Technological Ecosystems in Health Sector

[EN]Presentation about the technological ecosystems applied to the health sector related to dementia and other mental disorders. This presentation was made in the INTERDEM meeting in Budapest, April 21, 2016

Human Interaction in Learning Ecosystems based on Open Source Solutions

Technological ecosystems are software solutions based on the integration of heterogeneous software components through information flows in order to provide a set of services that each component separately does not offer, as well as to improve the user experience. In particular, the learning ecosystems are technological ecosystems focused on learning and knowledge management in different contexts such as educational institutions or companies. The ecosystem metaphor comes from biology field and it has transferred to technology field to highlight the evolving component of software. Taking into account the definitions of natural ecosystems, a technological ecosystem is a set of people and software components that play the role of organisms; a series of elements that allow the ecosystem works (hardware, networks, etc.); and a set of information flows that establish the relationships between the software components, and between these and the people involved in the ecosystem. Human factor has a main role in the definition and development of this kind of solutions. In previous works, a metamodel has been defined and validated to support Model-Driven Development of learning ecosystems based on Open Source software, but the interaction in the learning ecosystem should be defined in order to complete the proposal to improve the development process of technological ecosystems. This paper presents the definition and modelling of the human interaction in learning ecosystem

Entrepreneurial ecosystems: a dynamic lifecycle model

The concept of entrepreneurial ecosystems has been used as a framework to explain entrepreneurial activities within regions and industrial sectors. Despite the usefulness of this approach, the concept is under-theorized, especially with regard to the evolution of entrepreneurial ecosystems. The current literature is lacking a theoretical foundation that addresses the development and change of entrepreneurial ecosystems over time and does not consider the inherent dynamics of entrepreneurial ecosystems that lead to their birth, growth, maturity, decline, and re-emergence. Taking an industry lifecycle perspective, this paper addresses this research gap by elaborating a dynamic entrepreneurial ecosystem lifecycle model. We propose that an ecosystem transitions from an entrepreneurial ecosystem, with a focus on new firm creation, towards a business ecosystem, with a core focus on the internal commercialization of knowledge, i.e., intrapreneurial activities, and vice versa. Our dynamic model thus captures the oscillation that occurs among entrepreneurs and intrapreneurs through the different phases of an ecosystem’s lifecycle. Our dynamic lifecycle model may thus serve as a starting point for future empirical studies focusing on ecosystems and provide the basis for a further understanding of the interrelatedness between and co-existence of new and incumbent firms

Networking Innovation in the European Car Industry : Does the Open Innovation Model Fit?

The automobile industry is has entered an innovation race. Uncertain technological trends, long development cycles, highly capital intensive product development, saturated markets, and environmental and safety regulations have subjected the sector to major transformations. The technological and organizational innovations related to these transformations necessitate research that can enhance our understanding of the characteristics of the new systems and extrapolate the implications for companies as well as for the wider economy. Is the industry ready to change and accelerate the pace of its innovation and adaptability? Have the traditional supply chains transformed into supply networks and regional automobile ecosystems? The study investigates the applicability of the Open Innovation concept to a mature capital-intensive asset-based industry, which is preparing for a radical technological discontinuity - the European automobile industry - through interviewing purposely selected knowledgeable respondents across seven European countries. The findings contribute to the understanding of the OI concept by identifying key obstacles to the wider adoption of the OI model, and signalling the importance of intermediaries and large incumbents for driving network development and OI practices as well as the need of new competencies to be developed by all players.Peer reviewe

Enabling Entrepreneurial Ecosystems

Inspired by research on the importance of entrepreneurship for sustained economic growth and improved wellbeing, many governments and non-governmental grantmaking organizations have sought over the past decade to implement policies and programs intended to support entrepreneurs. Over this interval, growing appreciation of the limits of strategies focused narrowly on financing or training entrepreneurs has prompted a number of such entities to shift their efforts toward more broadbased strategies aimed at enabling "entrepreneurial ecosystems" at the city or sub-national regional scale.This paper takes the metaphor of the "ecosystem" seriously, seeking to draw lessons from evolutionary biology and ecology to inform policy for entrepreneurship. In so doing, the paper provides a framework for data gathering and analysis of practical value in assessing the vibrancy of entrepreneurial ecosystems

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 organisations’ 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

Looking into the future: Learning services-based technological ecosystems

La gran distancia existente entre la tecnología y las metodologías docentes provoca que los nuevos avances tecnológicos no tengan fácil su integración en los contextos y prácticas metodológicas implantados, y que las tecnologías educativas maduras y los métodos educativos aplicados no respondan a las demandas de la sociedad ni al potencial transformador de la tecnología para la mejora del aprendizaje. Esta contribución plantea la necesidad de ofrecer un entorno tecnológico para el soporte de servicios de aprendizaje, el ecosistema educativo, que rompa con las limitaciones tecnológicas y de proceso de las actuales plataformas tecnológicas para conseguir una mejora de los procesos educativos. La propuesta de ecosistema educativo se concreta en 6 líneas de actuación: 1) arquitectura para la implantación de ecosistemas de servicios de aprendizaje; 2) toma de decisiones basadas en analíticas de aprendizaje; 3) sistemas de gestión de conocimiento adaptativos; 4) formación gamificada; 5) porfolios semánticos para la recogida de evidencias de aprendizaje; 6) metodologías educativas que hagan un uso efectivo de los avances tecnológicos en pro de la mejora del aprendizaje.The existing distance between technology and learning methods have two consequences: on the one hand, it makes the fit of new technological advances and existing educational methods and practices difficult; on the other hand, mature educational technologies and methods might not give an adequate answer to actual society needs and demands, and they may not fully use their transforming potential to improve learning processes. This study discusses the need for a new technological environment supporting learning services: the educational ecosystem. The educational ecosystems must be able to break the technological constraints of existing learning platforms and achieve an effective improvement of learning processes. Our proposal of educational ecosystems pivots around six specific lines of action: 1) an arquitecture that gives support to learning service-based ecosystems; 2) learning analytics for educational decision making; 3) adaptive knowledge systems; 4) gamifications; 5) semantic porfolios to collect learning evidences; 6) learning methods that make and effective use of technology for the improvement of learning processes