The sharing economy holds promise for the way we consume, work, and interact. However, consuming in the sharing economy is not without risk, as institutional trust measures (e.g. contracts, regulations, guarantees) are often absent. Trust between sellers and buyers is therefore crucial to complete transactions successfully. From a buyer ́s perspective, a seller ́s profile is an important source of information for judging trustworthiness, because it contains multiple trust cues such as a reputation score, a profile picture, and a textual self-description. The effect of a seller’s self-description on perceived trustworthiness is still poorly understood. We examine how the linguistic features of a seller’s self-description predict perceived trustworthiness. To determine the perceived trustworthiness of 259 profiles, 189 real buyers on a Dutch sharing platform rated their trustworthiness. The results show that profiles were perceived as more trustworthy if they contained more words (which could be an indicator of uncertainty reduction), more words related to cooking (indicator of expertise), and more words related to positive emotions (indicator of enthusiasm). Also, a profile’s perceived trustworthiness score correlated positively with the seller’s actual sales performance. These findings indicate that a seller’s self-description is a relevant signal to buyers, eventhough it is cheap talk (i.e. easy to produce). The results can guide sellers on how to self-present themselves on sharing platforms and inform platform owners on how to design their platform so that it enhances trust between platform users.
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In order to empower more people to become more selfreliant in society, interactive products and services should better match the skills and values of diverse user groups. In inclusive design, relevant end-user groups are involved early on and throughout the design and development process, leading to a better user experience. However, for IT businesses not operating in the academic domain, getting access to appropriate user research methods is difficult. This paper describes the design and prototype development of the Include Toolbox, in close cooperation with practitioners of small to medium sized enterprises (SMEs) in IT. It consists of an interactive app paired with a book. The app helps to find suitable research methods for diverse user groups such as older people, people with low literacy, and children. The book offers background information on the advantages of inclusive design, information on different user groups, and best practices shared by other companies.
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Background: Phantom limb pain is a frequent and persistent problem following amputation. Achieving sustainable favorable effects on phantom limb pain requires therapeutic interventions such as mirror therapy that target maladaptive neuroplastic changes in the central nervous system. Unfortunately, patients’ adherence to unsupervised exercises is generally poor and there is a need for effective strategies such as telerehabilitation to support long-term self-management of patients with phantom limb pain. Objective: The main aim of this study was to describe the user-centered approach that guided the design and development of a telerehabilitation platform for patients with phantom limb pain. We addressed 3 research questions: (1) Which requirements are defined by patients and therapists for the content and functions of a telerehabilitation platform and how can these requirements be prioritized to develop a first prototype of the platform? (2) How can the user interface of the telerehabilitation platform be designed so as to match the predefined critical user requirements and how can this interface be translated into a medium-fidelity prototype of the platform? (3) How do patients with phantom limb pain and their treating therapists judge the usability of the medium-fidelity prototype of the telerehabilitation platform in routine care and how can the platform be redesigned based on their feedback to achieve a high-fidelity prototype?
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Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
Over the past decade, the trend in both the public sector and industry has been to outsource ICT to the cloud. While cost savings are often used as a rationale for outsourcing, another argument that is frequently used is that the cloud improves security. The reasoning behind this is twofold. First, cloud service providers are typically thought to have skilled staff trained in good security practices. Second, cloud providers often have a vastly distributed, highly connected network infrastructure, making them more resilient in the face of outages and denial-of-service attacks. Yet many examples of cloud outages, often due to attacks, call into question whether outsourcing to the cloud does improve security. In this project our goal therefore is to answer two questions: 1) did the cloud make use more secure?and 2) can we provide specific security guidance to support cloud outsourcing strategies? We will approach these questions in a multi-disciplinary fashion from a technical angle and from a business and management perspective. On the technical side, the project will focus on providing comprehensive insight into the attack surface at the network level of cloud providers and their users. We will use a measurement-based approach, leveraging large scale datasets about the Internet, both our own data (e.g. OpenINTEL, a large- scale dataset of active DNS measurements) and datasets from our long-term collaborators, such as CAIDA in the US (BGPStream, Network Telescope) and Saarland University in Germany (AmpPot). We will use this data to study the network infrastructure outside and within cloud environments to structurally map vulnerabilities to attacks as well as to identify security anti-patterns, where the way cloud services are managed or used introduce a weak point that attackers can target. From a business point of view, we will investigate outsourcing strategies for both the cloud providers and their customers. For guaranteeing 100% availability, cloud service providers have to maintain additional capacity at all times. They also need to forecast capacity requirements continuously for financially profitable decisions. If the forecast is lower than the capacity needed, then the cloud is not able to deliver 100% availability in case of an attack. Conversely, if the forecast is substantially higher, the cloud service provider might not be able to make desired profits. We therefore propose to assess the risk profiles of cloud providers (how likely it is a cloud provider is under attack at a given time given the nature of its customers) using available attack data to improve the provider resilience to future attacks. From the costumer perspective, we will investigate how we can support cloud outsourcing by taking into consideration business and technical constraints. Decision to choose a cloud service provider is typically based on multiple criteria depending upon the company’s needs (security and operational). We will develop decision support systems that will help in mapping companies’ needs to cloud service providers’ offers.
