In the literature about web survey methodology, significant e orts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.
To what extent can the application of blockchain technologies be employed toward civic empowerment, organizing local civic and circular economies, reinstating trust in civic institutions, or, perhaps, creating entirely new types of institutions?
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Maintaining the child-robot relationship after a significant break, such as a holiday, is an important step for developing sustainable social robots for education. We ran a four-session user study (n = 113 children) that included a nine-month break between the third and fourth session. During the study, participants practiced math with the help of a social robot math tutor. We found that social personalization is an effective strategy to better sustain the child-robot relationship than the absence of social personalization. To become reacquainted after the long break, the robot summarizes a few pieces of information it had stored about the child. This gives children a feeling of being remembered, which is a key contributor to the effectiveness of social personalization. Enabling the robot to refer to information previously shared by the child is another key contributor to social personalization. Conditional for its effectiveness, however, is that children notice these memory references. Finally, although we found that children's interest in the tutoring content is related to relationship formation, personalizing the topics did not lead to more interest in the content. It seems likely that not all of the memory information that was used to personalize the content was up-to-date or socially relevant.
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For a number of years now, NGO Milieu Centraal has been running the website www.klimaatwijsopreis.nl, which informs Dutch consumers about the climate impact of holidays and also allows them to calculate the climate impact of their own holiday. This calculator is based on, among other things, a number of calculation models and a series of emission factors for transport, accommodation, activities, and holiday types. These emission factors are subject to change and should be updated regularly. This project provides an update and substantiation of emission factors for a number of accommodation types, activities, and holiday types.Societal issueThe contribution of holidays to climate change is substantial, depending on choices regarding transport, distance, accommodation, and activities.Benefit to societywww.klimaatwijsopreis.nl informs consumers about the climate impact of holidays, so they can make more informed choices. Up to date and sound emission factors enable giving the most accurate advice.
