (2008) find that scientists strategically offer resources to others in exchange for access to knowledge, visibility, reputation or other benefits that help them gain competitive advantage.

Access to biological material is related to bioprospecting activities of seed, pharmaceutical and cosmetic companies and has been subject to fierce global political debates over equity and ownership since the early 1980s. These debates led to new regulations on biological material exchange that affect access to biological material by the research sector. Despite attempts to negotiate regulatory exemptions based on the need to increase knowledge about biodiversity, the new regulatory requirements are affecting the ability of biologists, geneticists and others to access research materials previously considered to be freely available (Young et al., 2006; Rosegrant and Cline, 2003; Byerlee and Dubin, 2009; Cock et al., 2010; Chakraborty and Newton, 2011).

Researchers are increasingly required to negotiate access to material property with national governments and their representatives, to acquire and use materials. Moreover, the institutional controls are not uniform; they depend upon the ways in which individual countries and national institutions interpret and implement the treaties, and the type of biological organism of interest. For example, countries such as Brazil or the Philippines enacted restrictive legislation to manage access and use of materials in response to the Nagoya Protocol (NP) to the CBD. The US has strict national laws that control access and exchange for biosecurity, ecological and phytosanitary reasons.

While some work has examined the role of material transfer agreements on exchange (Rodriguez, 2007) and proprietary issues (Shibayama et al. 2012) prior work is either limited in scope, generally assume that scientists are autonomous actors and have control over the material resource they exchange and use, fail to recognize the range of potential institutional constraints on access and use of materials, or have not looked at the effect of resource constraints on collaboration or collaboration outcomes. In general, the science policy literature has not effectively addressed the fundamental role that resource constraints play on science collaboration and outputs.

In response, this paper aims to better understand the extent to which scientists are facing constraints on access to biological material for research and the factors that predict events of non-access to biological materials, which we call material exchange blockage or ‘blockage’ for short. We ask: To what extent are scientists experiencing constraints on access to biological materials? What factors explain the blockage of the flow of biological materials for scientific research? To what extent can blockage be attributed to regulatory institutions, characteristics of the biological material, scientists’ attitudes or relationships among suppliers and providers?

The paper makes use of survey data collected from agricultural scientists in industry, government and universities in the US on access, exchange and use of non-plant biological material inputs for research. It first develops a conceptual framework for understanding how material, regulatory, compensation, relational and individual characteristics predict blockage of biological material flows for research. Framework derived hypotheses are tested using survey data on exchange and use of biological material inputs for research collected from a national sample of scientists in the US. Findings show the value of the biological material is strongly significant positive predictor of blockage. Monetary compensation is not associated with blockage; however, non-monetary compensation is negatively related to blockage and positively related to the value of the material for research. Regulatory constraints do not directly constrain access. However, national regulatory barriers are indirectly associated with blockage through the value of the biological material. Overall, the findings demonstrate that access to biological materials is determined by a complex interaction among institutional, material and individual behavioral factors. Conclusions discuss implications for policy related to biological material access, exchange and use, and suggest future directions for research on this topic.

ABSTRACT.             Scientific advances in diverse fields ranging from stem cell research to nanotechnology increasingly pose challenging ethical questions. At the same time, efforts to strengthen the competiveness of the science, technology, engineering and mathematics workforce are viewed as crucial to maintaining the nation’s economy. Relatively little is known, however, about how working in contentious fields shapes students’ experiences in graduate school and the development of their careers. This paper seeks to contribute to efforts that address this gap in knowledge and advance our understanding of the frequency, severity and nature of ethical controversy that doctoral students in the life sciences encounter associated with their research. More generally, this effort aims to contribute to our understanding of how the increasingly common contentious nature of modern science and technology may shape the next generation of scientists.

            Substantial research addresses research ethics from the perspective of research integrity, focusing on issues such as falsification, fabrication, and plagiarism.  These important issues often go by the term “micro-ethics.”  In this paper, we focus on a different, broader set of issues – sometimes termed “macro-ethics” – that address questions about the use of specific research tools, model organisms or even the appropriateness of entire lines of scientific inquiry. In our analysis, we analyze novel data from the first wave of a nationally representative panel survey of more than 2,000 doctoral students – primarily drawn from fields in the life sciences – in the United States.

            In particular, we examine two novel measures of ethical controversy in research – personal and public ethical controversy – designed to assess whether doctoral students’ research raises ethical concerns for them personally or if they believe their research would raise ethical concerns for the general public (if the public was aware of the research).  For each measure, we examine both the frequency of such ethical concerns as well as their severity.  In addition to descriptive statistics illustrating the frequency of these sorts of ethical controversy, we also present regression analyses seeking to identify the predictors of ethical controversy among life science doctoral students.

            Overall, our results suggest that ethical controversy is an important contextual factor affecting students’ experiences in graduate school.  We conclude by discussing the implications of these findings for STEM education.

ABSTRACT. This paper investigates the development of evaluative bibliometrics (EB) from 1972 to 2013 using a sociology of professions framework. According to Abbott (1988), a defining feature of modern professions is the application of abstract knowledge to practical cases through processes of diagnosis, inference and treatment. Each profession requires an abstract knowledge system as a precondition for claims to professional jurisdiction in a socially contested domain. Consequently, professions have close relations to academic fields which produce abstract knowledge, introduce neophytes, and legitimize claims to professional authority.

The past two decades have witnessed a strong increase in the demand for and production of quantitative research assessment. Yet does this signal the formation of a new profession in the domain of research evaluation? The paper analyses the development of the academic specialty of EB as a precondition for the professionalization of research assessment practice. Since Abbott does not analyse science and engineering, we use Whitley's (2000) definition of reputational work control in academic fields as a complementary concept; he argues that the establishment of a scientific specialty implies a certain closure towards contributions from outside. Thus, requirements of specialized expertise and perspective constitute thresholds for field access.

The specialty of EB is delineated using research breakthroughs and follow-up research on these breakthroughs as a dynamic field definition (Heinze et al. 2013). Garfield's journal impact factor (1972) and the Hirsch-index (2005) are two most influential citation indicators ("breakthroughs"). In addition, we found 61 citation indicators that were introduced as follow-up inventions between 1972 and 2013. All publications in Web of Science that cite any of these 63 indicators (“flag articles”) are included in our definition (3153 publications).

The paper argues that the establishment of reputational work control is a strong indication for the institutionalization of an academic specialty. Reputational work control is operationalized, first as the “number of new entrants to the field”, and second as the “origin of invention”, which distinguishes whether follow-up inventions originate from the core or the periphery of EB’s longitudinal citation network. We define the following variables: number of institutions in core, periphery and outside of EB (V1), number of stable field members (V2) and new field entrants (V3), and contributions of core versus peripheral institutions to follow-up invention (V4).

Existing approaches for the analysis of core-periphery structures in social networks cannot be used for longitudinal analysis of small fields. Therefore, we use Characteristic Scores and Scales (CSS), an approach developed to define groups of ranked observations in highly skewed distributions (Glänzel & Schubert, 1988). We apply CSS to network indegree of institutions per year resulting in simple group definitions of core, periphery and outside that are consistent over time. Using five-year citation windows, we analyse the development of field structure over four decades (1972-2013).

We find two distinct periods in terms of the number of new entrants: a period of moderate field growth from 1972 to 2004 and a period of rapid expansion from 2005 to 2013. The percentage of new entrants is persistently high. During the first three decades, more inventions originate from the periphery and outside than from the core of the academic field, a clear indication of low thresholds for field access and weak reputational work control. During the expansion period since 2005, there is still a substantial inflow of inventions from periphery or outside, but two thirds of follow-up inventions originate from the network core. This indicates an intensified methodological discourse among an enlarged set of core institutions in the latter period.

We conclude that the academic specialty of EB has permeable boundaries which limit its ability to underpin professional jurisdiction in quantitative research evaluation. The most significant invention from outside was the introduction of the Hirsch-index which contributed to field expansion and an intensified methodological discourse. The paper introduces a new approach to bibliometric mapping of EB and connects bibliometric methods with sociological analysis.

References

Abbott, A. (1988). The system of professions: An essay on the division of labor. Univ. of Chicago Press.
Glänzel, W., Schubert, A. (1988). Characteristic scores and scales in assessing citation impact. Journal of Information Science, 14(2), 123–127.
Heinze, T. et al. (2013). New patterns of scientific growth. How research expanded after the invention of Scanning Tunneling Microscopy and the discovery of Buckminsterfullerenes. JASIST, 64: 829–843.
Whitley, R. (2000). The social and intellectual organization of the sciences. 2nd ed. Oxford Univ. Press.

ABSTRACT. Science, technology and innovation (STI) policy research and policy analysis are two relative new specialties in the field of public policy. The first one originated based on discussions among natural scientists, philosophers and social scientists about the social relations of S&T. Simultaneously, growing demands for evidence based policy generated governmental requirements for data to make informed decisions, particularly for the design of research instruments and policies(Morlacchi & Martin, 2009). The second one, policy analysis, seems to be related to the problem-solving methodologies and approaches needed to create, critically asses and communicate useful information for understanding and improving policies (Dunn, 2004). Both of them are “intellectual technologies” which facilitate the framing of STI policy problems. The literature on innovation systems recognizes that designing new policies and instruments on STI has been influenced from the supply-side (Borrás & Edquist, 2013; Velho, 2011). Particularly in Latin America and south countries, this type of STI policies lack legitimacy and are seen as an endless re-negotiation of the limited public resources for R&D. It is necessary to widen the approach and the scope of innovation policies and conceiving them as social policies (Arocena & Sutz, 2010). Increasingly, the social process behind designing and implementing policies has become an issue of academic interest (Borzel, 1998). In the case of STI policy, the consolidation of academic communities had an important role in shaping the objects of interests, the reachable and assessable goals and the incentives articulated through the regional policy(Serafim & Dias, 2010). The creation of STIPA, (STI Policy Research Network of the Americas) demanded the identification of scholars whose research trajectories would provide valuable insights. We are interested in how STI policy research could provide a theoretical foundation for policy planning and how policy analysis could strengthen the links between policy-makers, policy scholars and knowledge users. A bibliometric analysis to identify how Latin-American scholars are approaching policy research and analysis and what type of theories and methodologies are they using would provide a point of departure for this exploration. Relying on bibliometric methods, we searched for this community of interest in two steps. First, following a top-down approach we identified relevant scientific journals in research and scientific policy. We searched issues of the last five years (2008-2013) for contributions whose authors were affiliated to Latin America and Caribbean (LAC) countries in the moment of publication. Next, we followed a bottom-up strategy and searched for the networks were the identified authors play a role. We collected all the contributions of the identified corpus of LAC authors in order to provide inputs to the following questions: - Is there an academic community, or invisible college with cohesive ties contributing to our understanding of STI policy? - What are the institutions behind these authors? Are the identified authors from universities? Or do they belong to institutions in the interface between science and policy? (Newman, 2011). - From a deeper content analysis, what topics are highlighted in the identified contributions? What are the problems articulated? Are solutions proposed? What kind of theoretical approaches and methodological instruments are they using in their analysis? Is there evidence of a dialogue between the academia and the policy makers? Tensions? - Do the study of references and citations articulated in the identified contributions useful to provide insights into the conceptual frameworks schools of thought, or geographic influences of the LAC authors in these topics?

References

Arocena, R., & Sutz, J. (2010). Weak knowledge demand in the South: learning divides and innovation policies. Science and Public Policy, 37(8), 571–582.

Borrás, S., & Edquist, C. (2013). The choice of innovation policy instruments. Technological Forecasting and Social Change, 80, 1513–1522.

Borzel, T. (1998). Organizing Babylon — on the different conceptions of policy networks. Public Administration, 76: 253–273.

Dunn, W. N. (2004). Public Policy Analysis. An introduction. Pearson Prentice Hall.

Morlacchi, P., & Martin, B. R. (2009). Emerging challenges for science, technology and innovation policy research: a reflexive overview. Research Policy, 38, 571–582.

Newman, J. (2011). Boundary troubles: working the academic–policy interface. Policy and Politics, 39(4): 473-484.

Velho, L. (2011). La ciencia y los paradigmas de la política científica, tecnológica y de innovación. In Estudio social de la ciencia y la tecnología desde América Latina (pp. 99–125). Bogotá: Siglo del Hombre.

Serafim, M.; Dias, R. (2010). Brazilian science and technology Policy: Why is it not socially oriented? Central European Journal of Public Policy, 4(18).

ABSTRACT. Effective coordination of research and innovation policies has recently become an increasingly important challenge for governments across the globe (e.g. Magro et al. 2014). This call for coordination stems from several sources such as internationalisation, the growing importance of innovation for economic growth, the increasingly horizontal nature of policy problems as well as the simultaneous widening and deepening of innovation policies.

High-level research and innovation policy councils have traditionally been an important institutional mechanism to promote policy coordination in particular in Europe but also globally (OECD 2012; 2009). Although the councils’ composition, tasks, position and role differ in different countries, they often assume several important functions such as strategic advice to the government on S&T issues, planning and allocation of the R&D budget and providing strategic intelligence to the research and innovation system (OECD 2012). In the 2000s, due to the quest for coordination, the role of these councils has become increasingly salient (e.g. Pelkonen & Teräväinen-Litardo 2013). In many countries, high-level councils have become key mechanisms through which governments have tried to develop a more strategic approach and to provide leadership and common visions for research and innovation development.

Although the councils’ importance has increased, there is practically no research-based evidence on the effects and impacts of their activities. This paper tackles this gap in the literature by examining the impacts of the Research and Innovation Council of Finland. Headed by the Prime Minister and assuming a broad composition of political decision-makers and key stakeholders, the Council is charged with – among other things – coordinating and consolidating research and innovation policies. In terms of impact analysis, the Council is particularly interesting object of study as it has often been considered as a key element behind the success of Finnish research and innovation policy (e.g. Benner 2003). It has become an international benchmark and attempts have been made to transfer the model to several countries (e.g. Sweden, the Netherlands). Yet, prior to this study there is lack of evidence of the effects of the council’s work.

The paper will develop a conceptual framework for analysing the impacts of high-level research and innovation councils. The framework will take into account the nature of the councils’ activities and their impact logic. In particular, as such councils normally do not have decision-making power nor executive power, their impacts are primarily indirect and “soft” effects that become manifested in other actors’ activities and strategies. Hence the framework will combine perspectives from traditional logic-linear evaluation models (e.g. Lähteenmäki-Smith et al. 2006) and systemic evaluation models that take into account the broader operational environment and interaction therein (Dyehouse 2009). The paper will also draw attention to challenges in impact assessment of high-level policy councils that rely on indirect impact mechanisms in their activities.

In empirical terms, the paper will focus on analysing the impacts of the Research and Innovation Council of Finland during the period of 2005-2012 from three interrelated perspectives: influence on the macro-level development of the national research and innovation system, impact on governmental decision-making and impacts on the development of national R&D funding. In addition, the results will be compared to observations on a few similar European councils.

The paper is based on multiple data sets which comprises 32 personal interviews with key Council members, policy-makers and stakeholders, an extensive survey targeted at Council’s stakeholders (n=473) and document material including e.g. the Council’s reports, minutes of meetings and other policy documents.

References Benner, M. (2003) The Scandinavian Challenge: The Future of Advanced Welfare States in the Knowledge Economy. Acta Sociologica 46:2, 132–149. Dyehouse, M., Bennett, D., Harbor, J., Childress, A. & Dark, M. (2009) A comparison of linear and systems thinking approaches for program evaluation illustrated using Indiana Interdisciplinary GK-12. Evaluation and program planning 32, 187-196. Lähteenmäki-Smith, K., Hyytinen, K., Kutinlahti, P. & Konttinen, J. (2006) Research with an impact. VTT, Espoo. Magro, E., Navarro, M. and Zabala-Iturriagagoitia, J. M. (2014), Coordination-Mix: The Hidden Face of STI Policy. Review of Policy Research, 31: 367–389. OECD (2012) OECD Science, Technology and Industry Outlook 2012. OECD, Paris OECD (2009) Chile’s National Innovation Council for Competitiveness. OECD, Paris. Pelkonen, A. & Teräväinen-Litardo, T. (2013) Convergence and Divergence in Research, Higher Education and Innovation Policies: An Analysis of Nine European Countries. In Erkkilä, T. (Ed.) Global University Rankings. Palgrave MacMillan.

ABSTRACT. This research studies the intersection between Science and Innovation Policy and International Relations; specifically to analyze the use of a new framework to study how actors at the international level are using science and innovation to develop and improve their technological capabilities, pursue national strategic objectives, and improve their geopolitical positions. The main objective of this research is to apply a new theoretical and methodological framework that provides a comprehensive map of the current reality of science, technology and innovation policy within the international system. As an example, in this presentation the new model will be applied in the strategic sector of space technology, and a case study from Vietnam will be used to illustrate the analysis methods and key findings. The analysis uses an interdisciplinary, systems approach and applies systems models to study the main characteristics of science, technology and innovation in international relations. The result is the creation of a new framework to analyze science and innovation policy within the international system that describes and explains the actors, interactions among actors, internal processes, and emergent realities within the process. This research is part of broader collaboration between the Johns Hopkins, and the Massachusetts Institute of Technology.

The world order after the Cold War exhibits a new transitional scenario in which science, technology and innovation have become key factors. At the beginning of 21st Century, these variables are key drivers of economic growth and societal progress. Major actors in the global arena are investing in science, technology and innovation activities, and are using science and innovation policy as an instrument through which seek to build technological capability in order to improve their economic development and geopolitical power. As a consequence, the intersection between Science and Innovation Policy and International Relations is becoming in a new and very significant issue within the international relations agenda.

The use of an interdisciplinary, systems approach to study the characteristics of science, technology and innovation within the international systems is a new and powerful tool. This presentation presents preliminary results of this ongoing research effort, including the following three findings: 1) The creation of a new tool in order to analyze how actors are using science and innovation policy within the global system; 2) A description and explanation of the relevance of the Emergent Properties within the Model to understand the main characteristics of a science and innovation policy at the international level; 3) An application of the new Model to a specific technological sector, namely space technology in a case study of Vietnam.

The Systems Architecture Model is a new methodological tool to analyze the mutual impact between Science and Innovation Policy and International Relations. The new framework considers each science and innovation policy by defining the International Context, Actors, Interactions, and Internal Processes. The context forms the environment of science and innovation policy (i.e. the global international systems). The actors include States, Companies, IGOs, etc.; these actors tend to interact in modes such as Cooperation and Competition. This interaction leads to processes or mechanisms that generate policy via Production, Transmission, and Governance.

The use of the new framework as a new methodological tool also reveals the emergent properties that come up in each of case study. These emerging realities related to science and innovation policy appear in the international system as a result of the relationships among global actors, and these new phenomena are becoming key issues in international agenda. For example, the increasing use of Science Diplomacy as a tool for foreign policy, the relevance of Diaspora in the design and performance of science and innovation policy, the growing gaps in scientific knowledge at international and regional levels, or the ongoing competition among international actors to recruit and hire highly skilled personnel around the world, all are evidence of the intersecting concerns across science, innovation and international relations.

Also, the paper shows how the new model can be applied in a particular case study of science and innovation policy in the Vietnam space program. The main findings show the development of a new science and innovation policy in Vietnam has had a strong international component. This research has found many emergent realities that come up from the Vietnamese science and innovation project which are related to international relations, for example, increasing participation in multilateral intergovernmental fora, developing international training programs for Vietnamese researchers, or the participating in satellite projects in partnership with France, Japan or Belgium

ABSTRACT. This paper studies the structure and behavior of young companies established by entrepreneurs holding advanced post-graduate degrees (Ph.D.). In this respect, the paper explores the formation of new entrepreneurial ventures created by persons who have been previously exposed to academic research for a considerable amount of time, at a bare minimum of three years during the preparation of their Ph.D. thesis. This is a form of academic-related entrepreneurship- defined rather broadly- implemented by graduates of an advanced knowledge background. The paper focuses on the direct or indirect relationship of the founders with university education and research. In this framework, the paper contributes to the study and further understanding of the emergence and growth of Knowledge-Intensive Entrepreneurship (KIE). KIE can be considered as an important transformative mechanism which converts knowledge (tacit or codified) to economic activity.

The paper uses empirical data from a large-scale survey in ten country members of the European Union including the big four (France, Germany, Italy, United Kingdom), small advanced economies in the north (Denmark, Sweden), smaller catching-up country members (Czech Republic, Greece, Portugal), and a candidate member country (Croatia). We look at traditionally classified high-tech and low-tech manufacturing sectors as well as knowledge intensive business service sectors.

New companies can trace their origins to Universities in a variety of direct or indirect ways falling under the term of Academic Entrepreneurship: • “Spin offs that are new ventures dependent upon licensing or assignment of an institution’s IP for initiation (Strict definition used by AUTM in the US). • Companies established by University graduates after the completion of their studies. In this case it is not clear whether the company is based on specific knowledge created and transferred in the University setting or whether the founder(s) accumulated the knowledge they use outside the University in their career trajectory as professionals.

The founding team is considered a very important factor in the creation of new entrepreneurial ventures. In particular, the cognitive base and the educational background of the founders is an important variable for the study of KIE in general and academic entrepreneurship in particular.

In this paper we adopt the broader definition of Academic Entrepreneurship and investigate whether new ventures founded by Ph.D. holders exhibit different characteristics and/or different behavior patterns compared to the rest of the knowledge-intensive entrepreneurial firms established in the same period in Europe. The paper explores the extent to which this form of academic-related entrepreneurship possesses different properties in relation to knowledge sourcing, funding, attraction of postgraduate holders, etc. Our basic premise is that exposure of company founders to university research affects entrepreneurial incentives and behavior in ways that reflect higher levels of creation and use of scientific and technological knowledge and market niche specialization. In particular we look at the educational levels of employees, factors affecting firm formation, funding sources, factors to create and sustain competitive advantage, overall strategic direction, sources of knowledge, and innovativeness by two groups of new companies: those founded by at least one person holding a Ph.D. degree and the rest.

The empirical findings suggest that young European companies whose founders have been exposed to academic research, in the aggregate, indicate a fair degree of similarity in behavior to those whose founders have not had the same exposure. Important similarities between the two groups of companies include: • market focus and offering of novel products or services are the critical factors for creating and sustaining competitive advantage; • main company strategy is to offer unique products and services followed at some distance by exploiting new market niches; • clients are the most important source of knowledge.

However, more careful cross examination reveals for the former group of firms (PhD founders) a picture of: • higher knowledge intensity and innovativeness; • increased awareness of intellectual property protection; • higher dependence on internal R&D and external networks as sources of knowledge; and • more reliance on venture capital funding

The paper subsequently performs the analysis and reports results for three separate groups of sectors: (a) high and medium-high technology manufacturing; (b) low technology manufacturing; (c) knowledge-intensive services.

Important policy implications follow.

ABSTRACT. THIS PAPER IS SUBMITTED AS PART OF THE SESSION PROPOSED BY NICK VONORTAS TITLED "ACADEMIC ENTREPRENEURSHIP"

The National Cooperative Research Act (NCRA) of 1984 (Public Law 98-462) provided incentives for firms to form research joint ventures (RJVs). Those firms that did and that also filed their research intensions with the U.S. Department of Justice (DoJ) received certain antitrust indemnification. The DoJ published those filings in the Federal Register. The filings in the Federal Register have been viewed as the population of U.S. RJVs. Over time, the number of new RJVs followed an up and down trend. The expansion in the number of new RJV continued from the mid-1980s until late-1990s. This upward trend might have been influenced by the cyclical upturn in the business cycle in the United States that began in 1991. RJV activity waned beginning in the mid-2000s. Perhaps this downward pattern reflects an overall industrial trend toward open innovation. I have interacted with and interviewed over time a number of individuals in the firms that formed the RJVs published in the Federal Register. This identification/contact/discussion process began in the late-1980s. My motivation for identifying and nurturing these contacts was to build a database relevant to the formation of an RJV and to track its progress over time in an effort to understand its life cycle and related dimensions of its success and/or failure Of the 1,331 Federal Register filings through 2012, I have collected longitudinal project information on 117 RJVs. This decades-long data collection process was not designed to be random. Rather, I made an effort to identify the founder for all RJVs, but contact information was not always available and even once an individual was identified in the founding firm, his/her willingness to participate in the data collection process often diminished over time. Still, and simply by chance, the resulting sample of 117 RJVs, which I call the National Research Joint Venture Database (NRJVD), is balanced across years and by RJV membership size. I am proposing to investigate empirically covariates with the performance of each RJV. To the best of my knowledge, this paper is unique in its ability to quantify such dimensions: publications, patents, revenues to date and expected for the founding firm, employment growth in the founding firm, and formation of subsequent joint ventures. The covariate of particular interest is whether the RJV has a university as a research partner and the role that the university played in the venture (e.g., faculty member as a researcher, use of university equipment, etc.). A preliminary examination of the data suggests that university faculty involvement is correlated with RJV performance, holding constant characteristics of the RJV and of the founding firm. Assuming that through more detailed analyses this preliminary finding is robust, the paper will conclude with organizational as well as policy recommendations.

ABSTRACT. (NOTE: please consider this paper for the session organized by Nicholas Vonortas entitled "Academic Entrepreneurship"). If you do not approve his session proposal, please consider my paper for other sessions. Thank you.)

The recent financial crisis and an increasingly competitive global marketplace have heightened interest among policymakers and scholars alike regarding the economic impact of university entrepreneurship (Rothaermel et al. 2007). While universities play a well-understood role in the production of new knowledge and human capital (Utterback 1994; Romer 1986), policymakers promote academic entrepreneurship, typically defined as the establishment of new university spinoffs based on faculty research, believing that these new companies will generate new innovations, accelerate productivity, and generate prosperity for regional economies (van Praag and Versloot, 2007; 2006; Shane, 2004).

University spinoffs can be conceptualized as a window through which both the innovation and entrepreneurship contributions of universities are examined (Guerrero and Urbano 2013; Svensson et al. 2011). Technology commercialization among university spinoffs offers an intermediate outcome-based measure of economic development—and innovation—success but its realization by academic entrepreneurs is anything but guaranteed (Druilhe and Garnsey 2004; Franklin et al. 2001; Hayter 2013a; Hayter forthcoming; Mosey and Wright 2007).

Specifically, the entrepreneurial decision and spinoff establishment—a proxy of entrepreneurial propensity—are often tied to factors internal to the university, including faculty motivations (Hayter 2011), internal culture (Feldman and Desrochers 2004), and entrepreneurship education programs (Pittaway and Cope 2007). Contextual factors, especially institutional and regional policies and programs, including science parks, incubators, proof of concept centers, and venture funds, also matter (Blair and Hitchens 1998; Bradley et al. 2013a; Bradley et al. 2013b; Siegel et al. 2003). Finally, the impact of provincial (or state) and national level policies and programs on spinoff success has emerged as an important topic of examination (Shane 2004; Lerner, 1999). What is missing within the literature is a conceptualization of ‘structural optimality’: how do we concurrently maximize both the entrepreneurial and innovative contributions of universities to accelerate regional economic and social development?

In an effort to bridge this auspicious gap within the literature, the proposed paper will compare the evolution of social networks among academic entrepreneurs to the developmental trajectory of their university spinoffs. The paper will focus specifically on the role of institutional and regional context, especially the role of policies and programs intended to encourage the formation of university spinoff and support their success. By empirically examining the growth trajectory of university spinoffs within these disparate contexts, we seek to conceptualize the various components of an optimal entrepreneurial university ecosystem.

An analysis of social networks is performed under the assumption that what Wright et al. (2007) deem as social resources are, in addition to technical, human, and financial resources, a critical complement to spinoff success. Research shows that social networks enable entrepreneurial development, from enterprise founding and growth to turnover (Jack, 2010). Surprisingly, resource-based approaches often fail to acknowledge the antecedent role of social networks for spinoff development (Jack, 2010; Zimmer and Aldrich, 1987).

Data that would be used for the proposed paper were collected during a nation-wide study of spinoff success (see Hayter 2013) and a more recent study of entrepreneurship networks within New York State (see Hayter forthcoming). Davidsson (2004) recommends that researchers obtain data from a sample of cases that are theoretically relevant, reflect the critical unit of analysis, reflect relevant variances in phenomenon characteristics, and are “workable” from a practical point of view. Thus, the proposed paper will include six to ten case studies of university spinoffs located in the Northeast United States, emphasizing a substantial degree of variance including different stages of development.

Initial results show that composition, contributions, and evolution of social networks among academic entrepreneurs are critical to the entrepreneurial development of their respective spinoffs. Further, a sizable proportion of spinoffs within the aforementioned studies benefited from institutional, region, state, and federal policies intended to spur entrepreneurship and technology commercialization. We will explore the contributions of these contextual elements in detail through the lens of social network evolution in order to more fully conceptualize the optimal ecosystems of an entrepreneurial university.

ABSTRACT. This abstract is part of the session proposal "Academic Entrepreneurship", organized by Professor Nicholas Vonortas.

INTRODUCTORY ARGUMENT

Expectations behind knowledge-intensive entrepreneurship (KIE) are that these ventures can enhance levels of innovative potential and generate a wide array of socioeconomic gains at the system level. Nonetheless, history has shown that the distribution of these activities is concentrated in space, a function of agglomeration economies and the existence of a multidimensional structure that fosters the location of entrepreneurial activity (such as market size, human capital pool, presence of research-oriented universities, support systems, among others). Understanding the determinants and dynamics of emergence of these entrepreneurial ecosystems represents a fundamental aspect in defining public policies that aim at: i) reinforcing existing structures; ii) coordinating and facilitating the rise of latent systems. Although this seems like a straightforward conclusion, the economic environment that underlies these agglomerations is often poorly assessed by KIE-enhancing initiatives, generating inefficient allocation of public resources. We propose an in-depth evaluation of the geography of KIEs in the State of São Paulo, Brazil. We depart from the basic hypothesis that the generation of KIE in São Paulo (and in developing regions in general) is overestimated by public authorities. More than that, its existence is likely to be spatially concentrated around regional centers of excellence that already present main characteristics of entrepreneurial ecosystems. Our primary goal is to explore and identify the main conditions that characterize these hubs of entrepreneurship, assessing their role as determinants of KIE location. In order to so, we aim at building a thorough picture of entrepreneurial activity throughout the unit under scrutiny. Our interest is to generate empirical knowledge on the geography of innovation-oriented entrepreneurship concerning its potential public policy implications. Further aspects in the scientific realm, particularly connected to the dynamics of KIE in developing countries, are also in our agenda.

METHODOLOGICAL PROPOSAL

Our empirical exercise is based on data from the PIPE/FAPESP program (Innovative Research in Small Enterprises, managed by the São Paulo Research Foundation), an initiative that grants subsidies for entrepreneurial projects that present high levels of knowledge-intensity and innovative potential. Though we recognize this dataset represents a small fraction of the KIE scenario in the State of São Paulo, it also offers an interesting source of "certified" knowledge-intensive entrepreneurs for 1196 ventures. This poses the possibility of working with high-quality microeconomic data, instead of resorting to the analysis of knowledge-intensive sectors (and all of its internal heterogeneities). Data is mainly oriented towards the geographic location of ventures, their broad area of expertise, and the profile of entrepreneurs (formal education, prior work experience and professional relationship with HEIs). We have also gathered complementary data on economic conditions of firms' locations (skilled labor indicators, HDI, patents and technological specialization, jobs in STI) as indicators of the socioeconomic environment in which these new ventures are embedded. These variables function as proxies for the evaluation of the entrepreneurial ecosystems' rationale within the scope of our sample. From this we attempt to build a descriptive analysis of the KIE geographical structure within São Paulo, through standard procedures, such as locational Gini and explanatory explorations of socioeconomic data. More robust analyses consider the density of KIE according to local population and economic weight of regions within the State of São Paulo.

PRELIMINARY RESULTS

Preliminary results indicate a lack of critical mass of KIEs in peripheral regions of the State of São Paulo, particularly those that are apart from the São Paulo metropolitan area. Nonetheless, the "distance dimension" is by no means deterministic, as regions such as São Carlos represent core locations for knowledge-intensive entrepreneurship (provided it has major research-oriented universities and a highly skilled workforce). Our sample also provides suggestive information on the inadequacy of transactional approaches to entrepreneurship that cover relatively large areas. Such linear forms of assessing innovation policy are likely to be economically inefficient, whereas relational, systemic, forms of connecting agents within areas of a denser entrepreneurial activity can provide more satisfactory outcomes. In this regard, governmental strategies towards regional development must take into account that peripheral locations often lack the fundamental "critical masses" to become high-tech poles, a feature that can hardly be tackled by funding isolated KIEs.

ABSTRACT. Investments in research and development, especially in basic research, have been justified since Vannevar Bush’s 1945 report by arguments in favor of long-term patience and a faith in nonspecific but likely eventual benefits, both tangible and systemic (Arnold 2012). A 1992 NAE report the relationship between risk and time horizons for technology investments is affected by many factors, and recommended several steps to increase companies’ long-term perspectives. The role of time in shaping S&T practices and policies is crucial, but in recent years cognitive scientists, psychologists, organization theorists, and decision theorists have increased our understanding how people and institutions make decisions that involve varying time spans, and it appears that time, as a factor in individual and institutional behavior, is more complex than traditionally assumed. It is not linear or constant across individuals or institutions.

For example, some evidence suggests that we tend to think of time somewhat logarithmically, with compression of our perception of future events occurring over rather short time scales, perhaps with typical time horizons of days, weeks, or months, rather than years or decades. It appears that humans possess a dual temporal capacity that allows them to function in both near time and deep time thought (e.g., Koritzky 2013; Daly, Harmon, & Delaney 2009; Rubia & Smith 2004). Similarly, scholars have noted that many successful technology companies employ diversified temporal portfolios, with some activities aimed at satisfying short-term earnings per share and others aimed at long-term research investments. Superior financial performance in firms often is associated with “a diverse portfolio of time horizons,” requiring complex cognitive structures and strong managerial control (Judge & Speitzfaden 1995). Many other factors have been found to determine or shape an institutional time horizon: “coincidence, informal networks, intellectual capacities and creativity, … opinion leaders, individuals with strategic capabilities and powerful actors from business, policy, science or society and culture” (Loorbach, 2007, p. 105).

Advocates of long-term policies such as investments in research and development must consider the justifications – economic, ethical, and political -- for incurring current costs to achieve uncertain long-term benefits. Analysts of such policies must consider the effect of nonconstant time perceptions on questions such as the characteristics and predictability of time preferences and discounting of future costs and benefits (Arnold 2012; Loewenstein, Read and Baumeister 2002; Mowery, Nelson & Martin 2010; Council for Science and Society 1989). For S&T policy in particular, this paper will consider the implications of emerging understanding of time perception and time horizons on questions such as political processes for justifying long-term R&D investments, the appropriate discounting of economic and non-economic phenomena, and temporal aspects of public perceptions of the benefits and risks of scientific research and technological development.

Arnold, Erik. 2012. Understanding long-term impacts of R&D funding: The EU framework programme, Research Evaluation (2012) 21 (5): 332-343

Council for Science and Society (1989). The Value of 'Useless' Research: Supporting Science and Scholarship for the Long Run, (London: The Council for Science and Society).

Daly, Michael, Colm P. Harmon, and Liam Delaney. "Psychological and biological foundations of time preference." Journal of the European Economic Association 7.2-3 (2009): 659-669.

Judge, William Q., and Mark Speitzfaden. 1995. "The Management of Strategic Time Horizons within Biotechnology Firms The Impact of Cognitive Complexity on Time Horizon Diversity." Journal of Management Inquiry 4.2: 179-196.

Koritzky, Gilly, et al. 2013. "Processing of time within the prefrontal cortex: Recent time engages posterior areas whereas distant time engages anterior areas." Neuroimage 72: 280-286.

Loewenstein, George, Daniel Read, and Roy E. Baumeister. 2002. Time and Decision: Economic and Psychological Perspectives on Intertemporal Choice. New York: Russell Sage.

Loorbach (2007), Transition Management. New Mode of Governance for Sustainable Development, Utrecht: International Books, PhD thesis, June 7. 2007

Mowery, David C., Richard R. Nelson, and Ben R. Martin. "Technology policy and global warming: Why new policy models are needed (or why putting new wine in old bottles won’t work)." Research Policy 39.8 (2010): 1011-1023.

Rubia, Katya, and Anna Smith. 2004. "The neural correlates of cognitive time management: a review." Acta neurobiologiae experimentalis 64.3: 329-340.

ABSTRACT. The notion of innovation policy is relatively new, coming into currency in the early 1970s. However, there has been a massive expansion of use in scholarly, management and policy spheres in the 1990s (Fagerberg, 2014) and today. Considerable policy hopes are placed in the promise of innovation policy and a broad range of innovation policy instruments have been designed and implemented. While there is a recognition that many policy efforts to foster innovation fail to meet aspirations, there seems to be a broadly shared view – growing and broadening over the last 20 years or so (Stewart 2012) – that with well-defined intervention rationales, intelligent policy design, appropriate instrumentation and effective implementation, public intervention can make a difference, and deliver on the ever broader list of innovation policy goals. At the same time, it is suggested, notwithstanding much conceptualization about policy design and implementation, that we continue to lack a consistent underpinning theory that can guide design and analysis (Fagerberg, 2014, Martin, 2014), and thus policy intervention and the analysis of its effects are based, at best – on partial economic, systemic (Dodgson et al., 2011, Klein Woolthuis et al., 2005) and societal intervention rationales . Against the background of high and growing expectations vis-à-vis innovation policy and lack of a sound theoretical base for innovation policy, this paper (1) contributes to a better understanding of the functionality and impact of innovation policy instruments and, more importantly (2) critically reflects on broader insights related to impact analysis and its limits. The main aim of this paper is to better understand the conditions for sand limits of learning in innovation policy, and to suggest ways forward for innovation policy analysis and conceptualisation. The paper starts off with a discussion of the existing literature on rationales for and theoretical reflections on innovation policy and existing typologies. It then introduces its own typology of instruments which is conceptually based on the idea of multi layered instrumental goals. On the basis of this typology, the paper synthesis the findings of a meta-evaluation of 18 innovation policy instruments based on the Manchester COMPENDIUM (http://innovation-policy.org.uk/)) in terms of their effectiveness and the underlying conditions for effectiveness. With the existing evidence we have we will then demonstrate the limitations inherent in the question: “what works in innovation policy”, the answer to which must be: it depends. We will show how “what works” in innovation policy depends on a raft of factors, including the meaning of context conditions, the application of different analytical lenses and methods used to analyse effects, and insufficient means to capture spill over, interaction and long term effects of innovation policy instruments. Only if we understand these learning limitations more thoroughly can we make best use of existing evidence for future policy design. On that basis we reflect on the shortcomings both in policy practice and policy analysis that can be identified as a result of the meta-evaluation. The paper will suggest ways forward and a set of concrete principles as for instrument design, implementation and analysis in innovation policy. This will not deliver an instrumental or analytical “tool box” for innovation policy. If anything, it seeks to make a contribution to more reflexivity. The set of basic principles for instrument design and analysis will have to be applied flexibly, taking into account systemic contexts and evolution over time and allowing for informed policy experimentation. While this paper makes a contribution to the understanding of innovation policy and policy learning in the STI policy community, it builds bridges to political science approaches to policy learning more generally.

References DODGSON, M., HUGHES, A., FOSTER, J. & METCALFE, S. 2011. Systems thinking, market failure, and the development of innovation policy: The case of Australia. Research Policy, 40, 1145-1156. FAGERBERG, J. 2014. Innovation policy. In search for a useful theory. Lundvall Symposium: Innovation Policy - can it work. Aalborg. KLEIN WOOLTHUIS, R., LANKHUIZEN, M. & GILSING, V. 2005. A system failure framework for innovation policy design. Technovation, 25, 609-619. MARTIN, B. 2014. R&D policy instruments - a critical review of what we do and don't know. Lundvall Symposium: Innovation Policy - can it make a difference? Aalborg. STEWART, F. 2012. Europe’s challenge-led broad-based innovation policy revolution: a convoluted and contested transition. EU SPRI Annual Conference Karlsruhe, Germany

ABSTRACT. This paper sheds light into the policy coordination concept and explores its implications for science, technology and innovation (STI) policies. We develop a framework that brings together insights from institutional and public policy theories with concepts from STI policy and innovation systems. This model is evidenced in two regions, the Basque Country (Spain) and Skåne (Sweden). As a result, the paper discusses different types of STI policy coordination according to the institutional settings (i.e. centralized vs. decentralized, agency-based vs. multi-organizational forms) in which they are embedded. Policy coordination is an important and often neglected issue in STI policy that has been traditionally studied in the literature of institutional theory and public policy itself. However, little attention has been paid to it in other streams such as STI policy, innovation systems, or the recent developments in territorial strategy approaches. Both theoretical contributions and case studies recognize the role played by public bodies and new governance models in facing policy and institutional failures. This is particularly the case in the regional context, where a huge range of complex and multi-level institutional settings coexist and conflicts among interdependent actors take place. Two dimensions of complexity can be distinguished in STI policy: the policy- mix and multi-level governance. The policy-mix concept denotes the diversity of innovation instruments from different domains that can be applied, while multi-level governance focuses on the levels in which policies are designed and administered. In this sense, we can find STI policies administered at supranational, national, regional or even local levels and instruments belonging to STI domains but also to health, industrial or energy domains, influencing the orientation of regions towards innovation. In this sense, regions can be regarded as policy spaces in which different policies and instruments administered at various levels interact, and as also as the geographical area where their outcomes will be felt. STI policy and the instruments available for its implementation as well as regions have evolved, forming increasingly complex contexts where policy coordination mechanisms have not coevolved. In other words, STI policies are conceived as systemic policies by institutional settings that lack systemic policy-making processes. In fact, it is very common to find (in national or regional domains) policies designed and implemented by an isolated governmental department without any mode of coordination with other (related) policies implemented by other departments or at different levels. In this sense, the literature has provided some possible coordination mechanisms such as the creation of centralized agencies, coordination councils, creation of superministries, etc. In this paper we aim at making a step forward and explore which are the coordination modes and mechanisms that might best fit into different STI policy-mixes and contexts. We compare two regions which despite being different in many aspects, have many things in common when it comes to STI policy. The two territories count with economies focused on the international scene, register income per capita levels above the EU average and show intense collective organization concerning social cohesion. As to their research orientation, both regions show a similar pattern in the diversity of research areas covered and have a sophisticated innovation system in which STI activities play an important role. The paper contributes to the literature by analyzing the benefits and limitations of the coordination modes signaled by scholars in the STI stream and point out which coordination modes and mechanisms might best fit into diverging institutional settings.

ABSTRACT. The popularity of the term “innovation policy” is of relatively recent origin. To the best of our knowledge it comes – as so much within the field of innovation studies - from the intellectual environment that developed around the Science Policy Research Unit (SPRU) at the University of Sussex from the late 1960s onwards. However, the real surge of interest had to wait until the1990s, when international organisations such as the OECD (alongside various national governments) started to pay attention to the phenomenon.

By now we have several decades of experience with innovation policy (if not more) and it is time to take stock of what has been learnt and consider what the challenges for the theory and practice may be. This paper contributes to this process by focusing on the extent to which we have developed a theoretical framework that is sufficiently helpful.The discussion concentrates on innovation policies aiming at improving a nation’s economic performance.

The “innovation policy” term may be used in different ways. For example, it may be defined broadly as all policies that have an impact on innovation, or more narrowly as policies (or policy instruments) created with the intent to affect innovation. Nevertheless, if we are interested in the impacts of policy on innovation and economic performance, the former, broader definition appears more appropriate (although it arguably complicates life for the analyst). Different usages of the term may also reflect different understandings of innovation: Does it refer to the entire process from the emergence of new ideas to their economic exploitation (broad definition), or is it limited to the first occurrence of a new product, process or way do things (narrow definition)? However, while innovation may be a fascinating topic in its own right, this is not the reason why most policymakers are interested in it. Rather what they are interested in is the beneficial economic effects that innovation is assumed to have, not only for the innovator, but for a country or region as whole. From this perspective the broader definition makes most sense, since what mainly matters for the economy is not the first occurrence of an innovation but its subsequent diffusion and use including the effects that this gives rise to.

An important conclusion from the discussion is that a distinction needs to be made between the characteristics – or “structure” – of a national innovation system and its dynamics. National innovation systems have evolved through interaction between the economic and political system of a country. Since countries differ industrially, industries (or sectors) have different innovation dynamics (and requirements) and political systems differ in their origins and characteristics, national innovation systems may end up as looking rather different. Such differences are not necessarily a problem, however, as much policy-advice based on so-called “benchmarking” seems to take for granted. Arguably, an unsatisfactory state or “problem” cannot be revealed by studying a single component of a system. What is required is an analysis of the technological dynamics of the national innovation system as whole.

While the characteristics – or structures – of national innovation system may differ a lot, there may still be common features related to the technological dynamics occurring within these systems. This has to do with the fact that innovation and diffusion follow certain regularities, which have been extensively analyzed and documented by innovation research. Guided by recent advances in innovation systems research the paper outlines a synthetic framework for analyzing the technological dynamics of a country. It is shown that the technological dynamics of a country is the result of interaction between a number of different processes that are influenced by a range of policies, many of which do not carry the “innovation” label and primarily have other goals. An effective innovation policy, therefore, requires close coordination of policies across a number of different domains, and the development of new forms of governance and supporting knowledge bases that makes this possible. It is argued that greater attention should be paid to the policy experiments that have emerged in various countries to achieve some of these aims.

In recent years a lot of attention has been devoted to the evaluation of single innovation policy instruments in various countries. However, such evaluations risk missing their point if interactions between different policies, as well as system-wide effects and feedbacks, are not properly taken into account. What is needed are system-level evaluations . There has been little discussion, though, about the methodologies for carrying out such analyses, a topic on which the research community in this area should be well placed to contribute.

ABSTRACT. The solar photovoltaic (PV) industry in China has experienced rapid growth in the past decade, expanding from almost non-existent at the turn of the 21st century to an industry that is worth multi-billion dollars in 2014. Chinese solar PV installed capacity has doubled every year since 2007, reaching 7 GW in 2012, 11 GW in 2013 and 12 GW in 2014. Besides the fast deployment of solar PV, China also has the world’s largest PV manufacturing capacity. PV made in China accounted for 56% of global PV sales in 2013, and seven out of the world top ten PV manufacturers are Chinese.

Despite its strong manufacturing and deployment capacity, China is a latecomer to PV technology innovation. The U.S. National Renewable Energy Laboratory (NREL) tracks the world record lab efficiency of 23 types of PV technologies between 1975 and 2014 and none of them was set by Chinese entities. Even in the commercial production realm, Chinese companies traditionally are known as the producer of low- to medium-efficiency solar PV.

In this study, we examined a few areas related to solar PV innovation and discovered that the gap between China and the world leading PV innovators is indeed narrowing. For example, we tracked the record efficiencies of four types of popular PV technologies among Chinese players in the past 10 years and compared them to the world record efficiencies complied by NREL. The results indicate that three out of the four technologies have shown narrowing gaps over time between the Chinese records and the world records, indicating that the efficiencies of solar cells made in China are increasing at a higher rate than the rest of the world. Using a different metric, we analyzed the number of patents granted in China and found that the number of patents granted to Chinese entities has grown significantly in all the 16 types of PV technologies examined. For three quarters of the technologies, foreign entities used to hold the majority of the patents in Chinese market, but the trend started to reverse since 2009. In certain technology space such as dye-sensitized and organic solar PV, Chinese entities were even the major patent holders to begin with. These evidences suggest that players in the Chinese solar PV industry have become more innovative over time.

Given the evidences that the innovation capacity in the solar PV section in China is strengthening, we were curious about the drivers behind the change. Further analysis suggests that two factors have strong influence on China’s growing PV innovation strength.

First, China has a national strategy for solar energy, which was formulated since the 10th Five Year Plan (FYP) in 2001 and has since evolved in the subsequent two FYPs. The strategy has been carried out by the National Science Foundation of China (NSFC) and various programs under the Chinese Ministry of Science and Technology (MOST). Through NSFC and MOST, China has funded the research of a portfolio of PV technologies throughout the RD&D cycle. In particular, China has allocated a disproportionally large amount of financial resource to the research of emerging PV technologies and high-efficiency commercializable technologies because the historical innovation gap between China the world leading innovators are relatively small in these areas and therefore, the likelihood for China to leapfrog in innovation is higher.

Second, technology innovation has become a globalized enterprise and China is a likely beneficiary of such trend. In this paper, we mapped the global innovation network of five types of popular PV technologies. Three models of globalization of innovation emerged. For HIT technology, Chinese companies have been collaborating with domestic and foreign research institutes through official institutional channel. For technologies like CdTe, pervoskite and organic solar PV, oversea-trained Chinese researchers who returned to China to work in either academia or private companies have become the major driver of innovation improvement in these technology areas. The third model is represented by Hanergy, the largest Chinese thin film solar PV manufacture, who has made news headlines by purchasing 4 innovative CIGS companies in the U.S. and Europe. The merger and acquisition strategy has allowed Hanergy to become an industry leader for controlling high-efficiency technologies, although no argument can be made regarding the originality of its innovation capacity.

In summary, China has long been seen as the laggard in PV technology innovation albeit its strong record in PV manufacturing and deployment. This paper presents a suite of evidences suggesting that China had made strides to close the innovation gap between itself and the world leading PV innovators. The mechanisms behind the trends investigated. It is found that a national PV innovation strategy coupled with strong public finance support as well as a globalized innovation network have facilitated the growth of China’s PV innovation capacity.

ABSTRACT. Transformations and applications of scientific knowledge into new technologies are usually complex interactive processes.  In several situations, e.g. the optimal allocation of resources for R&D, it is desirable to be able to identify at early stage scientific discoveries that have the potency to lead to major changes in existing technologies or even to new technologies.

Scientific discoveries come in several flavours based on the impact of a discovery on the evolution of science. Some discoveries have a major impact on the evolution of science and are called a ‘major discovery’ or a ‘breakthrough’. Subject experts are able, especially in hindsight, to value the impact of a particular discovery on the evolution of a particular field of science. The fact that ‘breakthrough’ is not a well-defined term complicates the identification of such phenomena.  This lack of consensus applies to ‘breakthrough’ scientific discoveries, ‘breakthrough’ inventions, ‘radical’ technological innovations and other related concepts. In general the term ‘breakthrough’ refers to a major change that opens up new possibilities.

In the past few years we conducted four case studies in order to develop bibliometric heuristics and algorithms based on citation patterns in bibliographic data. These heuristics and algorithms should answer the question:

‘Is it possible to detect at early stage from bibliographic information structural alterations and significant events that indicate potential breakthrough discoveries in science that could evolve into new technologies?’

The objective of our research is to come up with analytical methods that are able to identify within 2-3 years after the publication of a research article if is to be considered a ‘breakthrough’ paper. To select the case studies we relied on expert opinions as to which discovery is to be seen as ‘major discovery’ or ‘breakthrough’.  All discoveries studied in the four case studies led to new technologies.

Several indicators pointing to structural changes in citation patterns for publications were uncovered in the analyses of the case studies. On the basis of the results of these case studies we developed and implemented five analytical methods that deviate from a ‘simple’ highly-cited criterion, and focus on structural changes in citation patterns. Each of the methods focuses on a different aspect of the process that we consider to be characteristic for a breakthrough. The developed algorithms are designed to differentiate between three different types of breakthrough discoveries. The three types are 1) discoveries not leading to a new theoretical framework, 2) discoveries resulting in a new or changing theoretical framework, and 3) discoveries that bridge basic research and applied research.

As our approach is based on case studies the general validity and applicability of the indicators has to be ascertained. To do so we applied our heuristics and algorithms to the entire Thomson Reuters Web of Science database. This validation study focuses on publications containing original research from the period 1990-1994. The base data set in this comparative study consists of all 2,715,651 publications with document type ‘article’ or  ‘letter’ in the Thomson Reuters Web of Science database published for that period. These publications are considered to contain the results of original scientific research. From this base data set we extracted, by using different algorithms, two document sets that contain 214,898, and respectively 253,689 publications. The period 1990-1994 was chosen to be able to verify and validate if the publications that were selected at early stage by the algorithms are currently – in retrospect – still regarded as ‘genuine’ breakthroughs, and are at the basis of new technological developments.

As there is no objective measure for a document to test if it is a ‘breakthrough’ the usual concepts like ‘accuracy’, and ‘recall’ to test an algorithm cannot be used. We therefore relied on an alternative approach to check if the publication that was identified by the algorithms was indeed an outstanding publication. To do so we applied our algorithms to two sets with original research papers from 1990-1994 that we have constructed. The test results show that we could identify 1) all publications that are in Nature’s Top-100 list of most cited publications ever; 2) for 60% of the Nobel Prizes in Physics, Chemistry, or Physiology or Medicine, that are based on publications from 1990-1994, at least one of the founding publications was identified. For all algorithms applied to the two datasets we included in an additional test set the Top-10 publications based on the total number of citations they received. For these publications we found that 1) they are among the publications most cited by review papers; 2) a number of these publications is cited in ‘altmetrics’ publications from 2012-2014; and 3) a considerable number of the identified papers is cited in patent publications, and a number are even highly cited.

We conclude that our findings strongly suggest that our suite of algorithms present a superior tool to trace ‘hot’ papers or even ‘breakthrough’ discoveries in science that evolve into new technologies.

ABSTRACT. A common objective in studying history is reconstruct patterns in events and behaviors, and to see if they provide insight into current situations. In the study of innovation, the case history method is used to reveal possible dynamics underlying the technology development process. This method is exemplified by the NSF-funded TRACES study (Technology in Retrospect and Critical Events in Science), conducted in 1968 by the Illinois Institute of Technology Research Institute. The TRACES study was among the first to reveal the important contribute of non-mission, fundamental research to downstream development. The methods used in technology tracing are largely unchanged today, consisting of bibliographical research supplemented by interviews with the principals involved in developing the technology under study. A later version of the TRACES study by SRI International, also for the NSF, relied on a very similar methodology.

Limitations of the case study method for innovation research include the expense and time required to compile a case study (especially when trying to interview key researchers and innovators), the inherent challenges in relying on the memory of key actors (including information missed if key actors are deceased or unreachable), and the potential pitfalls of generalizing insights from a relatively small number of case histories. Given current advances in scientometrics and text analytics, to what extent is it possibly to “automate” the production of a TRACES-type study? Would it be possible to conduct relatively large numbers of tracing studies around particular innovations in a short period of time? What new insights could this method generate, and would they enable us to improve how we organize and undertake technology innovation?

These questions motivated the Helios project, an effort funded by the U.S. Department of Energy’s SunShot Initiative to use text analytics in studying the evolution of photovoltaics innovation. The SunShot Initiative seeks to accelerate the development of photovoltaic systems and technologies to make solar-generated electricity cost-competitive with coal-generated power. Helios is an attempt to use retrospective analysis to provide possible approaches to accelerating breakthrough innovation in photovoltaics. This paper captures some of the key findings of the first year of this project.

The Helios project leverages a number of recent developments in the use of machine learning for technology analysis, including network science, topic modeling, and entity extraction. We take as our guiding framework the photovoltaic efficiency chart, updated regularly by the National Renewable Energy Laboratory. We have selected three alternate PV cell technologies for investigation: multijunction cells (MJ), thin-film (CdTe) cells, and dye sensitized solar cells (DSSC). These technologies were selected because they have attributes of interest to both academic and industrial researchers, and they have diverse characteristics in terms of their stage of maturity, performance-to-cost parameters, and the timeframe of technology development.

The Helios project applies a number of advanced analytical techniques to the study of relevant technical documents. The current Helios system includes five data sources:

• Bibliographic records of scholarly journal articles from the Thomson Web of Science database service • Granted patents from the U.S. Patent and Trademark Office • Full-text technical reports generated by the U.S. Department of Energy • Records of awarded research grants from the U.S. National Science Foundation • Selected full-text versions of scholarly journal articles from various sources

We conducted an experiment in which human technology analysts compiled case histories for each of the three technologies of interest: MJ, CdTe, and DSSCs. We then used different analytical techniques to generate findings comparable to elements of technology case histories. For example, we tested the use of various network science algorithms to identify the key researchers involved in the development of a technology. We also examined how topic models can be used to determine and characterize shifts in the nature and focus of research over time within a community of practice. One promising outcome is a set of techniques that may reveal how the emergence of a new method or technique within a community is related to later breakthroughs in technology development.

This paper provides a review of technology tracing, an overview of the Helios system development, descriptions of the analytical techniques used in Helios, and our initial findings. We then extend these findings to suggest new uses of text analytics and other advanced data analysis to create next-generation scientometric tools for innovation researchers. We also discuss how Helios may provide guidance on ways to make these analytical tools more accessible to and useful for the research community.

ABSTRACT. Newly Emerging Science and Technologies (NESTs) bring numerous innovation opportunities and challenges. At the same time, the highly uncertain dynamics of NESTs pose special challenges to traditional technology forecasting tools. Dye Sensitized solar cells (DSSCs), a promising third-generation photovoltaic technology, could add functionality and lower costs, enhancing the value proposition of solar power generation in the early years of the 21st century. DSSCs have witnessed increasing R&D activity since 1991 (Baxter 2012). We have analyzed DSSC R&D activity patterns and trends through a series of studies -- research profiling (Guo et al., 2010), innovation risk path estimation (Guo et al., 2012a), technology delivery system (Guo et al., 2012b), technology roadmapping (Zhang et al., 2013), technology opportunities analysis (Ma et al., 2014), technology evolutionary pathways (Zhou et al., 2014) and collaboration network analysis (Wang et al., 2014a; 2014b). This paper updates DSSC data collection to revies our earlier assessments and projections. Our team has been building a framework of 4 stages, containing 10 steps, to analyze NESTs to help Forecast Innovation Pathways (FIP) (Robinson et al., 2013). This method integrates (a) heavily empirical "Tech Mining" with (b) expert-based inputs and interpretations. We draw heavily on "Tech Mining" (Porter and Cunningham, 2005) to ascertain developmental patterns, key participants, and potential application targets by analyzing large datasets drawn from ST&I publication and patent databases, as well as contextual information resources (e.g., ABI Inform). The FIP process incorporates to various degrees-- trend analyses, actor analyses, and forecasting workshops (Guo et al. 2012c). Future-oriented technology analyses (FTA) purport to inform Management of Technology (MOT) and Science, Technology & Innovation (ST&I) policy processes. Yet, rarely do we revisit forecasts or projections to ascertain how well they fared. One aim is to check accuracy, to gain some sense of how valid those studies were and whether they proved useful to others in some ways. Additionally, we want to assess the degree to which such future-oriented analyses did or did not make good use of available information. Moreover, we seek indications of what information is key, and how FTA processes can better utilize such information. Pointing toward ways to improve the FIP methodology, we address several research questions: 1)What information most importantly characterizes the NEST’s developmental progression? How do particular trend projections fare when revisited 3-8 years later? 2)What are key elements of the Technology Delivery System (TDS) and the emerging value chain developments? How stable are those elements over moderate time periods? 3)Which analyses of R&D activities contribute vital information on developmental trends and leading players? How well do those analyses stand the test of time (for 3-8 years, at least)? 4)Which facets of our earlier studies have identified important factors affecting innovation pathway progressions? How can these be better treated? We update DSSC searches in four databases: Web of Science (WOS), EI Compendex, Derwent Innovations Index (DII), and Factiva. Retrievals of abstract records range from about 7,000 to 12,000 from each. This paper updates a series of Future-oriented Technology Analyses of DSSCs. It identifies strengths and weaknesses of those analyses in terms of data characteristics, projection accuracy, stability of actors and networks. Results suggest ways to improve the FTA methodology. They also offer a model to consider for validation of other FTA analyses.

ABSTRACT. We have been witnessing an important transition in the evaluation of research as assessment shifts from the custom designed and peer based to the routine and metric-based. The data for those metrics come primarily from publication, citation, other project outcomes, and may in the future be joined by web data. This creates the risk that evaluation is now led by the data rather than by the expert. In this environment, indicators proliferate: usually well-intentioned, not always well-informed, often ill-applied. Evaluation is implemented by organisations without knowledge of or advice on good practice and interpretation. Everyone struggles with the indicators and wonders how to interpret and use them. We thus risk damaging the system with the very tools with which we seek to improve it. Scientometricians, watch the pervasive application of indicators to the evaluation of scientific performance with an increasing sense of alarm, aware that too many instances fall far short of good evaluation practice. This challenge was the focus of the most recent conference of indicator experts at the Leiden University – STI2014. Discussion at the conference and follow on has produced a consensus statement on best practice in metrics based research evaluation, known as the Leiden manifesto. This talk will present the Leiden manifesto.

ABSTRACT. In the UK, the funding council HEFCE has been asked to organize an independent review of the role of metrics in the future Research Excellence Framework exercises as well as in research evaluation more generally by the former minister of science, David Willett. It installed a steering group chaired by prof. James Wilsdon, professor of science and democracy at SPRU, University of Sussex. As part of this assignment, the steering group issued a call for evidence on 1 May 2014. A total of no less than 152 submissions were sent in in response to this call.

The review report will be published in the Summer of 2015, and will address, among others, the following issues: • The relative metrics of bibliometrics, scientometrics and altmetrics in assessing the academic qualities and broader societal impacts of research • The positive and negative effects of metrics in creating an environment that enables and encourages excellent research and broader impact, including fostering inter- and multi-disciplinary research • The appropriate balance between peer review and metrics in research assessment and the consequences for administrative burden and research cultures across different disciplines • The extent to which metrics could be used by higher education institutions in their internal processes and by research funders in the assessment of research • Ethical considerations and guidance on how to reduce the unintended effects and inappropriate use of metrics • The potential contribution of metrics to other aspects of research assessment, such as the matching of reviewers to proposals, or research portfolio analysis.

In this paper, I will discuss, as member of the HEFCE steering group, the main topics addressed in the report, discuss the implications for the future of large-scale national research evaluations, and explore the future role of scientometrics, altmetrics and other forms of quantitative information. I will discuss the concept of "informed peer review" and related approaches to research assessment. I will also analyze the responses to the call for evidence, as well as the discussion that the report will have generated since its publication.

ABSTRACT. The range and types of performance indicators has recently proliferated in academic settings, with evaluative metrics as one of the most visible examples. Metrics are increasingly positioned as the basis for rationalising complex decision-making processes in assessment situations. They are used to redistribute resources, decrease costs, regulate access to public services, promote collaboration, etc. This raises questions about the appeal of metrics and their ability to enact value, excellence, reputation and impact; the consequences of metrics-based evaluation, how it works; what counts as quality, excellence or impact, and to whom. However, at present we know surprisingly little about how research evaluation actually shapes the production of knowledge. Despite a voluminous amount of literature having been written on research evaluation, explanatory knowledge about the varying effects metrics are having is far from satisfactory.

In this talk I will draw upon three qualitative research projects in biomedicine, social sciences and law to consider the ways in which evaluative metrics are increasingly shaping how research is carried out and communicated. The projects are part of a budding research program on the growing influence of metric-based evaluation on the organisation and content of research (https://evaluationpracticesincontext.wordpress.com/). The core methods of the studies are qualitative semi-structured interviews with researchers in different career stages and with research managers, as well as ethnographic observations of a number of settings in which research is assessed (lab or team meetings, supervision sessions, yearly appraisal talks, management and board meetings). This adds a level of empirical detail hitherto lacking in efforts to understand mechanisms through which performance evaluation shapes academic knowledge.

The first project (with Alex Rushforth) is based on fieldwork at six research groups in two Dutch academic medical centres, and focuses on interactions between evaluation and knowledge production in biomedical research. In this project we also benefit from collaborations with Roland Bal (Erasmus University Rotterdam) and Ruth Müller (Technical University Munich). The second project (with Thomas Franssen) is based on fieldwork at an internationally acclaimed Dutch social scientific research group. Here, we zoom in on how junior researchers relate to and use performance measures in academic identity formation and in the process of positioning themselves in the academic labor market. The third project (with Wolfgang Kaltenbrunner) draws on a comparison of ongoing debates about research evaluation in three Dutch Law faculties. The project aims to analyse the implications of the need to create new alignments between research and science policy on the one hand (including the requirement of (‘societal relevance’), and the everyday articulation work legal scholars engage in to bring research to closure on the other.

For the present talk I will mainly consider how indicators are becoming a central aspect of research activities themselves, by analysing three important dimensions of knowledge production: 1. Planning of research; 2. Navigating collaborations during research; 3. Concluding the research. Our material shows how research activities become increasingly assessed and defined by their potential for translation into quantitative measures of quality. Other criteria of scientific quality, e.g. epistemic originality, societal relevance and social responsibility become redefined through their relations to quantitative indicators. We understand this to be in tension with policy goals to encourage innovative, societally relevant and responsible research.

ABSTRACT. Publication and citation (bibliometric) data remain the predominant source of information used to construct indicators of relative research performance. Despite their utility and versatility, they also remain problematic because they inform us of only one part of the research ecosphere in terms of either activity (academic output) or achievement (academic usage). A growing range of accessible, structured data sources now provide alternative pathways into information about research. This is building a richer picture of the research process, which may provide better management information as well as creating perspectives to complement the bibliometric approach. The use of bibliometric data in research evaluation has become almost universal, driven in part by the accessibility of such data, their geographic and disciplinary versatility, and the evolving sophistication of analytical methods. Methodology can always be abused: like any simple yet powerful tool, bibliometric analyses and their interpretation can lead to damaging errors (reference to Hicks, Wouters in this session). Bibliometric indicators also have obvious limitations: they refer only to the immediate output from research; they are focused on just academic knowledge rather than other modalities; and they index only the extent to which this knowledge influences other academics. It would be valuable if the dominant – because powerfully informative - perspective given by bibliometrics could be broadened and balanced by other indicators. It would enable some sense-checking of bibliometric information and thus provide routes to mitigate inappropriate use. But, more widely, bibliometrics fail to capture the full diversity and richness of what research delivers. We would benefit from a much broader perspective on the research process. The evaluation of outputs has much value as a retrospective performance check for research funding agencies, and therefore for their paymasters, and it also provides an historical context for research policy development. Nonetheless, it lacks the immediacy desired by research managers and the focus on journal output alone is monotonic. In this paper we review additional forms of research-relevant data, now becoming accessible in a collated and normalised form that makes them potentially suitable for the creation of novel indicators. Recent projects have explored the utility of such data. For example, a study by Digital Science for the UK Nesta organisation looked at the value and limitations of altmetrics (social and news media mentions of journal articles) in identifying research papers relevant to non-academic knowledge networks. Digital Science is also expanding the range of other data that can be employed: altmetric.com data are now being gathered for other, non-journal outputs; figshare has established a system for tracking and indexing the use of specific data; ÜberResearch provides the first truly comprehensive database of research grants. These data sources can be mapped onto the research environment to expand our interpretation and strengthen the opportunity for management of research in progress. But although the new indicators show a great deal of promise, they remain limited in their explanatory power. Bibliometrics evolved over decades: time is required both to deepen the new databases and to develop our interpretative competency. Alongside the data sources which emerge from the research process, 2015 also saw the creation of the first nationally comprehensive database of research impact – the description of longer term economic and social benefit arising from research. The content of the UK’s Research Excellence Framework (REF) impact case studies database, which covered all disciplines across all universities and within a common time frame, will be discussed in terms of its value as a new information source for indicators. As for other data sources and indicators, the potential, limitations and developmental requirements of this impact methodology will be reviewed. However, the simple existence of this new UK information and the likely adoption of similar methodology in other countries raise important questions for the future of research performance indicators and the nature and purpose of the evaluation of publicly funded research.

ABSTRACT. This paper aims to explore the problems that emerge when S&T indicators are used in peripheral contexts, that is, in geographical or social spaces that are somehow marginal to (or marginalised by) the centres of scientific activity. In these situations evaluators and decision-makers are likely to use indicators that were designed to reflect variables relevant in the dominant social and geographical contexts --i.e. in the hegemonic countries, languages, gender, disciplines, etc.--, but that are usually not adequate in peripheral contexts.

We will examine various dimensions of periphery. First, the geographical: e.g. global south vs. global north, regions vs. metropolises (Aguado et al. 2014). Second, the social group dimension: women, the disenfranchised, the poor, or perhaps the elderly have social needs that are different from those of richer or more powerful groups --and the problems affecting the former tend be less researched than those of the later (Stirling, 2014). Third, the cognitive dimension: areas of research, such as epidemiology or surgery, that capture less attention in terms of publications or citations (and resources) than the more prestigious disciplines, such as molecular biology (van Eck et al, 2013).

This study investigates the mechanisms by which performance indicators tend to be biased against peripheral spaces. This would include for example, bias in language (van Leeuwen et al. 2011), or disciplinary/topic coverage in conventional databases (Martin et al., 2010). An interesting issue to consider is how the overlap across peripheries, i.e. how bias in language coverage has an effect on bias in disciplines or topics covered (Archambault et al., 2006; Piñeiro and Hicks, 2015). We discuss how these biases may have a tendency to suppress scientific diversity and shift research towards a higher degree of homogeneity (Rafols et al., 2012). We discuss how the "objectification" of excellence by means of indicators may support the diffusion of mainstream modes of research at the expense of critical or unorthodox modes.

References

Aguado-López, E., Becerril-García, A., Arriola, M. L., & Martínez-Domínguez, N. D. (2014). Iberoamérica en la ciencia de corriente principal (Thomson Reuters / Scopus): Una región fragmentada. Interciencia, 39(8), 570-579.

Archambault, É., Vignola-Gagne, É., Côté, G., Lariviere, V., & Gingras, Y. (2006). Benchmarking scientific output in the social sciences and humanities: The limits of existing databases. Scientometrics, 68(3), 329-342.

Martin, B. R., Tang, P., Morgan, M., & al. (2010). Towards a Bibliometric Database for the Social Sciences and Humanities – A European Scoping Project (A report for DFG, ESRC, AHRC, NWO, ANR and ESF). Brighton, UK: SPRU.

Piñeiro, C. L., & Hicks, D. (2015). Reception of Spanish sociology by domestic and foreign audiences differs and has consequences for evaluation. Research Evaluation, 24(1), 78-89.

Rafols, I., Leydesdorff, L., O’Hare, A., Nightingale, P., Stirling, A., 2012. How journal rankings can suppress interdisciplinarity. The case of innovation studies and business and management. Research Policy 41, 1262–1282.

Stirling, Andy. Towards Innovation Democracy? Participation, Responsibility and Precaution in Innovation Governance. No. 2014-24. SPRU-Science and Technology Policy Research, University of Sussex, 2014.

Van Eck, N. J., Waltman, L., van Raan, A. F. J., Klautz, R. J. M., & Peul, W. C. (2013). Citation Analysis May Severely Underestimate the Impact of Clinical Research as Compared to Basic Research. PLoS ONE, 8(4), e62395. doi:10.1371/journal.pone.0062395

ABSTRACT. Universities’ ‘institutional’ funding for research is increasingly subjected to quality control and competition, with significant impact on behaviour and productivity but also with negative effects. We analyse evidence about the effects of making institutional funding contested and draw conclusions for policy and the design of such systems. The paper is grounded not only in the literature but also in our work analysing and helping pilot changes in the UK system, national research assessment in Latvia and Lithuania, and analysis and redesign of the Czech system. State universities were traditionally financed through a block grant but have increasingly been gaining external income from research councils, other government agencies and industry, so the block grant is a declining proportion of the total. Curiously, despite wide variations in the share of income provided by the part of the block grant for research there is no clear relationship between the share of institutional funding and research performance. Since 1986, governments have used performance-based research funding systems (PRFS) to award institutional funding, aiming to stimulate performance by reallocating resources to strong performers. The UK Research Assessment Exercise (RAE, now REF) was the first, and like other first-generation PRFS relies heavily on peer review. From roughly 2000, more countries adopted a second generation of PRFS, mostly using bibliometric indicators. By that time barriers to doing bibliometric analysis had fallen dramatically, providing a cheaper solution than peer review. Up to this point, PRFS focused on scientific quality but a third generation of PRFS now incorporates the impact of research on innovation and society. Evidence used ranges from patents through innovation outputs like prototypes to text descriptions of research impacts. Key design decisions for PRFS include • Whether also to reward external project-based income, reinforcing the themes and types of research that already get funding • Whether to create a list of ‘approved’ national language publications, in addition to relying on journals indexed in bibliometric databases • Whether and how to address impact • Whether to extend to PRFS beyond the universities to other research organisations • How much of the institutional funding to make contestable, balancing stability against change. Many systems effect a lot of change while moving only small amounts of money • Whether to put different research fields in competition with each other or to arrange competition only within fields Despite their widespread adoption, there is little evidence about whether and how PRFS work. Evidence from the UK, Norway and the Czech Republic suggests that effects depend both on policy purposes and on the implementation. The UK RAE distributes most UK institutional research funding and has concentrated it on a small number of institutions. Long periods between exercises let the system adjust and an allocation formula that gives proportionately more to the most successful keeps the list of ‘winners’ fairly stable. There is a strong feedback to career progression and recruitment. The same academic elites populate the RAE and research council panels, so RAE and research council success correlate strongly. The RAE impedes interdisciplinarity and drives out heterodox approaches. It is thought to support the UK’s strong position in international bibliometric comparisons, but little evidence supports this idea. Norway introduced a PRFS in 2004 in the university ‘Quality Reform’ and then a separate system for the university hospitals and research institutes. Both affect a small fraction of institutional funding. The PRFS in practice rewards the newer and weaker universities for increasing their research efforts, building capacity rather than rewarding existing winners. It drove up the number of publications but not their quality (cp similar Australian experience.) The PRFS for the institutes had similar effects but failed to increase either the amount of institute-university collaboration or international income. Both were already high. The Czech Republic introduced an annual metrics-based PRFS in 2008, which was intended to become the only mechanism for allocating institutional research funding. Universities doubled their academic paper production in 3 years and the production of innovation-related outputs grew even faster. Allocations to individual organisations and fields became unstable. Gaming was widespread and despite repeated attempts to refine it, the system was abandoned in 2012 and is undergoing radical redesign. Comparative analysis of the ways in which different PRFS lead to changes in performance requires unpacking key dimensions of their design (noted above) and the institutional context within which they act. Critically revisiting the work of authors such as Glaser and Whitley with more recent evidence will allow further lessons for policy and assessment to emerge.

ABSTRACT. If science has been crucial to many governments for the past five centuries, the current position of knowledge is unprecedented (Pestre 2003). In this era of responsible research and innovation (European Commission, 2012), governments use steering mechanisms to impose their priorities, make scientists accountable and commercialize results (Nowotny, Scott & Gibbons, 2003). Public-sector research (PSR) funding is both a symbol of the relationship between the state and universities, and a powerful policy instrument to influence the direction and nature of research (Kalpazidou Schmidt, 2012). Correlating universities’ ranking scores with funding streams, Aghion et al. (2009) have conclude that per-student revenues, budget autonomy, less basic funding and more competitive grants led to higher performance. This type of study however relies on measures of central tendency that can hardly take into account smaller countries with fewer institutions. The Nordic countries differ from the Anglo-Saxon model (Benner, 2011) yet, on a per capita basis, they achieve higher results in terms of world-class universities publications, citations and patents. 
PSR funding partly explains this result. Public expenditure on higher education in the Nordic countries is the highest in the world (Kalpazidou Schmidt, 2012) and research funding is concentrated in universities (Henrekson & Rosenberg, 2001). Second, Nordic countries have achieved a particular balance between funding streams: basic, competitive, excellence and strategic funding. Despite an increase in competitive research grants, basic funding (often based on a formula) continues to cover almost half of PSR funding (Sörlin, 2007). Basic funding would increase institutional freedom, and competitive funding would make the system dynamic and socially relevant (Kalpazidou Schmidt, 2012). Nordic countries also count on mission-oriented agencies supporting research in strategic areas (Benner & Sandström, 2000). Finally, all Nordic countries have implemented centers of excellence, i.e. research groups counting on stable source of revenues developing high quality and relevant research (Brundenius, Göransson & Ågren, 2011). The objective of this paper is hence to assess the perceived importance of various funding streams on academic research production and to explore the mechanisms through which they might impact countries’ research output.
Method: Following Holmes’ (1981) hypothetico-deductive problem approach to comparative education, this study testes the PSR funding hypothesis in four contexts (Denmark, Finland, Norway and Sweden) according to a mixed method design and a vertical scheme. Systems being immaterial, empirical testing relied on the perception of major stakeholders belonging to the same 13 strata in each country: Nordic Council of Ministers, ministries of Higher Education, quality assurance agencies, research councils, innovation networks, university associations, faculty unions, university boards, university administrators, faculty members, temporary contract-researchers, doctoral students and non-university institutions. A thematic analysis was processed on more than 60 interviews and factorial analysis and means comparison on more than 400 questionnaires regarding the perceived strength of systemic factors in promoting academic research.
Findings: According to survey results, respondents in the four countries (i.e. no significant differences) perceive that a predominantly public research funding concentrated into universities has a positive impact on research production. Regarding funding streams, peer-reviewed competitive funding is perceived as having the greatest impact and, in Denmark, Norway and Sweden, block grants are also well considered. For Finnish interviewees, block grants have little impact on research since they only account for salaries, while external grants allow for hiring human and material resources. In Sweden, system-level stakeholders perceive excellence and strategic funding more positive than professors. While the former highlight that a small country should focus its investments, the latter explain that these two streams of funding imply that the government prioritizes research areas for reasons other than scientific excellence. Excellence funding is perceived positively by both Norwegian professors and system-level actors since it compensates for scattered grants for which academics are in competition with researchers in public institutes.  In sum, based on the difference and commonalities in stakeholders’ assessment of the importance of PSR funding streams, this study attempts to identify the key factors behind the success of Nordic countries.

ABSTRACT. The acknowledgement sections of scientific publications are a rich source of data that can inform us about how science is conducted. One application of this data is to link research inputs (grants) with research outputs (publications), in order to reveal details of the funding landscape without relying on the reports of individual funders. Yet, acknowledgement data do not conform to a single recognised standard. Instead they are reported in an unstructured manner, which complicates the identification and extraction of relevant funding data. Some commercial data providers provide extracted data on funders from publications but how well do they collate and aggregate data that relates to particular funders? Indeed how should such data be collected, extracted and coded to give an accurate picture of the funding landscape in a given field?

To address these questions the paper proposes a method and a series of guidelines for the collection and processing of acknowledgements sections contained publications in order to extract and codify relevant data on research funding. We then compare our findings with results from alternative datasets provided by searching MEDLINE/ Pubmed and ISI WoS.

We apply our approach to the field of cancer research in the UK. We identify a sample of 7,510 publications produced in the year 2011 that involved at least one author affiliated to a UK research host organisation. For each publication in the sample, we extracted the funding data from the acknowledgement section. We then developed a number of guidelines for funding data collection and coding. We describe the rationale for our approach with associated caveats and limitations.

We use the processed data to explore patterns of co-funding among funding organisations in the setting of cancer research in the UK. The analysis shows the UK contributes 6.9% of global research publications in the field ‘neoplasms’ (a term that captures research related to cancer and pre-cancerous growths) that year. The results are informative about the main features of the UK research funding landscape. Funding sources were acknowledged in half of publications, and revealed that almost two thirds of these papers benefited from multiple funders with a mean of 3.3 funders per publication. The analysis also reveals not only the major UK funders but also shows the important role of overseas funding in supporting the work of UK authors, through the funding of their co-authors – just under half benefited from overseas funding. The role of industry is was shown to be considerable, with almost a fifth were supported by firms in the UK or elsewhere. Finally this bottom up approach to identifying research funders reveals the contributions of myriad small charities to cancer funding.

We also compare our data with that obtained by querying on the same set of publications in MEDLINE/PubMed and ISI Web of Science (WoS). Results of the comparison demonstrate the poor coverage of funders in MEDLINE/PubMed, and the incomplete aggregation of funders in ISI Web of Science, relative to results obtain through manual collection, cleaning and aggregation of the data using a team approach, coordinated with the guidelines we describe in the paper.

ABSTRACT. As universities develop in-depth relationships with industries and non-profit organization, a growing number of researches in universities are steered by non-public funding. The same trend also affects the university research in China. The R&D expenditures performed by higher education institutions from business enterprise raised from 2,483 million Yuan in 2000 to 28,928 million Yuan in 2013. As the research finance system transforming from the government dominant model to a multi-agent-based model, there are concerns that non-public research contract might distract the researcher from traditional academic activities to short-term applied research, which would harm the knowledge production (Glenna,et al.,2011; Gulbrandsen &Smeby,2005; Slaughter & Rhoades, 2004). 
This study intends to explore the influence of non-public funding on the academic productivity of university faculties in China. We take use of the survey of “The Changing Academic Profession in Asia: China” (CAPA: CN), consisting of a 2400 academic faculties from China in 2012. Negative binomial regressions are applied to estimate the number of articles and patents by the faculty during 2009-2012, controlling for individual and institutional factors and public funding. To address the potential endogeneity problem of the grants, we employ an instrumental approach after the regression.
The findings demonstrate the positive influence of the non-public research funds on the amount of patents and technology transfer, confirming the assumption that non-public grant promotes the applied research. On the other hand, non-public research funding has no significant influence on scholarly output, especially the high quality research. When we don’t consider the endogeneity, non-public funding is positively associated with the amount of domestic published articles. However, after controlling for the endogeneity, non-public grant does not affect publication in domestic journal. Moreover, there is no significant influence of non-public grant on the international journal articles, which indicates the ineffectiveness of the non-public research funding to generate high-quality research. These results suggests that an increasing reliance on non-public funding can promote applied research, but may be ineffective to enhance the academic productivity in the long run if the publication output is not improved.
Another noteworthy finding is that the influence of non-public grant on the academic output varies across different types of institutions. Chinese higher education is a highly hierarchical system, with priority funding given to selective institutions in Project 211 and Project 985. Compared with the selective institutions, we find the effect of non-public funding on academic output is smaller in less selective institutions. The result implies that public priority funding can be complementary to non-public funding in terms of increasing academic productivity.

ABSTRACT. This paper aims to analyze public policies to encourage the development of the software industry and IT services in Brazil, implemented from 2003 to 2010, based on the normative dimension, objectives, goals and priorities, and the positive dimension, from the experiences and perceptions of firms. It discusses how the institutional construction of supporting public policy is articulated with the instruments and implementation mechanisms, and these, in turn, with the development and needs of the Brazilian software industry and IT services. This production system is at the center of the techno-economic paradigm based on ICTs, which is responsible for storing the knowledge liable of encoding, in order to process and make it available or executable at any time. Thus, based on the neo-Schumpeterian politics on scientific, technological and innovative development, we carried out an analysis of the major Brazilian supporting public policies, from the perspective of institutional building of legal instruments and devices to implement actions to encourage the productive sector. The study researched in official documents – guidelines and policy evaluations –, interviews with policy-makers and firms in the software industry, which allowed the identification of strengths and limitations of supporting public policies. It was noticed that policy implementation has some shortcomings that limit its power to intervene on the innovative efforts of firms, particularly related to the complex relations between the basis for politics and its instruments. Thus, it became clear that the policies are comprehensively designed for the entire national productive system, involving the compatibility of interests of general policies with micro-economic and / or marketing dimensions of the supported segments, restricting the possibilities of incentives and / or directing policies specific to particular stages of technological and innovative development. Under review more careful of the productive system of software and IT service, identifies important sets of fortress, debility and opportunities. Stand out the amplitude of the internal market, the dynamism of the associated industry to the flexibility and creativity of companies and technical staff, the sophistication and attractiveness of some of their segments and the capacity generate satisfactory solutions to the wide economic activities range. Among the main debility of the industry, it’s possible to mention the excessive fragmentation of the industrial structural and the difficulties to implement strategies more effectives internationalization, capable of translate increased penetration of the national products abroad. Despite these problems, It is possible to identify opportunities, given the overall growth of the productive system that can be exploited to maintain the dynamism of industry, (TIGRE et al.,2009b). The entrepreneurial heterogeneity, the dimension of the Brazilian market and the needs of guarantee mechanism of proprietorship increased the complexity of the productive system. The new policy of Brazilian innovation to software – Productive Development Policy (PDP) – is related to the national technological densification (adensamento), as well as to the greater insertion these activities in other productive systems. Thus, the discussion of tools to the new industrial policy of innovation to the segment has the institutional framework the intensification of actions that possibility that allow greater availability of qualified labor and mechanisms that enable the exploration of market niches to companies of national capital. The normative role of the support policy is compound by inherent challenges to the own intrinsic characteristics to the competitive dynamic of the software industry, considering that your structure of costs is quite different of the others productive systems, marked by the existence of the high fixed costs and marginal costs practically nonexistent. This characteristic determines the innovation process, because most of the costs are generated before of the product commercialization. Moreover, depending on the competitive dynamic between systems, the index of commercial success in introduction of new products is relatively low. Such characteristics imply two relevant elements to the dynamics of the software industry in Brazil. First of all, the costs have this peculiarity, because the most part of the initial investments is held on human resources hiring. Thus, the availability of qualified labor for the accumulation of knowledge that allow strengthen the productive system is considerate the main element of the industrial dynamics. In the second place, the outcome of the software and IT services industry is related to the existence of minimum customer contingency that guarantee returns to the initial investments.

ABSTRACT. Innovation has become a leitmotif of policy-making around the globe. Hardly a week passes without a government announcing an innovation strategy for a city, region, or country, or an institution branding itself as a driver of innovation. As a result of this flourishing of “innovation” as a policy category, innovation practice has become increasingly heterogeneous and decentralized, driven by diverse groups of practitioners (e.g. policy-makers, institutional managers, consultants) rather than academic discourse alone. Despite an empirical appreciation of this diversity in innovation policy, however, the innovation literature continues to focus on innovation’s supposedly universal mechanics and their operationalization in the form of innovation models. “Innovation” increasingly comes in the form of pre-packaged plug-in solutions and “best practices” – such as the “Silicon Valley” ecosystem model, or the “MIT” institutional model – that travel with the promise of solving socioeconomic woes almost independently of where and what these woes are.

In this paper, we argue that the current focus on universal mechanics and “best-practice transfer” is flawed and offer an alternative perspective. Using a cross-country comparative analysis of three implementations of the “MIT model” in the UK, Portugal, and Singapore, we show how key features in the design, implementation, and performance of innovation policy remain unexplained if treated as mere variations on a common theme that presupposes identical systems components, circulatable models, or globally shared rationales about development. We employ the concept of sociotechnical imaginaries as “imagined forms of social life and social order centering on the development and fulfillment of national scientific and/or technological projects” to show how the goals and activities that went into the each implementation of the MIT model – and with it into the conceptualization of “innovation” itself – are being locally constructed vis-à-vis contingent visions of desirable sociotechnical futures, and only minimally rely on what MIT practices “are.” In each partnership, different Imaginaries of Innovation shape how the need for innovation arises and is justified in light of a perceived socioeconomic ailment, how the “MIT model” is expected to cure this ailment, and how innovation should consequently be organized. Our study flips the conventional notion of “best-practice transfer” on its head: Instead of asking how well an innovation model has been adopted, we look at the differences in MIT’s partnerships as a window onto the unique social, political, and cultural determinants that underwrite innovation policy in each country. Our study is based which is based on more than 80 interviews and extensive document analysis for the three partnerships under consideration: the Cambridge MIT Institute (CMI); the MIT Portugal Program (MPP); and a suite of three partnerships between MIT and Singapore – the Singapore MIT Alliance, the Singapore MIT Alliance for Research and Technology, and the Singapore University for Technology and Design.

Our paper has three main theoretical ambitions. First, we wish to challenge the common notion of innovation and its “models” as panaceas for social woes, and replace it by that of innovation as a diagnosis – a “self-diagnosis of society,” if you want – as to what is perceived as lacking and which policy pathways are acceptable within a given political culture to address this lack. Second and related, we hope break new ground for theorizing politics and culture more seriously in the context of innovation policy. While most authors agree that “culture and politics matter” for innovation, a systematic theorization of where, when, and how they matter is strikingly absent. Here, Imaginaries of Innovation provide a way forward. Third, we provide a theoretical underpinning for the phenomenon of “best-practice transfer” and the common puzzle how a single practice can serve as a model for diverse countries and cultures. Our analysis has demonstrated the “MIT model” depends principally on what practices various societies seek and can accommodate within their political culture, much less on what MIT practices “are.” In each case, the “MIT model” – and with it the concept, purpose, and mechanics of innovation – is co-produced with contingent understandings of how a society is ailing and equally contingent visions of what is needed. It is not despite these country differences that the “MIT model” is effective or acquires any meaning. Rather, it is because of it.

ABSTRACT. Since the works of Schumpeter, innovation has been recognized as a driver of economic development. Scholars have focused on identifying the factors that drive innovation, covering from institutional settings to interaction patterns. Florida (2002, 2008) has proposed a conceptual framework considering urban, geographical and social conditions directly related to regions or cities. He stated that creativity rises in regions or cities where technology, talent and tolerance flourish, thereby individuals with greater innovation competences would pick them when addressing their location question. The result is a direct impact on the local economic development based on a higher likelihood of new technology-based firms. To build his argument, Florida has focused on the developed world. In the United States, and based on city-comparative studies, he has put forward the case of cities that despite following the “traditional paradigm” of promoting economic development by means of new technology parks and Science, Technology and Innovation (STI) subsidies have not become vibrant knowledge-based economies. Buffalo, New Orleans and Louisville, albeit their efforts, have not reached innovation and entrepreneurship standards easily found in Austin, Boston, San Francisco or Seattle. In that vein, the latter have been able to attract a greater share of the high-skilled innovative population with the underlying impact on their local economic development. Florida defines such group as the so-called Creative Class, which is composed by individuals who make intensive use of their minds in their daily jobs and constantly create and need novel combinations of ideas and knowledge. He arguments that regional development depends on a combination of factors that attract and retain the Creative Class, grouped under three concepts: Technology, Talent and Tolerance. To represent the capacity a city may have to attract the Creative Class, he builds indexes related to each concept that in turn add up to determine a region’s Index of Creativity (Florida 2002, 2007, 2008). Although Florida’s premises may have great effect for regional economic development policy, studies have been restrained to developed countries. Thus far, the developing world has not been a great focus for Florida’s framework, which raises questions on the validity of the current shape of the theory in those countries. New phenomenon directly related with developing nations and absent in the nations where the framework have been applied, may turn into new empirical and theoretical challenges. Inequality may be one of them. In that line, the article proposes the application of Florida’s Creative Class conceptual framework in a social and geographical unequal developing country: Chile. We expect to determine the effects produced by social inequality dynamics in the capacity to attract and retain the Creative Class in each of the 15 Chilean Administrative Regions. Chile, an OECD country member, is nowadays recognized as the safest country to invest in Latin America in light of its institutional and governance stability (MINECON, 2014). Furthermore, during the last decades, Chile has gone through a successful period of economic growth. However, two challenges related to the high social and economic inequality and the excessive institutional centralization the country has had for decades. Also, the excessive dependency on natural resources based industries and their cost minimization business models have made of innovation an economic driver to be actively promoted for the country to reach a higher development level. Therefore, Chile’s social inequality, high institutional centralization, and new development innovation-based perspectives make of it a good locus to extend Florida’s premises and explore their reach in the developing world. In regard to our methods, we first calculated normalized indexes of Technology, Talent, and Tolerance for each of Chile’s 15 Administrative Regions. We drew upon the National 2012 Census, the VIII National Innovation Survey, and the National Socioeconomic Survey (CASEN). Proxies were used in regard to the number of gay and lesbians and of artists and bohemians, as official data have not been recorded yet. Then, we determine the Creativity Index of each region. Secondly, each region’s Gini Coefficient is calculated and normalized. Using statistical tools and qualitative analysis, we compare inequality and creative class dynamics in each region. Finally, we extend our comparative analysis to two specific variables: Social Capital and Tolerance, to explore whether both follows similar patterns, and if not, describe the reasons behind it. We conclude that Chile’s regions can be grouped under five typologies resulting from a two-dimension framework crossing inequality and creativity. Likewise, attraction and retention of the Creative Class follow dissimilar patterns as the relation between social inequality and creativity differs at regional level.

ABSTRACT. Despite strides in assessing innovation potential of nations and firms, our collective understanding of innovation potential at the sectoral and regional levels remains nascent. The global community of practice has undertaken few efforts to translate emerging research in innovation systems and practices into user-friendly analytical and decision-making tools. In response to this, a team of researchers from the Global Knowledge Initiative and the Georgia Tech Research Institute have created an analytical framework and toolset that allows decision makers to assess the innovation potential within opportunity areas and problem spaces for specific contexts. We present the framework and toolset as developed and tested in a recent project. Significant global variation exists in different regions’ abilities to both create and absorb innovation such that their potential to deliver economic and social value is fully realized. These contextual variations shape how innovations are adapted, disseminated, and bundled. Differences in terms of how decision makers (government officials, civil society leaders, donors) frame innovation opportunities, evaluate trade-offs, and devise strategies to unlock the potential for innovation also bear on whether solutions prove valuable or transformative. As a result, many possibly game-changing innovations are not adopted, especially in lesser developed contexts. Correctly assessing innovation potential therefore requires analyzing the context and problem space in which an innovative solution is to be deployed. One cannot hope to fully represent the complexity inherent in large-scale social problems; there are too many interactions and unknowns. However, using a suite of systems analysis, strategic foresight, and data integration tools, decision makers can better visualize these complex problem spaces and clarify the interacting forces at play. Towards this goal, we developed a rigorously researched analytical framework and toolset for assessing innovation potential within a systems context. This framework and associated toolset is designed to allow users to contend with the many considerations that bear on whether an opportunity area is primed for outsized impact, or whether a problem space exhibits sufficient innovation potential to warrant investment. The toolset starts with two defining taxonomies that capture the components of sociotechnical systems in conjunction with the enablers and barriers that define an innovation system. The taxonomies recognize that the diffusion of innovation in sociotechnical systems occurs around different structures and behaviors that can be identified at micro, meso, and macro levels of the system. Part of the difficulty in assessing innovation potential is that there are two separate systems, the problem space and the innovation landscape. These two systems share regional characteristics and actors, but may be uncoupled in their relative structure and behaviors. Using established research from system sciences and innovation systems we define a framework for data collection in both contexts. Two system framing tools are used to aid in workshops, interviews, group dialog, and study of the respective problem space and innovation landscape. Both are established methods with long histories of effectiveness. A strategic foresight method derived from the “Three Horizons” toolset developed by the International Futures Forum is used to gain inclusive agreement on trends in the current system and scenarios for long and near term futures. A sensemaking and systems mapping methodology that combines an action oriented narrative with visual diagrams of the systems forms the heart of the systems analysis. A key aspect of this method is the development of generalized system descriptions across multiple contexts in order to find opportunities that might scale broadly. In addition to these tools, we suggest the use of a number of management and decision making tools that aid in collecting, characterizing, ranking, and quantifying problems and innovation enablers/barriers for decision makers. These tools support a qualitative ranking jointly of our two systems – the assessment of needs in the problem space and the assessment of innovation enablers/barriers in the innovation landscape, which provides a novel approach to quantify decision making compares these two assessments in the current and future desired systems. Finally, we discuss a tool to support an action based theory of change to create a framework for future development and execution. The entire framework supports a robust methodology for focused research on innovation as a potential solution for complex social problems.

ABSTRACT. Portfolio analysis in terms of journals may provide insights into the specialization of countries, cities, or knowledge-producing organizations such as universities and firms (for a literature review see Wallace & Rafols, in press). Analytically, the matrix of journals versus countries has been basic to evaluative bibliometrics (Narin, 1976). We introduce an instrument to generate such a matrix for the purpose of mapping and analyzing portfolios using tools available online.

The base map onto which the portfolios can be overlaid was developed by Leydesdorff, Rafols, & Chen (2013). Portfolios can be measured for any document set retrieved from WoS. In addition to the visuals, the data can be analyzed statistically using the matrix generated in formats compatible to SPSS and Pajek/UCINET. Analytically, this extension enables the user to compare among units (e.g., firms), whereas the visuals provide overviews of the results.

First, the user is invited to identify a document set by using the “Advanced Search” interface of WoS. The identified documents can be examined online using the analytical interface of WoS, “Analyze Results”. For example, we searched WoS using the search string: “cu=South Africa and py=2013.” In a number of steps (http://www.leydesdorff.net/portfolio provides an instruction), the user can thus generate Figure 1. ((This map can be web-started at http://www.vosviewer.com/vosviewer.php?map=http://www.leydesdorff.net/portfolio/sa.vos.)

The routine generates a file with Rao-Stirling diversity values and the modification of this measure (2D3) proposed by Zhang et al. (in press). Diversity can be considered as a measure of the interdisciplinarity of the portfolios under study (cf. Rafols & Meyer, 2010).The vector with the number of documents for each of the 10,000+ journals is saved each time as a column in the file “matrix.dbf”. This file can be read into SPSS for statistical analysis. One also obtains files for network visualization and analysis in Pajek.

We provide examples at three levels of aggregation. A sample of 43 leading nations covers 1,753,243 documents (that is 89.4% of the total in 2013). Using the matrix of these countries versus journals, we found a remarkably strong divide between advanced and less-developed nations. However, a more finely-grained analysis showed regional differences.

Cities can be expected to entertain different portfolios both in terms of their sizes and given differences among national cultures (e.g., Van Noorden, 2010). A diverse knowledge base provides more opportunities for knowledge development and related diversification (Heimeriks & Boschma, 2013). We selected four cities with sufficient variety in each of five different countries: China, France, Israel, the Netherlands, and the USA. Israel and Israeli cities (Haifa, Tel Aviv and Beer Sheva) scored highest on diversity.

Figure 2 shows the cosine-normalized comparison among three universities (LSE, Georgia Tech, and University of Amsterdam) and seven large corporations. (The map can be web-started at http://www.vosviewer.com/vosviewer.php?map=http://www.leydesdorff.net/portfolio/cos_univ.txt&label_size=1.40&view=3 .) The portfolios of the three universities are significantly different in terms of their industrial affinities. We noted that the consolidated names (“OG=”) in the database were reliable in the case of universities, but not when using company names. The instrument, however, can be used with any document set retrieved from WoS; for example, for analyzing and comparing individual authors or document sets retrieved on the basis of informed search strings.

References - Heimeriks, G., & Boschma, R. (2013). The path-and place-dependent nature of scientific knowledge production in biotech 1986–2008. Journal of Economic Geography, 14(2), 339-354. - Leydesdorff, L., Rafols, I., & Chen, C. (2013). Interactive Overlays of Journals and the Measurement of Interdisciplinarity on the basis of Aggregated Journal-Journal Citations. Journal of the American Society for Information Science and Technology, 64(12), 2573-2586. - Narin, F. (1976). Evaluative Bibliometrics: The Use of Publication and Citation Analysis in the Evaluation of Scientific Activity. Washington, DC: National Science Foundation. - Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263-287. - Van Noorden, R. (2010). Cities: Building the best cities for science. Nature, 467(7318), 906-908. - Wallace, M. L., & Rafols, I. (in press). Research Portfolios in Science Policy: Moving from Financial Returns to Societal Benefits. Minerva. - Zhang, L., Rousseau, R., & Glänzel, W. (in press). Diversity of references as an indicator for interdisciplinarity of journals: Taking similarity between subject fields into account. Journal of the Association for Information Science and Technology.

ABSTRACT. Context Increasingly governments are using national published strategies to help accelerate the development of novel technologies, improve competitiveness in sectors, and support and develop manufacturing. These strategies convene relevant actors to provide visibility and promote the intended actions and directions of the relevant community. Such strategies are often elicited by policymakers and are developed as part of a broader program. For example, the UK has recently developed a number of technology related strategies as part of its industrial strategy, including a composites strategy, a quantum technology landscape, and a robotics and autonomous systems landscape.

Purpose and aim A brief international review of technology strategies reveals that they focus on different levels of aggregation of economic activity and pay different attention to different aspects of innovation and competitiveness. For example, the National Nanotechnology Initiative: Strategic Plan (NSTC, 2014) provides a vision and focuses on providing visibility of the activities of the actors in the US nanotechnology community. However, Materials science and engineering in Germany (acatech, 2008) explores what particular aspects of materials research and development need to be targeted to accelerate development, including scale-up, data-compatibility, and simulation and modelling. The frameworks, guiding principles, and practices used to develop these technology strategies are clearly different.

Frameworks are ‘a basic structure underlying a system or context’ (Oxford English Dictionary). Technology development frameworks can help navigate technologies’ increasing complexity. Furthermore, they define a number of dimensions (e.g. time, vision, etc.) and ‘topics’ or areas related to innovation and deployment (e.g. standards, regulation, supply chain, etc.), helping to collect and structure relevant information. Finally, frameworks can be trialled, developed, and improved to create more established and tested structures for these exercises. This paper identifies what policymakers aim to achieve by developing technology strategies. It also explores: - what types of strategies both those who want a strategy developed (elicitors) and those who develop a strategy (developers) believe exist and what frameworks, guiding principles, and practices are used to develop them; - how strategy elicitors and developers understand the different ‘topics’ or areas related to innovation and deployment on which a strategy development exercise might focus and how these topics are selected; and - how elicitors’ and developers’ intentions and expected outcomes of the strategy exercises differ.

Particular dimensions underpin strategies, such as developing a vision and understanding what activities need to occur to reach this vision. This paper also draws out these underpinning dimensions and how they have been applied in the context of technology strategies.

Method Interviews were conducted with policymakers who recently elicited strategies and consulting experts who developed these strategies. The policy makers were asked why they wanted technology strategies. The consultants were asked what they thought these strategies were for. Both groups were asked what frameworks they used, what they believed to be the defining features of these frameworks, and what processes and practices were used to develop the strategies. They were also asked what different types of strategies they believed existed.

Results The interviews revealed only a vague understanding of the differences between different types of strategies, with technology roadmapping, landscapes, and horizon scans being the most commonly named. The interviews indicated that technology roadmapping is the most frequently employed framework. However, roadmapping took a number of different forms and Phaal and Muller’s (2009) framework was altered considerably for application in the public sector. Other frameworks, such and landscapes and horizons scans, were vague in detail and applied in vastly different forms, indicating few fixed frameworks for policy developers to choose between. The activities used to develop strategies also varied substantially, suggesting that these activities can be configured differently to achieve different objectives.

The strategy literature suggests that the fundamental questions of how, what, why, when, and who form the basis of a strategy. The interviews supported this view, with a vision (what they wanted), time, and how they were to get there the most common responses. Furthermore, strategy elicitors and developers both indicated that the topics for analysis were selected by the participants, suggesting that the make-up of the group developing the strategies is an important consideration. Finally, the findings suggest that there is little understanding of how technology strategies fit into the changing policies used over the cycle of technology emergence to emerged industry.

ABSTRACT. A cursory glance through recently published literature on advances in biotechnology reveals a determined effort to articulate the relevance and benevolence of emerging technoscience. While this trend almost certainly reflects the priorities of funding agencies and the requirement to link scientific research to its societal impacts, these statements also reveal and contribute to the resilience of an optimistic narrative about biotechnology as a field, an industry, and a necessary solution (Bud, 1998). The stories woven around agricultural biotechnology in particular have commonly framed genetic modification as the only way to avert crisis given the combined challenges of global climate change, population growth, and finite land and water resources (Jansen & Gupta, 2009). Importantly, these narratives, which are often partial or largely hypothetical, are sourced to experts who conduct biotechnology research and evaluate its impacts (Glover, 2010). Glover (2010) insists that these stories, repeated by politicians, policymakers, and the media, “[distort] public debate and [impede] the development of sound, evidence-based policy” (p. 484), and he calls upon academic scholars to fulfill their “responsibility” for honest communication of both the promise and limitations of their science.
As part of a much larger conversation about the responsibility of science to society, responsible innovation has gained traction as a governance framework for attending to the ethical implications of the products, processes, and, uniquely, the purposes of research. Unlike conventional governance, responsible innovation attempts to answer questions about why innovation is pursued and whether or not its ends are both transparent and in the public interest (Stilgoe et al., 2013). Emerging with a complicated legacy in genetically modified (GM) crops and in a moment of much attention to responsible innovation, new GM trees are subject to the normative expectation that their development should be rooted in the values and propelled by the problems that are important to the public. In response, the developers and proponents of GM trees have carefully constructed narratives about the socially and environmentally responsible purposes of tree biotechnology, including the restoration of ecosystems and economies. These messages are not benign. Stories play a central role in how individuals process and share information as they make sense of the world (Jones et al., 2014), and scholars of linguistics, public perception, and the political process have demonstrated that the language used by scientists, the media, and policy-makers both reflects and shapes debates and decisions regarding emerging technoscience (Cook, 2004). How will the narratives emerging around GM trees shape the political - and physical - landscape?
This paper uses the Narrative Policy Framework (NPF) (Jones & McBeth, 2010) to systematically examine the policy narratives used by the developers of GM trees as they seek to influence both public perception and the regulation of tree biotechnology. Specifically, it compares the notions of responsibility embedded in those narratives as policy belief systems related to commodification, intellectual property, naturalness, and power. As individuals whose personal narratives reverberate through the coalitions in which they engage, researchers are considered in this paper to inhabit the fluid boundary between the micro- and meso-level of NPF analysis. Consequently, this paper combines content analysis, the standard in meso-level studies, with interviews, which are characteristic of micro-level studies (Jones et al., 2014). Early attention to the stories being told at this new frontier of genetic engineering may contribute to the refinement of concepts in both NPF and responsible innovation literatures.
References
Bud, R. (1998). Molecular Biology and the Long-Term History of Biotechnology. In A. Thackray (Ed.), Private Science: Biotechnology and the Rise of the Molecular Sciences (3-19). Philadelphia: University of Pennsylvania Press. 
Cook, G. (2004). Genetically Modified Language: The Discourse of Arguments for GM Crops and Food. Routledge; New York.  
Glover, D. (2010). Is Bt Cotton a Pro-Poor Technology? A Review and Critique of the Empirical Record. Journal of Agrarian Change, 10(4), 482–509.
Jansen, K., & Gupta, A. (2009). Anticipating the future: “Biotechnology for the poor” as unrealized promise? Futures, 41(7), 436–445. 
Jones, M. D., & McBeth, M. K. (2010). A Narrative Policy Framework: Clear Enough to Be Wrong? Policy Studies Journal, 38(2), 329–353. 
Jones, M.D., E.A. Shanahan, & M.K. McBeth. (2014). Science of Stories: Applications of the Narrative Policy Framework in Public Policy Analysis. Gordonsville, VA: Palgrave Macmillan. 
Stilgoe, J., R. Owen, and Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568-1580.

ABSTRACT. Emerging technologies, such as transgenic trees, are theoretically born into what Stilgoe, Owen, and Macnaghten (2013) describe as an “institutional void...there are few agreed upon structures or rules that govern them.” Responsible Innovation (RI) may be one way to fill that void, calling for dialogue involving a broad array of stakeholders in the social process of integrating emerging technologies. Gaining traction as a framework with the rise of genetically engineered organisms, RI aims to prevent rejection of promising technologies due to public misunderstanding or anxiety through such inclusions. RI, in short, aims to ensure that science and innovation generate socially responsible and beneficial outcomes. 

Further, RI may offer the space for the “amplification...of the still, small voices of folks previously excluded from offering constructive visions of futures” (Guston, 2014) that had been initiated, however incompletely, with anticipatory governance frames. However, because RI has been critiqued for its exclusion of politics and therefore power (von Oudheusden, 2014), and because race represents such an important factor in the distribution of political power, RI would be strengthened with the addition of Environmental Justice (EJ) principles.

Ottinger (2013) links STS and EJ, highlighting the political nature of knowledge. There are different and equally valid knowledge bases - expert and local/lay are common categories - and that knowledge, generally, is dynamic. As noted, RI has called for increased public engagement throughout the innovation process. Ottinger (2013), however, has raised concerns that the types of knowledge(s) and information required to make informed decisions are generally absent at the moment of decision-making, thus minimizing their potential effectiveness. RI could represent a more intentional, longer-term vision of engagement, as outlined by Stilgoe, Owen & Macnaghten’s framework (2013), which include anticipation, reflexivity, inclusion, and responsiveness. Merging the RI and EJ frameworks could create the kind of important space for dynamic knowledges required of meaningful, informed, shared decision-making. 

Historically, EJ has, with good reason, focused primarily on specific industrial processes’ disproportionate effects on communities of color. Applying these frameworks to an emerging technology such as GE trees would allow us to expand the application of EJ and RI alike, responding to Finney’s suggestion that uniquely constituted problems, such as environmental change or emerging technology, create space for integrating diverse socio-cultural perspectives that are often challenged or resisted: traditionally invisible people, alternative perspectives, and excluded voices (Finney, 2014). 

GE trees make for a particularly compelling case for these frameworks; the narratives surrounding GE trees fall under the shadow of GE crops, which have had polarizing effects globally. Importantly, for RI and EJ principles, GE chestnuts trees represent a range of conditions to which these analytical frameworks could be applied. The developers of the GE chestnut have actively garnered public support of their project; how has their public engagement addressed issues of power and race? Situated in physical proximity to a number of active Native American nations in New York State, how will these communities perceive eventual release of the transgenic chestnut, especially in the context of indigenous sovereignty? How do the GE chestnut technoscientists perceive these understandings in their own landscape? An analytical frame that merges RI and EJ would allow us to address these complex negotiations. Similarly, such an approach would allow us to explore the controversy over power and racial/ethnic identities surrounding the release of GE eucalyptus as a biofuel crop in South Africa. Although the regulatory regimes and end uses are distinct, there are clear parallels: how traditionally invisible communities of color are engaged with respect to an emerging technology, and how the developers of said technologies see their own role in the process. 

Finney, C. (2014). Black Faces, White Spaces: Reimagining the relationship of African Americans to the great outdoors. Chapel Hill: The University of North Carolina Press. 

Guston, D. H. (2014). Understanding ‘anticipatory governance.’ Social Studies of Science, 44(2), 218-242.

Ottinger, G. (2013). Changing knowledge, local knowledge, and knowledge gaps: STS insights into procedural justice. Science, Technology, & Human Values, 38 (2), 250-270.

Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580. 

Von Oudheusden, M. (2014). Where are the politics in responsible innovation? European governance, technological assessments, and beyond. Journal of Responsible Innovation, 1(1), 67-86.

ABSTRACT. The Institute of Forest Bioscience was founded to promote forest biotechnology in a responsible manner, cognizant of the mistakes and challenges that developers of genetically modified crops have faced over the last twenty years. As a non-governmental organization, their normative commitments are to “science, dialogue, and stewardship,” and they have developed programs to engage with academic scientists, corporate developers, regulators, political representatives, and laypersons. In a sense, they embody an effort to achieve responsible innovation in an anticipatory way. Preliminary interviews suggest that they actively anticipate regulatory and public responses to potential technologies, and they work with experts to manage those responses in ways that promote “responsible use” (their term) of forest biotechnology.

Anticipatory governance and responsible innovation offer conceptual frameworks for suffusing the innovation process with avenues for public input and expert reflection. Guston (2014) describes anticipatory governance as “an ensemble of activities designed to build research and societal capacities for foresight, public engagement, and integration of social and natural sciences” (p. 226). Importantly, a broad definition of governance activities specifically includes the technological, political, and rhetorical choices that experts make in the course of their research. Previous work on anticipatory governance in nanotechnology development has focused largely on scientists who are not initially attuned to their implicit role in governing their own innovations (p. 232). We diverge from this body of work by studying scientists and expert organizations largely cognizant of the potential long-term and large-scale ramifications of forest biotechnology. Thus, governance is not only anticipatory, but anticipated.

Stilgoe et al. (2013) offer a broad definition of responsible innovation, describing it as simply “taking care of the future through collective stewardship of science and innovation in the present” (p. 1570). More explicitly, Owen et al. (2012) codify the responsibility of science to be ethical in its products, processes, and purposes. In a similar vein, Stilgoe et al. (2013) identify four integrated and overlapping dimensions of responsible innovation: anticipation, reflexivity, inclusion, and responsiveness. Anticipation is not about predicting, modeling, or divining the future (Guston, 2014), but as a principle “prompts researchers and organizations to...consider contingency, what is known, what is likely, what is plausible and what is possible” (Stilgoe et al., 2013, p. 1570) and to prepare for an uncertain future. Reflexivity requires individual scientists and institutions to examine their “own activities, commitments and assumptions” and “directly challenges assumptions of scientific amorality” (Stilgoe et al., 2013, p. 1571). Inclusion refers to democratic participation and links to notions of procedural justice. Finally, responsiveness in “responsible innovation requires a capacity to change shape or direction in response to stakeholder and public values and changing circumstances” (Stilgoe et al., 2013, p. 1572). And at the extreme, such ideas raise the possibility of “responsible non-innovation,” a relevant question to pose in the multiple pathways of GM tree innovation.

We focus on GM trees for several reasons. First, GM trees represent a technological and regulatory extension of well-established agricultural biotechnologies. Existing techniques of genetic engineering and existing frameworks for the governance of GM plants form a landscape upon which GM trees may take root. Second, GM trees offer intriguing comparisons across specific technological applications. On one hand, efforts are underway to rescue the American chestnut from extinction through genetic modification - a project with conservation/cultural goals. On the other hand, much of the research in GM trees focuses on improving feedstocks for biofuel applications - projects with clear commercial relevance that could mirror the R&D and intellectual property pathways of existing agricultural biotechnologies. Third, GM trees offer the opportunity for comparison of research occurring in industrial, academic, and hybrid settings, which could contribute to broad debates about how the context of research impacts scientific practice.

In this paper we analyze the Institute of Forest Bioscience (formerly Institute of Forest Biotechnology) programs and activities through the lens of scholarship on anticipatory governance and responsible innovation. Our data come from interviews with IFB staff, interviews with affiliated scientists, analysis of formal IFB documents, and a review of media coverage of IFB activities.

ABSTRACT. Consumer, farmer, and activist opposition to genetic engineering (GE) are predominantly aimed at the shortcomings of safety and risk assessments. Science studies scholar Brian Wynne (2007) argues that the risk discourse-based regulatory approach the US takes towards the governance of GE is common in the science-policy culture, but is problematic in that it “preempts any mainstream democratic policy debate about the proper human purposes and cultural meanings of knowledge” (367). Scientists are beginning to notice that the scope of public concern exceeds that which is accounted for directly in the US regulatory structure. Thus, scientists themselves are increasingly anticipating and planning for public objection to certain forms of GE development.

This paper examines the mechanisms by which research scientists in the area of tree biotechnology — the American chestnut, more specifically — are beginning to self-govern, in the absence of a US Coordinated Framework for the Regulation of Biotechnology that adequately addresses the wide range of public concerns about GE. According to Erik Fisher (2011), governance of emerging technologies has “expanded from risks, impacts, implications and dimensions to include science, technology and innovation as political issue areas in themselves,” including increasing consideration of ethical, legal, and social implications of technology development through engaged scholarship and responsible innovation (609). My research will consider how current, second-generation biotechnologists themselves are identifying risk, and in turn how they are accounting for increased public concerns directed towards their own work.

I will use the American Chestnut Research and Restoration Project at the SUNY College of Environmental Science and Forestry in Syracuse, New York, as a case study. Responding to the near-eradication of the American chestnut tree population from Cryphonectria parasitica (chestnut blight) over the past century, the SUNY-ESF team has successfully utilized GE techniques to transfer the oxalate oxidase gene from wheat into the tree, “detoxifying” the oxalic acid released by the tree-killing cankers the blight produces. Scientists are working to obtain deregulated status for the engineered trees, and are engaging with the public to crowd-source funding and support.

Tree crops present a unique case study of the “next generation” of plant biotechnology, which is likely to be received by the public in ways that differ from the products currently on the market. These tree crops have intrinsic relevance to the consumer in a way that currently available GE traits for staple crops do not. Many projects in tree engineering utilize public sector research programs, and not those using multinational corporations as a dominant funding source. Biologically, GE in trees is different from in field crops. The 1980s regulatory framework still utilized is no longer relevant to these newer kinds of technology, or questions from the “public.”

This critique is not new, as evidenced by a July 2015 memo from the White House OSTP acknowledging plans to modernize the outdated Coordinated Framework in order to “ensure public confidence in the regulatory system and to prevent prevent unnecessary barriers to future innovation and competitiveness” (1). This research will explore the extent to which the SUNY-ESF chestnut program scientists not only perceive these policies to contribute to negative public reaction to their research, but also the extent to which they believe science communication is the antidote. 

This paper will draw from participant observation and interviews with laboratory scientists working on the American Chestnut Research and Restoration Project. In addition, the laboratory’s interactions with their academic institution, with the biotechnology industry, with policymakers, and with the public, will be considered. Archival material will serve as additional sources for analysis. By studying biotechnology researchers, this paper aims to elucidate the ways in which scientists themselves are already navigating the Coordinated Framework, and the concerns about GE they recognize the policies fail to accommodate for. Finally, this work intends to clarify some of the gaps within current regulatory policies, and to provide an improved understanding of how scientists attend to public concerns and engagement in their work.

References:

Fisher, E. (2011). Editorial Overview. Science and Engineering Ethics, 17(4), 607-620.

Holdren, J. (2015). “Memo: Modernizing the Regulatory System for Biotechnology Products,” White House Office of Science & Technology Policy.

Wynne, B. (2007). Risky Delusions: Misunderstanding Science and Misperforming Publics in the GE Crops Issue. In Taylor, I.E.P. (Eds.), Genetically Engineered Crops: Interim Policies, Uncertain Legislation (340-372). New York: Haworth Food & Agricultural Products Press.

ABSTRACT. Temporary employment has been becoming more and more prevalent in many sectors in the Netherlands (Bertrand-Cloodt et al. 2012). However, it has been common in academia for a longer time, especially for recent PhD graduates (Association of Dutch Universities 2014). Temporary contracts can serve different goals for both the employer and the employee. For the employer, the main benefits are the possibility to adjust the size and composition of their workforce to the economic situation, and the possibility to determine the suitability of employees (CPB Netherlands Bureau for Economic Analysis 2011). In terms of job satisfaction, high-educated employees (i.e., those with a bachelor or higher degree) on a temporary contract are significantly less satisfied with their job security than high-educated employees on a permanent contract. However, there is no difference in overall job satisfaction between the two groups (De Graaf-Zijl 2012). This is due to a higher satisfaction with job content for high-educated employees on a temporary contract, which makes up for the gap in satisfaction with job security.

In our study, we determine whether the type of employment (contract) affects the job satisfaction of recent PhD graduates. Our study is based on a survey among 2,193 PhD graduates who obtained a PhD from one of five Dutch universities between 2008 and early 2012. The universities encompass all major fields of research. Of the 2,193 PhD graduates, 1,133 responded to our survey, making for a 52% (partial) response rate. Variables were constructed to measure: 1) type of employment (permanent, probation period of permanent contract, temporary with tenure track, temporary without prospect of permanence, and self-employment) and 2) satisfaction with three factors (job content, terms of employment, and work-life balance). Other factors were measured as well, such as the sector of employment (academia, non-academic research, or non-research), educational level required for the job (bachelor or lower, master, PhD, or professional degree), whether the PhD graduate has a supervisory role in their job, received support in the job (having a mentor), PhD characteristics, and personal characteristics.

We find PhD graduates on temporary contracts to be especially less satisfied with their terms of employment than PhDs on permanent contracts. Unsurprisingly, this difference in satisfaction is especially pronounced between PhDs on a permanent contract and PhDs on a temporary contract without prospect of permanence. In addition, we find that temporary contracts without the prospect of permanence negatively affect satisfaction with job content. On the other hand, self-employed PhDs are more satisfied with job content. Furthermore, we find that PhDs on a tenure track are less satisfied with their work-life balance, whereas PhDs on other types of temporary contracts are not. Hence, the type of non-permanent employment matters for PhDs.

Other results from our study show that the level of the job heavily influences job satisfaction, especially satisfaction with job content. Close to a quarter of our respondents indicates they have a job at master level, or even at bachelor or lower level. These PhDs are less satisfied with their job content than those working at PhD level. PhDs working at bachelor level or lower are also less satisfied with their terms of employment. Another effect was found for sector of employment: PhDs in non-academic research are more satisfied with their terms of employment than PhDs in academia.

Finally, we find some gender effects in our survey on job satisfaction. First of all, females are less satisfied with their work-life balance than males. Furthermore, we find that having a mentor positively influences satisfaction with job content. However, it only does so for women, suggesting that women benefit much more from a mentor than men.

In conclusion, we show that temporary employment affects the job satisfaction of recent PhD graduates, not only with their terms of employment, but also with job content. Other factors also play an important role, such as level of the job, sector of employment, having a mentor, and gender.

References Association of Dutch Universities (2014). Samenstelling universitair personeel per 31 december 2013. http://www.vsnu.nl/files/documenten/Feiten_en_Cijfers/website_WOPI_per_31-12-2013.xls. Accessed 20 November 2014. Bertrand-Cloodt, D., Cörvers, F., Kriechel, B & van Thor, J. (2012). Why do recent graduates enter into flexible jobs? De Economist, 160(2): 157-175. CPB Netherlands Bureau for Economic Policy Analysis (2011). Labour market flexibility in the Netherlands. Edited by F. Cörvers, R. Euwals and A. de Grip. De Swart: Den Haag. de Graaf-Zijl, M. (2012). Job satisfaction and contingent employment. De Economist, 160: 197-218.

ABSTRACT. While scientific production is one of the primary functions of academic institutions, education is another (Hackett, 1990). Formal teaching set aside, senior scientists (or principal investigators) are expected to train junior scientists including PhD students and postdocs as future scientists. Although these dual roles of academic institutions are indispensable for sustainable advancement of science, they could cause conflict, often resulting in undersupply of training. Apparently, senior scientists’ effort for training is made at the sacrifice of that for scientific production. Thus, if scientists’ primary objective is scientific production, less effort for training is made than is socially desirable. The academic institution has been offering a solution to this problem in that trainees often directly benefit their trainers by providing labor, raising trainers’ reputation by citing their papers, and so forth. This solution functions well when the relation between trainees and trainers lasts for a long term. Such used to be the case when the master-apprenticeship form of training was common, and perhaps is still the case in contexts where inbreeding is the norm (Horta et al., 2010). When junior scientists continue working in the same lab after earning degrees, or when they succeed their trainer’s research topics even after moving out, trainers can gain sufficient benefit through citation and coauthorship. However, such incentives may be weakened when evaluation of scientists is performed on a shorter-term basis, and when the practice of inbreeding is considered inappropriate. Implication of this transition for production of science is rather unclear. Strong ties between masters and apprentices are likely to facilitate knowledge transfer and probably promote scientific production. However, it can simultaneously compromise the diversity of science, which is another key ingredient for sustainable science. Therefore, weakening apprenticeship may allow junior scientists a greater extent of autonomy and facilitate the expansion of knowledge, while it may decrease the quality of training, and perhaps deterr the over-generational knowledge transfer. Although this complex nature in the production of science and scientists is of clear relevance in science policy, academic training has been one of the seriously understudied subjects in policy literature (Shibayama et al., 2013). The primary objective of this study is to theoretically model the over-generational dynamics and empirically test it. On top of the general analyses, this study aims to examine contextual contingency of the issue. First, resource intensity substantially differs between scientific fields. When manpower plays a small role in science, direct incentives for training should be weak. Second, the extent of specialization of research skills may be relevant. When it is high, trainers may want to specialize each trainee in a certain skill area so that trainees as a team can efficiently achieve a certain research goal, but none of them may be independently functional. This is sometimes observed in life science labs whose organizational design is similar to that of assembly line in factories. Third, trainers’ and trainees’ bargaining power may differ by context. This is related to popularity of academic career in certain contexts, the asymmetry of information about post-graduate career, and so forth. As a field of empirical study, we focus on the Japanese academia, where the destruction of apprenticeship may be occurring due to a series of policy reforms since the 1990s; e.g., evaluation has become shorter-term oriented, inbreeding has made less common. A resulting transitional state is expected to provide us with a heterogeneous set of observation. This study draws on a few data sources. First, we conducted a questionnaire survey of 360 principal investigators (PIs) in the field of life sciences in Japanese universities, inquiring into their training policy and career information of their trainees. In addition, we created a database of all Japanese PhD graduates, with which we identified the PIs’ former students. Then, we collected publication information of the PIs and their former students from Thomson’s Web of Science. The survey was conducted in 2013 and all the necessary data is ready for analyses.

ABSTRACT. A scientist’s “academic pedigree” has been shown to be a defining factor early in an academic scientist’s career (Long et al., 1998; Long 1978) with implications for cumulative career effects (Merton, 1968). While individual scientists develop identity and reputation based on their own accomplishment, other aspects of their training and development, such as the reputation of their doctoral granting institution and notoriety of their primary advisor may add, or in other instances, detract, from their pedigree or prestige.  As a scientist’s career develops, their own institutional affiliation lends prestige and recognition to their own identity, also having implications for productivity and recognition (Long, 1978).  Importantly, prestige and reputation are by definition, hierarchical, and evidence points towards a relatively rigid “caste” (Burris, 2004) where mobility, especially upward (in institute ranking/prestige) mobility, is rare and difficult, but also desirable.  This hierarchy is evidenced in hiring patterns, and variation in productivity and professional visibility, as well as rankings and prestige based on professional reputations (e.g. U.S. News and World Report).

There is a substantial literature that has addressed the importance of prestige and reputations within labor markets, especially within academia (Crane, 1965; Baldi, 1995; Long, Allison and McGinnis, 1979; Long, 1978; McGinnis and Long, 1988). Studies in management and the social sciences, as well as some science disciplines, have demonstrated that departmental or institutional prestige is a better predictor of job placement post-PhD than any other individual measures (and, in fact, outweigh productivity and other personal characteristics) (Baldi, 1995; Burris, 2004; Keith and Babchuk, 1998; Miller, Glick and Cardinal, 2005; Wiggins, 2007; Long 1978). Further, placement in prestigious institutions reaps other benefits, important for career advancement. As prior work has demonstrated, movement into prestige institutions has important positive impacts on tangible employment rewards in the short term, but also scientific productivity in the long term (e.g. Allison and Long, 1990; Long, 1978; Clemente 1973).  The notion of a prestigious “academic pedigree” suggests a certain additional advantage, where access to colleagues at the prestigious institutions yields additional resources and rewards, as Crane (1965) noted some time ago. 

What Crane (1965), Long (1973), Hagstrom (1967) and others since then have suggested is that the productivity and mobility effects of institutional affiliation may also be due to other unexplained factors.  An “organizational advantage” suggests different types of social capital of its members (Nhapiet and Ghoshal, 1998), which in turn may have implications for career outcomes. Studies of hierarchy in networks is consistent with the view that higher prestige may yield markedly different resources and advantages. Studies in science have shown that scientists demonstrate some level of preference when selecting and continuing collaborative relationships (Bozeman and Corley, 2005; Wagner and Leydesdorff, 2008), whether for strategic or other reasons. If the prestige of one’s academic pedigree suggests certain desirable qualifications, it may be that academic scientists with more prestigious academic origins may have markedly different opportunities via their social networks, and therefore have access to great social capital relevant to career productivity and advancement.

The reality is that both actual and perceived prestige of institutions, and in many cases individual departments, matters for career advancement and outcomes.  Yet, these studies also have some limitations. While a great deal of work in past decades has addressed the identification and effects of prestige on career mobility, advancement, and productivity, there remain some gaps in our understanding of how prestige functions and the types of benefits it yields. Most have focused almost exclusively on doctoral-granting, research-focused institutions, although the academic workforce is employed in a much broader set up of institutions, including for example prestigious liberal arts colleges.  Further, women and underrepresented minorities in STEM (science, technology, engineering and mathematics) disciplines are disproportionality represented in non-doctoral-serving (NSF, 2008.)

Yet, it is unclear of whether prestige factors function in similar ways across this set of institutions, and how this creates cumulative effects on career productivity. This paper tries to help correct that, by looking at the impact of institutional prestige on social capital resources for STEM faculty across different institutional types and across demographic groups. Specifically, we use NSF-funded survey data from a nationally representative sample of faculty members across 4 disciplines[1] in 4 different institutional types, as defined by the Carnegie Foundation: research extensive, research intensive, masters, and liberal arts colleges and universities. Data include 9,000 faculty in more than 450 academic institutions in the United States. Our goal is to understand the ways in which both STEM faculty doctoral and current institutional prestige affects an academic scientist’s professional networks, and the resources derived from them, and ultimately, their productivity. We will use structural equation models (SEM) to disentangle direct and indirect effects.

[1] Biology, biochemistry, civil engineering and math.

ABSTRACT. Policy makers and industry observers claim there exists a shortage in the market for agricultural scientists. However, National Science Foundation (NSF) statistics indicate the total number of students earning advanced degrees in agricultural sciences from U.S. universities has remained remarkably constant since the early 1980s. Some part of this apparent discrepancy may be due to the increasing share of degrees earned by foreign temporary resident students. For example, Finn (2010) estimates only half (53%) of the foreign agricultural sciences PhDs graduating in 2000 were still in the U.S. by 2007. But, even holding supply of agricultural scientists constant, real U.S. agricultural R&D investments have grown at an average annual rate of 1.6% since 1970, with the private sector driving growth since 1993. BLS employment projections further indicate growth in employment for agricultural and food scientists over the next decade will mirror jobs growth in the economy as a whole.
In this paper, we employ multiple years of data from the NSF Scientists and Engineers Statistical Data System (SESTAT), obtained under restricted-use license, to investigate demographic, human capital, and employment-related characteristics of the U.S. agricultural sciences workforce. For example, we examine whether career age-earnings profiles and employment sectors for agricultural scientists have shifted over time, relative to alternative occupations commonly held by graduates trained in agricultural sciences or related, substitutable fields. Descriptively, we find about one-third of U.S. workers in agricultural sciences occupations hold degrees in some other field. Most commonly, these are native U.S. citizens who studied other life sciences, e.g., general biology. In addition, approximately 20% of agricultural scientists and engineers in 2010 were foreign-born.
If the supply of agricultural scientists is, in fact, relatively inelastic, then growth in agricultural R&D investment and related indirect demand for labor should be driving up relative wages for agricultural scientists. Human capital theory suggests that individuals select their occupation based on the expected lifetime earnings, net of investment. Based on this theory, we would expect to observe a significant positive effect of increases in relative wages on probability that an individual with training in agricultural sciences or potentially substitutable fields would hold a job in the agricultural sciences workforce. Secular changes in international labor flows may also have impacted elasticity of the agricultural sciences labor supply over time.
To examine how earnings and other employment-related factors have affected occupational choice over time, we estimate conditional binomial and multinomial logistic regression models, with dependent categorical variables representing either (a) agricultural sciences versus all other occupations, or (b) explicitly modeling other life sciences occupations as an alternative to both agricultural sciences and other unrelated occupations. Key explanatory variables include nativity and immigration status, gender, career age, educational attainment and field(s) of degree(s), higher education institutional characteristics, and cumulative student debt load at graduation.
Preliminary results suggest that, controlling for demographics (e.g., gender, immigration status) and human capital investment, the career age-earnings profile may be less steep for agricultural scientists than for other life scientists, with higher expected earnings among early-career agricultural scientists than in other potentially-substitutable life sciences occupations. Further analyses will investigate whether the relatively high share in 2010 among agricultural scientists of native U.S. workers trained in other life sciences fields might be due to more to oversupply and declining real wages for recent graduates in biomedical sciences (push), as opposed to the hypothesized increase in demand and a resulting higher real wage among agricultural scientists (pull). We will also evaluate the extent to which foreign labor flows have been sensitive to changes in expected earnings, over time.

ABSTRACT. The impacts of the economic crisis, set off in 2008 and still ongoing in many European countries, have been far reaching on the ability of the EU economy to innovate and create jobs. Overcoming the current economic crisis and ensuring long-term competitiveness and growth is thus a key challenge for European policy.
Research and development (R&D) and innovation are regarded as key drivers for the competitiveness of firms and, consequently, for employment growth. This is why R&D and innovation has been placed at the centre of the “Europe 2020 Strategy” – a strategy that prioritize the mid- and long-term implementation of a knowledge- and innovation-based economy, a more sustainable and competitive economy and, finally, a high-employment economy reducing social disparities.

A key question in the current debate on R&D, innovation and employment is to what extent countries hit by the crisis are able to seize growth opportunities offered by new ideas and technologies. An answer to this question greatly hinges upon the relationship between innovation and employment growth, and how this relationship changes over the business cycle.
However, innovation, employment growth and the business cycle are interlinked in a complex way. Up to now, empirical research has been largely either focusing on the cyclicality of innovation or on the relationship between R&D and innovation on employment growth. There is no firm-level study yet considering business cycle effects of innovation on employment growth.

Our paper disentangles this complex relationship by estimating the employment model of Harrison et al. (2008, 2014), which has been a seminal theoretical foundation for estimating employment effects of innovation in the recent past. Our empirical analysis is based on the Community Innovation Survey (CIS) covering information on 26 European countries. We observe 234,406 firms of the manufacturing sector during the period 1998-2010.
In order to analyze the different effects over the business cycle, we have split our sample into the four phases of a business cycle – economic upturn, boom, economic downturn, recession. Moreover, we perform employment decompositions to show the contributions of the different estimated effects on the average employment growth rate. 
We find product innovations to have a positive effect on gross and net employment growth. These effects are quite stable over the business cycle except for recession periods. But the decomposition analysis shows that product innovators are more resilient to econonomic recessions than non-product innovators. Product innovators do not strongly reduce employment growth during recessions. Process and organizational innovations have negative gross employment effects during upturns and downturns. These effects, however, are very small in terms of a reduced employment growth rate.
Further, we have classified firms of the included countries into three different European regions: North-West, South and East Europe. Accordingly, product innovations induce gross as well as net employment growth in all observed regions during all business cycle phases except for recession periods. Both process and organizational innovations play a minor role for employment growth in North-western European countries. The pattern is more mixed for South and East Europe. In both regions, organizational innovations tend to have a negative effect on gross employment in all phases of the business cycle. Boom periods in South Europe are an exception. The effect of process innovations is similar to the base case across the European regions.

Basically, our results show that employment creation is larger in innovative firms than in non-innovative firms. This pattern can be observed in all phases of the business cycle but it is particularly pronounced in downturn and recession periods where the gap between innovating and non-innovating firms is particularly large.

ABSTRACT. The entrepreneurial university plays an important role in firm growth and regional development (Kenney 1986; Shane 2004; Goldstein and Renault 2004; Lane and Johnstone 2012). Existing studies demonstrate regional benefits from university investment, making universities a highly desirable and almost essential resource for a region. The service component of universities, or what has been called the “Third role” of universities encompasses many different roles. In particular, universities provide additional services to local firms such as technology transfer, incubators, and patent assistance. However, the service levels at universities vary. Some provide just basic technology commercialization services. Others provide extensive services to all new startups in the region/State. These services include but are not limited to: teaching entrepreneurship, writing business plans, consulting, helping firms recruit employees and funding, directly funding firms, etc. The purpose of this paper is to evaluate the level of services provided by universities on the growth and success of startups.

The university’s entrepreneurial culture is shaped to support risk-taking, innovation, new business creation, and a willingness to collaborate with industry (Bercovitz and Feldman 2007; Clark 1998; Etzkowitz 1998; James 2005; Kenney and Goe 2004; Schoenberger 1997). The simplest way to assess the economic development created by a university is to evaluate the amount of technology commercialization it generates. Patents, licenses, and spinout firms are easy to quantify and use as a measurement of university entrepreneurial productivity. Technology firms tend to develop near universities as a result of the knowledge spillover generated by university research. However, and as claimed by several authors, universities’ contribution to a region goes beyond the basics of technology commercialization (Audretsch 2012, Rodin 2007, Moreau and Forrant 2008). Numerous studies outline the role of universities in innovation-driven social networks. While most focus specifically on incubators as the main source of network building activities at universities, universities have been found to effectively occupy the role of anchor tenant in innovation networks (Powell, Packalen and Whittington 2012). Incubators are important for firms’ initial network connections, though their long-term impact on member firm networks is less clear (Casper 2007; Fleming, Colfer, Marin and McPhie 2012). Network building involves costs and may be time intensive. University incubators are able to leverage resources to facilitate networking activities for fledging firms. Because new firm network needs vary depending on development stage (Cooper, Hamel, & Connaughton, 2012; McAdam & McAdam, 2008), incubators are in a position to provide targeted network links and networking activities for member firms. Network contingencies such as cost, development-stage requirements, and organizational form may provide a unique service niche for public research universities to fill and we find numerous studies that outline the role of universities in innovation-driven social networks.

Using both the entrepreneurial university and social networks literature, we seek to understand the impact of the level of universities’ services on the longevity and success of local firms. The uniqueness of the data used in this paper allows us to break down the different services and identify the most effective university programs. Considering universities’ limited funding coupled with pressure by local and national government to make a local economic contribution, identifying the programs that add the most value to startups could make a dramatic difference to both universities and regional economies. While other papers analyze incubator impact and some have delineated different services offered (Von Zedtwitz and Grimaldi 2006), we have not found anything looking at impact of service level on firm success and regional impact. This is our paper’s unique contribution.

ABSTRACT. During the last decades, Chile has been through a period of economic growth that has brought wealth and welfare to the country . However, varying challenges have to be addressed for Chile to become a developed country. Inequality and the excessive institutional and economic centralization remain significant barriers to development. Chile is nowadays administratively organized in 15 regions, each of them responding to a specific local economic and social structure shaped by a high dependency upon natural resources based industries. That makes extremely inefficient to address local needs from a single, centralized, perspective, thereby questioning the soft role regional governments may have nowadays. Chile’s regional diversity may turn into an actual competitive advantage as long as the right institutions and public policies are set.

Efforts have been made to make of Science, Technology and Innovation (STI) a driver for development. Several policies have been proposed and implemented, and the role of several public agencies reformulated. Although new STI capacities have been built, Chile is not yet a knowledge-based economy. Its natural resources dependency and a cost minimization rationale that crosses its main industries seem to be behind that phenomenon. Scholars have raised the question whether the institutional centralization that rules the country has been replicated in regard to STI policy thereby limiting the effect that STI may have for regional development.

We identify the local STI dynamics in each of Chile’s 15 administrative regions drawing upon the Regional Innovation Systems (RIS) conceptual framework, which allow us to identify the national and local agents involved in the regional innovation process and their interaction and learning patterns. We expect that a better understanding of regional STI dynamics will result in a better perspective of regional capabilities and shortcomings.

Our main motivation is to establish the significance for STI public policy to understand the peculiarity of each region and to strengthen that in future scenarios. Our analysis builds on previous work by applying, as mentioned above the RIS conceptual framework, and a set of statistical tools following a three-stage methodology.

Firstly, we apply factor analysis over an empirical RIS model which considers 35 STI variables in six hypothetical factors: a) Regional Environment; b) High Education Public and Private Non Profit Organizations; c) Productivity and Competitiveness; d) Regional STI Institutions; e) Firms; and f) National STI Public Organizations. The result explains more than 90 percent of the original variance.

Secondly, drawing on information extracted from the model and cluster analysis, regions are grouped in seven clusters with different types of RIS. An analysis of variance (ANOVA) is carried out to test if differences between regions-clusters are statistically significant. To have a better historical perspective in regard to STI regional dynamics we apply the same methodology for two different periods : 2007-2008 and 2011-2012.

Thirdly, we defined each region according to the two-dimensions Government-Firms methodology proposed by Cooke et al (2004), noticing that most of all 15 Chilean regions may not be considered yet as globalized RSIs and respond to a centralized orientation with significant participation of the national government.

We present an analysis in regard to each region to establish whether our results are consistent with the current regional STI situation. Our cluster analysis deviates from a regular RIS definition of dimensions to consider. At first, the identification of the “productivity and competitiveness” dimension was expected to directly relate to firms’ innovation capacities. That was not the case. We point to the local firms’ cost minimization business models and the low-rewarding perception they have over the national intellectual property rights system as drivers of such dynamic. Likewise, we expected that variables under the dimension “National STI Public Organizations” would have been grouped along with either those under the dimension “High Education Public and Private Non Profit Organizations”, as both refer to STI human resources and funding or the dimension “Regional STI Institutions”, as both refer to public STI institutions. We propose that the low interaction among actors in the system may have an effect in that clustering and thereby in thus far weak RISs.

We conclude that the systemic dynamics of innovation in Chile follow dissimilar patterns at the regional level. Efforts should be made to design new strategies pointing to a higher regional STI productivity particularly in regard to technology transfer, capacity building and technology-based business. From there, regions may advance to the so-called “interactive” RIS, as improvements in funding, coordination and human capital should lead them to a new stage of economic development.

ABSTRACT. This paper and presentation describes a theory-based evaluation framework for measuring the performance and impacts of large scale research infrastructure (LSRI) – with particular emphasis on the unique features of these facilities. LSRI are research facilities with unique capabilities that serve users through merit-based access, and are usually of a scale or complexity that exceeds the capacity of a single organization, region or nation to fund, build and manage. Facilities may be aimed at fundamental discoveries to drive our understanding, focused on specific missions to address the requirements of their users, or some combination of both. They have both scientific impacts and social and economic impacts.

The literature review and consultations leading up to development of this new evaluation framework found that there are three major gaps in current practice and that if these are filled, wider impacts can be assessed earlier in the process and these can increase understanding of the contributions of LSRI as well as inform improvements.

1. The wide variance in the nature and context of LSRI conditions for assessment of performance is not described. The variety of purposes, target audiences, activities and how these activities will lead to desired results given specific existing conditions are seldom explicit. Logic models with underlying causal assumptions have not been done. 2. Related to the first, while LSRI typically consider a number of important quantitative indicators for management, not all of the pathways by which outcomes occur have been clearly explained and pursued. Current practice rarely considers public policy and effects of government-funded technical infrastructure. Also detailed descriptions of common sequences of outcomes currently used in assessment of more ordinary S&T organizations are not used for LSRI. Examples are stages of building trusted relationships with partners or stages of technology development and commercialization. 3. Monitoring and evaluation approaches are more limited. Opportunities exist to monitor and track more impact pathways, document sequences of outcomes, and improve on both success stories and periodic assessment with careful case studies building on monitoring data and assessment of progress along impact pathways.

The framework builds on accepted best practice in assessment of organizational impacts and adapts them for LSRI. The essence of this is that assessment answers questions about program/organization logic: why is the program important to science and society, what sequence of results occur from which target audiences, and how did the program influence that through its activities, taking into account differing conditions. Analysis suggests that there are some common sequences of results from various groups which can serve to outline performance expectations. These can be summarized as six impact pathways.

1. Create a Research Structure that Supports Discovery and Innovation. 2. Build Research Capacity – Knowledge Base, Qualified People, Research Tools. 3. Contribute to New Technologies, Competitive Companies, Markets and Clusters. 4. Inform Government Policies and Decisions. 5. Inspire Students and Public Appreciation of Science and Technology. 6. Contribute Directly to Local and High Tech Economic Activity.

Combinations of impact pathway can be defined in consultation with key stakeholders. These form the core of a logic model (or models) which then guides the LSRI performance measurement strategy. The Framework’s generic logic models and indicators with data sources and analysis approaches can be tailored to the evaluation planning of specific LSRI.

The LSRI framework recommends that in order to appropriately assess performance each LSRI implement a multi-year assessment plan that has three levels of analysis integrated over a period of time from four to ten years depending on funding cycles and LSRI context.

The three levels of analysis are:

1. Routinely collect data on inputs, activities, outputs, and engagement (users, partners, others involved), This includes looking at the quality of facility operations, relevance of research options provided, people trained, outreach events, and research projects enabled. It also includes documenting major external influences (e.g., funding for the whole field, breakthroughs elsewhere). What is collected depends on the LSRI logic model and context. 2. As required, mid-term reviews will assess progress and early effects. This includes looking at the continued relevance of research options provided, research results as measured by publications, collaborations, significance of that research, and user satisfaction. 3. Periodically, larger assessment efforts can determine longer-term scientific and socio-economic impacts, both qualitative and quantitative, by using, updating, and expanding on the data and analysis completed in levels 1 and 2.

ABSTRACT. The U.S. National Science Foundation (NSF) uses external evaluations to gather systematic data on program effectiveness and for program improvement and policy decisions. A key challenge in investigating the effects of NSF (and other Federal RD) programs is constructing a comparison group to represent the counterfactual and isolate the role of these programs in outcomes. Although random assignment designs are the most rigorous test of a program’s causal impact, they are not feasible when award selection is merit-based. Evaluators have turned to other approaches, including regression discontinuity designs (Jacob and Lefgren, 2011) or propensity score matching (Martinez, et al., 2012; Pion and Cordray, 2008), to compare outcomes of successful to unsuccessful applicants. However, these methods are limited in application by the award selection process or the availability of data on pre-selection characteristics of applicants. When criteria for these methods are not met, evaluators must find alternative means to represent the counterfactual. We describe a novel approach used to construct a comparison group for a study of NSF’s Partnerships for International Research and Education (PIRE) program.

The PIRE program supports U.S. scientists and engineers who wish to pursue research in collaboration with international partners. PIRE is intended to promote opportunities for U.S. researchers to forge international collaborations that enhance research excellence; to provide international research and educational experiences for U.S. students and faculty; and to strengthen the capacity for U.S. researchers and institutions to build and sustain international partnerships. The program funds projects across a broad array of disciplines. To receive a PIRE award, applicants must demonstrate that the research agenda requires an international collaboration. PIRE awards last five years and range in size from relatively small, bi-national consortia to large, multi-national, multi-institutional awards. By June 2013, PIRE had funded 59 awards across the 2005, 2007, 2010 and 2012 cohorts with award amounts up to $5 million each.

Key objectives of the evaluation of PIRE required comparing outcomes of the PIRE program (where international collaboration is a required component) to an appropriate comparison group (where international collaboration may or may not occur in the absence of any requirement). These outcomes included the quantity and quality of research produced; educational and career milestones; and the formation and duration of international collaborations.

To satisfy these objectives, we constructed a comparison group of non-PIRE, NSF-funded projects that were as similar as possible to PIRE projects except for requiring international collaboration. This comparison group represents the counterfactual: the baseline level of international collaboration that arises (or not) in the absence of PIRE, but with sufficient similarity to PIRE projects in terms of award amount, duration and other key criteria.

We adopted this approach after determining that the PIRE selection process does not meet requirements for a regression discontinuity design and that insufficient data on pre-award characteristics of unsuccessful, proposed PIRE projects were available to support the use of propensity score methods.

Fifty-five of the 59 PIRE projects were successfully matched to other, non-PIRE projects funded by one of NSF’s other programs on the following criteria. First, although projects with international collaborations were eligible for the comparison group, such collaborations could not be required in order to receive an award under the NSF program. Eligibility was further restricted to projects where research was the primary focus (e.g., programs providing equipment or instrumentation support were ineligible). Priority was given to continuing grants, the grant mechanism for PIRE, although some comparison projects were standard grants. In addition, each comparison project had to satisfy the following:

•An award amount within 20 percent of its corresponding PIRE project’s award; •Total duration within one year of the PIRE project’s duration; •Start and end dates within one year of the PIRE project’s corresponding dates; •Similar disciplinary focus of senior investigators; •At least two participating institutions (U.S. or international); •Graduate student involvement; and •Lead PI must not have received a PIRE award.

Furthermore, within each matched PIRE-comparison project pair, a “greedy matching” algorithm was used to match PIRE-comparison projects’ principal investigators, postdoctoral and graduate students for analysis of participant-level outcomes. Details of this algorithm are included in the paper. We provide evidence of the comparability of the resulting comparison group, describe the conditions that made this approach feasible and discuss both advantages and limitations of the approach for use in other contexts.

ABSTRACT. Government funding agencies often provide supplemental funding opportunities to individuals and groups that it currently funds. This practice is common place across various agencies (NSF, NIH, DoD). Since these funding opportunities usually involve a modest amount of support and are thought of as minor expansion or enhancements to the awardee’s mission and/or capabilities, they are rarely subjected to serious evaluation scrutiny. Supplemental funding opportunities might be used to produce significant synergistic effects between programs. Although little research has been devoted to this topic, similar results may be possible within the realm of organizational or programmatic innovation wherein synergistic effects are produced by creative combination of different programmatic innovations (Sutton & Hargadon, 1997). The Small Business Innovation Research (SBIR) Membership Supplement in IUCRCs (SBIR Supplement), launched in 2008 by NSF provides an interesting example of an attempt to produce synergistic programmatic effects. The initiative builds on two highly regarded and carefully evaluated STI programs. SBIR/STTR program provides funding to small businesses, often startups, to assist them in moving from research to commercialization. It has been the subject of a variety of evaluation efforts and the consensus of these studies is that it has been effective (Audretsch, Link, & Scott, 2002). The IUCRC program (started 1980s), supports pre-competitive research via multidisciplinary, team, and consortial processes between universities and primarily large firms. It has been the subject of an extensive program of evaluation research and has also been judged effective (Gray, 2008). The SBIR Supplement attempts to leverage the strengths of these two programs by providing Phase II SBIR/STTR firms who have limited R&D capabilities and technical networks with a subsidized membership in an IUCRC while center stakeholders are exposed to the type of entrepreneurial start-up that rarely have the resources to participate in an IUCRC. Given the novelty of this type of arrangement and limited evaluation research on similar combinatorial programmatic interventions, we choose to understand the effect for both the IUCRCs and the SBIR participants. This paper will focus on the SBIR company benefits. This part of the study explored how SBIR firms get involved involved in an SBIR Membership Supplement, their membership experience, and the benefits of their participation. Data were collected from firms that had received supplements between 2008-2013. Fifty-six firm representatives participated in a structured telephone interview. Respondents reported their interests aligned with center research agendas and most reported regular participation in center meetings. The majority of SBIR representatives reported a variety of networking, R&D and commercialization benefits from their participation including identification of new applications and improved products. Novel impacts included enhanced understanding of and access to customers via interactions with existing center members. About half of all participants listed operational, supplement,or research issues (mostly minor) that that could have been improved. Almost 90% of respondents indicated membership participation was worth the time and money they invested in the arrangement. Implications for policy and program development will be discussed.

Audretsch, D. Link, A. Scott, J. (2002). Public/private technology partnerships: Evaluating SBIR-supported research. Research Policy, 31, 145-158. Gray, D.O. (2008). Making team science better: Applying improvement-oriented evaluation principles to the evaluation of cooperative research centers. New Directions for Evaluation, 118, 73-87 Hargadon, A. & Sutton, R. (1997). Technology brokering and innovation in a product development firm. Administrative Science Quarterly, 42, 716-749. Varian, H. Farrell, J. and Shapiro, C. (2004). Economics of Innovation Technology: An introduction. Cambridge: Cambridge University Press

ABSTRACT. Emerging technologies are knowledge-intensive and attractive starting points for emerging economies wanting to build high-value industries. In the past three decades, many such countries have invested heavily in developing their biotechnology industry through the creation of new start-up firms. Technology late developers are widely perceived to rely on foreign technologies to develop their economies (Nelson 1993, Wong 2005, Breznitz 2007, Dodgson, Mathews et al. 2008, Dodgson 2009, Wong 2011). The biotechnology industry is an intensively knowledge-based sector and an attractive starting point for relatively small economies wanting to build high-value industries. In particular, small economies, which lack natural resources and have a comparatively tiny domestic market, can overcome their resource limitations by establishing knowledge-intensive industries. Many such countries have invested heavily in both creating a strong bioscience base and supporting small- and medium-sized biotechnology enterprises, using a range of policy tools. In these countries, particular attention has been paid to the creation of new start-up firms, and there has been a number of recent policy initiatives designed to stimulate commercialization within the biotechnology industry.

South Africa is the richest African nation. It has the highest per capita GDP on the continent, a plethora of minerals like diamond and platinum, and world class universities. But South Africa also has a lot of poverty and inequality. Twenty-two percent of its population lives below the poverty line and in general, South Africa has a two-tiered economy (Cozzens et al 2014), the rich and poor (World Bank, 2011). Since its first democratic elections in 1994, South Africa has striven to develop a robust innovation system. It has succeeded in improving its production and research capabilities, yet it has a long way to go before the country overcomes the problems of its past.

Since Enriquez-Cabot proposed the concept of Bioeconomy in 1998, this concept has been gradually emerged in the policy documents world-widely, especially after in 2010s. “Bioeconomy refers to all economic activity derived from scientific and research activity focused on biotechnology industrial process” (1998; p925-926). OECD proposed a policy whitepaper namely "The Bioeconomy to 2030: designing a policy agenda" in early 2000s with the expectation of developing biotechnology to result in an emerging “bioeconomy” where biotechnology contributes to a significant share of economic output. The Obama Administration has seen building the US bioeconomy, a growing sector of this technology-funded economy, as a means of retaining the dominance of US research capacity through enhancing technology transfer and public-private partnership (PPP) (White House 2012, p1 and p5) in April 2012. A new South African Bio-economy strategy was launched in early 2014, with a focus on the economy and how biotechnology could be used to create a positive socioeconomic impact.

Whilst biotechnology industry is a knowledge and capital intensive sector, whether the industrial and social development needed to be ready for incubating a high-tech sector (such as biotechnology industry in the process of social-economical transformation or the development of such high tech sector to transform the socio-economic structure. Empirically, this project start with a study of the interaction between policy and the development of innovation networks in South Africa and Taiwan in the past three decades by implementation of scientometrics mapping technique developed by Leydesdorff et al (Leydesdorff and Persson 2010, Leydesdorff, Rotolo et al. 2012). Particular attention was paid to selected landmark cases that exemplify the interrelationships between relevant actors, especially the actors (firms and research institutes) specialized at biomedical research in South Africa. A small amount of elite interviews (6 interviews) were made with the key actors of firms, research organisations, non-profit organisations, and intermediaries in South Africa, including the institutional intermediaries and NGOs involved in the development of biotech sector. In addition, the role of policy interventions in enhancing innovation networks within the context of recent socio-technical transitions in South Africa will be discussed.

South Africa is proactively on its way of integrating resources and trying to strengthen the connectedness of the local actors. Therefore, this study suggests that more productive strategies would enhance the research capabilities and the diversified specialties of the local firms, improve the research capacity of local academia and support them in engaging with global innovation networks more broadly. Only by building the local research capacity can the nascent Bioeconomy in South Africa be successfully developed and transformed.

ABSTRACT. In 1986 following National Science Foundation (NSF) model, Chinese government established National Natural Science Foundation of China (NSFC) to support basic scientific research in China. On the one hand, following NSF’s management model, NSFC supports scientists to do research on free topics and uses peer review as the proposal selection method. On the other hand, NSFC also explores Chinese characteristics funds management methods. For example, beyond free topic research, NSFC also supports proposition research to meet national needs as well as training young scientists. Especially in the aspect of funds management, NSFC is different from NSF. Upper total budget line is set for projects instead of doing cost reimbursement. Meanwhile, labor fee and indirect cost budget are made by fixed rate. In the beginning, the funding amount as well as the financial management capability in China was quite limited, so simplified management also worked well. However, after near 30 years of development, government scientific research investment has been substantially increased. The previous simplified Chinese characteristic funds management model in NSFC could no longer adept to the new status. Even worse, it becomes the obstacles in NSFC’s funding efficiency improvement. Keep the old way or make changes thus becomes an important choice in NSFC’s development. Firstly, based on the above background, this paper analyzes 7 main problems NSFC facing in its funds management. (1) What is the base of the total project budget? It is the real needs or upper line set by NSFC? (2) Should labor fee cap in project budgeting be kept? If kept, how to decide the proper line? (3) Should indirect cost rate cap in project budgeting be kept? If kept, how to decide the proper line? (4) Could performance salary of permanent staff be charged from NSFC project funds? (5) Should NSFC project budget caliber be unified to other government scientific research funding? How to manage the flexibility of project budgeting? (6) Should the policy of retaining the balance of project funds be kept? (7) What is the role of the universities and research institutes in the funds management? How to stimulate and guarantee institutes’ management? Secondly, in-depth analysis to cause of the above problems is given. NSFC funds management development in the past 29 years and the development of Chinese government research funding from macro view are analyzed as the internal and external cause. Focus discussion, questioner analysis and feasibility analysis are conducted to provide solutions for the problems. Finally, based on the above analysis, Changing toward a new funds management model according with the rules of scientific research activities is the ideal goal of reform in NSFC. However, considering the practical status of financial management and government scientific research funding system, we propose a periodical plan: (1) Majority kinds of projects still keep the upper budget limitation. Cost reimbursement policy applies to some kinds of huge projects, such as key project, major project and major equipment project. (2) The labor fee cap should be canceled to alleviate the performance salary shortage in universities and research institutes. (3) Instead of canceling the cap, improving indirect cost rate cap to 20% is a stage solution, as in the present stage institutes lack the ability to account the accurate indirect rate. (4) NSFC will not consider directly compensate performance salary of permanent staff, instead increasing indirect cost rate cap and allowing institutes to compensate performance salary from indirect cost might be a stage solution. (5) Budget caliber for scientific research projects at government funding level should be unified. Given the uncertainty of scientific research, NSFC should delegate the budget adjustment right to universities and research institutes. (6) The policy of retaining the balance of funds for future research should be continued while the supervision should be reinforced. (7) Universities and research institutes are the first level agent in principal-agent relationship built by project funding. Some of the funds management duties and powers should be assigned to them. Allocating indirect cost directly into institutions is a good choice to stimulate and guarantee institutes’ management. Research conclusions and suggestions in this paper were adopted by NSFC and suggestions about labor fee cap and indirect cost rate have already been written in to the new funds regulation.

ABSTRACT. Nuclear power has long been a controversial issue around the world since decades. But, unlike many nuclear power controversies, the actors in Indonesia’s nuclear power controversy involve not only scientific facts but also Islamic considerations. In September 2007, using combined scientific facts and Islamic arguments, some local Islamic leaders declared fatwa haram (forbidden in Islamic law) to forbid a nuclear power plant in Muria, the Central Java. They argued that the risks of the prospective of nuclear power plant outweigh the benefits. However, three years later, just a year before the Fukushima Daiichi “triple disaster” occurred in Japan, some national Islamic leaders claimed to support the prospective Indonesia’s nuclear energy, and published a book What do They Say about Nuclear?. Despite having the same source of Islamic interpretation, the national and the local Islamic groups interpreted differently nuclear power technology. With combined a controversy study in STS approach, social construction of nuclear risks and the sociology of religion, this poster analyzes the events that the actors involved in the controversy, and their roles in making the decision. This poster is part of my prospective thesis research looking at the intersection of scientific controversy and the sociology religion in the Indonesia’s controversy of nuclear energy. The poster also highlights the relationship between religious belief and social construction of nuclear risks during the controversy. Lastly, this poster identifies that Indonesian nuclear experts seem to have dual duties: to think scientifically and to consider Islamic arguments in communicating their knowledge.

ABSTRACT. In evaluating competing energy system options, aside from technologies, it is important to recognize how the form of business model affects sustainability in at least three ways: intended outcome(s), revenue and pricing mechanism(s), implied resource throughput.

This overview examines implications (and impacts) inherent to an investor-owned business model, versus a municipal approach; for planning, pricing and future risks.

Comparison of these can inform policy for energy systems development in relation to multiple recognized priorities and risks.

More broadly, examining factors implied by these two business models, widely used in the U.S., may also inform emerging energy system design, planning, and policy.

ABSTRACT. With increasing demand for emerging technologies to help accelerate sustainable economic growth and overcome global social and environmental challenges, many national governments and executive agencies are taking strategic approaches to supporting key emerging technologies and relevant innovation. At the same time, there is also a growing attention to the role and importance of standardisation in supporting technological innovation, and the potential for technical standards to offer a source of competitive advantage in new industries. Although there have been prevailing perceptions that standards obstruct innovation by imposing certain constraints, there appears progressive understanding that standards, more generally, play critical roles in supporting various activities of technological innovation.

Despite such increasing awareness on importance of standards, most government policies for emerging technologies and associated foresight analyses address standardisation issues only in a limited way. This is due to the lack of understanding and careful analyses on dynamics between standards and innovation, resulting not only from the highly non-linear and uncertain nature of the innovation process itself, but also from the complexity and variations involved in standardisation; standardisation processes involve high levels of technical detail and consensus, various types of standards in terms of both roles and Standards Developing Organisations (SDOs), different motivations and requirements of diverse stakeholders to be considered, and the integration of information relevant for standards development which is distributed among a variety of innovation system actors. Such complexity leaves significant challenges for SDOs and policymakers in developing strategies for standardisation to support innovation, as poorly timed or organised standardisation activities may result in either competing standards visions or premature consensus to emerge, leading to ineffective or even counterproductive standards that inhibit innovation.

Given these difficulties and opportunities, the current research carefully explores such complex dynamics between standards and innovation across the technology lifecycle, examining how standards have impacted the innovation and development of emerging technologies. An in-depth case study is carried out, using historical data on standardisation and development of photovoltaic (PV) technology; PV technology has been selected because of not only its long history of innovation and development, but also high level of systems complexity, various application areas, and a variety of stakeholders involved, all of which add intricacy and variety to its standardisation activities, providing rich information to explore such complex dynamics. Quantitative analysis of over a hundred standards published regarding PV technology has been conducted, in order to investigate if there are any patterns and trends to be observed on evolving functions of standards as well as varying levels of technical details involved in standardisation during the innovation journey. Then, qualitative analysis has been carried out using documentary archives and interviews with experts involved in PV standardisation, to identify any causal relationships and interactions between standardisation and development of PV technology. It is found that a variety of standards actually played different roles and functions to support various innovation activities of PV technology across its development journey.

Based on these empirical analyses, a new conceptual framework is proposed for standardisation in technological innovation, summarising the findings of the current research. It is expected that this research broadens the evidence base to inform SDOs and relevant policymakers for more anticipatory standardisation activities, ensuring an effective management of standardisation to support technological innovation and emerging technologies.

ABSTRACT. Objectives: What factors influence graduate students’ career interests and decisions? For those in academic or non-academic positions what influences them to stay in or leave their original track? To answer this, we use both quantitative and qualitative analysis techniques from an NIH funded evaluation project of the Atlanta BEST program. From this evaluation study, we selected a subset of the data of recent alumni from several biomedical related departments from both Georgia Tech and Emory University.

We look specifically at the differences in the academic, advising, and work experiences of alumni while they were doctoral students that may explain why some maintained their career preferences while others changed. In total, we have four different populations of alumni in this study: 1) those who initially envisioned a career in academia and then began a career in academia; 2) those who initially envisioned a career in academia but began a career in a non-academic job; 3) those who initially envisioned a career in a non-academic job and then began a career in a non-academic job; and 4) those who initially envisioned a non-academic career, but began a career in academia. This study will allow for us to compare experiences across these groups of alumni with different career outcomes.

Theoretical Foundation: The literature regarding career development and career interest extends several decades and can be found in a variety of fields. Perhaps the most influential framework regarding career choice theory is the social cognitive career theory (SCCT). This theory emphasizes interest development and performance, while additionally giving credence to an individual’s personal demographics and surrounding environment, abilities, and values (Lent, Brown, and Hackett, 1994). According to this theory, career choice behavior comes as a result of academic or personal performance and success (Brown, Lent, 1996; Kaminsky, Behrend, 2014; Schaub, Tokar, 2005), skill confidence (Brown, Lent, 1996; Schaub, 2005), social persuasion (Flores, O’Brien, 2002), expectations of support from peers and authority figures (Flores, O’Brien, 2002; Lent, Lopez, 2008), and perceived barriers to success (Brown, Lent, 1996; Flores, O’Brien, 2002; Lent, 2000; Lindley, 2005). These factors are have been found to influence, career interests, goals, and actions.

Based on such career choice literature, we expect to find that the differences in career outcomes (selection of an academic vs. a non-academic career) of alumni are a result of different academic, advising, and work experiences as graduate students.

Methodology: This study is an exploration of the influences on career decisions of doctoral students. As stated above, the data for this study comes from an alumni survey conducted as part of the evaluation of the Atlanta BEST Program. It provides information on the work experience of alumni prior to their PhD training, the types of activities they participated in as graduate students (i.e. research assistantship or teaching assistantship), skill development, their advising experiences, perceptions of career goal support from academic and non-academic persons, and job market perceptions.

The analysis is both quantitative and qualitative. First, descriptive and inferential statistics (primarily by means of regression analysis) will be used to determine the relationships (if any) between alumni doctoral experiences and career outcomes, as well as to determine the strength of those relationships. Direct and indirect causal modeling will also be used to better understand these relationships to determine what intervening variables might exist. Second, to complement the quantitative work, there will be a qualitative analysis of open-ended comments regarding career choices of the alumni. This will allow for a more thorough understanding of the career decisions and doctoral experiences of alumni, as the open-ended comments provided opportunity for alumni to expand on previous close-ended answers, as well as the ability to discuss additional issues that may not have been addressed in the survey. These responses will be categorized and coded into themes for analysis.

Results: Analysis is still in progress.

Conclusions: Analysis is still in progress.

Contribution to the Studies of the STEM Workforce: Our study contributes to the study of science and innovation policy and processes through the exploration of factors that contribute to the career decisions of emerging leaders in science. These “if” and “how” questions regarding the role of such factors point to an opportunity to learn about how we can structure graduate training in the sciences to better prepare students for a variety of careers. In studies of STEM workforce, this is relevant for the attraction and retention of both academic and non-academic scientists in the field.

ABSTRACT. Abstract Technological change is a key driver of economic growth and increase in the standard of living. This thesis has been argued by a number of scholars working in different academic traditions and relying on different methodologies. In his historical accounts of economic catch-up among European countries, Gerschenkron (1962) observed how increasing technological prowess was a part of the catch-up process. Using a quantitative approach, Solow (1956; 1957) arrived at a similar conclusion. Decomposing the sources of growth for the US economy, he found that during the 40-year period ending 1949 only 12.5 perent of the increase in American output per man-hour could be traced to capital deepening, with the remaining 87.5 a result of technical change. In the decades following, the centrality of technical progress to economic growth has only been reinforced.

Because technological innovation stands at the source of economic growth, determinants of innovative performance have become of keen interest to a broad community of scholars in growth theory and economics of innovation, as well as to practitioners in technology and development policy. Yet, in the decades following the recognition of the link between technical progress and economic growth, a number of questions remain unanswered. These concern the factors that determine innovative activity, the distribution of innovation in time and space, and the scope for policy to improve innovative performance. While there exists a consensus on the importance of technological change and innovation to overall economic growth, there remain unanswered questions about the determinants of technological change at the country level.

Innovation patterns differ enormously across countries. While a small number of countries are cynosures of innovation, most exhibit levels of innovative activity that are considerably less stellar. Furthermore, innovation patterns change over time, raising questions about causal factors.

This article aims to explain why some countries innovate, while others do not, as well as the probable causes of change in innovative activity over time. The dynamics of country-level innovation are explored by estimating an international production function for new-to-the-world ideas using a relatively novel measure based on Triadic patent counts. This measure is more robust to own-country bias than patent measures originating in a single patent system (e.g.: USPTO or EPO), and is therefore more appropriate as a measure of innovation for the purpose of international cross-country comparison.

The conceptual approach integrates ideas from Endogenous Growth Theory and the literature on National Systems of Innovation. Analysis is based on a panel dataset of 40 developed and developing countries. The results bear on several aspects of national innovation-creation processes: existence of nonlinearities in the response of innovation outcomes to determinants of innovation, complementarity between different types of research effort, and existence of country-specific dynamics in innovation.

REFERENCES Gerschenkron, A. 1962. Economic backwardness in historical perspective. “The” Belknap Press. Solow, R. M. 1956. “A contribution to the theory of economic growth.” The Quarterly Journal of Economics 70 (1): 65. ———. 1957. “Technical change and the aggregate production function.” The Review of Economics and Statistics 39 (3): 312–320.

ABSTRACT. E-Government in U.S. Cities: Enabling Engagement or Reinforcing Tradition

ABSTRACT This research examines the adoption of e-government information and communication technologies (ICTs) for the purpose of promoting engagement and participation in municipalities in the United States. An important promise of ICTs is its potential to transform government by providing new forms of access to and participation in the policy process. Scholars from the early 2000s were optimistic about e-government development and progression, describing it as a process towards transformation. However, recent research concludes that the adoption of ICTs has been incremental with governments favoring the adoption of technologies that provide e-service or information over those that promote civic engagement and participation. Little is known about the factors that explain the adoption of transformative ICTs among U.S. cities or within departments in those cities. This study takes the first steps at systematically investigating the adoption of ICTs that aim at e-participation using data from a national survey of government managers and content analysis of 500 city websites. Accordingly, this study will address three research questions: How do organizational, environmental, and institutional factors influence the adoption of e-participation ICTs among U.S. municipalities? Does the adoption of e-participation ICTs vary among five municipal departments within those cities: mayor’s office, finance, community development, police, and parks and recreation? Whether and to what extent do municipal websites promote engagement and participation? Institutional theory, models of e-democracy and stage models of e-government development, and the Technology Enactment Framework provide the three theoretical perspectives that guide the development of the hypotheses. The study employs a cross-sectional, mixed methods design, using a concurrent triangulation approach, which will be carried out in two phases. Phase I includes the analysis of survey data on technology use in local government departments. Negative binomial regression will be used analyze the survey data and secondary data to determine the extent and predictors of e-government ICT adoption. Phase II involves the evaluation of municipal websites in the U.S. based on 29 criteria capturing website content, accessibility, and usability. Municipal websites will be evaluated based on their composite score on criteria guided by the literature, yielding a ranking of online civic engagement. Multivariate analysis will then be used to explain the factors associated with higher overall scores on the e-government civic engagement index. This research will make important contributions to the e-government literature. It will use survey and content analysis to tell a more complete story of adoption behavior in public organizations. Using a random selection of 500 U.S. cities accounts for variation and enables generalizability of findings. This study is guided by theory, which can aid in developing e-government theory, and possibly, a theory of innovation in public organizations. Moreover, using the department as the unit of analysis will reveal the influence of functional roles in ICT adoption. This research will also have important implications for practice as understanding the predictors of adoption can help governments elucidate barriers to citizen responsiveness and enable the enhancement of online experiences and interactions between government and citizens.

ABSTRACT. Technology adoption through punctuated equilibrium and diffusion are widely studied across a variety of disciplines, such as economics, policy, marketing, and history, and often studied congruently. While punctuated equilibrium describes the sudden shift from the incumbent technology to the new technology, diffusion refers to the spread of a technology that leads to the punctuation. The main focus of punctuated equilibrium and diffusion research is between two competing technologies, one incumbent and one new, and their penetration and adoption in a specific market. Less focused upon, however, are punctuations and diffusions of a single technology across industries. 

Here, we adapt the model described in 1999 by Loch and Huberman, which addresses technology diffusion and punctuation of a new technology that is competing with an established technology.  As innovation naturally cycles through industries, short and sudden punctuations, often referred to as “radical change,” can occur, signifying a major shift from an old technology to the new technology. Punctuations are more likely to occur if there are positive externalities associated with the technology, thereby making the adoption beneficial to the user. Initial uncertainties associated with adopting the technology also increase the likelihood of adoption, as do high learning-rates and the speed at which users adopt the technology.

The Loch and Huberman model focuses on “when and how fast technology adoption occurs, and how adoption is related to the underlying technology performance.” The model assumes that decision makers are “profit driven” and only choose the best of the available technology. How well a technology is preferred depends upon the learning curve and process associated with the technology. Performance improves over time, and firms choose a technology based on performance. 

The Loch and Huberman model has previously been applied only to competing technologies in the same industry. For example, Loch and Huberman note that the widespread adoption in the 1980s of gas turbines over steam turbines for power generation is viewed as a punctuation, as is the dominance of Microsoft Office’s Excel software over the incumbent software, Lotus, established in the mid 1990s. We propose that the diffusion and punctuation patterns described and documented using the Loch and Huberman model are evident when applied to a single technology’s market penetration across at least two industries. This allows for insight and a new way to view cross-industry technology transfer. 

To conduct this analysis, we use hydraulic fracturing technology as a case study. Hydraulic fracturing has been used in the oil and gas industry since the mid-1940s and, combined with horizontal drilling, has been credited with the recent boom in natural gas production from shale rock. It has also been suggested that hydraulic fracturing technology could be utilized in Enhanced Geothermal Systems (EGS) drilling. To support the linkage of these technologies, we characterize the applicability of fracturing technology to EGS and estimate the potential implications of the technology for EGS development. 

The Loch and Huberman model is applied, where a technology with the highest performance dominates an industry, to the fracturing technology and the potential market penetration from the oil and gas industry to the EGS industry is analyzed. Here, the performance of the single technology (hydraulic fracturing) is calculated for both the EGS and the oil and gas industry. Performance is characterized by parameters that may support or hinder the transfer of fracturing technology from oil and gas to EGS. Parameters in the model include the usage of the technology in both industries, performance of the technology over time, learning curves, and a measure of uncertainty in each industry. Based on this analysis, we identify and discuss which policy mechanisms might be available to support this technology transfer.

ABSTRACT. This paper seeks to answer the innovation and diffusion of CFL and LED lighting technologies. Since EU first started to phase out incandescent light bulbs, innovation is more likely to occur in EU-based firms than in U.S.-based firms. Using patent data, I plan to answer the following questions. First, what role does incandescent light bulbs ban play in inducing CFLs and LEDs patenting? Second, does the U.S. incandescent light ban spur innovations by U.S. inventors or non-U.S.-based inventors?

Technological advances were made because of the ban of incandescent light bulbs, because firms were forced not to produce incandescent light bulbs. Instead, firms produced Light-Emitting Diode (LED) bulbs and Compact Fluorescent Light Bulbs (CFLs). EU started to phase out incandescent light bulbs since 2009. On the other hand, the United States banned most of the current incandescent light bulbs since 2012. Advocates of the Porter Hypothesis argued that well-designed environmental regulation may increase firms’ competitiveness. It implies that EU-based firms could enjoy a first-mover advantage when the U.S. has started to ban incandescent light bulbs.

In order to answer these questions, I plan to collect the data to analyze firm innovation behaviors related to related to CFLs and LEDS gathered from the Derwent Innovations Index (Thomson Reuters). I will identify a set of utility patents filed by lighting technology firms using keywords search: CFLs and LEDs. Second, I plan to identify the impact of the policy implementation using a difference-in-differences estimation method between 2010 and 2014. In this context, incandescent light bulbs are the treatment group while CFLs and LEDs are the control group. Third, I will analyze the patenting behavior of firms by comparing the location of the first inventor that I can identify where R&D occurred. Fourth, I will compare between U.S. inventors and non-U.S. inventors patenting.

A gradual incandescent light bulbs ban across countries seems to be an imperative policy stream. Followed by Brazil and Venezuela’s controversial light bulbs phase-out in 2005, many countries participated in implementing the policy. A light ban could have cascading effects from the country which implemented the policy early on to the country which implemented the policy later. Therefore, it is meaningful to examine the different effects of energy-efficient technology patenting across different firms in these countries.

ABSTRACT. INTRODUCTION Entrepreneurship has been touted as a catalyst for economic development. In that regard governments at the national, provincial (state) and local government levels have put in place supporting measures that include loan guarantees, tax incentives, research, credit schemes designed to boost innovation, or systems to encourage self-employment all in an effort to promote entrepreneurship (OECD 2011:4). The advent of green economy/growth has widened the range of possible entrepreneurship activities. While global inequality and rising unemployment pose major challenges to policy makers, green entrepreneurship is seen as the driving force for the establishment of a holistic and sustainable economic, environmental, social system. Green economy has been defined as one that results in improved human well-being and social equity, while significantly reducing environmental risks and ecological scarcities (UNEP, 2011:14)Green entrepreneurship could be defined in terms of the technology used for green production in any sector of the economy. Green entrepreneurship’ is an increasingly relevant phenomenon from a development perspective which, however, is still largely under-researched.Farinelli et al.(2013:5) state that a green economy cannot be mandated from at macro level but needs to be driven by entrepreneurs who respond to policy incentives through innovations in management and technology. The majority of policy mechanisms that have tried to enable green growth are aimed at identifying the technological innovations capable of mitigating the human impact on the environment including issues such as climate change, land degradation and loss of biodiversity. However, there is a paucity of studies exploring the characteristics of green entrepreneurs, the policy and institutional regime that determine green entrepreneurship comparing the experiences of industrialized and developing countries.Seeking to address this gap within a South Africa context the research sets two objectives. Research objectives • To determine the size (number) and range of green entrepreneurs in South Africa. • To identify some of the challenges being faced by green entrepreneurs in South Africa Methodology/Approach The study is qualitative and will gather secondary qualitative and quantitative data relating to the entrepreneurial environment in South Africa. The secondary data will examine seven issues that are considered critical for entrepreneurship to thrive. These are: •cultural and social norms related to entrepreneurship, •legal framework, •current state of entrepreneurship, •current sectors with more entrepreneurs, •access to finance, •available education and training for entrepreneurs, •supporting organisations and intermediaries. The second stage involves the analysis of selected case studies on green entrepreneurship that will help to acquire a deeper understanding of the entrepreneurship environment from the aspect of green entrepreneurs and understand motives, challenges and obstacles they face in developing their businesses. The study draws on some of the vantage points presented by the multi-level perspective, sustainability transition and the strategic niche management theories. The paper will employ thematic analysis. Findings Results of the study reveal that there interesting and innovative ideasamomg green entrprenurs but the challenge is how to bring innovations to societies. Green entrepreneurs try new business opportunities and undertake ventures, which usually involve a very high risk,but normally the outcome is unpredictable. Financing of green entrepreneurship activities is a major obstacle to the development of green entrepreneurship activities. Research limitations The major limitation of the study lies in separating unambiguously relevant activities within “green” sectors from activities occurring in the rest of the economy. References Farinelli, F, Bottini, M, Akkoyunlu, S & Aerni, P 2011, Green entrepreneurship: the missing Link towards a Greener Economy, ATDF Journal, vol. 8, no. 3/4, pp. 42–48. OECD (2011), “Measuring Green Entrepreneurship”, in Entrepreneurship at a Glance 2011, OECD Publishing.http://dx.doi.org/10.1787/9789264097711-4-en UNEP ( 2011). Towards a Green economy. Pathways to sustainable development and poverty reduction. Available online atwww.unep.org/green economy. Accessed 11/03/2015. ISBN 978-92-801-3143-9

ABSTRACT. The phenomenon of interest in this proposal is the variation in success levels resulting from the application of industry-wide (or sector-wide) Technology Roadmaps (“industry TRMs”). Generally TRMs are instruments that articulate a future goal-state for a technology, the current state of a technology, and the sequence of scientific and technological advancements between the two states. The articulation of the present and future states of the technology may include economic and social attributes of the technology, such as price, consumption, and public perception of the technology. TRMs are created and distributed across an industry in order to coordinate R&D efforts among members of that industry so that R&D costs are reduced and R&D advancements are accelerated.

A specific case of a TRM is the US Department of Energy (USDOE)’s Solid State Lighting TRM (SSL TRM). The SSL TRM concerns two broad categories of technology – light-emitting diodes (LEDs) and organic light-emitting diodes (OLEDs). While both technologies were integrated into the SSL TRM during its outset, USDOE-funded R&D has advanced LEDs more rapidly than OLEDs. More targets for LED research have been met than for OLED research, making the SSL TRM more successful for LEDs and less successful for OLEDs. At the outset, it is not clear what caused this differential in success, hence the need for an explanation.

An explanation for the differential success between LEDs and OLEDs from application of the SSL TRM would offer valuable lessons for future industry-wide R&D coordination by government actors. It would offer managers of government research programs guidance on how to better-design and better-use TRMs to further reduce costs of government-funded R&D and accelerate advancements of government-funded R&D. This guidance could include suggestions to abandon TRMs if they are not found to be useful instruments. Improved design and use of TRMs would assist the achievement of policy goals related to R&D, which often include goals for economic development, health, medicine, energy, the environment, and national security.

Data from USDOE reports and reports by the National Research Council could offer an explanation of why the SSL TRM appears to have been more successful for LEDs than for OLEDs. The USDOE has issued two streams of documents related to the SSL TRM, which are both updated annually: the Multi-year program plan for solid-state lighting (MYPP), and the Annual Technology Roadmap for solid-state lighting (the SSL TRM). In each year of its issuance, the SSL TRM revises targets for LED and OLED research each year, while also evaluating prior R&D progress in comparison to the targets of the prior year’s issuance. The MYPP reports annual R&D progress and outlines the budget and expected performance of R&D projects for the year following its issuance. In addition, the National Research Council has offered reports evaluating the activities of the USDOE’s LED and OLED R&D activities. The National Research Council compare R&D progress in LED and OLED technologies to the expected progress articulated in the MYPP and SSL TRM and to R&D progress by other nations and US R&D progress more broadly. Since these sources both articulate the features of the SSL TRM and the R&D outcomes of projects affected by the SSL TRM, these sources have the potential to answer the question of why the SSL TRM appears to have been more successfully applied to LEDs than to OLEDs. Answering this question will contribute toward answering the broader question of what drives successful application of TRMs.

ABSTRACT. This research studies the intersection between Science, Technology, Innovation and International Relations. The main objective of this research was to create an original theoretical and methodological framework that provides a wide comprehensive map of the current reality of scientific knowledge in the world system at the beginning of the 21st Century. The analysis uses an interdisciplinary, systems approach and applies systems models to study the characteristics of scientific knowledge within the international systems. The result is a Systems Architecture Model that examines the structure of scientific knowledge in the context of the post-Cold War international system.

The profound changes occurring in the international system over the past 25 years have resulted in new rules and new world patterns. One of the most relevant topics in the new global agenda is scientific knowledge. The deep impact that the technological revolution is generating has special incidence in the scientific knowledge and in the role that is playing in the international system. At the beginning of the 21st Century, scientific knowledge has acquired a significant economic value that generates a very strong impact on the economic, social and political system. The new aspect is that it becomes central to this new stage of international relations, and a main source that generates economic richness, political power and social development. The main hypotheses of this research refers to the transition to a new international system after the Cold War era that has modified the geopolitical and geo-economic structure of the scientific knowledge, and has created a new one, characterized by the participation of new actors; by producing a new dynamic of interaction between the international stakeholders; by new internal processes and mechanisms; by the emergence of new systemic realities; and by the configuration of a global structure more unequal, multi-polar, conflictive and competitive of the scientific knowledge within the international system.

The analysis uses an interdisciplinary, systems approach and applies systems models in order to study the holistic characteristics of scientific knowledge within the international systems. The research model was developed with the following criteria: i) A research project within the academic area of Social Sciences that pretends to have a multi and interdisciplinary point of view that allows us to combine different disciplines, methods and techniques; ii) Complementary with this interdisciplinary approach, the research pretends to focus on the autonomous field of the International Relations as a principal academic discipline; iii) The research uses quantitative and qualitative models and techniques; iv) The Systems Approach was adopted as main methodological model; and, v) Uses specific systems models that are combined to develop an integrated System Architecture Model.

The world order after the Cold War is a new transitional scenario in which science, technology and innovation have become key factors reconfiguring the New World Order. In today’s Knowledge Society, scientific knowledge is enabling a new geopolitical and geo-economic structure of power at the national and international level. This research reached the three initial established objectives and contributed with findings in three different aspects: - Theoretical: The creation of a theoretical systematization of the role of scientific knowledge in the world system using a new approach within the Social Sciences and International Relations. - Methodological: The use of interdisciplinary, systems approach and application of systems models to study the characteristics of scientific knowledge within the international systems and the creation of a Systems Architecture Model as a new methodological tool to analyze the impact of S&T in the international relations. - Empirical: The application of the Systems Architecture Model to examine the structure of scientific knowledge in the context of the post-Cold War period within the international system.

ABSTRACT. Over the last three decades, grand challenges in the current economic structure come together with the high-speed economic development, such as environment pollution, resource exhaustion and increasing costs of labors. To transit to a sustainable and inclusive development model, China has carried out the strategy of innovation-driven development. The efficiency of China’s R&D institutes, the key actors of national innovation system, is significant to this strategy. Recently, several new R&D institutes has become more and more influential and attracted the attention of both policy makers and researchers. Why some of them succeed in their fields and what is novel in these institutes? These questions are related not only to the ongoing institutes reform but also to the whole national innovation system. Despite the many useful and informative studies focusing on particular aspects of these institutes (such as He & Long, 2014; Zeng &Lin 2013,2014), there is a conspicuous dearth of studies providing a systematic study to explore the mechanism behind these institutes. To fill this void, we choose two extremely successful institutes as cases. One is BGI, which is famous for its outstanding publications (Figure 1) as well as commercial profits in DNA sequencing. The other on is National Institute of Biology Science (NIBS), which is famous for its special invectives to young researchers and also their outstanding publications (Figure 1) and worldwide reputations. We interview the researchers and the managers of these institutes to investigate their missions and strategies. Further more, we take Beijing Institutes of Genomics of Chinese Academy of Sciences (BIG) as an example to study what is novel in these new institutes. We use the “Mission-Management-Governance” framework (Xue and Chen,2012) to compare these three cases . Organization mission refers to “what to do”, which product or service the institute produce; operational management refers to “how to do”, how to organize researchers to work and how to pay them; governance means the power to evaluate the institutes both from outside and inside of the organization. The main findings are: 1.BIG is highly organized as a private corporation, which brings its success. Basic research is some kind a by-product of its commercialization. However, BIG is lack of clear property structure and this restricts its further development. 2.NIBS’s success comes from its clear mission and matched management model. It follows the mechanism of basic research to provide incentives to its scientists. Its week governance power might bring risk in the future. 3.BGI is restricted in basic research by its outside governance power. But mismatched management lowers its productivity. Further, it can be concluded that: 1. The key to success of new R&D institutes is the match of mission and management, which is not so novel. 2. Week governance power might bring risk in the future. 3. Clear mission, matched management and strong governance are key to the further reform of science and technology institution.

ABSTRACT. Scientometrics, especially evaluation bibiometrics, has been developed in a big step in both theoretical and empirical aspects in recent two decades. Scientometrics has become one of the basic methodologies to promote research of S&T (ie. science and technology) policy. More and more scientific management organizations from many countries focus on applying scientometric methods in S&T policy-makings to optimize and support S&T policy-makings and scientific management process. This research aims at studying the profile of applying scientometric methods in S&T policy-makings from research papers. Through the analysis, we try to answer the following questions: in which research topics of S&T policy field, scientometric methods have been applied? in which research topics of S&T policy field, scientometric methods have been applied more/less frequently? which research topics of S&T policy field are the potential areas where scientometric methods can be applied?

In the research, we select all papers from Scientometrics and Research Policy as two separate datasets in 2010-2014, since the two journals are the most specialized and typical journal in the fields of scientometrics and research policy separately. We compare the 2742 keywords from Scientometrics and 1644 keywords from Research Policy after cleaning the keywords, and there are 270 common keywords between the two datasets. We make a deep analysis about the 270 common keywords by dividing the common keywords into two groups (high frequency keywords & low frequency keywords ) and from two dimensions(words' frequency in Scientometrics & words' frequency in Research Policy). Then we get four different types of keywords which represent four different kind of research topics related to both scientometrics and S&T policy.

From the research, we find that: there are 25 keywords which are high frequency words in both Scientometrics and Research Policy. These words are the main general concepts and core/hot topics in scientometrics or S&T policy field, such as Bibliometrics, Citation analysis, Research productivity, Innovation, Patents and Research collaboration. It shows that in macro-level scientometric methods are applied more frequently in S&T policy.  There are 25 keywords which are high frequency words in Research Policy but low frequency words in Scientometrics. These words are some specific topics of the main general concepts and core/hot topics in S&T policy field, such as Open innovation, Innovation systems, Knowledge transfer, Innovation performance, which are specific aspects of innovation . It shows that in micro-level or in some specific topics scientometric methods are applied less frequently in S&T policy.  There are 60 keywords which are high frequency words in Scientometrics but low frequency words in Research Policy. These words can generally be divided into three parts: some of them are the hot topics in scientometics, such as co-authors, peer review, indicators, patent citations; some of them are the topics from research policy which are not hot in the field, but they are hot in scientometrics, such as research performance; others are topics in social science or science' science, such as Impact, Higher education and Academic disciplines. It shows that, some hot topics and important methods from scientometrics are little mentioned in S&T policy, and some issues related to S&T policy which are studied in scientometric methods are not core or hot topics in S&T policy field. What's more, there are some topics from social science which are studied both in scientometric and S&T policy field separately, and these topics can be studied by the combined methods from these two fields. There are 160 keywords which are low frequency words in both Scientometrics and Research Policy. These words are related to many aspects from scientometrics or S&T policy or even social science. And some of the related topics can be seen as potential topics of S&T policy field where scientometric methods can be applied, such as academic career, evolution of research fields.  

In conclusion, it shows that in macro-level scientometric methods are applied more frequently in S&T policy, but in micro-level or in some specific topics scientometric methods are less applied. Some hot topics and important methods from scientometrics are infrequently applied in S&T policy. Some issues related to S&T policy which are studied in scientometric methods are not core or hot topics in S&T policy field, but just some periphery topics. It shows that Scientometric methods applied in S&T policy field are limited, not sufficient and not meeting the needs of S&T policy-makings well. There are some other applicable and potential subfields in S&T policy-makings which can be studied in scientometric methods having seldom been noticed. We hope this research could help apply scientometric methods in S&T policy-makings in a more sufficient way.

ABSTRACT. This paper examines the role of government in promoting innovation in large-scale energy technologies to address climate change, drawing on the experience of Carbon Capture and Storage (CCS) development in China and the United States (US). Aimed at scholars, policymakers, and sectoral analysts, the paper also offers recommendations on policy instruments and implementation.

Clean energy technology development and deployment plays a central role in tackling climate change and progressing toward a 'low-carbon' world. As early as 2004, Pacala & Socolow introduced seven 'wedges' to achieve stable emissions by 2050 (to ~500 ppm CO2 concentrations in the atmosphere). The seven wedges are mainly about the gradual increasing deployment of clean energy technologies over the next four decades, including renewable energy, nuclear energy, carbon capture and storage, electrical vehicles, etc. The International Energy Agency (IEA) also publishes its annual Energy Technology Perspective, tracking clean energy technology development and discussing countries’ policy actions.

Economic and technological efficiencies of large-scale, clean energy systems can be achieved only through intensive research and development (R&D) and learning-by-doing. Due to market limitations/ failures in realizing environmental externalities and producing 'public goods' technologies, industries lack sufficient incentive for the scale of investment necessary for research, development, and dissemination (RD&D) of climate-friendly technologies. Compared to 10-20% in the information technology and pharmaceuticals sectors, in the energy sector the technology turnover rate is only 0.4% (Neuhoff, 2005; Margolis & Kammen, 1999). With strong market incentives, the former sectors receive robust private financing to upgrade technology. Many critically important clean energy systems are highly capital intensive, with a decades-long processing time. The IEA (2010) estimated that a total investment cost of $26.6 billion is needed to deliver early CCS demonstrations. After these initial demonstration projects, much knowledge remains to be obtained at a progressive scale, preventing private sectors from undertaking the full learning process alone. Therefore, governments fund research institutes and private firms to conduct clean energy technology RD&D; they also create markets in which firms can benefit financially from creating and using low-carbon technologies – this is referred to as 'technology push' and 'demand pull'.

China, the US, UK, and Australia, among other large economies, have taken aggressive action to support basic research, pilot and demonstration projects for CCS, a technology holding significant promise for reducing carbon dioxide from large point sources. The world’s first industry-scale, Integrated Gasification Combined Cycle plant, in Kemper, Mississippi, received a $270 million grant from the Department of Energy, plus $133 million in investment tax credits approved by the Internal Revenue Service. Meanwhile, the Mississippi Public Service Commission approved the retail rate increases for 186,000 ratepayers to support the IGCC facility effective in March 2013. All four nations also published their own CCS roadmap(s), planning to develop CCS to meet their climate goals. As a result, a critical question emerges, how effective is government action in advancing CCS technologies?

Building on and contributing to the literature on innovation in clean energy technology, this paper examines the effects of the US and China’s government actions through the lenses of RD&D and 'learning-by-doing'. A possible optimal analytical model on the respective roles of government, research institutes, and firms in technology learning to maximize clean energy technology is developed and tested. The paper is organized in five sections: The first briefly introduces the scale of clean energy technology development required for combating climate change and the US and China’s major government actions on CCS. The literature on technology learning and government roles, and the scholarly contributions of this paper are discussed in the second section. Section three describes the research design and methods, with a focus of the analytical model. That model is applied in the US and China’s context, and the results and discussion is presented in section four. The paper concludes with a discussion of policy implications.

ABSTRACT. OBJETIVES: This poster aims at introducing Policy Analysis field as an interesting tool to investigate International Science and Technology Cooperation Policy (IS&TCP). IS&TCP can be defined as explicit actions performed by government officials such as official agreements that influence the intensity, content and direction of collaboration between scientists, researchers and policymakers across borders¹. IS&TCP can be established at the national level - bilateral or multilateral – or at the transnational level, involving a supranational body or a specific governance. Considering this definition, it can be argued that IS&TCP cannot be treated as ordinary policies in that they present certain particularities that should be taken into account: (i) their scope is very broad covering a wide range of topics such as research financing, innovation incentives to companies and knowledge diffusion; (ii) like regular Science and Technology policies they serve as basis for other public policies such as health, education and industrial policies and constitute one of the main axis to national development; (iii) they affect the political, social, economic and diplomatic realms of States and are not only part of the national Science and Technology Policy of a country but also its Foreign Policy. METHODOLOGY: This work applies the concepts of Policy Analysis to a case study in order to assess its contribution to aspects of the International S&T Cooperation Policies (IS&TCP) such as: decision making process and actors involved; strategies implemented and alternative options of policy. Other approaches – Foreign Policy Analysis for example – were also considered but discarded due to their lower contribution when compared to Policy Analysis. The chosen approach was considered to cope well with IS&TCP complexities. São Paulo Research Foundation (FAPESP) was selected as case study firstly because it is one of the most important funding agencies in the world. FAPESP finances the biggest and most developed research community in Brazil (State of São Paulo's research community) with a budget that represented in 2013 around 34% of the United State's National Science Foundation investment (about US$2,4 billion; nothing bad for a third world country!). It is also one of the research foundations with the higher degree of security of funds and autonomy from the federal government². Considerable data concerning its S&T policies are available online at FAPESP's website and in addition to data collection interviews were also conducted. RESULTS: the Policy Analysis approach was successful applied and revealed important aspects of FAPESP's IS&TCP. It is important to note however, that this positive outcome was only possible with the theoretical support of International Relations field and concepts like International Regimes, Cooperative Games, Dependency Models. This paper contribution is twofold: firstly it offers practical tools to analyze, understand and improve Science and Technology Policies, connecting S&T topics to the political process. Applying those tools to policy analysis results in more adequate policies that most achieve a given set of goals. Secondly it shows how Policy Analysis approach and International Relations theories offer clear and deep understanding of the political process and how these concepts should be applied by policymakers – who then are able to acknowledge both the national and the transnational character of these policies; and to apprehend the international cooperative environment and the actors participating in it.

¹ - European Commission. Drivers of International Collaboration in Research. Luxembourg: Publications Office of the European Union, 2009. ² - The Royal Society. New Frontiers of Science Diplomacy. London: The Royal Society, 2010.

ABSTRACT. Trends of information use can be obtained by investigating popular topics and issues of information users through visualization of related search words. Consecutive patterns of search words used were investigated, and relationships between search words were developed into Topic Maps to examine the keywords for major issues in each country. A network of search words was created to find the issues of interest. Meaningful keywords among issue-related keywords were reviewed to see whether they could be recommended as related data. This paper proposed methods for systematically analyzing search data(ex] log), for converting analyzed search patterns into Topic Maps, and for creating a network of related search words. The results were analyzed as well.

ABSTRACT. Innovation policies are no longer the responsibility of national-level governments alone, because regions and supra-national organizations also implement these policies.National governments try to safeguard budgets and think of new ways to support innovation. Regional, local and supra-national authorities also increasingly consider innovation policy to be an important activity. This creates new policy coordination challanges. At one government level horizontal coordination is important, but in the multi-level system vertical coordination has to be considered. Proposed poster aims to identify the character of the relations between different government levels which implement innovation policy in six Baltic Sea Region countries (Sweden, Denmark, Finland, Latvia, Estonia and Lithuania) by concentrating on policy coordination. In the Baltic States there are two parallel government levels (national and supra national), but in the Nordic countries – three (regional, national and supra national). 

Expansion of innovation policy to different government levels may create a risk of overlapping between various policy initiatives, therefore a distribution of tasks and policy coordination is very important. The theoretical background of the paper will focus on the concept of policy mix which emphasizes the role of interactions between different policies in different dimensions. Coordination is sone of the core elements of the concept. Innovation policy strategies and in-depth interviews with policy-makers will be analysed in the paper to characterize the multi-level innovation policy mixes. The results  will demonstrate how innovation policy mixes in the Baltic Sea Region countries are coordinated.

ABSTRACT. The “100-talents” policy of Chinese Academy of Sciences (CAS), has been the first influential S&T talents policy in China since 1980s. This policy was initiated in 1994, aiming at recruiting and funding 100 talented scientists from the world to address CAS’s problem of lacking top scientists at that time, although the number of selected scientists has marvelously been enlarged since CAS obtained specific funds for this policy from Chinese center government in 1998. Till the end of 2013, CAS “100-talents” policy have recruited 2145 scientists, with the average funds of 2 million RMB yuan provided to each of them for research activities and personal allowance. In 2014, Evaluation Center of CAS was contracted with CAS headquarter to review the effect and impact of CAS “100-talents” policy.

Effect review of “100-talents” policy aims at assessing direct effect of this policy on CAS’s innovative capability defined below: structure of CAS scientists’ team, quality and quantity of high level scientists in CAS, major S&T programs undertaken in CAS, major breakthrough and contribution by CAS scientists, development of disciplines and platforms, international collaboration level, quality of training graduates students. We collected quantitative data mainly from CAS headquarter and CAS institutions.

As the quantitative analysis shown, most scientists recruited and funded by CAS “100-talents” policy have developed well in CAS, and innovative capability of CAS has been greatly promoted after 20 years’ implementation of this policy, which means problems of lacking top scientists in CAS have been solved successfully. 1/3 of scientists selected by CAS “100-talents” policy had obtained Ph.D. degree from world’s top 100 universities or had academic experiences in world’s top 59 research institutions . What’s more, although scientists selected by this policy only counted for less than 4% of the total number of CAS employees, they have contributed 1/3 or even 1/2 in CAS significant outputs during last two decades of years. For example, number of top 1% SCI/SSCI papers published by “100-talents” scientists have occupied 41% of total in CAS from 2008 to 2011.

Impact review of “100-talents” policy aims at analyzing broader impact of this policy on Chinese science community, policies and culture. Desk work on “100-talents” policy was done to understand background and purpose of this policy. We also interviewed the following stakeholders: 24 representatives of scientists selected by CAS “100-talents” policy; 5 directors of CAS institutions where scientists selected by this policy were working; 10 representatives of officials from Chinese government departments such as MOST, NSFC, SAFEA, MHRSS; 3 academicians of CAS.

Qualitative methods were employed to compare and conclude opinions of different stakeholders. In terms of impact in China, firstly it is clear that CAS “100-talents” policy has attracted lots of talent scientists working for not only CAS but also Chinese universities, S&T management departments, and Chinese industries, as 160 scientists selected by “100-talents” policy had moved to these institutions now. Secondly, Chinese government have already launched many talents policies, such as Changjiang Scholars Program (by MOE), Thousand Talents Program (by COD), etc., based on experiences of CAS “100-talents” policy. These talents policies have clearly similar organizational methods as CAS “100-talents” policy.

Further, cultural impact of CAS “100-talents” policy has been observed. Background of initiating CAS “100-talents” policy was associated with scarcity of well-trained scientists and lack of scientific culture in 1980s. At that time, scientists in China led poor lives, and had few funds to undertake researches, which resulted in lot of scientists’ quitting their academic careers and prohibiting ethnic Chinese scientists from returning back to China. However, this situation has been enormously changed since CAS “100-talents” policy started in 1994. Selected by this policy, scientists were able to build advanced research facilities and better their personal lives. Because of this policy, Chinese academic atmospheres have been aligned to western countries, which make many ethnic Chinese scientists willing to work in China in recent decade of years.

Nevertheless, our review also found some problems and challenges for this policy. For example, some interviewees called for special attention to inequity between indigenously trained scientists and exotically introduced talents selected by CAS “100-talents” policy. Some scientists also pointed that levels of scientists selected by CAS “100-talents” policy were declining because of national talents policies’ rapid emerging.

In conclusion, CAS “100-talents” policy has obvious positive effects and impact on CAS and China, even though some problems also exist.

ABSTRACT. Precise time standard forms the basis of sciences such as precision spectroscopy and the determination of fundamental constants. It is also a fundamental technology with a significant impact on applications found throughout modern society, such as global positioning systems and high-speed communications network. An atomic clock is a precise type of clock that stabilizes the oscillator frequency by referring and locking it to an atomic resonance. At present, the most accurate time measurements are made by atomic clocks and the precision is increasing with rapid developments. A better understanding of the global development status of atomic clocks is necessary to provide support for emerging research areas and highlight areas where further research is necessary. Results from individual studies show considerable variations in key characteristics of atomic clocks. Global patterns may be much more important than individual studies when assessing the development status of atomic clocks. There is a clear need to quantitatively synthesize existing results on the key characteristics of various types of atomic clocks in order to either get the general consensus or summarize the differences. Meta-analysis is a quantitative synthetic research method that statistically integrates results from individual studies to find common trends and differences. There has been a widespread discussion about the state of the research on atomic clocks within the academic circles, however few synthetic research based on the rich resources of available literatures has been performed. This paper reviews the general methodology of meta-analysis, and synthesizes its use in assessing the global development status of atomic clocks and discusses future direction. 1 Meta-analysis Meta-analysis provides a means of quantitatively integrating results to produce the average effect, so as to be used to develop general conclusions, delimits the differences among multiple studies and the gap in previous studies, and provides new research directions and insights. Criticisms of meta-analysis are due to its shortcomings and misapplications, including publication bias, subjectivity in literature selection, and non-independence among studies. Various methods are developed to verify the publication bias. The steps of performing the meta-analysis method are as follow: (1) formulating research question, (2) collecting all relevant researches from individual studies related to the problem, (3) designing the special forms for recording information extracted from selected literatures, (4) selecting the effect size metrics and analysis models, and (5) conducting summary analyses and interpretation. 2 Meta-analysis of the development status of atomic clocks Literature searches were conducted using ISI Web of Science database and other similar search engines for the relevant keywords: atomic clock, cold atom clock, optical clock, ionic clock, space atomic clock, etc. We also checked the published studies of the world’s major atomic clock research institutions, including National Institute of Standards and Technology (NIST), Physikalisch Technische Bundesanstalt (PTB) and so on. Qualifying data constituted any reported numerical findings applicable for a quantitative meta-analysis, i.e., the key characteristics of various types of atomic clocks. Studies were collected for analysis until February 2013. Data were manually retrieved from publications due to the different information formats in the scientific publications. To explore the main characteristics of the wide variety of atomic clocks, we selected transition frequency, medium/long-term frequency stability, frequency accuracy, natural/actual linewidth, quality factor, etc. Other response variables were not available in enough quantity to include in meaningful quantitative analyses. The result indicates that the investigations on fountain clock, optical clock, optical lattice clock, and nuclear clock have become the active field in atomic clock research. The accuracy of primary frequency standards contributing data to International Time Bureau (BIH) has greatly increased since 1950’s and has reached 10^–16 level. The femtosecond optical frequency combs associated with the laser cooling and trapping has also reached maturity, thereby providing various isolated and nearly motionless quantum references for the optical clocks. The best of these new clocks are now surpassing the accuracy of even the best Cs fountain clocks. NIST have succeeded in demonstrating two optical clocks based on single trapped Hg and Al ions with the stability of 1.9×10^–17 and 8.6×10^–18, respectively. For a well chosen transition, the systematic uncertainties of the optical clocks are expected to be as low as 10−18. A “solid-state nuclear clock” from 229Thorium nuclei implanted into VUV transparent Calcium fluoride crystals will open new possibility of reaching a fractional instability on the 10^−21 level.

ABSTRACT. Debating Formal and Informal Innovation

Introduction The history of innovation is as old as of the mankind itself, with its prime focus to help human beings live a comfortable life. The evolution of innovation studies has not only added a new dimension to the domain of scientific knowledge production, but has also influenced individual as well as nations. Innovation is being defined as “…implementation of a new organizational method in business practices, workplace organizational or external relations” [OECD, Eurostat 2005]. Despite the advancement in the discipline, the categorization of formal and informal has been quite a significant debate over time, the early contributor like Schumpeter , scholar like Nelson and Winter tracks the importance of firm-level knowledge as the driving force in the Innovation process (Jan Fagerberg, 2012). Despite the upper edge of the formal sector, innovation in the informal sector has been the central focal point over the years, with countries like India were the major work force (almost 90%) is being engaged in the informal sector. Grassroot Innovation has added a new dimension to the field, where the central theme of innovation is not guided by a structural framework, rather the motivation and self-guided framework results into fascinating discoveries (Hemant Kumar, 2014). Formal, Informal Sector and Innovation The outmost view of innovation includes technological, economic, and social solution, in the step of Idea formulation, Production/development, and Commercialization. According to (Giuliano Bianchi, 1996) the formal innovation undergoes various cycles to end up with the product in the market for mass consumption Informal sector has been one of the more complex area to define, the concepts evolves in 1971 by Hart at the International Labour organization. Since its inception it has been reframed continuously, India is being a dual economic country with over 70 percent of its population is rural and 60 percent are engaged in agriculture, and only 11 percentage of workforce is engaged in public sector with the rest relies on informal sector (Dutz, 2007). However, there has been considerable amount of transformation from the old view model to the new view model, where the former was considered to be problematic due to labour exploitation, wage rate, rather the new view act as a support system for the organized sector. Methodology According to various scholars informal innovation has been thought of the wrong choice of import technology, mainly occurred in developing country due to low capital, and the unaffordability to technology, but in other way it act as sustainability as the waste of formal sector is often the platform for product innovation. To holistically understand the nexus of formal and informal innovation, two cases has been taken off one each from the two sector followed by an overview of IPR (Intellectual Property Right) in agriculture manufacturing, focusing the above mention debate. The research is exploratory in nature, and the data are collected mainly from the secondary sources. The research will mainly focus through certain key issues which have been framed in accordance with the literature available. Conclusion The paper trace that informal and formal though differ a lot in their structure and form , the former being free from organizational constrain and lack of R&D support, while the latter is mainly equipped with all these features. Idea generation and financial support is mainly guided by self-motivation in case of former but the essence of these innovations is quite important in the context of development in the developing countries. The paper tries to look forward the debate by tracing the current innovation in both institution and public sphere, the objective of the paper is to theorize that informal and formal are not entirely different set of innovation process, yet the process are different but the intermediator like NIF and other government bodies tries to accommodate the unstructured to structured form through various schemes and policies.

References Dutz, M. (2007). Unleashing India's Innovation. Washington DC: The World Bank Giuliano Bianchi, F. B. (1996, August). On the Concept of Innovation. European Regional Science Association. Zurich,Swizerland. Hemant Kumar, S. B. (2014). Juggad to grassroot innovation: undestanding the landscape of the informal sector innovation in India. African Journal of Science,Technology,Innovation and Development. Jan Fagerberg, M. F. ( 2012). Innovation: Exploring the knowledge base. Research Policy, (41)1132-1153. Kumar, H. (2014). Dynamic Network of grassroots Innovation in India. African Journal of Science, Technology, Innovation and Development . OECD, E. (2005). Oslo Manual- Guideline for Collecting and Interpreting Innovation Data. Paris Steward, F. (1987). Macro-Policies for Appropiate Technology in the developing countries. Boulder: Westview Press.

ABSTRACT. The Science of Science and Innovation Policy (SciSIP) is motivated by a desire to improve the science and innovation enterprise from the “top down,” by improving the decision making of policy makers and program managers. Much of this work, however, can be “co-opted” and extended to improve the science and innovation enterprise from the “bottom up,” by improving processes used in-the-trenches by working scientists and innovators. Moreover, trying to improve the science and innovation enterprise from the bottom up has many advantages over trying to improve it from the top down: bottom-up mistakes can be more easily tolerated than top-down mistakes, so more and riskier experiments can be tried and tested quickly, and learning can be faster. Indeed, the life sciences have already embraced the possibility of this kind of bottom-up self-improvement, addressing one of the key issues associated with modern science and innovation – the “Science of Team Science” (SciTS).

The physical sciences, surprisingly, have not embraced this kind of bottom-up self-improvement. One possible reason: the phenomenal record of success of the physical sciences in understanding and manipulating our universe. The physical sciences have remade the world many times over in the past centuries – why wouldn’t they want to just be left alone so they can continue to do so? In fact, there is much room for improvement. Recently, Sandia National Laboratories sponsored an Art & Science of Science & Technology Forum & Roundtable which brought together distinguished practitioners of the art of research in the physical sciences, and experts in the social science of research. The conclusion of that Forum & Roundtable was that it is time for physical scientists to take seriously the idea that their research practice can be improved through the systematic research of research.

In this paper, we review the findings of that Forum & Roundtable. We organize our review around the two key but opposing types of thinking, divergent and convergent, necessary to advance science and technology; and around the three “micro,” “meso,” and “macro” levels of the research enterprise -- the individual researcher, the research team, and the research institution. By organizing our review in this way, we implicitly follow Linus Pauling’s dictum, “the way to get good ideas is to get lots of ideas and throw the bad ones away,” and indeed go beyond it by positing that these processes must be nurtured, if not executed, at each level of the research enterprise.

At the individual researcher level, we discuss potential science-based strategies for overcoming human cognitive constraints and biases: for overcoming the idea fixation which is detrimental to divergent thinking; for overcoming the sloppy thinking that is detrimental to convergent thinking; and for balancing divergent and convergent thinking. At the research team level, we discuss potential science-based strategies for overcoming social constraints and biases: for overcoming the “strong links” in social networks that are detrimental to divergent thinking; for overcoming the “groupthink” that is detrimental to convergent thinking; and for optimally distributing divergent and convergent thinking across individuals and teams. At the research institution level, we discuss potential science-based strategies for understanding, assessing and improving research ecosystems: for overcoming the GPA and performance fixation that is detrimental to divergent thinking; and for overcoming the existential (losing one’s funding or job) and social (losing the friendship or respect of one’s colleagues) stress that is detrimental to convergent thinking.

Reference: http://belfercenter.ksg.harvard.edu/publication/23766/art_and_science_of_science_and_technology.html

ABSTRACT. This study presents an empirical analysis of the factors that contribute to the determination of whether firms collaborate with public research organizations (PROs) for a sample of emerging biotechnology enterprises. Here we define PROs as universities and government-funded research centers. In this study, the research question we would like to answer is what kind of firms in the emerging high-tech sectors tend to collaborate with PROs? To answer this question, we drew an analysis of Taiwanese biotechnology industry and investigated company profiles and patent information of all 136 companies who went IPO before 2014. Based on the available data, we tried to identify critical determinants that contribute to Academia-industry (A-I) collaborations. Different from previous studies regarding A-I collaborations, we applied classification method from the field of machine learning along with interviews for our analysis. 
A-I collaboration have drawn wide attentions from the academia, abundant literature (Fontana et al. 2006, Rasiah and Chandran 2009, Saito 2010) have been trying to understand and explain this phenomenon. To understand A-I collaboration is crucial. Firms now compete in an environment where sciences and technologies are evolving rapidly. Firms have to take advantage of research results from PROs to speed up R&D processes, stay ahead of their competitors and to explore new or potential development directions. In advanced economies, the high-tech industries has developed strong internal R&D capabilities, but has also relied heavily on basic science carried out in the public sector (Bartholomew, 1997; Carlsson 2010; McMillan et al., 2000). Traditionally, catching-up economies are viewed as users of technology developed abroad. According to Malerba et al. (Malerba and Nelson, 2010), internal capabilities for accessing complementary assets, absorptive capabilities (Cohen and Levinthal, 1990), and innovation capabilities are required by firms in order to use technologies developed abroad. Therefore, S&T policies have focused on A-I collaboration, since 1980's, many countries have implemented policies to promote and sustain A–I partnerships. 
A-I collaboration data used in this study were obtained primarily from publicly available financial reports of 136 biotechnology companies, which listed on Taiwan Stock Exchange, in Taiwan. Collaboration data from those financial reports were then cross-examined and validated with data from those companies’ websites. In our dataset, we also included patent data from Taiwan Patent Search System of the abovementioned 136 companies. Company names were used as search terms to search for patent data. After data preprocessing, we have total 136 records with 26 attributes (independent variables). Attributes are categorized as basic company information (include names, established year, location etc.), patent data (include file date, IPC code, number of invention patents etc.). Whether the firm build collaboration with PROs is used as dependent variable (class label).
We facilitated classification analysis here first to categorize our data sample into collaborator with PROs or non-collaborators, and then we examine determinants contributes the collaboration. Classification analysis, which can be explanatory and predictive, uses a divide-and-conquer manner to evaluate the past performance independent variables with respect to a dependent variable. Examples of classification applications included building a classification model based on large history application data for loan approve. Another example would be categorizing, comparing, and summarizing articles in automated fraud detection within the last decade to help businesses achieve cost saving (Phua et al., 2010). The classification algorithm used here took advantage of attribute selection classifier algorithm to constructs decision tree model to identify whether firms collaborate with PROs.  This model is then evaluated by two measures. First, classification accuracy (correctly classify firms into collaborator and non-collaborator). And second, proportional error reduction (PRE, to see how the model performs in comparison with pure guessing) (Chen, 2013).
Our preliminary results indicated those firms’ technological specialties, R&D/innovation activity status/intensity and age matter for university-industry collaboration in the overall sample. Those findings are consistent with previous studies of different regions and of the variety of sectors (Fontana et al., 2006, Rasiah and Chandran 2009, D'Este et al., 2012).

ABSTRACT. At universities, department climate is fundamental for faculty productivity and satisfaction. Relationships with colleagues in particular may provide faculty members with the necessary support to thrive in their professional careers (Bilimoria et al., 2006). Inclusion within the department community translates into access to an important network of support for dealing with professional and work-life related issues. Moreover, recognition by colleagues can result in power and influence within the department and in the field, thus fostering self-esteem and motivation. In science, technology, engineering and mathematics (STEM) disciplines, the sex-related fragmentation of departments has often led to marginalization and isolation of women faculty members. Women scientists face the challenge of integrating into male-dominated work environments and gaining access to networks of power, support, and resources. Several policies at the national and university level have aimed to directly alter this “chilly climate” within STEM departments, leveraging integration among faculty members and equal opportunities for women scientists. Current trends indicate that the number of women scientists at U.S. universities is steadily increasing (NSF, 2013) and research shows that women are increasingly integrating into faculty networks (Ceci et al., 2014; Feeney & Bernal, 2010). However, there is little empirical evidence as to whether this increased presence of women in STEM departments and STEM networks has resulted in a more inclusive climate for faculty members. While researchers have focused on sex as a significant variable to explain the climate of marginalization and isolation, few studies have investigated what diverse networks mean for organizational outcomes and scientists’ success. Networks are the mechanisms through which scientists are able to access to the symbolic, information, and material resources that are necessary to gain influence within STEM departments. And networks provide an invaluable source of support and advice to scientists – for instance, through friendship ties - that may enhance a scientist’s sense of inclusion (Coleman, 2010). Because the sex composition of a group (e.g. predominance of same-sex or cross-sex relationships) strongly influences psychological and social perceptions among co-workers, sex-homophily and heterophily are expected to differently affect men and women scientists (Callister, 2006; Ibarra, 1992). This research investigates the following questions: are sex-heterophily ties advantageous for women and men scientists? How do scientists perceive sex-heterophily ties? How is the composition of networks related to the perceptions of influence and inclusion for both men and women scientists? To test our hypotheses, we use data from the “Women in Science and Engineering II: Breaking Through The Reputational Ceiling: Professional Networks As A Determinant Of Advancement, Mobility, And Career Outcomes For Women And Minorities In Stem” (NETWISE II) survey, an NSF-funded study on professional networks for women and minorities in STEM, conducted in 2011 (CO-PIs: Julia Melkers, Eric Welch, Monica Gaughan). The data consist of ego-centric networks of 9,925 U.S. scientists at higher education institutions in four fields: biology, biochemistry, engineering, and mathematics. The results of this research will inform the literatures on sex diversity in STEM networks and university and federal policies aimed at integrating women in STEM fields.

ABSTRACT. Balancing benefits of emerging technologies against civic concerns is a challenging task. Doing it without compromising international competitiveness complicates things further. Previous experience of regulatory action for emerging technologies can help policy makers better address such issues and better anticipate future problems. By examining four notable cases of overcoming barriers to emerging technologies in the U.S., we are establishing a framework for classification of approaches that address civic concerns preventing uptake and acceptability of the use of emerging technologies. The case of National Childhood Vaccine Injury Act demonstrates how regulatory action can protect supply side firms that introduce early-stage technologies. The case of Genetic Information Nondiscrimination Act provides an example of the regulation protecting demand side by upholding public interests through establishing the anti-discriminatory environment. Development of technology in the case of induced pluripotent stem cells makes it more acceptable for general public compared to stem cells extracted out of embryos. The case of in vitro fertilization is an example of a cultural change, which lead to acceptance of emerging technology. In 2009, Nobel Prize to Robert G. Edwards reflects final acceptance of IVF as a medical procedure after rather mercurial history of societal attitudes. We are proposing a framework for classification of existing practices in addressing civic concerns preventing uptake and acceptability of the use of emerging technologies. Its central premise is that emerging technologies often encounter barriers in the form of public acceptance. We identify three main pathways for navigating the barriers: through establishing regulatory setting addressing concerns, through technological advancement that eliminates previous concerns, and through changing the cultural perception of emerging technology. The framework aims to provide a more coherent and concise explanation of already-existing practice to provide instruments for policy-makers to address current issues in emerging technologies. For example, approach to liability limitation in case of vaccine injury might be adapted for automated vehicles (Anderson, James M., Nidhi Kalra, Karlyn D. Stanley, Paul Sorensen, Constantine Samaras and Oluwatobi A. Oluwatola. Autonomous Vehicle Technology: A Guide for Policymakers. Santa Monica, CA: RAND Corporation, 2014.) The GINA serves as another instance of regulatory approach that can be adjusted for neural technologies, which probably would face the same discrimination concerns genetic technologies have faced (Kostiuk, Stephanie A. "After GINA, NINA? Neuroscience-Based Discrimination in the Workplace." Vanderbilt Law Review 65, no. 3 (April 2012): 933-977.)