111,451 research outputs found
Impact in networks and ecosystems: building case studies that make a difference
open accessThis toolkit aims to support the building up of case studies that
show the impact of project activities aiming to promote innovation
and entrepreneurship. The case studies respond to the challenge
of understanding what kinds of interventions work in the Southern
African region, where, and why. The toolkit has a specific focus on entrepreneurial ecosystems and proposes a method of mapping out the actors and their relationships over time. The aim is to understand the changes that take place in the ecosystems. These changes are seen to be indicators of impact as
increased connectivity and activity in ecosystems are key enablers of innovation. Innovations usually happen together with matching social and institutional adjustments, facilitating the translation of inventions into new or improved products and services. Similarly, the processes supporting entrepreneurship are guided by policies implemented in the common framework provided by innovation systems. Overall, policies related to systems of innovation are by nature networking policies applied throughout the socioeconomic framework of society to pool scarce resources and make
various sectors work in coordination with each other. Most participating SAIS countries already have some kinds of identifiable systems of innovation in place both on national and regional levels, but the lack of appropriate institutions, policies, financial instruments, human resources, and support systems, together with underdeveloped markets, create inefficiencies and gaps in systemic cooperation and collaboration. In other words, we do not always know what works and what does not. On another level, engaging users and intermediaries at the local level and driving the development of local innovation
ecosystems within which local culture, especially in urban settings, has evident impact on how collaboration and competition is both seen and done. In this complex environment, organisations supporting entrepreneurship and innovation often find it difficult to create or apply relevant knowledge and appropriate networking tools, approaches, and methods needed to put their processes to work for broader developmental goals. To further enable these organisationsā work, it is necessary to understand what works and why in a given environment. Enhanced local and regional cooperation promoted by SAIS Innovation Fund projects can generate new data on this little-explored area in Southern Africa. Data-driven knowledge on entrepreneurship and innovation support best practices as well as effective and efficient management of entrepreneurial ecosystems can support replication and inform policymaking, leading thus to a wider impact than just that of the immediate reported projects and initiatives
Living Innovation Laboratory Model Design and Implementation
Living Innovation Laboratory (LIL) is an open and recyclable way for
multidisciplinary researchers to remote control resources and co-develop user
centered projects. In the past few years, there were several papers about LIL
published and trying to discuss and define the model and architecture of LIL.
People all acknowledge about the three characteristics of LIL: user centered,
co-creation, and context aware, which make it distinguished from test platform
and other innovation approaches. Its existing model consists of five phases:
initialization, preparation, formation, development, and evaluation.
Goal Net is a goal-oriented methodology to formularize a progress. In this
thesis, Goal Net is adopted to subtract a detailed and systemic methodology for
LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps.
Big data, crowd sourcing, crowd funding and crowd testing take place in
suitable steps to realize UUI, MCC and PCA throughout the innovation process in
LIL 2.0. It would become a guideline for any company or organization to develop
a project in the form of an LIL 2.0 project.
To prove the feasibility of LIL Goal Net Model, it was applied to two real
cases. One project is a Kinect game and the other one is an Internet product.
They were both transformed to LIL 2.0 successfully, based on LIL goal net based
methodology. The two projects were evaluated by phenomenography, which was a
qualitative research method to study human experiences and their relations in
hope of finding the better way to improve human experiences. Through
phenomenographic study, the positive evaluation results showed that the new
generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf
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How to design for persistence and retention in MOOCs?
Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme.
In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context
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Seeking togetherness: moving toward a comparative evaluation framework in an interdisciplinary DIY networking project
There is renewed interest in community networks as a mechanism for local neighbourhoods to find their voice and maintain local ownership of knowledge. In a post-Snowden, big data, age of austerity there is both widespread questioning of what happens to public generated data shared over āfreeā services such as Facebook, and also a renewed focus on self-provisioning where there are gaps in digital service provision. In this paper we introduce an EU funded collaborative project (āMAZIā) that is exploring how Do-It-Yourself approaches to building community networks might foster social cohesion, knowledge sharing and sustainable living through four pilots across Europe. A key challenge is to develop a shared evaluation approach that will allow us to make sense of what we are learning across highly diverse local situations and disciplinary approaches. In this paper we describe our initial approaches and the challenges we face
The role of network administrative organizations in the development of social capital in inter-organizational food networks
This paper is concerned with the role of network administrative organizations (NAOs) in the development of social capital in interāorganizational networks aiming at supporting their members to innovate in the food sector through interacting with one another. A multiācase study approach is used whereby three Belgian interāorganizational networks are investigated i.e. Wagralim, RéseauāClub and Flanders Food. Our study shows that there are many options available to NAOs to build social capital within the networks they are responsible for; options which we propose to categorize in three main distinct groups: creation of boundary objects, careful selection of members and effective communication.</p
Transition UGent: a bottom-up initiative towards a more sustainable university
The vibrant think-tank āTransition UGentā engaged over 250 academics, students and people from the university management in suggesting objectives and actions for the Sustainability Policy of Ghent University (Belgium). Founded in 2012, this bottom-up initiative succeeded to place sustainability high on the policy agenda of our university. Through discussions within 9 working groups and using the transition management method, Transition UGent developed system analyses, sustainability visions and transition paths on 9 fields of Ghent University: mobility, energy, food, waste, nature and green, water, art, education and research. At the moment, many visions and ideas find their way into concrete actions and policies.
In our presentation we focused on the broad participative process, on the most remarkable structural results (e.g. a formal and ambitious Sustainability Vision and a student-led Sustainability Office) and on recent actions and experiments (e.g. a sustainability assessment on food supply in student restaurants, artistic COP21 activities, ambitious mobility plans, food leftovers projects, an education network on sustainability controversies, a transdisciplinary platform on Sustainable Cities). We concluded with some recommendations and reflections on this transition approach, on the important role of āpolicy entrepreneursā and student involvement, on lock-ins and bottlenecks, and on convincing skeptical leaders
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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