Fraunhofer-Institut für Angewandte Informationstechnik FIT

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  • Publication
    Advancing Process Mining from the Core: Managing Process Mining Project Portfolios from Data Processing to Process Improvement
    ( 2023)
    Fischer, Dominik
    ;
    Grünberger, Michael
    ;
    Röglinger, Maximilian
    Process mining is a specialized form of data-driven process analysis that organizations use to understand and improve their business processes. Applying process mining techniques such as process discovery, conformance checking, or enhancement using event logs as the central data source generates insights into process behavior, performance, and compliance. Turning these insights into action supports evidence-based process improvement and strategic decision-making. Therefore, process mining supports multiple phases of the business process management lifecycle (i.e., process discovery, process analysis, process improvement and implementation, and process monitoring and controlling) using data about the execution of a process. The groundwork for these outstanding developments has been laid in academia, where a huge research stream focuses on developing and improving new process mining algorithms for various use cases, resulting in a strong technology core for process mining and analysis techniques. The overall purpose of this dissertation is to advance process mining by building on its solid technological core around the numerous process mining analysis algorithms by adding the missing pieces in preceding and subsequent steps of end-to-end process mining projects. Furthermore, this dissertation also abstracts from a single-project perspective and contributes on the managerial side to the broad applicability of process mining in organizations. Applying design science research principles, the research objectives of this thesis are primarily addressed through design-oriented research by creating and evaluating multiple artifacts in the form of reference architectures, methods, and instantiations. Ultimately, researchers in the process mining field as well as practitioners on the vendor and adopter side should benefit equally from the contributions of this thesis. Therefore, this cumulative dissertation comprising five research papers addresses three challenges that slow down the widespread adoption of process mining in organizations. First, research on adopting process mining at the enterprise level is somewhat fragmented, leading to a call for better guidance on managing process mining project portfolios, complemented by a holistic understanding of the opportunities and challenges of using PM in organizational settings. Therefore, this dissertation provides two deliverables to address this research need: Research Paper P1 provides a holistic overview of the opportunities and challenges of using process mining in organizations. Further, Research Paper P2 developed a method to manage portfolios of process mining projects in a value-oriented manner. Second, for process data quality management, there is a need for a dedicated environment focused on detecting, measuring, and repairing data quality problems. Research Paper P3 proposes a reference architecture for process data quality management to address this research need. The reference architecture is designed to be comprehensive and flexible.
  • Publication
    The Secret Key to the Heart of Decentralized Finance: Unlocking the Potential of Crypto Assets and Currencies to Advance the Financial Sector
    ( 2023)
    Schellinger, Benjamin
    The rise of decentralized financial applications, such as crypto assets and currencies, challenges traditional banking and finance’s roles and value proposition. To remain competitive traditional finance organizations and institutions need to learn from innovations in decentralized finance. Hence, they must gain a broad and deep understanding of this emerging ecosystem. In addition, it is crucial to acquire specific design knowledge, to develop innovative but regulatorily compliant crypto assets and currencies. Furthermore, centralized institutions in this domain need to manage crypto assets and currencies effectively to mitigate risk while improving portfolio performance. In light of this paradigm shift, this dissertation seeks to guide the financial sector in designing and managing crypto assets and currencies. To achieve this objective, I structure my dissertation along three research goals: first, establishing an understanding of decentralized finance, and second and third, providing guidance in the design and, respectively, management of crypto assets and currencies. To address the first research goal, I capture the state of the art in decentralized finance, develop a consolidating definition, devise a research framework, and propose future research directions (Essay 1). To achieve the second goal, I focus on guiding traditional finance organizations in designing crypto assets and currencies by developing novel information technology artifacts and proposing design suggestions, specifically, for blockchain-based equity tokens (Essay 2) and privacy-enhanced but regulatorily compliant digital payment systems (Essay 3). Lastly, to achieve the third goal, I provide investors with valuable insights into the efficient portfolio management of crypto asset and currency (Essay 4). The findings of this dissertation contribute to the body of knowledge on crypto assets and currencies through exploratory, prescriptive, descriptive, and analytical research methods. Specifically, the pluralistic research approach permits the generation of richer and more reliable knowledge for both the information systems and finance research domains. Overall, this dissertation provides theoretical and practical insights into the design and management of crypto assets and currencies.
  • Publication
    Managing Emerging Technologies - A Socio-Technical Analysis of Opportunities and Tensions for Incumbents
    ( 2023)
    Stohr, Alexander
    ;
    Fridgen, Gilbert
    ;
    Germelmann, Claas Christian
    Emerging technologies are changing today’s economic environment with unprecedented speed and in unpredictable ways. These dynamics threaten incumbent organizations, which are caught between continuing to effectively deliver their outcomes to existing customers and leaving established paths to leverage the opportunities afforded by emerging technologies that are still changing and developing. To address these tensions, incumbents often must implement a variety of structural and contextual changes. However, these changes strongly depend on the respective environment the incumbents are embedded in. In managing emerging technologies, incumbents thus need to understand and address a broad range of interrelated techno-organizational factors. At the same time, the increasing autonomy and intelligence of emerging technologies challenges the effectiveness of established concepts of information systems research for managing traditional information technology. To address this gap, this thesis presents a socio-technical perspective on the management of emerging technologies that is informed by the ideas of critical realism and considers opportunities and tensions for incumbent organizations as well as their contextual embedding. First, it delves into a deeper understanding of the potentials of emerging technologies in relation to their respective context and organizational actors. In particular, the thesis focuses on two such technologies: blockchain and artificial intelligence. Second, it elaborates on how incumbents can prepare emerging technologies for effective use that lack established use cases and patterns. It explores how the techno-organizational context gives rise to a variety of interrelated mechanisms that can stimulate or constrain experimentation activities with these technologies. Moreover, this thesis investigates how incumbents prepare for effective technology use by building necessary digital capabilities and managing tensions between leveraging digital opportunities and effectively delivering outcomes despite disruption. Resolving these tensions often leads to an accumulation of digital debt, technical and informational obligations that will need to be addressed in the future. Incumbents must manage this digital debt carefully to avoid negative in the long-term. This thesis contextualizes the contribution of seven embedded research papers and provides a holistic perspective on managing emerging technologies, contributing to a better understanding of opportunities and tensions for incumbents.
  • Publication
    Anywhere, Anytime, Autonomous - Meeting Customer Needs in the Digital Age through Omni-Channel and Proactive Service Management
    ( 2022)
    Wenninger, Annette
    ;
    Schlüchtermann, Jörg
    ;
    Röglinger, Maximilian
    ;
    Baier, Daniel
    The increasing proliferation of digital technologies enables novel value propositions, closer customer relationships, and greater automation of customer-facing business processes, softening the boundaries between the physical and digital world. Whether it is a smart fridge informing customers when food is running low, digital fitting rooms in stores offering extensive knowledge about the garments, or the permanent availability of information through smart devices, the opportunities to provide a unique customer experience appear endless in the digital age. However, with these opportunities, customer behavior is also changing to favor empowered customers who determine how they interact with organizations. These empowered customers expect a seamless and personalized customer experience anytime, anywhere. Hence, organizations must shift their mindset from organizational-defined solutions to customer-oriented solutions to meet customer needs in the digital age. Against this backdrop, this cumulative doctoral thesis aims to identify pathways to fulfill customer needs based on omni-channel and proactive service management insights. Considering omni-channel management, Research Article #1 presents an economic decision model that helps organizations seamlessly manage hybrid customers moving fluently between channels by evaluating omni-channel strategies that meet customers’ channel preferences and can also be operated efficiently. Considering proactive service management, Research Article #2 analyses proactive service features through the empirical and conceptual design of a taxonomy and provides further a list of 45 examples. This taxonomy helps organizations and researchers understand the proactive service phenomenon and to identify valuable conceptualizations. Based on this research article, Research Article #3 shows that the implementation of certain proactive service features has the potential to delight customers. Organizations can, therefore, design appropriate services leading to higher customer satisfaction. The classification and prioritization of the features are determined by applying the well-established Kano model and the self-state importance method. Further, the popular Five Factor model allows investigating the influence of customers’ personality traits on the evaluation. Finally, Research Article #4 presents a contextualized acceptance model of proactive services drawn from insights of general acceptance theory to identify antecedents influencing customers’ acceptance. The results provide further indications for a tailored service design meeting customer needs. In sum, this cumulative doctoral thesis analyzes customer needs in the digital age through different theoretical lenses by using qualitative and quantitative research methods in the research field of omni-channel and proactive service management. In this regard, the research articles build upon (i.e., Kano model, Five Factor model, taxonomy design) and extend relevant theory (i.e., contextualized UTAUT2 model) to answer the different underlying research questions, whereby providing valuable empirical evidence for researchers and practitioners.
  • Publication
    Data-Driven Business Process Management: Advancing Process Data Quality and Process Improvement
    ( 2022)
    Dun, Christopher van
    ;
    Schlüchtermann, Jörg
    ;
    Röglinger, Maximilian
    ;
    Wynn, Moe
    Business processes are at the core of every organisation’s effort to deliver services and products to customers and, thus, achieve the organisation’s goals. The discipline that deals with the design, analysis, execution, and improvement of such business processes is called business process management (BPM). Over the years, the BPM research discipline has created a large number of methods and tools to support practitioners in managing and improving their business processes. In recent years, the increasing abundance of process data available in organisational information systems and simultaneous progress in computational performance have paved the way for a new class of so-called data-driven BPM methods and tools, the most prominent of them being process mining. This cumulative doctoral thesis concentrates on two challenges related to data-driven BPM methods and tools that impede faster and more widespread adoption. First, while data-driven methods and tools have found quick adoption in BPM lifecycle phases such as process discovery and process monitoring, the lifecycle phase of process improvement has so far been neglected. However, process improvement is considered to be the most value-adding BPM lifecycle phase since it is the necessary step to address existing issues in as-is processes or to adapt these processes to constantly changing environments and customer needs and expectations. Process improvement is often expensive, time-consuming, and labour-intensive, which is why there is a particular need to support process stakeholders in redesigning their processes. Second, there is a need for high-quality process data in all phases of the BPM lifecycle. In practice, process data, e.g., in the form of event logs for process mining, is often far from the desired quality and process analysts spend the majority of their time on identifying, assessing, and remedying data quality issues. Thus, in the BPM community, the interest in exploring the roots of data quality problems and the related assurance of high-quality process data is rising. Hence, it is essential to have a means for detecting and quantifying process data quality. Against this backdrop, this cumulative doctoral thesis comprises five research articles that present advances in process data quality management on the one hand and data-driven process improvement on the other hand. Taking on a design-oriented research paradigm and applying different qualitative and quantitative research methods, this thesis proposes several IT-enabled artifacts that support stakeholders in managing process data quality and improving business processes. The insights contained in this thesis are relevant for academia and practice as they provide both scientific perspectives and practical guidance. Concerning process data quality management, research article #1 presents an approach for (semi-) automated and quality-informed event log extraction from process-agnostic relational databases. It applies metrics for data quality dimensions that are relevant to process mining in order to quantify the data quality of the source data in selected database tables and simultaneously allows users to extract event logs in XES format from the database tables. Research article #2 presents an approach for detecting and quantifying timestamp data quality issues in events logs already present in XES format. The approach applies metrics for identifying timestamp imperfection patterns and allows users to interactively filter, repair, and annotate the event log. Furthermore, this thesis provides several concrete approaches to data-driven business process improvement. First, it focuses on process improvement in itself and aims to create artifacts for supporting process improvement initiatives. Therefore, research article #3 provides a model based on generative adversarial networks to create new process designs. Specifically, it uses event logs and annotated information on process variants and process deviance to generate a new process model which provides suggestions for process improvement to the user. Second, this thesis targets data-driven decision support in business processes. In particular, research article #4 uses multi-criteria decision analysis to extend traditional vehicle routing problems in last-mile delivery with a customer-centric perspective. The customer-centric vehicle routing uses process and customer data and the concept of customer lifetime values to predict customer satisfaction and, thus, optimise delivery routes. Finally, research article #5 presents a modelling approach for IT availability risks in smart factory networks based on Petri nets. The modelling approach uses modular components of information systems and production machines to model, simulate, and analyse production processes. The thesis concludes by pointing to limitations of the presented research articles as well as directions for future research. Overall, this thesis contributes to several important research streams in BPM while applying a broad range of qualitative and quantitative research methods such as simulation, normative analytical modelling, multi-criteria decision analysis, and interview studies within an overarching design science research paradigm. It builds upon and extends existing research on process data quality management and business process improvement.
  • Publication
    Are we Human, or are we Users? Understanding the Inter-Twinement of Technology Acceptance, (IT) Identity, and Self-Concept-Related Implications
    ( 2022)
    Diel, Sören
    As information technology (IT) has become an indispensable part of people’s everyday lives (Yoo, 2010), being human is more than ever influenced by IT. Thereby, a growing psychological - often subconscious - intertwinement between human beings’ social roles and relationships and their interactions with IT can be observed (Carter and Grover, 2015). For example, human beings use IT to understand, expand, or represent their self (c.f., Carter and Grover, 2015), determine online who they are, and evaluate their self-worth (e.g., Wenninger et al., 2019; Yang et al., 2018), strive in online environments for belonging and meaningful existence (Baumeister and Leary, 1995; Bernstein, 2016), and internalize IT as part of their identity (Carter et al., 2020a; Carter et al., 2020b). As human beings are essentially social beings (Riva and Eck, 2016), those social processes are essential for individuals to cope with a complex social world. Moreover, they relate to an individual’s psychological and physiological well-being. Information systems (IS) research that examine the intertwinement between human beings’ socio-psychological nature and IT use behavior indicates a reciprocal relationship between so-called digital users and IT. For example, socio-psychological concepts like emotional attachment, relatedness, and dependency (i.e., IT identity) determine IT use behavior (Carter and Grover, 2015), expanding traditional technology acceptance research and offering a new lens to understand individuals’ IT use behavior (Venkatesh et al., 2003; Venkatesh et al., 2012; Ven-katesh et al., 2016). Moreover, IS research suggests that due to growing opportunities to interact with others and enabled by IT’s functionalities, IT use triggers physiological and psychological reactions, ranging from severe consequences (e.g., depression, anxiety, bipolar mania) to individuals who report higher life satisfaction due to the ability for social participation in online environments (e.g., Verduyn et al., 2017; Krasnova et al., 2015). Building on first investigations and in light of the increasing integration of IT into human beings’ everyday life, IS research calls for (1) the integration of socio-psychological perspectives in IS research to understand better and predict individuals IT use behavior and (2) insights on new outcomes of technology use like subsequent thoughts, physiological, and emotional reactions within socio-technical contexts (Carter and Grover, 2015; Venkatesh et al., 2016). Accordingly, this thesis replies to this calls by following the overarching research objective to enhance the understanding of the reciprocal relationship of how IT use influences one as a hu-man being and how being human influences IT use. This thesis takes on a Service-Dominant-Logic (SDL) perspective by understanding that a digital user’s value perception of IT goes beyond the mere fulfillment of tasks and reflects deeper basic human needs and values in everyday life (Vargo and Lusch, 2004, 2008, 2016; Yoo, 2010). Moreover, this thesis integrates socio-psychological perspectives (e.g., Social Comparison Theory, Social Identity Theory, Temporal Need Threat Model) and established theories from IS research (e.g., Uni-fied Theory on Acceptance and Use of Technology) to explain individuals’ use behavior, social processes when using IT, and self-concept related consequences of IT use. Overall, the thesis encompasses seven research articles. Three research articles enhance the context-dependent understanding of technology acceptance from the perspective of a digital user by providing theoretical explanations for use intentions and actual use of IT regarding new types of IT used in new contexts, new forms of use behavior, and new antecedents that (indirectly) predict IT use behavior. Furthermore, three research articles enhance the understanding of why and how IT use influences self-concept-related aspects of a digital user by providing empirical evidence that digital users utilize IT to determine their self-concept in digital environments. Thereby, digital users make digitally mediated experiences through its functionalities (e.g., paralingual digital affordances, editability, asynchronicity). Which enable and trigger socio-psychological processes and relate to users’ self. Moreover, one article enhances the understanding of IT identity’s role in integrating technology acceptance and a digital user. In this regard, this thesis provides empirical evidence that individuals perceive IT as part of their self. Furthermore, the results indicate that users’ IT identity significantly mediates use behavior. Overall, this thesis contributes to IS research by thoroughly investigating the human-IT relationship. By putting the individual in the center of interest, the thesis proposes further research on digital users’ intentions and actual use of IT, investi-gations of why and how social-psychological processes extend into the online world, and the mediating role of one’s self on context-dependent technology acceptance factors and use behavior.
  • Publication
    Data-driven support and risk modeling for a successful heat transition in the building sector
    ( 2022)
    Wenninger, Simon
    Growing international interest in climate change and the ambitious climate goals of the Paris Climate Agreement requires policy decisions and actions to curb the adverse effects of human-made climate change. The buildings sector accounts for more than one-third of global greenhouse gas emissions and energy consumption, with space heating and water heating accounting for most of these, and offers great potential for progress toward climate goal achievement. Moreover, most of today's existing buildings were built before introducing more strict building codes than today and are therefore not sufficiently energy efficient. Due to the low number of new buildings compared to the existing building stock, extensive retrofitting is necessary, as the current building stock will continue to account for the largest share of energy consumption in buildings in the future. However, retrofits of these buildings are sparse, and the retrofit rate - the percentage of buildings that undergo retrofits in a year - is too low to meet climate goals. Therefore, this cumulative doctoral thesis examines two aspects for a successful heat transition in the building sector. The first aspect deals with the identification of general factors influencing energy efficiency and retrofitting practices on a regional level. It is not yet fully understood which local differences exist in building performance, energy efficiency, and retrofitting practices and how socio-economic factors influence these. Thus, this doctoral thesis follows the call to use the opportunities of advancing digitalization and data availability to examine this aspect. The findings indicate strong evidence for regional differences in building energy efficiency, confirm existing qualitative and small-scale studies regarding the influence of socio-economic factors and classify retrofitting-related CO2 taxes as reasonable and easy to implement. The second aspect shifts the focus from a regional level to individual retrofit decisions. It examines risk in general and inaccurate predictions of building energy performance in particular as barriers to individual retrofit decisions. The results show that promoting energy efficiency reduces the variance - and thus the risk - of future energy bills and opens up opportunities for more sustainable investment behavior. In addition, policy instruments such as energy efficiency insurance are more effective and cost-efficient than subsidies in mitigating the risk of environmentally friendlier investments. Regarding building energy performance prediction, data-driven approaches exceed the currently prescribed engineering method (in Germany) by almost 50% in prediction accuracy and provide insights into influencing factors. In summary, this doctoral thesis provides insights using data-driven and risk-modeling approaches for a better understanding of factors influencing energy efficiency and retrofitting on a regional level and risk in retrofit decisions and contributes managerial and policy implications that support a successful heat transition in the building sector.
  • Publication
    Big Data in der Automobilindustrie
    (Dfv-Mediengruppe, 2018)
    Karikari, B.A.
    Im Wettkampf um die Mobilität der Zukunft konkurrieren Fahrzeughersteller inzwischen nicht mehr nur untereinander, sondern gleichzeitig mit großen IT-Konzernen sowie Start-ups um Marktanteile. Der Wegbereiter für die Transformation der Automobilhersteller zu Mobilitätsdienstleistern ist das vernetzte Fahrzeug und die darin enthaltenen Daten. Aufgrund der Masse an Daten, die Automobile erfassen, beinhalten sie ein enormes wirtschaftliches Potential, da die hieraus gewonnenen Erkenntnisse zur Schaffung neuer und/oder Optimierung bestehender Produkt- und Serviceangebote verwendet werden können. Vor diesem Hintergrund beschäftigt sich die vorliegende Arbeit mit dem durch neuartige Technologien wie Big Data-Analytics verursachte Bedeutungszuwachs von Daten, die zu einem wirtschaftlich motivierten Verteilungskampf um diesen Rohstoff des 21. Jahrhunderts geführt hat. Nach einer technischen Einführung, wie dieses ""Öl der Zukunft"" geschöpft werden kann und einer Neubewertung der Werthaltigkeit von Daten in der Automobilindustrie folgt eine juristische Auseinandersetzung mit Blick auf die Befugnisse zur Erhebung und Verarbeitung der entsprechenden Daten sowie die rechtliche Zuordnung dieser Daten unter Berücksichtigung unterschiedlicher Rechtsbereiche. Ziel der Arbeit ist es, die Fragestellungen in Bezug die Befugnisse zur Erhebung und Verarbeitung sowie die Zuordnung von Daten im Automobil rechtswissenschaftlich zu evaluieren und aus den daraus gewonnenen Erkenntnisse Handlungsempfehlungen zu definieren.