Abstract The paper illustrates the role of political and social dynamics upon technological change. Examining dynamics transforming excavation practices on South Africa’s goldmines, it describes how a technology that opened up a range of social and economic opportunities became a constraint on those opportunities. This technology’s development and diffusion thereby established a critical precedent in the spread of racial occupational mobility restrictions. Collaborative innovation is also a dominant feature in the analysis, highlighting both its historical importance in South Africa as well as the need for caution in its contemporary promotion.

The research outputs within this portfolio demonstrate sustained inquiry over seven years which has added progressively to the understanding of collaborative educational research contribution. This research developed out of my work with the iPED (inquiring pedagogies) research network which I co‐founded in 2005. The portfolio contextualizes and critiques four journal articles and two peer‐reviewed book chapters published between 2007 and 2012. Through these studies of educational research at Coventry University, I make a significant and original contribution to knowledge, firstly by synthesizing evidence of the facilitators, drivers and barriers to contribution within collaborative educational research; and secondly by offering three new models which help to make the contribution of collaborative educational research more transparent. The Influence Wheel provides a visualization of research contributions as an interactive image. The model can also reveal gaps in contribution, and can be adjusted to display the aspects of contribution of interest to an intended audience. Product‐Based Analysis provides a holistic view of the strategic drivers, goals and interim outcomes of research. This model can be used to analyze, develop, track or communicate a research strategy. Finally, the Enhanced Three‐Phase Model articulates the social and cultural transitions through which a collaborative educational research community may evolve. Used alongside an analysis framework I devised using themes from works of fiction, this model exposes the problems and opportunities a collaboration community may encounter. These three tools exemplify my creativity in devising new ways of visualizing information, an approach adapted from the field of management where graphics are commonly used to supplement text. Methodologically, all the portfolio outputs employ insider inquiry strategies which capture different perspectives on the research context. Conceptually, all the outputs offer social representations of collaborative educational research. These studies offer questions and interim findings which provide opportunities for future research.

This thesis takes a theoretical framework constructed for transdisciplinary research within different natural science disciplines and investigates what kind of new knowledge is produced when this framework is applied to projects at the interface of art and natural science. The main case study is “Sauti ya Wakulima – The Voice of the Farmers”, which involves collaboration with another intervention artist, and with natural scientists and farmers. This is a collaborative knowledge project with small-scale urban as well as rural farmers in Tanzania who have created an online community archive of their farming practices by using mobile phones to upload images and sounds onto a website. The research uses an open-ended participatory methodology that gives the participants as much creative agency as possible within the given power structures and practical and technical parameters. A second work examined is the Climate Hope Garden, an installation by the author in collaboration with ecologists and climate scientists at the Swiss Federal Institute of Technology, Zürich (ETHZ). The installation consisted of a garden grown in climate-controlled chambers based on the climatic conditions proposed by IPCC climate scenarios. The project aimed to enact these scenarios on a spatial and temporal scale to which visitors could relate. Transdisciplinary research has become a key reference point in funding proposals. Despite many references in the literature, and calls for research involving both the natural sciences and humanities to solve complex world problems such as adaptation to climate change, there seems to be little consensus about exactly what kind of knowledge might be produced from such projects, and how transdisciplinary research proposals might be evaluated, especially those at the interface of art and the natural sciences. Several theoretical frameworks have been suggested for designing transdisciplinary research between and within scientific disciplines, or between the natural and social sciences and humanities. The present study applies the framework proposed by Christian Pohl and Gertrude Hirsch Hadorn (2007) to a real-world transdisciplinary art-science project in a development context in order to examine the balance between the collective, locally embodied experience and the nomothetic knowledge that arises from it. This thesis found that transdisciplinarity is a different question from that of types of knowledge on the nomothetic-idiographic scale. Transdisciplinarity is a pragmatic question of definitions and inherited boundaries of disciplines. The framework categories do not differentiate between nomothetic and idiographic, just to which part of the problem-solving puzzle they fit. This is perfectly valid for goal-oriented, problem-solving research and can be applied to art-science research, but there are other ways of describing this work, such as using a philosophical description of the knowing process which comes closer to encompassing the richness of the knowledge produced. It is in this sense that the new type of knowledge generated by the transdisciplinary projects required an expansion of the given theoretical framework.

Collaborative action was undertaken in response to the continued criticisms of formal coach education. It is strongly felt that we can no longer merely criticise what is not happening in terms of coach learning, but a key requirement now is to demonstrate other options. In the UK up to 80% of coaches are volunteers who reach out to around eight million people involved in sport. This valuable workforce is largely forgotten and the bureaucratic structures which oversee formal coach education are merely concerned with quotas and income generation. A fundamental problem with formal coach education is the way in which learning is decontextualized and a knowledge deficit remains. Coaching is multifarious and complex and we need to consider better ways in terms of how we prepare people for this. The Coach Learning and Development (CLAD) programme was devised and implemented in October 2013 to May 2014 at a community rugby club in Wiltshire. Over this 8 month period a range of strategies for coach learning were integrated into CLAD to evidence methods which benefitted the transition of knowledge(s). The theoretical endeavours of Basil Bernstein are introduced to SCR for the first time, particularly the ‘pedagogical device’ to understand, theorise and develop insight into the type of educational contexts that can better support the learning of volunteer coaches. Findings suggest that CLAD as collaborative action learning was successful in transforming coaches to engage with more positive and contemporary forms of coaching pedagogy. Namely ‘game based pedagogies’ argued to be theoretically underpinned by the ‘constraints based approach’. Empirical insights are given in the hope that this can spur further methodological enquiries that move beyond the mere criticism of coach education. SCR needs research endeavours that shift beyond the ‘bricolage’ where knowledge is transferred into the real world to influence real change. Therefore, the findings also draw on the pivotal features of CLAD to not only support more value laden research commitments, but to inform policy developments and practice that can re-configure more successful outcomes for coach education and coaches.

Are foreign-born scientists different from native-born scientists with respect to research activity and performance? This question has important policy implications not only for immigration policy but also for science policy because a substantial part of scientific research in the United States is conducted by foreign-born scientists. This study examines the differences between foreign-born and native-born scientists in research collaboration, grants, and publication productivity. The data for this study are 443 curricula vitae (CVs) and survey of scientists and engineers that Research Value Mapping Program (RVM) at Georgia Tech conducted from 2000 to 2001. By using the multiple indicators, the findings show that foreign-born scientists do not differ significantly in research collaboration and grants from their native-born counterparts. But in terms of publication productivity, foreign-born scientists are consistently more productive than their native-born counterparts. This study also examines the impact of being foreign-born on research collaboration, grants, and productivity, and which factors account for the differences between foreign-born and native-born scientists in collaboration, grants, and productivity. When other relevant variables are controlled for, being foreign-born still has a strong positive effect on publication productivity. Collaboration and grants have a significant positive effect only on the productivity of native-born scientists, whereas strong research preference of foreign-born scientists contributes to their relatively higher productivity. Differences are also found among foreign-born scientists, largely depending on their national origin categorized by the similarity of language and culture. The theoretical and policy implications are also discussed.

This dissertation studies the relationship between collaboration networks and scientific creativity. It finds significant knowledge spillover from new collaborations to repeated collaborations, and proposes a network approach to understand scientific creativity at the egocentric network level beyond the boundary of teams. To understand the network effect (specifically, effects of tie strength) on creativity, it integrates literature on small groups and social networks and adopts a creative-process model. An inverted U-shaped relationship between tie strength and creativity is observed, because of the mixed impacts of tie strength at different stages of the creative process. Furthermore, it explores the effect of tie configurations and finds that the skewness of tie strength distribution moderates the effect of tie strength. In addition, it also tests two competing explanations for the association between strong tie and low creativity: creativity-decline hypothesis versus cost-reduction hypothesis. Finally, there is no evidence that collaboration networks would raise the visibility of previously published papers, but there is a significant prestige effect in gaining citations.

Cette thèse porte sur la création de connaissances scientifiques et technologiques,et son lien avec la géographie et le réseau social. En ce sens la thèse s’attacheà mieux identifier le rôle du réseau social dans la production de connaissance, et à éclairerle lien entre réseau social et géographie dans la formation des collaborations, en mettanten avant dans quelles conditions le réseau permet de s’affranchir de cette dernière. Acet égard, cette thèse apporte plusieurs contributions théoriques, méthodologiques et empiriques.L’essentiel de la thèse s’applique à assembler les mécanismes qui lient le réseausocial à la production de connaissances. La discussion théorique est ensuite appuyée parune analyse empirique dans deux contextes liés la création de connaissances. D’une partla thèse analyse la formation du réseau des collaborations scientifiques en Europe dansle domaine de la chimie, mettant en avant l’interaction réseau versus géographie dansla formation des collaborations. D’autre part, elle évalue le rôle du réseau d’inventeurdans la performance des zones d’emploi françaises en termes de production d’innovation,en se focalisant sur le type de structure de réseau qui favorise le plus l’innovation. Lesrésultats principaux sont que l’expansion du réseau social – mesuré par la connectivitédes inventeurs – a un effet bénéfique sur l’innovation. De plus, il est montré que le réseausocial permet en partie de s’affranchir de la barrière géographique pour collaborer. Cesrésultats apportent des éclairages sur le rôle du réseau dans l’organisation spatiale desactivités scientifiques et technologiques / This thesis pertains to understanding how social networks and geography affect thecreation of new knowledge. More precisely, this thesis will question how the social networkof collaboration can influence the production of knowledge, how do geography and thesocial network interact, and whether the social network can help to bypass geography. Answeringthese questions required to make some theoretical, methodological and empiricalcontributions. One part of the thesis gathers the mechanisms linking the social network toknowledge creation, while another focuses on the interplay of geography and the networkinto the collaboration process. Following this theoretical discussion, two empirical studiesare laid out. First, it assesses the formation of scientific collaborations in Europe in thefield of chemistry. This study focus on the competing role between the social network andgeography to shaping new collaborations. Then, the thesis comes to evaluate how thenetwork of inventors influence the innovation performance of French employment areas.In particular, a specific methodology is set up to address what kind of network structurefavours the most collaboration. The main results of this thesis are that an increase inthe connectedness of inventors is always beneficial to urban innovation performance. Wealso show that social network act as a substitute to geographic distance, so that socialnetwork allows to alleviate the burden of distance. These results shed light on the role ofthe network in shaping the spatial distribution of the scientific and technological activity.

The Argus Commission identified three major federal priorities related to health care, including the precision medicine initiative, the Cancer Moonshot and the opioid abuse epidemic. Current activities at the federal level were summarized and an analysis of activities within the profession, and academic pharmacy specifically, was prepared. The implications for pharmacy education, research and practice are compelling in all three areas. Recommendations, suggestions and two policy statements aim to optimize the attention to these priorities by the academy. Further, aligning the AACP Strategic Engagement agenda with the opportunities and threats acknowledged in the analysis is essential.

Open innovation that Henry Chesbrough introduced in 2003 promotes the usage of the input of outsiders to strengthen internal innovation processes and the search for outside commercialization opportunities for what is developed internally. Open innovation has enabled both academics and practitioners to design innovation strategies based on the reality of our connected world. Although the literature has identified and explored a variety of benefits and costs, to the best of our knowledge, no study has reviewed the benefits and costs of open innovation in terms of their importance for strategic performance. To conduct such a study, we need to take into account two main issues. First, the number of benefits and costs of open innovation are multifold; so, to have a comprehensive comparison, a large number of benefits and costs must be compared. Second, to have a fair comparison, benefits and costs must be compared in terms of different performance criteria, including financial and non-financial. Concerning the issues above, we will face a complex process of exploring benefits and costs. In this regard, we use multiple criterion decision-making (MCDM) methods that have shown promising solutions to complex exploratory problems. In particular, we present how using a stochastic multi-criteria clustering algorithm that is one of the recently introduced MCDM methods can bring promising results when it comes to exploring the strategic importance of benefits and costs of open innovation. Since there is no comprehensive understanding of the nature of the benefits and costs of open innovation, the proposed model aims to cluster them into hierarchical groups to help researchers identify the most crucial benefits and costs concerning different dimensions of performance. In addition, the model is able to deal with uncertainties related to technical parameters such as criteria weights and preference thresholds. We apply the model in the context of open innovation for universities concerning their research collaboration with industries. An online survey was conducted to collect experts' opinions on the open-innovation benefits and costs of university-industry research collaboration, given different performance dimensions. The results obtained through the cluster analysis specify that university researchers collaborate with industry mainly because of knowledge-related and research-related reasons rather than economic reasons. This research also indicates that the most important benefits of university-industry research collaboration for universities are implementing the learnings, increased know-how, accessing specialized infrastructures, accessing a greater idea and knowledge base, sensing and seizing new technological trends, and keeping the employees engaged. In addition, the results show that the most important costs are the lack of necessary resources to monitor activities between university and industry, an increased resistance to change among employees, conflict of interest (different missions), an increased employees' tendency to avoid using the knowledge that they do not create themselves, paying time costs associated with bureaucracy rules, and loss of focus. The research's findings enable researchers to analyze open innovation's related issues for universities more effectively and define their research projects on these issues in line with the priorities of universities.

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible.