Zoekresultaten

Cooperative behaviour at public electric vehicle charging stations

Underutilised charging stations can be a bottleneck in the swift transition to electric mobility. This study is the first to research cooperative behaviour at public charging stations as a way to address improved usage of public charging stations. It does so by viewing public charging stations as a common-pool resource and explains cooperative behaviour from an evolutionary perspective. Current behaviour is analysed using a survey (313 useful responses) and an analysis of large dataset (2.1 million charging sessions) on the use of public charging infrastructure in Amsterdam, The Netherlands. In such a way it identifies the potential, drivers and possible obstacles that electric vehicle drivers experience when cooperating with other drivers to optimally make use of existing infrastructure. Results show that the intention to show direct reciprocal charging behaviour is high among the respondents, although this could be limited if the battery did not reach full or sufficient state-of-charge at the moment of the request. Intention to show direct reciprocal behaviour is mediated by kin and network effects.

Explaining Curational Behaviour

Lecturers in higher education are expected to make use of the expanding amount of available resources when designing their courses(Baron & Zablot, 2015). This use of resources includes selecting, structuring and presenting materials; behaviour often referred to as curation (e.g. Anderson, 2015). Little is know about how lecturers approach this task of educational curation and what shapes their curational behaviour (Leighton & Griffioen, 2021). This poster aims to share the first findings of our study into the attitudes and beliefs that influence how lecturers in higher education approach this task.

Influencing behaviour for safe working environments

Safety at work The objective of the project Safety at Work is to increase safety at the workplace by applying and combining state of the art artefacts from personal protective equipment and ambient intelligence technology. In this state of the art document we focus on the developments with respect to how (persuasive) technology can help to influence behaviour in a natural, automatic way in order to make industrial environments safer. We focus on personal safety, safe environments and safe behaviour. Direct ways to influence safety The most obvious way to influence behaviour is to use direct, physical measures. In particular, this is known from product design. The safe use of a product is related to the characteristics of the product (e.g., sharp edges), the condition of people operating the product (e.g., stressed or tired), the man-machine interface (e.g., intuitive or complex) and the environmental conditions while operating the product (e.g., noisy or crowded). Design guidelines exist to help designers to make safe products. A risk matrix can be made with two axis: product hazards versus personal characteristics. For each combination one might imagine what can go wrong, and what potential solutions are. Except for ‘design for safety’ in the sense of no sharp edges or a redundant architecture, there is a development called ‘safety by design’ as well. Safety by design is a concept that encourages construction or product designers to ‘design out’ health and safety risks during design development. On this topic, we may learn from the area of public safety. Crime Prevention Through Environmental Design (or Designing Out Crime) is a multi-disciplinary approach to deterring criminal behaviour through environmental design. Designing Out Crime uses measures like taking steps to increase (the perception) that people can be seen, limiting the opportunity for crime by taking steps to clearly differentiate between public space and private space, and promoting social control through improved proprietary concern. Senses Neuroscience has shown that we have very little insight into our motivations and, consequently, are poor at predicting our own behaviour. It seems emotions are an important predictor of our behaviour. Input from our senses are important for our emotional state, and therefore influence our behaviour in an ‘ambient’ (invisible) way. The first sense we focus on is sight. Sight encompasses the perception of light intensity (illuminance) and colours (spectral distribution). Several researchers have studied the effects of light and colour in working environments. Results show, e.g., that elderly people can be helped with higher light levels, that cool colours like blue and green have a relaxing effect, while long-wavelength colours such as orange and red are stimulating and give more arousal, and that concentration and motivation of pupils at school can be influenced with light and colour settings. Identically, sound (hearing) has physiological effects (unexpected sounds cause extra cortisol -the fight or flight hormone- and the opposite for soothing sounds), psychological effects (sounds effect our emotions), cognitive effects (sounds effect our concentration) and behavioural effects (the natural behaviour of people is to avoid unpleasant sounds, and embrace pleasurable sounds). Smell affects 75% of daily emotions and plays an important role in memory, itis also important as a warning for danger (gas, burning smell). Research has shown that smell can influence work performance. Haptic feedback is a relative new area of research, and most studies focus on haptic feedback on handheld and automotive devices. Finally, employers have a duty to take every reasonable precaution to protect workers from heat stress disorders. Influence mechanisms: Cialdini To influence behaviour, we may learn from marketing psychology. Robert Cialdini states that if we have to think about every decision

A Sound of silence

In 2017, I introduced a new theoretical framework in Archival Science, that of the ‘Archive–as–Is’. This framework proposes a theoretical foundation for Enterprise Information Management (EIM) in World 2.0, the virtual, interactive, and hyper connected platform that is developing around us. This framework should allow EIM to end the existing ‘information chaos’, to computerize information management, to improve the organizational ability to reach business objectives, and to define business strategies. The concepts of records and archives are crucial for those endeavours. The framework of the ‘Archive–as–Is’ is an organization–oriented archival theory, consisting of five components, namely: [1] four dimensions of information, [2] two archival principles, [3] five requirements of information accessibility, [4] the information value chain; and [5] organizational behaviour. In this paper, the subject of research is component 5 of the framework: organizational behaviour. Behaviour of employees (including archivists) is one of the most complicated aspects within organizations when creating, processing, managing, and preserving information, records, and archives. There is an almost universal ‘sound of silence’ in scholarly literature from archival and information studies although this subject and its effects on information management are studied extensively in many other disciplines, like psychology, sociology, anthropology, and organization science. In this paper, I want to study how and why employees behave as they do when they are working with records and archives and how EIM is influenced by this behaviour.

Adverse effects of anxiety on attentional control differ as a function of experience

Simulation of free-floating vehicle charging behaviour at public charging points

As society has to adapt to changing energy sources and consumption, it is driving away from fossil energy. One particular area of interest is electrical driving and the increasing demand for (public) charging facilities. For municipalities, it is essential to adapt to this changing demand and provide more public charging facilities.In order to accommodate on roll-out strategies in metropolitan areas a data driven simulation model, SEVA1, has been developed The SEVA base model used in this paper is an Agent-Based model that incorporate past sessions to predict future charging behaviour. Most EV users are habitual users and tend to use a small subset of the available charge facilities, by that obtaining a pattern is within the range possibilities. Yet, for non-habitual users, for example, car sharing users, obtaining a pattern is much harder as the cars use a significantly higher amount of charge points.The focus of this research is to explore different model implementations to assess the potential of predicting free-floating cars from the non-habitual user population. Most important result is that we now can simulate effects of deployement of car sharing users in the system, and with that the effect on convenience for habitual users. Results show that the interaction between habitual and non habitual EV users affect the unsuccessful connection attempts based increased based on the size of the car-sharing fleet up to approximately 10 percent. From these results implications for policy makers could be drawn.