As supply chains (SCs) face increasing pressure from ecological demands, ethical expectations, and global disruptions, Distributed Ledger Technology (DLT) is gaining attention as a potential enabler of transparent, secure, and automated processes, helping to meet the expectations of customers and regulatory authorities. Nevertheless, the lack of generally valid design recommendations hinders its implementation. Therefore, we adopted grounded theory principles within a design science research approach to address this gap. Subsequently, we derived 11 overarching expert insights for developing a DLT operating model in SCs and 15 for its implementation by conducting 16 expert interviews. These insights were finally used to extract 19 generally valid design recommendations for applying DLT in SC processes that contribute to practical implementations and the framing of realistic adoption expectations by guiding researchers and practitioners.

Programming skills have been a crucial skill for (computer science) students. The level of technology affinity, encompassing a student's confidence in technology, can impact skill development. Students with high technology affinity may engage more confidently with programming tasks, while those with lower affinity might struggle with the initial learning curve. We used the adaptive Digital Game-Based Learning System (DGBL) system " Lost in Code", and conducted a field experiment with 66 students majoring in Business Administration or Information Systems. We analyzed the relationships between students' technology affinity, motivation, and learning outcomes in a PLS-SEM. Our findings show that the technology affinity impacts the perceived enjoyment, pressure and competence positively. Additionally, prior knowledge does not influence the motivation. We combined these findings with qualitative feedback and discuss the potential of adaptive DGBL systems.

Centrally administrated systems have historically facilitated inter-organizational data exchange in supply chains (SC), relying on the message standard electronic data interchange (EDI). However, the current use of EDI fails to meet information needs, as point-to-point interfaces complicate information sharing among multiple partners and batch processing lacks real-time capabilities. This results in information asymmetries, leading to inefficiencies. Distributed ledger technology (DLT), which offers decentralized communication and data storage, presents a potential solution. In this paper, we present a systematic literature review comparing the centralized architectures utilizing EDI applications with the decentralized architecture of DLT within SCs. We identified the limitations of the current systems and assessed whether DLT offers a solution. The findings show that DLT enhances real-time data exchange, automation potential, and transparency, but also faces shortcomings. Integrating EDI with DLT offers a promising approach to leverage synergies and address the weaknesses of both technologies, e.g., lacking standards for DLT.

Software testing plays a critical role in the success of software development projects. As the complexity of software grows, the demand for qualified software testing specialists is rising. Despite this, a gap persists between the knowledge required in the industry and the competencies offered in academic curricula. To delve deeper into educational strategies, this study investigates the state of software testing education in German higher education by analyzing module manuals from 162 Universities and Universities of Applied Sciences manually, focusing on Information Systems, Computer Science, and Information and Media Technology-related study programs. We assess in 754 study programs the integration of teaching methods and competencies of 1.552 modules related to software testing. The research identifies variations in educational approaches and gaps between the instructional content and industry standards, e.g., the ISTQB Foundation Level. The findings emphasize the importance of aligning software testing education with industry standards and suggest incorporating practical, project-based learning to better prepare students for the professional demands of software testing. Therefore, based on teaching approaches from the module manuals, we provide recommendations to enhance the integration of software testing in curricula, focusing on the broader inclusion of ISTQB standards, practical exercises, and interdisciplinary teaching approaches. This study contributes to the ongoing discourse on improving software testing education and highlights areas for future research in academic and practical contexts.

This paper evaluates a prototypical hierarchical digital twin (HDT) for industrial production environments, addressing the gap in practical evaluations of digital twin concepts. The HDT integrates data from various production levels, offering a comprehensive virtual representation of the physical production environment. A qualitative interview study was conducted with 14 practitioners from different industrial sectors to assess the HDT's utility and gather feedback. The study identified key data classes, performance indicators, and functions necessary for effective HDT implementation. Results indicate that the HDT provides significant benefits in monitoring, simulation, and control of production processes, aligning with scientific perspectives while highlighting practical enhancements. This evaluation informs future HDT development and implementation strategies in industrial settings.

The intended automation in the financial industry creates a proper area for artificial intelligence usage. However, complex and high regulatory standards and rapid technological developments pose significant challenges in developing and deploying AI-based services in the finance industry. The regulatory principles defined by financial authorities in Europe need to be structured in a fine-granular way to promote understanding and ensure customer safety and the quality of AI-based services in the financial industry. This will lead to a better understanding of regulators’ priorities and guide how AI-based services are built. This paper provides a classification pattern with a taxonomy that clarifies the existing European regulatory principles for researchers, regulatory authorities, and financial services companies. Our study can pave the way for developing compliant AI-based services by bringing out the thematic focus of regulatory principles.

In computer science education, students perceive programming as complex as it requires a broad set of technical and logical skills. These difficulties are further compounded by the varying levels of prior knowledge among students. This study presents "Lost in Code", an adaptive digital game-based learning system (DGBL) designed to enhance programming skills in introductory computer science courses. It leverages the complexities of learning programming by integrating educational content into a gaming context that adapts to individual student knowledge levels. The system was developed following a design science research approach, including two iterations of design and evaluation. Evaluations showed positive student engagement and learning outcomes, highlighting the system's potential to address challenges in computer science education by making learning interactive, engaging, and tailored to individual needs. Thus, reflect a timely response to the demand for educational approaches and provide insights into the development and evaluation of DGBL systems.

Financial institutions are increasingly turning to artificial intelligence (AI) to improve their decision-making processes and gain a competitive edge. Due to the iterative process of AI development, it is mandatory to have a structured process in place, from the design to the deployment of AI-based services in the finance industry. This process must include the required validation and coordination with regulatory authorities. An appropriate dashboard can help to shape and structure the process of model development, e.g., for credit assessment in the finance industry. In addition, the analysis of datasets must be included as an important part of the dashboard to understand the reasons for changes in model performance. Furthermore, a dashboard can undertake documentation tasks to make the process of model development traceable, explainable, and transparent, as required by regulatory authorities in the finance industry. This can offer a comprehensive solution for financial companies to optimize their models, improve regulatory compliance, and ultimately foster sustainable growth in an increasingly competitive market. In this study, we investigate the requirements and provide a prototypical dashboard to create, manage, compare, and validate AI models to be used in the credit assessment of private customers.

Changes in customer demand and technological advances increase production scheduling complexity. A solution to handle the increased complexity is to facilitate machine learning. Current reviews of the research of scheduling algorithms focus on summarizing existing methods. They lack the characteristics of practical implications arising from the increased complexity of the scheduling problem by Industry 4.0 and do not consider how the scheduling algorithms cope with the added complexity. Therefore, we address these issues and synthesize the current literature in a comprehensive literature review and taxonomy. We discuss how they address changeovers, buffers before and after machines, prioritization of jobs, dynamics of production, and transportation on the shop floor. Afterward, we summarize the concepts of scheduling approaches and derive five research gaps.

Changes in customer demands and technological advances increase the complexity of production scheduling. Hence, current production scheduling algorithms are not sufficiently good. Additionally, advances in the research of Machine Learning algorithms drive the development of new scheduling algorithms. Each algorithm’s quality is problem-dependent, making it challenging to find the best algorithm for a given production scenario. Benchmark problems only provide guidance as they do not reflect real-world situations. To address this issue, in this article, we develop the software artifact Simfia that allows researchers and practitioners to evaluate production scheduling algorithms in highly customizable experiments. To create the solution, we follow a design science research approach. We identify 30 functional requirements, develop the solution prototypically and demonstrate it in an exemplary production scenario. The artifact is developed in a service-oriented architecture where scheduling algorithms are provided as individually deployable scheduling services that Simfia manages. This solution enables researchers and practitioners to experiment with production scheduling algorithms in highly configurable production scenarios.

Corporate credit ratings provide multiple strategic, financial, and managerial benefits for decision-makers. Therefore, it is essential to have accurate and up-to-date ratings to continuously monitor companies' financial situations when making financial credit decisions. Machine learning (ML)-based internal models can be used for the assessment of companies' financial situations using annual statements. Particularly, it is necessary to check whether these ML models achieve better results compared to statistical methods. Due to the multi-class classification problem when forecasting corporate credit ratings, the development, monitoring, and maintenance of ML-based systems are more challenging compared to simple classifications. This problem becomes even more complex due to the required coordination with financial regulators (e.g., OECD, EBA, BaFin, etc.). Furthermore, the ML models must be updated regularly due to the periodic nature of annual statements as a dataset. To address the problem of the limited dataset, multiple sampling strategies and machine learning algorithms can be combined for accurate and up-to-date forecasting of credit ratings. This paper provides various implications for ML-based forecasting of credit ratings and presents an approach for combining sampling strategies and ML techniques. It also provides design recommendations for ML-based services in the finance industry on how to fulfill the existing regulations.