In this brief chapter of the report we focus on the model that was developed as part of the evaluation strategy: the local CoP impact measurement model. This model has been described as part of the strategy report as well. For purposes of clarity (as it is one of the main deliverables of work package 3) we briefly present it in this document. Background: Promoting Healthy Ageing, and specifically an Active & Healthy Lifestyle, is one of the biggest societal and economical challenges the EU is facing. A paradigm shift from health care and cure to prevention is essential since the traditional ways have proven to be insufficient to solve this complex problem. An impact-driven multi-sector approach is necessary to develop innovative products and services to change this for the better.ObjectivesThe Knowledge Alliance for Communities of Practice for Healthy Lifestyle aimed at developing and sustaining communities of practice (COP) in order to stimulate innovation and socio-economic development in the area of Healthy Ageing.ImplementationThe Consortium comes from 7 EU Member States and in 5 countries Local COP were developed. A European COP Support Lab and a European COP Alliance were developed that facilitate the set-up and sustainability of COP. An open access Community Knowledge Hub provides pilot-tested formal and informal blended learning material for managing COP and implementing interventions; whilst an entrepreneurship competition lead into an intensive program to develop entrepreneurial skills and stimulate innovation.AchievementsIn total 6 local COP were fully established who all defined their shared interest, organized learning opportunities, meetings and effective local activities that contributed to a common agenda setting for Healthy Lifestyle. Furthermore, the Alliance between businesses and HEI was extended exponentially and over 30 businesses, 18 HEI and 73 public authorities were involved. All 6 COPs are still running beyond the project funding period and supported by an open online platform www.yanuz.eu.
The impact communities of practice (CoPs) make can be understood in several different ways, depending on which theoretical perspective is used. For example, CoPs have been studied from a learning-theory perspective, from organizational development theory, and from a small-group theory. To understand the effects of participating in a CoP on individuals, groups or the organization in which they function, we could use traditional learning theory, organizational learning theory, information-processing theory or small-group process theory, etc. Or we could look at the internal processes of CoPs; the output they generate, or employ a synthesized view. CoPs can also be seen as impacting different actors in the organization in which they operate; individuals, groups or the whole organization. This means, for example, that we could look at CoPs from an organizational learning perspective to see how CoPs impact strategy development or renewal. At the level of the group, we could look at how CoPs lead to increased group performance and how that in turn leads to a higher output of knowledge products. And as learning is one of the key processes in a CoP, an important aspect of we need to study is how the individual learns, as well as what the individual learns. The complexity of impact a CoP can have on the diverse actors requires a pluralistic and multiperspective approach. However, a review of the literature showed no comprehensive model that neither integrates these different levels of impact nor employs multiple theoretical perspectives. Furthermore, most models of measurement or assessment use traditional types of output measurement, such as ROI, or anecdotal evidence that the CoP has improved organizational capability. Much like any human resource development initiative – which is the perspective of CoPs we take in this paper – there has been no real attempt to develop measures for assessing impact. We try to fill this gap by presenting a comprehensive, multidisciplinary, conceptual model that approaches measuring certain aspects a CoP has on individuals, groups and organizations.
Communities of practice (CoPs) impact different actors in different ways. Because using a singular approach would not do justice to the complexity that surrounds CoPs, a multi-disciplinary and pluralistic approach is used here to develop a model for measuring the impact CoPs may have on individuals, groups and the organisations in which they are situated. A review of the literature showed no such comprehensive model. In fact, empirical work on CoPs, in general, is scarce and evaluations of them are underdeveloped. Most assessments are look at process alone, or try to link output to anecdotal evidence. I try to fill this gap by presenting a multi-disciplinary conceptual model that approaches measuring certain types of impact a CoP has on individuals and groups that are functioning as CoPs. I also make a theoretical link to how CoPs may contribute to organisational capability.
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Size measurement plays an essential role for micro-/nanoparticle characterization and property evaluation. Due to high costs, complex operation or resolution limit, conventional characterization techniques cannot satisfy the growing demand of routine size measurements in various industry sectors and research departments, e.g., pharmaceuticals, nanomaterials and food industry etc. Together with start-up SeeNano and other partners, we will develop a portable compact device to measure particle size based on particle-impact electrochemical sensing technology. The main task in this project is to extend the measurement range for particles with diameters ranging from 20 nm to 20 um and to validate this technology with realistic samples from various application areas. In this project a new electrode chip will be designed and fabricated. It will result in a workable prototype including new UMEs (ultra-micro electrode), showing that particle sizing can be achieved on a compact portable device with full measuring range. Following experimental testing with calibrated particles, a reliable calibration model will be built up for full range measurement. In a further step, samples from partners or potential customers will be tested on the device to evaluate the application feasibility. The results will be validated by high-resolution and mainstream sizing techniques such as scanning electron microscopy (SEM), dynamic light scattering (DLS) and Coulter counter.
In order to achieve much-needed transitions in energy and health, systemic changes are required that are firmly based on the principles of regard for others and community values, while at the same time operating in market conditions. Social entrepreneurship and community entrepreneurship (SCE) hold the promise to catalyze such transitions, as they combine bottom-up social initiatives with a focus on financially viable business models. SCE requires a facilitating ecosystem in order to be able to fully realize its potential. As yet it is unclear in which way the entrepreneurial ecosystem for social and community entrepreneurship facilitates or hinders the flourishing and scaling of such entrepreneurship. It is also unclear how exactly entrepreneurs and stakeholders influence their ecosystem to become more facilitative. This research programme addresses these questions. Conceptually it integrates entrepreneurial ecosystem frameworks with upcoming theories on civic wealth creation, collaborative governance, participative learning and collective action frameworks.This multidisciplinary research project capitalizes on a unique consortium: the Dutch City Deal ‘Impact Ondernemen’. In this collaborative research, we enhance and expand current data collection efforts and adopt a living-lab setting centered on nine local and regional cases for collaborative learning through experimenting with innovative financial and business models. We develop meaningful, participatory design and evaluation methods and state-of-the-art digital tools to increase the effectiveness of impact measurement and management. Educational modules for professionals are developed to boost the abovementioned transition. The project’s learnings on mechanisms and processes can easily be adapted and translated to a broad range of impact areas.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
