Fraunhofer-Institut für Angewandte Informationstechnik FIT

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Now showing 1 - 10 of 3970
  • Publication
    Trusting the trust machine: Evaluating trust signals of blockchain applications
    ( 2023)
    Völter, F.
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    Urbach, N.
    ;
    Padget, J.
    Information systems research emphasizes that blockchain requires trust in the technology itself. However, we lack knowledge on the applicability of established trust cues to blockchain technology. Thus, this paper's objective is to empirically evaluate the effectiveness of several established IS trust formation factors on end user trust. We do so by conducting a between-groups experiment. While we can validate the applicability of previous IS trust research for blockchain technology to some extent, we find that trust signals emphasizing the technology's underlying trust-building characteristics are most effective. Hence, we highlight the need for contextualization of trust research on blockchain technology. We provide both researchers and practitioners with insights for building trustworthy blockchain applications that enable trust-less interactions not only in theory but in practice.
  • Publication
    Fairness, integrity, and privacy in a scalable blockchain-based federated learning system
    ( 2022)
    Rückel, T.
    ;
    Sedlmeir, J.
    ;
    Hofmann, P.
    Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regressions illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system.
  • Publication
    AI-based industrial full-service offerings: A model for payment structure selection considering predictive power
    ( 2022)
    Häckel, Björn
    ;
    Karnebogen, Philip
    ;
    Ritter, Christian
    Artificial Intelligence and servitization reshape the way that manufacturing companies derive value. Aiming to sustain competitive advantage and intensify customer loyalty, full-service providers offer the use of their products as a service to achieve continuous revenues. For this purpose, companies implement AI classification algorithms to enable high levels of service at controllable costs. However, traditional asset sellers who become service providers require previously atypical payment structures, as classic payment methods involving a one-time fee for production costs and profit margins are unsuitable. In addition, a low predictive power of the implemented classification algorithm can lead to misclassifications, which diminish the achievable level of service and the intended net present value of the resultant service. While previous works focus solely on the costs of such misclassifications, our decision model highlights implications for payment structures, service levels, and - ultimately - the net present value of such data-driven service offerings. Our research suggests that predictive power can be a major factor in selecting a suitable payment structure and the overall design of service level agreements. Therefore, we compare common payment structures for data-driven services and investigate their relationship to predictive power. We develop our model using a design science methodology and iteratively evaluate our results using a four-step approach that includes interviews with industry experts and the application of our model to a real-world use case. In summary, our research extends the existing knowledge of servitization and data-driven services in the manufacturing industry through a quantitative decision model.
  • Publication
    Explainable Long-Term Building Energy Consumption Prediction using QLattice
    ( 2022)
    Wenninger, Simon
    ;
    Kaymakci, Can
    ;
    Wiethe, Christian
    The global building sector is responsible for nearly 40% of total carbon emissions, offering great potential to move closer to set climate goals. Energy performance certificates designed to increase the energy efficiency of buildings require accurate predictions of building energy performance. With significant advances in information and communication technology, data-driven methods have been introduced into building energy performance research demonstrating high computational efficiency and prediction performance. However, most studies focus on prediction performance without considering the potential of explainable artificial intelligence. To bridge this gap, the novel QLattice algorithm, designed to satisfy both aspects, is applied to a dataset of over 25,000 German residential buildings for predicting annual building energy performance. The prediction performance, computation time, and explainability of the QLattice is compared to the established machine learning algorithms artificial neural network, support vector regression, extreme gradient boosting, and multiple-linear regression in a case study, variable importance analyzed, and appropriate applications proposed. The results show quite strongly that the QLattice should be further considered in the research of energy performance certificates and may be a potential alternative to established machine learning algorithms for other prediction tasks in energy research.
  • Publication
    Sustainable behavior in motion: designing mobile eco-driving feedback information systems
    ( 2022)
    Gimpel, Henner
    ;
    Heger, Sebastian
    ;
    Wöhl, Moritz
    Emissions from road traffic contribute to climate change. One approach to reducing the carbon footprint is providing eco-driving feedback so that drivers adapt their driving style. Research about the impact of eco-feedback on energy consumption is the basis for designing a mobile eco-driving feedback information system that supports drivers in reducing fuel consumption. This work develops design knowledge from existing knowledge. Subsequently, we implement a prototypical instantiation based on the derived knowledge. Insights from a field study suggest that our design artifact allows most drivers to decrease fuel consumption by 4% on average. The paper's theoretical contribution is a set of design principles and an architecture of the proposed mobile eco-driving feedback information system. One recommendation is to provide normative feedback that compares drivers with each other. This feedback appears to encourage drivers to decrease their fuel consumption additionally. The design knowledge may support researchers and practitioners in implementing efficient eco-driving feedback information systems.
  • Publication
    Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
    ( 2022)
    Bachmann, N.M.
    ;
    Drasch, B.
    ;
    Fridgen, G.
    ;
    Miksch, M.
    ;
    Regner, F.
    ;
    Schweizer, A.
    ;
    Urbach, N.
    The phenomenon of a blockchain use case called initial coin offering (ICO) is drawing increasing attention as a novel funding mechanism. ICO is a crowdfunding type that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although more than $7bn has been raised globally via ICOs as at 2018, the concept and its implications are not yet entirely understood. The research lags behind in providing in-depth analyses of ICO designs and their long-term success. We address this research gap by developing an ICO taxonomy, applying a cluster analysis to identify prevailing ICO archetypes, and providing an outlook on the token value market performance for individual archetypes. We identify five ICO design archetypes and display their secondary market development from both a short-term and a long-term perspective. We contribute to an in-depth understanding of ICOs and their implications. Further, we offer practitioners tangible design and success indications for future ICOs.
  • Publication
    Not yet another digital identity
    ( 2022)
    Rieger, A.
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    Roth, T.
    ;
    Sedlmeir, J.
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    Weigl, L.
    ;
    Fridgen, G.
  • Publication
    Impact of socio-economic factors on local energetic retrofitting needs - A data analytics approach
    ( 2022)
    Ahlrichs, Jakob
    ;
    Wenninger, Simon
    ;
    Wiethe, Christian
    ;
    Häckel, Björn
    Despite great efforts to increase energetic retrofitting rates in the residential building stock, greenhouse gas emissions are still too high to counteract climate change. One barrier is that policy measures are mostly national and do not address local differences. Even though there is plenty of research on instruments to overcome general barriers of energetic retrofitting, literature does not consider differences in local peculiarities. Thus, this paper aims to provide guidance for policy-makers by deriving evidence from over 19 million Energy Performance Certificates and socio-economic data from England, Scotland, and Wales. We find that building archetypes with their respective energetic retrofitting needs differ locally and that socio-economic factors show a strong correlation to the buildings' energy efficiency, with the correlation varying depending on different degrees of this condition. For example, factors associated to employment mainly affect buildings with lower energy efficiency whereas the impact on more efficient buildings is limited. The findings of this paper allow for tailoring local policy instruments to fit the local peculiarities. We obtain a list of the most important socio-economic factors influencing the regional energy efficiency. Further, for two exemplary factors, we illustrate how local policy instruments should consider local retrofitting needs and socio-economic factors.
  • Publication
    Welcome
    ( 2022)
    Cauchard, Jessica R.
    ;
    Jarke, Matthias
    ;
    Oliver, Nuria