One of the main causes of numerous health problemsis a lack of physical activity. To promote a more active lifestyle,the Hanze University started a health promotion program. Participants were motivated to reach their daily goal of physical activityby means of an activity tracker in combination with two-weeklycoaching sessions. Employing the data of the experiment, weinvestigated the manners in which the predictability of physicalactivity of a participant during the day can be improved. Thecollected step count data was used to construct personalisedmachine learning models, by taking into account the differencebetween physical activities during weekdays on the one handand weekends on the other hand. The training of algorithmsper participant in combination with the time-slices weekdays,weekend and the whole week improves the accuracy of theprediction model. The performance of the models improveseven further when the individualised time-sliced models arecombined. More contextual data, like free time and workinghours, might even extend the accuracy. The use of personalisedprediction models, based on machine learning and time slices,could become an addition in preventive personalized eHealthsystems and mobile activity monitoring. For instance, this canconstitute as a viable addition to a virtual coaching system to helpthe participants to reach their daily goal. As the individualisedmodels allow for predictions of the progression of the physicalactivity during the day, they enable the virtual coaching systemto intervene at the appropriate moment in time.
Purpose: This study, a conceptual paper, analyses the growth of curation in tourism and hospitality and the curator role in selecting and framing products and experiences. It considers the growth of expert, algorithmic, social and co-creative curation modes and their effects. Design/methodology/approach: Narrative and integrative reviews of literature on curation and tourism and hospitality are used to develop a typology of curation and identify different curation modes. Findings: Curational techniques are increasingly used to organise experience supply and distribution in mainstream fields, including media, retailing and fashion. In tourism and hospitality, curated tourism, curated hospitality brands and food offerings and place curation by destination marketing organisations are growing. Curation is undertaken by experts, algorithms and social groups and involves many of destination-related actors, producing a trend towards “hybrid curation” of places. Research limitations/implications: Research is needed on different forms of curation, their differential effects and the power roles of different curational modes. Practical implications: Curation is a widespread intermediary function in tourism and hospitality, supporting better consumer choice. New curators influence experience supply and the distribution of consumer attention, shaping markets and co-creative activities. Increased curatorial activity should stimulate aesthetic and stylistic innovation and provide the basis for storytelling and narrative in tourism and hospitality. Originality/value: This is the first study of curational strategies in tourism and hospitality, providing a definition and typology of curation, and linking micro and macro levels of analysis. It suggests the growth of choice-based logic alongside service-dominant logic in tourism and hospitality.
MULTIFILE
This data set contains the results of a survey about reviewing Data Management Plans (DMPs). The survey was carried out by the Research Support and Advice working group of the Dutch National Coordination Point Research Data Management (LCRDM). Sixty people shared their experiences and feedback on DMPs by responding to the survey. The data set contains a concise report, the survey questions and the anonymised data.
This project researches risk perceptions about data, technology, and digital transformation in society and how to build trust between organisations and users to ensure sustainable data ecologies. The aim is to understand the user role in a tech-driven environment and her perception of the resulting relationships with organisations that offer data-driven services/products. The discourse on digital transformation is productive but does not truly address the user’s attitudes and awareness (Kitchin 2014). Companies are not aware enough of the potential accidents and resulting loss of trust that undermine data ecologies and, consequently, forfeit their beneficial potential. Facebook’s Cambridge Analytica-situation, for instance, led to 42% of US adults deleting their accounts and the company losing billions. Social, political, and economic interactions are increasingly digitalised, which comes with hands-on benefits but also challenges privacy, individual well-being and a fair society. User awareness of organisational practices is of heightened importance, as vulnerabilities for users equal vulnerabilities for data ecologies. Without transparency and a new “social contract” for a digital society, problems are inevitable. Recurring scandals about data leaks and biased algorithms are just two examples that illustrate the urgency of this research. Properly informing users about an organisation’s data policies makes a crucial difference (Accenture 2018) and for them to develop sustainable business models, organisations need to understand what users expect and how to communicate with them. This research project tackles this issue head-on. First, a deeper understanding of users’ risk perception is needed to formulate concrete policy recommendations aiming to educate and build trust. Second, insights about users’ perceptions will inform guidelines. Through empirical research on framing in the data discourse, user types, and trends in organisational practice, the project develops concrete advice - for users and practitioners alike - on building sustainable relationships in a resilient digital society.
