Understanding the success of regional entrepreneurial startup ecosystems is crucial for the advancement of local economies, job creation and growth. The current paper takes a unique theoretically focused look at common ecosystem elements that regional startup ecosystems may need to pay extra attention to in order to become as competitive as top-tier startup ecosystems. In our analysis, we compare two prominent models on elements of ecosystems: the Entrepreneurial Ecosystem Elements by Stam and van de Ven (2019 and the Entrepreneurial Ecosystem Diagnostic Toolkit (2013). We conclude this paper with drawing up eight propositions and an invitation for future empirical research to test these propositions in practice.
LINK
Managing and supporting the collaboration between different actors is key in any organizational context, whether of a hierarchical or a networked nature. In the networked context of ecosystems of service providers and other stakeholders, BPM is faced with different challenges than in a conventional hierarchical model, based on up front consolidation and consensus on the process flows used in collaboration. In networked ecosystems of potential business partners, designing collaboration upfront is not feasible. Coalitions are formed situationally, and sometimes even ad-hoc. This paper presents a number of challenges for conventional BPM in such environments, and explores how declarative process management technology could address them, indicating topics for further research.
MULTIFILE
Sustainability and economic growth—the integration and balance of social, environmental, and economic needs—is a salient concern for sustainable development and social well-being. By focusing on a sustainable innovation project, we explore how entrepreneurial ecosystems become sustainable entrepreneurial ecosystems and investigate the interactions of entrepreneurial actors. We conducted an inductive, single-case study of a specific collaborative innovation project in the denim industry specialized in a specific geographic location. From our data, we show that the presence of four conditional aspects foster sustainable entrepreneurial ecosystems. These include sustainability orientation of actors, recognition of sustainable opportunities and resource mobilization, collaborative innovation of sustainability opportunities, and markets for sustainable products. We make two observations that contribute to the literature. First, we see that in a sustainable entrepreneurial ecosystem, entrepreneurial experimentation is a highly interdependent and interactive process. Second, we see that recognition of sustainable opportunities is distributed among different actors in the ecosystem. Our findings also have implications for practitioners and policy-makers.
DOCUMENT
The European creative visual industry is undergoing rapid technological development, demanding solid initiatives to maintain a competitive position in the marketplace. AVENUE, a pan-European network of Centres of Vocational Excellence, addresses this need through a collaboration of five independent significant ecosystems, each with a smart specialisation. AVENUE will conduct qualified industry-relevant research to assess, analyse, and conclude on the immediate need for professional training and educational development. The primary objective of AVENUE is to present opportunities for immediate professional and vocational training, while innovating teaching and learning methods in formal education, to empower students and professionals in content creation, entrepreneurship, and innovation, while supporting sustainability and healthy working environments. AVENUE will result in a systematised upgrade of workforce to address the demand for new skills arising from rapid technological development. Additionally, it will transform the formal education within the five participating VETs, making them able to transition from traditional artistic education to delivering skills, mindsets and technological competencies demanded by a commercial market. AVENUE facilitates mobility, networking and introduces a wide range of training formats that enable effective training within and across the five ecosystems. A significant portion of the online training is Open Access, allowing professionals from across Europe to upgrade their skills in various processes and disciplines. The result of AVENUE will be a deep-rooted partnership between five strong ecosystems, collaborating to elevate the European industry. More than 2000 professionals, employees, students, and young talents will benefit from relevant and immediate upgrading of competencies and skills, ensuring that the five European ecosystems remain at the forefront of innovation and competitiveness in the creative visual industry.
The IMPULS-2020 project DIGIREAL (BUas, 2021) aims to significantly strengthen BUAS’ Research and Development (R&D) on Digital Realities for the benefit of innovation in our sectoral industries. The project will furthermore help BUas to position itself in the emerging innovation ecosystems on Human Interaction, AI and Interactive Technologies. The pandemic has had a tremendous negative impact on BUas industrial sectors of research: Tourism, Leisure and Events, Hospitality and Facility, Built Environment and Logistics. Our partner industries are in great need of innovative responses to the crises. Data, AI combined with Interactive and Immersive Technologies (Games, VR/AR) can provide a partial solution, in line with the key-enabling technologies of the Smart Industry agenda. DIGIREAL builds upon our well-established expertise and capacity in entertainment and serious games and digital media (VR/AR). It furthermore strengthens our initial plans to venture into Data and Applied AI. Digital Realities offer great opportunities for sectoral industry research and innovation, such as experience measurement in Leisure and Hospitality, data-driven decision-making for (sustainable) tourism, geo-data simulations for Logistics and Digital Twins for Spatial Planning. Although BUas already has successful R&D projects in these areas, the synergy can and should significantly be improved. We propose a coherent one-year Impuls funded package to develop (in 2021): 1. A multi-year R&D program on Digital Realities, that leads to, 2. Strategic R&D proposals, in particular a SPRONG/sleuteltechnologie proposal; 3. Partnerships in the regional and national innovation ecosystem, in particular Mind Labs and Data Development Lab (DDL); 4. A shared Digital Realities Lab infrastructure, in particular hardware/software/peopleware for Augmented and Mixed Reality; 5. Leadership, support and operational capacity to achieve and support the above. The proposal presents a work program and management structure, with external partners in an advisory role.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
