In order to decrease the consumption of risky products (such as alcohol, tobacco, gambling products, and detrimental media) among youngsters, many governments (also in the Netherlands) have implemented legal age limits which forbid the sales of these products to people under a certain age. Within the Netherlands compliance to these age limits is low. Mystery shopping studies in the fields of alcohol, tobacco, gambling products and media show compliance levels ranging from 0% up to the 62% maximum. Although these compliance levels are commonly used when referring to availability, in our most recent study (Van Hoof & Gosselt, 2013) we argue that (at least) two factor should be added; the effort minors want to put in the purchase attempt (the number of vendors they are willing to visit), and the knowledge they might have on structural (non-)compliance. Within this proposal we want to make the next step in the compliance ? availability discussion, both from a scientific and a practical point-of-view. In the current scientific literature mostly single interventions aimed at improving compliance (in a broad range of fields) are investigated or the focus is on one compliance predictor (e.g., effective communication). In this project real life compliance behaviour is explained (Study III). The second scientific outcome will be an elaboration on a recent study (Van Hoof & Gosselt, 2013 ) in which more relevant factors related to availability are incorporated in addition to compliance. The third scientific insight will be derived from Study IV in which we will establish to what extent vendors are able to comply. This study related to a practical issue at the same time, since the previous years, various risky products industries always promised that they were going to increase compliance with age limits. But on the other hand, the highest compliance level we ever established by humans was 62% (one technical intervention we studies reached a 96% compliance level). In the end, all five studies together will lead to an acceptable and effective system of age validation. Research proposal outline Study I: large scale (N = 1,500) questionnaire study among adolescents focusing on (non-)compliance knowledge, knowledge transfer, and purchase attempts. Study II: interviews with 100 vendors (cashiers and managers) investigating the daily routines in sales situations related to age restricted products. Study III: mystery shopping study and questionnaire study (N = 200) among cashiers explaining (non-)compliance. In this study actual compliance behaviour is combined with self-reported data on compliance predictors (such as change to get a fine, support for legislation). Study IV: series of lab experiments investigating maximum levels of compliance possible. Study V: literature review and interviews analysing worldwide age verification systems resulting in an adjusted system of age verification in the Netherlands.

Power4FitFoot focuses on an ecosystem to support data-driven personalized and persuasive monitoring & coaching for cardiovascular patients with diabetic foot. The result will be an early warning system (EWS) on risk detection of deterioration (healthcare providers), with feedback and coaching (patients and informal caregivers). This project shows that the combination of big data technologies and real-time sensor data analytics is the key ingredient to tailor and personalize self-management programs. Power4FitFoot follows a participatory multidisciplinary development approach: researchers (medicine, computer and behavioral sciences) work together with patients, caregivers and industrial companies to develop personalized self-management products and services to prevent Diabetic Foot Ulcers (DFU) or amputations. We outline 4 complementary studies to accomplish our goals. Study 1 focuses on the identification of the major risk factors using existing retrospective data sets. Outcomes will be transformed into dependent variables we aim to predict. Study 2 entails the co-creation of a smart monitoring ecosystem (platform & body sensors) to enable personalized feedback via data analyses. Data sets from various sources (medical, lifestyle, geo-spatial) and with high variety will be collected real-time to further develop the prognostic models. Study 3 focuses on real-time coaching dealing with high risk contexts using the insights of the big data sets from both previous studies. Prognostic models will facilitate a dynamic and context aware heuristics for a self-management support system. Study 4 is an overarching study, to guarantee the infrastructure for data-driven monitoring & coaching and to develop a sustainable data management plan.

Steeds meer leerlingen in het voortgezet onderwijs hebben moeite met tekstbegrip. Het doel van dit onderzoek is achterhalen hoe dit komt en vervolgens hoe het tekstbegrip van leerlingen verbeterd kan worden. Dit onderzoek bestaat uit drie delen: ten eerste wil ik achterhalen wat er in lesmethodes wordt verstaan onder tekstbegrip en wat tekstbegrip volgens docenten inhoudt, ten tweede wil ik onderzoeken hoe leerlingen te werk gaan als ze een tekst proberen te begrijpen, en ten slotte wil ik op basis van deze data een interventie ontwerpen, implementeren en testen die tot doel heeft het tekstbegrip van de leerling te verbeteren.

This research project connects a national network of research groups, Smart Industry communities, 450 SMEs, social and education partners in a large scale, eight-year program to realise a major societal breakthrough towards a robust and resilient workforce in Smart Industry and a human-centric, sustainable and resilient Smart industry sector. We realize a scalable skills approach that continuously and integrally supports the operational workforce and organisations in adopting and adapting technology and incorporating those technologies into production processes. This approach rests on four cohesive pillars: smart production systems, smart jobs & careers, smart workplaces and smart inter-organisational infrastructures.

This project investigates the consequences of robot usage in organizations, specifically concerning employee well-being and motivation. The project investigates how work practices change when robots are being introduced, and how ways of collaboration change. Therefore, empirical research is aimed at mapping how employees employ robots in their work and how they experience working with robots.