Recent computing advances are driving the integration of artificial intelligence (AI)-based systems into nearly every facet of our daily lives. To this end, AI is becoming a frontier for enabling algorithmic decision-making by mimicking or even surpassing human intelligence. Thereupon, these AI-based systems can function as decision support systems (DSSs) that assist experts in high-stakes use cases where human lives are at risk. All that glitters is not gold, due to the accompanying complexity of the underlying machine learning (ML) models, which apply mathematical and statistical algorithms to autonomously derive nonlinear decision knowledge. One particular subclass of ML models, called deep learning models, accomplishes unsurpassed performance, with the drawback that these models are no longer explainable to humans. This divergence may result in an end-user’s unwillingness to utilize this type of AI-based DSS, thus diminishing the end-user’s system acceptance. Hence, the explainable AI (XAI) research stream has gained momentum, as it develops techniques to unravel this black-box while maintaining system performance. Non-surprisingly, these XAI techniques become necessary for justifying, evaluating, improving, or managing the utilization of AI-based DSSs. This yields a plethora of explanation techniques, creating an XAI jungle from which end-users must choose. In turn, these techniques are preliminarily engineered by developers for developers without ensuring an actual end-user fit. Thus, it renders unknown how an end-user’s mental model behaves when encountering such explanation techniques. For this purpose, this cumulative thesis seeks to address this research deficiency by investigating end-user perceptions when encountering intrinsic ML and post-hoc XAI explanations. Drawing on this, the findings are synthesized into design knowledge to enable the deployment of XAI-based DSSs in practice. To this end, this thesis comprises six research contributions that follow the iterative and alternating interplay between behavioral science and design science research employed in information systems (IS) research and thus contribute to the overall research objectives as follows: First, an in-depth study of the impact of transparency and (initial) trust on end-user acceptance is conducted by extending and validating the unified theory of acceptance and use of technology model. This study indicates both factors’ strong but indirect effects on system acceptance, validating further research incentives. In particular, this thesis focuses on the overarching concept of transparency. Herein, a systematization in the form of a taxonomy and pattern analysis of existing user-centered XAI studies is derived to structure and guide future research endeavors, which enables the empirical investigation of the theoretical trade-off between performance and explainability in intrinsic ML algorithms, yielding a less gradual trade-off, fragmented into three explainability groups. This includes an empirical investigation on end-users’ perceived explainability of post-hoc explanation types, with local explanation types performing best. Furthermore, an empirical investigation emphasizes the correlation between comprehensibility and explainability, indicating almost significant (with outliers) results for the assumed correlation. The final empirical investigation aims at researching XAI explanation types on end-user cognitive load and the effect of cognitive load on end-user task performance and task time, which also positions local explanation types as best and demonstrates the correlations between cognitive load and task performance and, moreover, between cognitive load and task time. Finally, the last research paper utilizes i.a. the obtained knowledge and derives a nascent design theory for XAI-based DSSs. This design theory encompasses (meta-) design requirements, design principles, and design features in a domain-independent and interdisciplinary fashion, including end-users and developers as potential user groups. This design theory is ultimately tested through a real-world instantiation in a high-stakes maintenance scenario. From an IS research perspective, this cumulative thesis addresses the lack of research on perception and design knowledge for an ensured utilization of XAI-based DSS. This lays the foundation for future research to obtain a holistic understanding of end-users’ heuristic behaviors during decision-making to facilitate the acceptance of XAI-based DSSs in operational practice.

Companies are expected to act as international players and to use their capabilities to provide customized products and services quickly and efficiently. Today, consumers expect their requirements to be met within a short time and at a favorable price. Order-to-delivery lead time has steadily gained in importance for consumers. Furthermore, governments can use various emissions policies to force companies and customers to reduce their greenhouse gas emissions. This thesis investigates the influence of order-to-delivery lead time and different emission policies on the design of a supply chain. Within this work different supply chain design models are developed to examine these different influences. The first model incorporates lead times and total costs, and various emission policies are implemented to illustrate the trade-off between the different measures. The second model reflects the influence of order-to-delivery lead time sensitive consumers, and different emission policies are implemented to study their impacts. The analysis shows that the share of order-to-delivery lead time sensitive consumers has a significant impact on the design of a supply chain. Demand uncertainty and uncertainty in the design of different emission policies are investigated by developing an appropriate robust mathematical optimization model. Results show that especially uncertainties on the design of an emission policy can significantly impact the total cost of a supply chain. The effects of differently designed emission policies in various countries are investigated in the fourth model. The analyses highlight that both lead times and emission policies can strongly influence companies' offshoring and nearshoring strategies.

Increasing global competition forces organizations to improve their processes to gain a competitive advantage. In the manufacturing sector, this is facilitated through tremendous digital transformation. Fundamental components in such digitalized environments are process-aware information systems that record the execution of business processes, assist in process automation, and unlock the potential to analyze processes. However, most enterprise information systems focus on informational aspects, process automation, or data collection but do not tap into predictive or prescriptive analytics to foster data-driven decision-making. Therefore, this dissertation is set out to investigate the design of analytics-enabled information systems in five independent parts, which step-wise introduce analytics capabilities and assess potential opportunities for process improvement in real-world scenarios. To set up and extend analytics-enabled information systems, an essential prerequisite is identifying success factors, which we identify in the context of process mining as a descriptive analytics technique. We combine an established process mining framework and a success model to provide a structured approach for assessing success factors and identifying challenges, motivations, and perceived business value of process mining from employees across organizations as well as process mining experts and consultants. We extend the existing success model and provide lessons for business value generation through process mining based on the derived findings. To assist the realization of process mining enabled business value, we design an artifact for context-aware process mining. The artifact combines standard process logs with additional context information to assist the automated identification of process realization paths associated with specific context events. Yet, realizing business value is a challenging task, as transforming processes based on informational insights is time-consuming. To overcome this, we showcase the development of a predictive process monitoring system for disruption handling in a production environment. The system leverages state-of-the-art machine learning algorithms for disruption type classification and duration prediction. It combines the algorithms with additional organizational data sources and a simple assignment procedure to assist the disruption handling process. The design of such a system and analytics models is a challenging task, which we address by engineering a five-phase method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks. The method facilitates the integration of heterogeneous data sources through dedicated neural network input heads, which are concatenated for a prediction. An evaluation based on a real-world use-case highlights the superior performance of the resulting multi-headed network. Even the improved model performance provides no perfect results, and thus decisions about assigning agents to solve disruptions have to be made under uncertainty. Mathematical models can assist here, but due to complex real-world conditions, the number of potential scenarios massively increases and limits the solution of assignment models. To overcome this and tap into the potential of prescriptive process monitoring systems, we set out a data-driven approximate dynamic stochastic programming approach, which incorporates multiple uncertainties for an assignment decision. The resulting model has significant performance improvement and ultimately highlights the particular importance of analytics-enabled information systems for organizational process improvement.

Das Verbundprojekt „Plattform für das integrierte Management von Kollaborationen in Wertschöpfungsnetzwerken“ (PIMKoWe – Förderkennzeichen „02P17D160“) ist ein Forschungsvorhaben im Rahmen des Forschungsprogramms „Innovationen für die Produktion, Dienstleistung und Arbeit von morgen“ der Bekanntmachung „Industrie 4.0 – Intelligente Kollaborationen in dynamischen Wertschöpfungs-netzwerken“ (InKoWe). Das Forschungsvorhaben wurde mit Mitteln des Bundesministeriums für Bildung und Forschung (BMBF) gefördert und durch den Projektträger des Karlsruher Instituts für Technologie (PTKA) betreut. Ziel des Forschungsprojekts PIMKoWe ist die Entwicklung und Bereitstellung einer Plattformlösung zur Flexibilisierung, Automatisierung und Absicherung von Kooperationen in Wertschöpfungsnetzwerken des industriellen Sektors.

Innovative Software kann die Position eines Unternehmens im Wettbewerb sichern. Die Einführung innovativer Software ist aber alles andere als einfach. Denn obgleich die technischen Aspekte offensichtlicher sind, dominieren organisationale Aspekte. Zu viele Softwareprojekte schlagen fehl, da die Einführung nicht gelingt, trotz Erfüllung technischer Anforderungen. Vor diesem Hintergrund ist das Forschungsziel der Masterarbeit, Risiken und Erfolgsfaktoren für die Einführung innovativer Software in Unternehmen zu finden, eine Strategie zu formulieren und dabei die Bedeutung von Schlüsselpersonen zu bestimmen.