Fraunhofer-Institut für Angewandte Informationstechnik FIT

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  • Publication
    Trusting the trust machine: Evaluating trust signals of blockchain applications
    ( 2023)
    Völter, F.
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    Urbach, N.
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    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
    Measuring intra-generational redistribution in PAYG pension schemes
    ( 2022)
    Klos, J.
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    Krieger, T.
    ;
    Stöwhase, S.
    Voters in ageing societies expect pension reforms to be both inter-generationally and intra-generationally fair. In this paper, we propose a global measure of intra-generational redistribution in pay-as-you-go pension schemes as a basis for voters' evaluations of reforms. Our novel index only requires information on contributions by and pension benefits paid to retirees, enabling us to measure intra-generational redistribution isolated from possible inter-generational redistribution. We rely on the contribution records of approximately 100,000 Germans, who progressed into retirement in 2007-2015, to measure the level of intra-generational redistribution in the German statutory pension scheme (GRV). A recent reform of the childcare benefit provision, which became effective in 2014, confirms the predictions of our index. The reform introduced additional benefits for a substantial subgroup of German mothers, owing to which the index value for women, but not for men, jumps up. Our findings suggests that GRV fulfills the ideal of a Bismarckian pension system without intra-generational redistribution for men, while women benefit significantly from intra-generational redistribution.
  • Publication
    Impact of socio-economic factors on local energetic retrofitting needs - A data analytics approach
    ( 2022)
    Ahlrichs, Jakob
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    Wenninger, Simon
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    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
    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
    A computer science perspective on digital transformation in production
    ( 2022)
    Brauner, Philipp
    ;
    Dalibor, Manuela
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    Jarke, Matthias
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    Kunze, Ike
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    Koren, István
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    Lakemeyer, Gerhard
    ;
    Liebenberg, Martin
    ;
    Michael, Judith
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    Pennekamp, Jan
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    Quix, Christoph
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    Rumpe, Bernhard
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    Aalst, Wil van der
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    Wehrle, Klaus
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    Wortmann, Andreas
    ;
    Ziefle, Martina
    The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production, two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI. Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure. Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.
  • Publication
    The role of domain expertise in trusting and following explainable AI decision support systems
    ( 2022)
    Bayer, S.
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    Gimpel, H.
    ;
    Markgraf, M.
    Although the roots of artificial intelligence (AI) stretch back some years, it currently flourishes in research and practice. However, AI deals with trust issues. One possible solution approach is making AI explain itself to its user, but it is still unclear how an AI can accomplish this in decision-making scenarios. This study focuses on how a user's expertise influences trust in explainable AI (XAI) and how this influences behaviour. To test our theoretical assumptions, we develop an AI-based decision support system (DSS), observe user behaviour in an online experiment, complemented with survey data. The results show that domain-specific expertise negatively affects trust in AI-based DSS. We conclude that the focus on explanations might be overrated for users with low domain-specific expertise, whereas it is vital for users with high expertise. Investigating the influence of expertise on explanations of an AI-based DSS, this study contributes to research on XAI and DSS.
  • Publication
    Aligning observed and modelled behaviour by maximizing synchronous moves and using milestones
    ( 2022)
    Bloemen, V.
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    Zelst, S. van
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    Aalst, W. van der
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    Dongen, B. van
    ;
    Pol, J. van de
    Given a process model and an event log, conformance checking aims to relate the two together, e.g. to detect discrepancies between them. For the synchronous product net of the process and a log trace, we can assign different costs to a synchronous move, and a move in the log or model. By computing a path through this (synchronous) product net, whilst minimizing the total cost, we create a so-called optimal alignment - which is considered to be the primary target result for conformance checking. Traditional alignment-based approaches (1) have performance problems for larger logs and models, and (2) do not provide reliable diagnostics for non-conforming behaviour (e.g. bottleneck analysis is based on events that did not happen). This is the reason to explore an alternative approach that maximizes the use of observed events. We also introduce the notion of milestone activities, i.e. unskippable activities, and show how the different approaches relate to each other. We propose a data structure, that can be computed from the process model, which can be used for (1) computing alignments of many log traces that maximize synchronous moves, and (2) as a means for analysing non-conforming behaviour. In our experiments we show the differences of various alignment cost functions. We also show how the performance of constructing alignments with our data structure relates to that of the state-of-the-art techniques.
  • 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
    An Update for Taxonomy Designers
    ( 2022)
    Kundisch, D.
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    Muntermann, J.
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    Oberländer, A.M.
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    Rau, D.
    ;
    Röglinger, M.
    ;
    Schoormann, T.
    ;
    Szopinski, D.
    Taxonomies are classification systems that help researchers conceptualize phenomena based on their dimensions and characteristics. To address the problem of 'ad-hoc' taxonomy building, Nickerson et al. (2013) proposed a rigorous taxonomy development method for information systems researchers. Eight years on, however, the status quo of taxonomy research shows that the application of this method lacks consistency and transparency and that further guidance on taxonomy evaluation is needed. To fill these gaps, this study (1) advances existing methodological guidance and (2) extends this guidance with regards to taxonomy evaluation. Informed by insights gained from an analysis of 164 taxonomy articles published in information systems outlets, this study presents an extended taxonomy design process together with 26 operational taxonomy design recommendations. Representing an update for taxonomy designers, it contributes to the prescriptive knowledge on taxonomy design and seeks to augment both rigorous taxonomy building and evaluation.